**Catalytic E**ff**ect of NaCl on the Improvement of the Physicochemical Structure of Coal-Based Activated Carbons for SO2 Adsorption**

**Dongdong Liu <sup>1</sup> , Rui Su 1, Zhengkai Hao 1, Xiaoman Zhao 1, Boyin Jia 2,\* and Liangjie Dong <sup>1</sup>**


Received: 25 April 2019; Accepted: 28 May 2019; Published: 5 June 2019

**Abstract:** The utilization of coal-based activated carbons focuses on improving the physicochemical structure for achieving high-capacity. Herein, the catalytic effect of NaCl (1 and 3 wt%) in the presence of oxygen functional groups on the improvement of the physicochemical structure of coal-based activated carbons is studied in this work. A large quantity of Na can be retained in 1NaJXO and 3NaJXO with the presence of oxygen functional groups to promote further its catalytic characteristics during pyrolysis, resulting in the disordered transformation of the carbon structure. In addition, the development of micropores is mainly affected by the distribution and movement of Na catalyst, whereas the growth of mesopores is mainly influenced by the evolution of oxygen functional groups. Then, the active sites of 3NaJXO-800 can no longer be consumed preferentially in the presence of Na catalyst during subsequent CO2 activation to facilitate the sustained disordered conversion of the microstructure and the rapid development of the micropores, resulting in the obvious high SBET value as activation proceeds. And the high SBET/burn-off ratio value (41.48 m2·g−1/%) of 3NaJXO-800 with a high value of SBET (1995.35 m2·g<sup>−</sup>1) at a low burn-off value (48.1%) can be obtained, presenting the high efficiency of pore formation. Finally, the SO2 adsorption efficiency of 3NaJXO-800-48.1 maintains at 100% within 90 min. After 180 min, 3NaJXO-800-48.1 still presents a high adsorptive capacity (140.2 mg/g). It is observed that a large micropore volume in the case of hierarchical pore structure necessarily assures optimal adsorption of SO2.

**Keywords:** activated carbons; catalytic activation; physicochemical structure; SO2 adsorption

#### **1. Introduction**

Recently, gas sorption, storage and separation in porous nanocarbons and metal–organic frameworks have received increasing attention. In particular, the tunable porosity, surface area and functionality of the lightweight and stable graphene-based materials open up great scope for those applications [1]. Activated carbons (ACs) as an adsorbent material is a promising choice to achieve the gas pollutants (such as SO2, NOx) adsorption [2]. In the case of ACs, the gas pollutants are adsorbed and catalyzed in abundant active sites within micropores, then can be further migrated and stored in developed mesopores [3],so the effective removal of gas pollutants is closely related to the physicochemical structure of ACs, including more amounts of active sites, a hierarchical distribution of pores and a high specific surface area (SBET).Among some synthesis methods (such as physical or chemical activation [4], soft/hard template [5,6], hydrothermal carbonization and self-assembly [7,8]) and sources of raw materials (such as coal, biomass, wastes, MOF and ordered mesoporous carbons), the traditional physical activation using H2O, CO2, flue gas, or their mixtures as activation agents

can partially etch the carbon-based framework to obtain desirable porosity and more active sites over other methods, in addition, the coal as the most suitable raw materials instead of other materials can satisfy the industrial application of ACs [9]. Therefore, coal-based ACs produced by physical activation process meets the requirements of economic and environmental friendliness.

In order to improve the physicochemical structure of coal-based ACs, some researchers concentrate mainly on various activation modes (such as the single activation and mixing activation) [10,11]. However, the obtained products present the low SBET values between 600 and 1000 m2/g at the high burn-off value of approximately around 60–85% and the rapid consumption of active sites during activation, finally resulting in a higher cost [12–16]. Thus, it is difficult to obtain the ideal AC only by adjusting the activation conditions during activation. Our previous research found that the number of initial pores and active sites of chars produced by pyrolysis and the disordered conversion of carbon structure of chars during activation have important effects on the ideal AC production [17]. Furthermore, more initial pores can promote rapidly the diffusion of the activated gas into the particles' interior to avoid external loss of quality effectively, resulting in the high efficiency of pore formation. In addition, a lot of active sites and the disordered conversion of carbon structure at high activation temperatures can further promote etching of carbon-based framework to obtain desirable porosity and more new active sites.

Alkali metals (such as Na and K) in raw coal play an efficient catalytic role in gasification which is similar to physical activation reaction, and the corresponding catalytic mechanism has been investigated by some researchers [18–22]. Alkali metal not only can fundamentally change the reaction path between activated gas and active sites, but also can accelerate the reaction between the active gas and coal matrix by providing catalytic active sites, finally, the increase of new active sites and the disordered conversion of carbon structure during gasification. Importantly, most of the works in the literature reported that large amounts of alkali metals have been released into the gas phase during the temperature range of 300–900 ◦C, resulting in the absence of a large number of catalyst in the subsequent activation stage [23–27]. However, some studies also reported that the introduction of oxygen functional groups play an important role in the movement and catalytic effect of alkali metals [28–30]. Alkali metals (M) can be fixed inside char during pyrolysis as intermediates (such as C-O-M and -COOM) form which act as catalytic active sites to react with activated gas at the activation stage. In addition, in our previous study [31] and Francisco et al. [32] found that more active sites in chars can be produced by the introduction of oxygen functional groups during pyrolysis.

In this work, we systematically investigated the catalytic effect of alkali metal under the introduction of oxygen functional groups regarding the improve of the physicochemical structure of Jixi bituminous coal-based activated carbons. A series of samples were prepared by loading various amounts of the NaCl (1 wt% and 3 wt%) and/or the subsequent pre-oxidation in the air at 200 ◦C for 48 h. To verify the application potentials of the ideal activated carbons with developed pore structure, SO2 removal tests also were conducted by portable FTIR. The characteristic parameters of all samples were determined by scanning electron microscopy (SEM), nitrogen adsorption, X-ray diffraction (XRD) and Raman spectroscopy.

#### **2. Materials and Methods**

#### *2.1. Materials*

Jixi bituminous coal was collected from the southeast of Heilongjiang province in China, and acted as the source material for the preparation of ACs. Different particle sizes of 250–380 μm from the Jixi bituminous coal were obtained through crushing and sieving. Importantly, the raw materials were demineralized sequentially using 6 mol·L−<sup>1</sup> HCl and 40 wt% HF [33]. Afterwards, the sample was treated with deionized water and dried in an oven at 80 ◦C overnight. After that, the proximate and ultimate analysis of a demineralized sample (denoted as JX) were shown in Table 1.


**Table 1.** Proximate and ultimate analyses of JX (wt%).

\* By difference; ad (air-dried basis): The coal in dry air was used as a benchmark; daf (dry ash free basis): The remaining component after the removal of water and ash in coal was used as a benchmark.

Na-loaded samples were prepared by liquid impregnation. A known amount of NaCl powder was first added into a beaker and dissolved by deionized water under magnetic stirring. NaCl powder was obtained from Kemiou, Tianjin, China. A pre-weighed amount of JX was then added into the beaker to make a coal-water slurry. The coal-water slurry was dried at 80 ◦C with magnetic stirring of 300 r/min, until the water was completely evaporated. The mass content of Na in the mineral-loaded coal samples is controlled at 1 wt% and 3 wt%. The Na-loaded samples were denoted as 1NaJX and 3NaJX. In addition, JX was oxidized in air at 200 ◦C for 48 h, the oxidized sample was marked as JXO. Then, a predetermined amount of NaCl powder (0.03 g and 0.09 g) and 3 g of JXO were mixed using above liquid impregnation to prepare the Na-loaded oxidized samples and they were marked as 1NaJXO and 3NaJXO.

#### *2.2. Experimental Process*

The 3 g of samples were heated at a constant rate of 10 ◦C/min in nitrogen (99.999%) flow of 300 mL/min by the three-stage fixed-bed reactor. Thermal upgrading stopped when the final temperature reached 300, 400, 500, 600, 700, 800, 900 and 1000 ◦C then maintained for 10 min, and after that, the samples were quickly cooled in a nitrogen atmosphere and was marked as JX, JXO, 1NaJX, 3NaJX, 1NaJXO and 3NaJXO- pyrolysis temperature. After that, the atmosphere was switched to CO2 (99.999%) at the same flow rate for a certain time to produce ACs with different porous structures, which were marked as the samples-pyrolysis time—burn-off value. In order to eliminate the interference of Na-based compounds in chars for the results of XRD and Raman. The 10 g of testing samples, including Na-based compounds were washed with 300 mL of 0.2 mol L−1 HCl at 60 ◦C for 24 h using magnetic stirring, then the residual acid-soluble inorganic salts on the surface of coal particles were removed by filtering and washing with 300 mL deionized water twice, respectively. In addition, the dissolved Na element in the residual liquid was quantified by an ICP-AES. This testing process was repeated three times, and the test results were averaged.

#### *2.3. Measurement Analysis*

The visualized results of surface topography of samples were obtained by SEM (Quanta 200, FEI, Hillsboro, OR, USA) a t 200 kV. The crystal parameters of the samples were obtained by a D/max-rb X-ray diffractometer (XRD, D8 ADVANCE, Brooke, Karlsruhe, Germany) in the 20 range from 10◦ to 80◦, and the scanning rate of XRD was set at a stable value of 3◦/min. The different hybrid carbon structures of the samples were obtained by Raman spectroscopy via a 532 nm wavelength laser, and the scanning scope was determined from 1000–1800 cm<sup>−</sup>1. The information of pore structure was obtained by a micromeritics adsorption apparatus (ASAP2020, Micromeritics, Norcross, GA, USA) in a relative pressure (P/P0) range from 10−<sup>7</sup> to 1, and the analysis temperature was set at 77 K [34]. The vacuum degassing process for samples was performed before the test and analysis experiment, and the temperature and time were set to 473 K and 12 h. Furthermore, the specific parameters of pore structure, such as the specific surface area (SBET), the micropore area (Smic), and the micropore volume (Vmic), were calculated using the following formulas: Brunauer–Emmett–Teller (BET) equation, the t-plot method, Horvath–Kawazoe (HK) method, and nonlocal density functional theory (NLDFT), respectively [35]. In addition, the parameter of the total pore volume (Vt) was obtained at 0.98 relative pressure. Elemental analysis (EA) was performed using an analyzer (Vario MACRO cube, Elementar, Langenselbold, Germany) for determination of the total carbon and oxygen content of the bulk samples. The quantification of sodium was obtained by an ICP-AES (Optima 5300 DV, PerkinElmer, Boston, MA, USA).

#### *2.4. The SO2 Adsorption Test*

The SO2 adsorption experiments were carried out in a fixed bed reactor using an on-line Fourier transform infrared gas analyzer (Dx4000, Gasmet, Vantaa, Finland) to continuously monitor the SO2 concentrations. The experimental system consists of a tubular reactor (20 mm diameter), placed in a vertical furnace, with a system of valves and mass flow controllers in order to select the flow and the composition of the inlet gas, as shown in Figure S1. In each typical running, 2.5 g of the sample was put into the tubular reactor at 80 ◦C within 120 min. The gas volumetric composition used in experiments was: SO2, 1500 ppm; O2, 5%; water vapor, 10%; N2, balance, total flow rate 200 mL·min<sup>−</sup>1. SO2 removal efficiency and rate versus time were evaluated by detected concentrations of SO2 at the inlet and outlet in real time through the gas analyzer. The SO2 removal capacity of coal-based activated carbons was calculated by the integrating area above the removal curves and reaction time [36].

#### **3. Results and Discussion**

#### *3.1. Sodium Release of NaCl Loaded Chars at Pyrolysis*

Figure 1 shows the retention of Na (%) in different chars during pyrolysis. With the increase of pyrolysis temperature, the retention of Na in the char all decreases.

**Figure 1.** The retention of Na (%) in different chars at pyrolysis.

The trends of the retention of Na in 1NaJX and 3NaJX are similar, especially when the pyrolysis temperature is higher than 500 ◦C, the retention of Na rapidly decrease from 85 to 5%, indicating the release of Na from chars cannot be changed only by adding the amount of catalyst. Li et al. 26 found that the repeated fracture and recombination processes are presented between alkali metal and coal/coke system (CM), resulting in the production of more stable chemical bonds as the increase of pyrolysis temperature, the entire reaction process can be expressed as follows:

$$(\text{CM} - \text{Na}) = (-\text{CM}) + \text{Na} \tag{1}$$

$$(-\text{CM}) = (-\text{CM}') + \text{gas} \tag{2}$$

$$(-\text{CM}') + \text{Na} = (-\text{CM}' - \text{Na})\tag{3}$$

In addition, it is noteworthy that large quantities of Na are retained in 1NaJXO and 3NaJXO during pyrolysis. Some intermediates (such as -O-Na and −COONa) are formed to fix Na within chars in the temperature range from 300 ◦C to 600 ◦C, resulting in the release of a small amount of sodium. Then these intermediates with low thermal stability (such as −COONa) may further be dissociated

into the volatile matter to produce more active sites, promoting the re-bonding process between Na and carbon matrix (CM), the reaction process can be expressed as follows [37]:

$$(-\text{COO-Na}) + (-\text{CM}) \to (\text{CM-Na}) + \text{CO}\_2 \tag{4}$$

However, other intermediates with high thermal stability (−O−Na) can continue to stabilize alkali metal Na within chars even at a higher temperature.

#### *3.2. Carbon Structure Analysis of Chars at Pyrolysis*

The Raman spectra of the different chars are shown in Figure S2. To obtain more information about the hybrid carbon structure of chars, each Raman spectrum was treated further into five band areas by the fitting method [38]. Importantly, the ratios of the different band areas, such as AD1/AG, AD3/AG, AD4/AG, and AD1/AD3, represent the different types of hybrid carbo as follow in turn: The defect degree of the microcrystalline structure; the amorphous carbon; the relative quantity of cross-linking bonds; the ratio between big rings relative to small fused rings in chars [39,40]. The parameters of the carbon structure of carbonized chars are given in Figure 2.

**Figure 2.** Raman data from chars at pyrolysis (**a**) AD1/AG; (**b**) AD3/AG; (**c**) AD4/AG; (**d**) AD1/AD3.

First of all, the values of AD1/AG, AD3/AG and AD4/AG of JX, 1NaJX and 3NaJX decrease obviously with the increase of Na loading during temperature rising from 25 to 500 ◦C, whereas the AD1/AD3 value increases gradually. These results indicate that the splitting of big aromatic rings (AD1/AG) and the remove of a large number of amorphous sp<sup>2</sup> bonding carbon atoms (AD3/AG) as volatile matter at the beginning of pyrolysis result in the increase in the AD1/AD3 value. Moreover, these reactions can be further strengthened under the presence of Na. The value of AD3/AG of 1NaJX and 3NaJX continues to decrease in this stage, due to the release of more organic components caused by the catalytic

decomposition of Na. Alternatively, the value of AD1/AG decreases slightly and AD1/AD3 decrease quickly, whereas the values of AD3/AG and AD4/AG of JXO increase obviously. The cross-linking reaction of oxygen-containing functional groups not only promotes the formation of cross-linking bonds (such as -COO- and -O-) and more small aromatic rings (such as some oxygen-containing heterocycles), but hinders the intense decomposition of aromatic structure. However, the values of AD1/AG and AD1/AD3 decrease obviously and the AD3/AG and AD4/AG of 1NaJXO and 3NaJXO increase slightly as compared with that of JXO. The formation of some intermediates can fix Na within chars to promote further its catalytic decomposition and reduce the number of cross-linking bonds.

Then, the values of AD1/AG, AD3/AG and AD4/AG of JX, 1NaJX and 3NaJX increase obviously with the increase of Na loading during temperature rising from 500 to 800 ◦C, whereas the AD1/AD3 value decreases gradually. The increase of some aromatic rings originates from the formation of more new cross-linking bonds at this stage. Especially, there is no significant change in all parameters for JX from 700 to 800 ◦C, where the process of cross-linking reaction can be shortened to hinder the production of sp2-sp3 bonding carbon atoms; the amorphous sp<sup>2</sup> bonding carbon atoms and transformation of small aromatic rings to form big aromatic rings, these results indicate the—graphitization conversion of microcrystalline. In addition, a large amount of Na is released as volatile, the remaining Na is bonded to carbon matrix (CM) to form the stable chemical bonds (CM-Na) at high temperature during which more free radical fragments have been produced. Thus, the cross-linking reaction can be strengthened continuously accompanied by the combination of free radical fragments, resulting in an obvious increase in all parameters of 1NaJX and 3NaJX. The values of AD1/AD3, AD1/AG, AD3/AG and AD4/AG of JXO increase at this stage. The break of oxygen-containing structures with low thermal stability not only helps the production of new cross-linking bonds with high thermal stability to form furthermore small aromatic rings, but also promotes the transformation of small aromatic rings to big aromatic rings. However, the value of AD1/AD3 of 1NaJXO and 3NaJXO decreases, and the values of AD1/AG, AD3/AG and AD4/AG continue to increase than that of JXO. The retention of more Na caused by the existence of oxygen-containing structures enhances further the catalytic and cross-linking reaction, resulting in the higher activity of coal chars.

Finally, the values of AD1/AG, AD3/AG and AD4/AG of JX decrease obviously, whereas the AD1/AD3 value increases from 800 to 1000 ◦C, and the change range of the corresponding parameters of 1NaJX, 3NaJX, JXO, 1NaJXO and 3NaJXO reduces gradually. These results indicate the relevant reactions (including the break of cross-linking bonds; the transformation of the isolated sp2 structure and the amorphous sp<sup>2</sup> bonding carbon atoms into the crystalline sp<sup>2</sup> structure) all are promoted at higher pyrolysis temperature, presenting the more ordered conversion of carbon structure with lower reactivity. In addition, the change of AD1/AD3 value indicates that the amorphous sp<sup>2</sup> bonding carbon atoms are more easily transformed into the crystalline sp2 structure (G peak). In particular, Na can promote further the stability of some oxygen-containing structures to hinder the graphitization conversion of the carbon structure.

#### *3.3. Crystal Structure Analysis of Coal Chars at Pyrolysis*

The XRD profiles of the different chars are shown in Figure S3. Some important structure feature information of aromatic layers (such as layer distance (d002), stacking height (Lc) and width (La)) can be obtained through the fitting treatment of two obvious broad diffraction peaks at 2θ = 24◦–27◦ and 41◦–44◦ in all samples [41]. The results of themicrocrystalline structure are shown in Figure 3.

First of all, the La value of JX, 1NaJX and 3NaJX decreases and the Lc value first decreases and then increases, and the d002 first increases and then decreases from 25 to 500 ◦C. These changes may be related to the break of chemical bonds and the release of organic fragments (such as •CnH2n+1, •OCnH2n+1, and substituted benzene) [41]. The break of chemical bonds and the slow release of organic fragments facilitate the production of the metaplast material, resulting in the movement, the orientation adjustment and the stacking of aromatic layers (namely the presence of a plastic behavior) for JX. However, the addition of NaCl accelerates the depolymerization of aromatic structure unit to produce

more volatile matters and the smaller the aromatic structure, which weakens the production of the metaplast material, the movement and the stacking of aromatic layers [42]. Alternatively, the values of La, Lc and d002 of JXO, 1NaJXO and 3NaJXO increase. The growth and stacking of aromatic layers are promoted by cross-linking reaction of oxygen-containing functional groups, and the significant reductions of organic fragments during the pre-oxidation stage is beneficial to the increase of the layer distance, whereas the formation of some intermediates can reduce the number of cross-linking bonds.

**Figure 3.** XRD data from chars at pyrolysis (**a**) La; (**b**) Lc; (**c**) d002.

Then, the values of La and d002 of JX,1NaJX and 3NaJX increase and Lc value decrease first and then increase obviously from 500 to 800 ◦C, these changes may be related to the competition between the break and production of cross-linking bonds. Some disordered aromatic structure units are formed by cross-linking reaction of aromatic layers, resulting in the disordered array and connection of aromatic layers. The formation of stable chemical bonds (CM-Na) at a high temperature cannot only enlarge the spacing of aromatic layer, but produces more organic fragments to increase the amount of cross-linking bonds [43]. However, the presence of more ordered aromatic structure units facilitates the break of chemical bonds instead of the cross-linking reaction of aromatic layers, presenting a graphitization tendency. Alternatively, the values of La and d002 increase of JXO, 1NaJXO and 3NaJXO, and Lc value decrease with the increase of Na loading. The break of cross-linking bonds with low thermal stability promotes the depolymerization of aromatic layers, and the production of new cross-linking bonds with high thermal stability helps the formation of more aromatic rings with disordered structure. The retention of a large number of Na enhances further the break and formation of cross-linking bonds with different thermal stability, presenting the obvious non-graphitized tendency.

Finally, the values of La, Lc and N of JX, 1NaJX, 3NaJX and JXO increase and the d002 value decreases rapidly from 800 to 1000 ◦C. These changes indicate the microcrystalline structure has transformed into a highly ordered graphite-like structure, and the ordered stacking and rapid growth of aromatic layers are presented at higher pyrolysis temperature. The variation of these parameters can be weakened with the increase of Na loading or the existence of oxygen-containing structures, but it is not easy to hinder the ordered condensation of aromatic layers at a high temperature. Remarkably, the values of La and d002 of 1NaJXO and 3NaJXO increase gently and the values of Lc gradually decrease, these results indicate that the coexistence of oxygen-containing structures and Na can help further the disordered transformation of aromatic structures at high temperature.

#### *3.4. Specific Surface Area (SBET) Analysis of Coal Chars at Pyrolysis*

The value of SBET and Vt of chars is shown in Table 2. First of all, the SBET value of JX increases first slightly and then decreases rapidly from 25 to 500 ◦C. The formation of pores is related to the release of volatile matter at the beginning of pyrolysis, but the increase of pore volume is limited. With the increase of pyrolysis temperature, a large number of metaplast materials are formed to block the pores of chars. However, the release of more volatile matter and the formation of fewer metaplast materials are performed with the increase of Na loading, leading to the relatively developed pore structure of 1NaJX and 3NaJX compared to that of JX. Alternatively, the SBET value of JXO, 1NaJXO and 3NaJXO increases continuously with the increase of Na loading. The depolymerization and recombination of aromatic structure promote the release of volatile matter and disordered stacking of aromatic layers, and there are no metaplast materials to block the pores for oxidized coal, resulting in the development of the pore. In addition, the depolymerization and cross-linking recombination of aromatic structure can be strengthened under the existence of Na, therefore, the pores structure of 1NaJXO and 3JXNaO can be developed further.


**Table 2.** The value of SBET (m2/g) of chars at pyrolysis.

Then, the SBET value of JX, 1NaJX and 3NaJX increases from 500 to 800 ◦C. The formation of cross-linking bonds between aromatic layers at this condensation stage promotes the production of pores. However, the pores have never been fully developed, due to blockage of metaplast materials. Alternatively, the SBET value of JXO, 1NaJXO and 3NaJXO increases obviously with the increase of Na. The break of oxygen-containing functional groups and oxygen heterocycles promotes the formation of cross-linking bonds and the release of volatile matter, resulting in the disordered condensation of aromatic structure and the formation of more pores. These processes can be strengthened further with the increase of Na loading.

Finally, the SBET value of JX, 1NaJX, 3NaJX, JXO decreases obviously from 800 to 1000 ◦C. This result is related to the collapse and expansion of microporous to form mes- or marc-opore and the further collapse of mesopore and macropore can be presented at higher pyrolysis temperature. However, it is difficult to prevent the collapse of pores only in the presence of Na or oxygen-containing functional groups. Remarkably, the SBET value of 1NaJXO and 3NaJXO indicates that the coexistence of oxygen-containing structures and Na can stabilize and develop the three-dimensional spatial structure of aromatic layers, thus resulting in the sustained development of porosity.

#### *3.5. Crystal Structure Analysis of Typical Chars During Activation*

3NaJXO-800 is the most suitable precursor for subsequent activation, due to its disordered carbon structure, abundant initial pores and active sites. JX-800 and JXO-800 are used as the contrastive precursor. Therefore, the change of the physicochemical structure for these typical chars during activation can be analyzed in detail. The XRD profiles and crystal parameters of JX-800, JXO-800 and 3NaJXO-800 at different burn offs are shown in Figure S4 and Table 3.


**Table 3.** XRD data of typical chars at different burn offs.

First of all, there is a sustained increase in the values of *L*<sup>a</sup> and *N* of JX-800 and decrease in the *d*<sup>002</sup> value, however, the *L*<sup>c</sup> value first decreases from 0 to 19.5% and then increases from 19.5 to 51.3%. These changes indicate that the microcrystalline of JX-800 always develops towards a highly ordered structure during activation. At the beginning of activation, the reaction between activated gas and the active sites and some sandwich materials in the longitudinal aromatic layers results in a decrease in the values of d002 and Lc. With an increase in carbon loss, the aromatic layers with the more ordered orientation rapidly begin to condense and distort that promotes the thickness and size of the microcrystalline.

Alternatively, the values of Lc, La and N of JXO-800 first decrease and then increase, whereas d002 value first increases and then decreases with an increase in burn offs. These changes indicate that the existence of oxygen-containing structure of JXO-800 may hinder the ordered transformation of aromatic layers at the beginning of activation. More active sites (including the defects and the oxygen-containing side chains and bridge of basic unit in aromatic layers) are removed by activated gas to strengthen the etching of carbon-based framework, thus resulting in the decrease of the thickness and size of the microcrystalline structure. With an increase in carbon loss, the more active sites of JXO-800 have been consumed, therefore the horizontal and longitudinal condensation of aromatic layers have been presented; moreover, the rapid reduction of lamellar spacing also indicates the highly ordered transformation of aromatic structures.

Finally, there is a sustained decrease in values of La, Lc and N of 3NaJXO-800 and the increase on d002 value during activation, indicating that the addition of Na catalyst has promoted a continuous disordered conversion of aromatic structures. The distortion of the longitudinal aromatic structure can accompany with its catalytic cracking, thus resulting in an obvious decrease in the Lc value. Na bonded and fixed in carbon matrix may destroy the parallelism of the layer and the constancy of the interlayer spacing, thus increasing the interlayer spacing; however, Na-based compounds can further accelerate the etching of aromatic layer instead of their ordered condensation and growth.

#### *3.6. Carbon Structure Analysis of Typical Chars During Activation*

The Raman spectra and corresponding parameters and of JX-800, JXO-800 and 3NaJXO-800 at different burn offs are shown in Figure S5 and Table 4.


**Table 4.** Raman data of typical chars at different burn offs.

There is a sustained decrease in the values of AD3/AG and AD4/AG, and the values of AD1/AG and AD1/AD3 first increase at the low burn offs and then decrease at the high burn offs. At the beginning of activation, the active sites are consumed by activated gas preferentially, resulting in a decrease in the AD3/AG and AD4/AG values. In addition, the growth and conversion of aromatic ring and its conversion into big aromatic ring structures promote the increase in the AD1/AD3 and AD1/AG values. With an increase in burn-offs, the interior of aromatic structure can be activated by the continuous penetration of the activated gas to further induce the condensation of the aromatic ring [42]. Alternatively, there is a sustained decrease in AD1/AG value of JXO-800, whereas the values of AD3/AG and AD4/AG first increases and then decreases, and the AD1/AD3 value first decreases and then increases. At the beginning of activation, the existence of more active sites promotes further the etching of carbon structure, thus hindering its growth. In addition, the existence of more oxygen-containing structure facilitates the production of new cross-linking bonds and small aromatic ring, but the reaction path between active sites and active gas still can't be changed. With an increase in burn-offs, consistent reduction of oxygen-containing structure and self-consumption of the small aromatic ring and its conversion into a big aromatic ring are presented to promote the formation of more crystalline sp2 structure. Finally, there is a sustained decrease in the values of AD1/AD3 and AD1/AG of 3NaJXO-800 and the increase in the values of AD3/AG and AD4/AG. It can be inferred that the presence of Na catalyst can change the reaction pathways between the carbon structure and activated gas. More concretely, the active sites can no longer be consumed with activated gas preferentially, and the big aromatic rings would begin to decompose into small aromatic rings and the Na catalyst also hinder the formation of the crystalline sp2 structure. In other words, a large number of broken fragments are produced by the catalytic effect of Na, resulting in the formation of newer cross-linking structure. Moreover, the presence of oxygen-containing structures is conducive to the reorganization of aromatic fragments.

#### *3.7. Pore Structure Development of Typical Chars During Activation*

To analyze the pore development of JX-800, JXO-800 and 3NaXO-800 at different burn-off values during activation, N2 adsorption isotherms and parameters of porous structure are shown in Figure 4 and Table 5.

First of all, the SBET value of 101.78 m2·g<sup>−</sup>1, Vmic value of 0.06 m3·g−<sup>1</sup> and non-Vmic value of 6.25% of JX-800-19.5 are obtained, showing the development of micropores at the beginning of activation. These values increase gradually with an increase in burn-offs from 19.5% to 51.3%, that are related to the enlargement of micropores into mesopores and the production of some new micropores. Remarkably, the rapid increase of non-Vmic value indicates the more obvious development of mesopore rather than that of new micropores. At a higher burn offs, an SBET/burn-off ratio value of 13.23 m2 g−1/% of JX-800-51.3 is obtained. Many macropores from 2 μm to 35 μm on the particle surfaces can be found in JX-800-51.3 from Figure 5a, indicating the severe carbon losses on the particle surfaces during

activation. This result may be related with the ordered conversion of the aromatic structure of JX-800 with less initial pores can hinder the penetration of activated gas into the interior of char structure during activation, thus resulting in the occurrence of more reactions on the particle surfaces rather than in the interior to decrease the production of the pores.

**Figure 4.** N2 adsorption isotherms (**a**), (**c**) (**e**) and pore-size distributions (**b**), (**d**) (**f**) of activated carbons at different burn offs.

Then, the increase in Vt, Vmic and SBET values and the decrease in the non-Vmic value of JXO-800 with the increase of burn offs from 0 to 29.4% show an obvious growth of micropores. These changes are related to the initial pores of JXO-800 act as channels to promote the diffusion of activated gas and more active sites produced by pyrolysis can strengthen the etching of carbon structure. All pore parameters of JXO-800 increase gradually with the increase of burn offs from 29.4 to 47.2%, indicating the formation of new micropores slows down; however, a rapid development of mesopores mainly results from the widening of the pores. The oxygen functional groups of JXO-800 as active sites are consumed gradually with continuous activation, resulting in limited micropores development. In particular, an SBET/burn-off ratio value of 22.99 m2 g−1/% of JXO-800-47.2 is obtained. No severe carbon losses and macropores on the particle surfaces of JXO-800-47.2 are found from Figure 5b, these changes are related to the penetration of activated gas into the interior of the particle during activation.


**Table 5.** Pore structure parameters of typical chars at different burn offs.

**Figure 5.** SEM images of typical chars under final burn-off values (**a**) JX-800-51.3; (**b**) JXO-800-47.2; (**c**) 3NaJXO-800-48.1.

Finally, the rapid increase in Vt, Vmic and SBET values and the rapid decrease in non-Vmic value of 3NaJXO-800 during the whole stage of activation are shown in Table 5. Along with the gradual consumption of oxygen functional groups, the disordered conversion of carbon structure and more active sites in the presence of Na-based catalysts facilitate a sustained formation of more micropores. Although the catalysts might have moved and agglomerated on the particle surfaces with an increase of burn-off at high temperature activation, the simultaneous existence of oxygen functional groups and Na-based catalyst can constantly enhance the development of micropores. Importantly, am SBET/burn-off ratio value of 41.48 m<sup>2</sup> g−1/% of 3NaJXO-800-48.1 is obtained. Moreover, the surface morphology of 3NaJXO-800-48.1 is similar to that of JXO-800-47.2 from Figure 5c, indicating no severe carbon losses on the particle surfaces of 3NaJXO-800-48.1.

#### *3.8. Study of SO2 Adsorption*

The SO2 adsorption test from a simulated flue gas in which JX-800-51.3, JXO-800-47.2 and 3NaJXO-800-48.1 as testing samples is carried out under 80 ◦C, the result of SO2 removal of the samples is shown in Figure 6.

**Figure 6.** SO2 removal of typical activated carbon (**a**) SO2 breakthrough curve; (**b**) SO2 adsorption quantity.

The SO2 adsorption capacities of JX-800-51.3 are mainly exhibited within only 30 min, after that, its desulfurization performance is obviously reduced from 30 to 180 min. The SO2 concentrations of gas outlet for JX-800-51.3 have achieved 1200 ppm about 30 min, showing that it has been penetrated basically by SO2. The SO2 adsorption capacity of JX-800-51.3 can only achieve 50.2 mg/g. Then SO2 adsorption efficiency of 3NaJXO-800-48.1 maintains at 100% within 90 min. The SO2 concentrations of gas outlet for 3NaJXO-800-48.1 slowly increase from 90 to 180 min and only achieve 700 ppm at 180 min, indicating the adsorptive capacity of 3NaJXO-800-48.1 with 140.2 mg/g is still strong. However, the SO2 adsorption capacities of JXO-800-47.2 are presented between JX-800-51.3 and 3NaJXO-800-48.1. In the case of AC, the hierarchical structure (micro- and mesopores) is critical to the SO2 removal process, the adsorption and catalysis processes of SO2 are performed within the micropores, and the developed mesopores promote the migration and storage of produced sulfuric acid [3]. In addition, a high specific surface area (SBET), which is related to the degree of well-developed pores, promotes desulfurization [44]. In addition, Zhu et al. [16,17] found that with the increase of burnout rate, the amount of basic functional groups has a good positive correlation with micropore. When the micropore originates from the microcrystalline structure etched by activated gas, they have a better linear relationship. The more active sites and high micropore volume of 3NaJXO-800-48.1 promote the adsorption and catalysis processes of SO2 within the micropores, then a well-developed mesopore is conducive to the migration and storage of sulfuric acid to release consistently the active sites as adsorption sites within the micropores, which ensures the persistent adsorption capacity. However, the SO2 adsorption capacities of JX-800-51.3 has reached saturation quickly in the initial stage, due to its less active sites and low micropores value, then undeveloped mesopore of JX-800-51.3 is unable to meet the storage of more sulfuric acid to release consistently the active sites, thus presenting a low SO2 adsorption capacity.

#### **4. Conclusions**

A catalytic effect of NaCl (1 and 3 wt%) in presence of oxygen functional groups (created by air pre-oxidation at 200 ◦C for 48 h) has provided control of the physicochemical structure of Jixi bituminous coal-based ACs for high efficiency of SO2 adsorption. In the phase of pyrolysis, a large number of Na catalyst can be fixed first in the interior of chars by the oxygen functional groups in the form of some intermediates (such as -O-Na and -COONa), then Na can be re-bonded with carbon matrix (CM-Na) at high temperatures, during which the catalytic cracking characteristics of Na catalyst plays a more important role, finally resulting in the disordered conversion of microstructure and the number of more active sites. Moreover, Na catalyst also helps the development of micropores; however, the evolution of oxygen functional groups mainly facilitates the production of mesopores. In the phase of activation, the reaction pathway of active sites and CO2 was changed by the presence of Na catalyst, leading to a consistent disordered conversion of the microstructure and the production of new active sites of 3NaXO-800. With an increase in burn offs, the existence of Na catalyst facilitates the etching of

the carbon structure to develop continuously the micropores rather than only widening of the pores to form mesopore and macropore. Finally, the SBET values (1995.35 m2·g<sup>−</sup>1) of 3NaXO-800-48.1 with the high SBET/burn-off ratio values of 41.48 m2·g−1/%. is obtained, presenting a persistent high adsorption efficiency (100%) within 90 min and a high SO2 adsorption capacity with 140.2 mg/g after 180 min.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2227-9717/7/6/338/s1, Figure S1: Schematic figure of the fixed bed reactor system for SO2 adsorption, Figure S2: Raman spectra from chars produced by pyrolysis, Figure S3: XRD profiles from chars produced by pyrolysis, Figure S4: XRD profiles of coal chars at different burn offs during activation (**a**) JX-800; (**b**) JXO-800; (**c**) 3NaJXO-800; Figure S5: Raman spectra of coal chars at different burn offs during activation (**a**) JX-800; (**b**) JXO-800; (**c**) 3NaJXO-800.

**Author Contributions:** D.L., B.J. and L.D. conceived and designed the experiments; X.Z., R.S. and Z.H. carried out the experiments; D.L. wrote the paper; D.L. and B.J. reviewed the paper.

**Funding:** This research was funded by National Natural Science Foundation of China, grant number 51806080, and Scientific Research Fund Project of Jilin Agricultural University, grant number 201801, and Jilin Province Education Department Science and Technology Program during the Thirteenth Five-year Plan Period, grant number JJKH20190940KJ.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Article* **The E**ff**ect of Various Nanofluids on Absorption Intensification of CO2**/**SO2 in a Single-Bubble Column**

#### **Soroush Karamian 1, Dariush Mowla <sup>2</sup> and Feridun Esmaeilzadeh 2,\***


Received: 23 May 2019; Accepted: 21 June 2019; Published: 26 June 2019

**Abstract:** Application of nanoparticles in aqueous base-fluids for intensification of absorption rate is an efficient method for absorption progress within the system incorporating bubble-liquid process. In this research, SO2 and CO2 were separately injected as single raising bubbles containing nanofluids to study the impact of nanoparticle effects on acidic gases absorption. In order to do this, comprehensive experimental studies were done. These works also tried to investigate the effect of different nanofluids such as water/Al2O3 or water/Fe2O3 or water/SiO2 on the absorption rate. The results showed that the absorption of CO2 and SO2 in nanofluids significantly increases up to 77 percent in comparison with base fluid. It was also observed that the type of gas molecules and nanoparticles determine the mechanism of mass transfer enhancement by nanofluids. Additionally, our findings indicated that the values of mass transfer coefficient of SO2 in water/Al2O3, water/Fe2O3 and water/SiO2 nanofluids are, respectively, 50%, 42% and 71% more than those of SO2 in pure water (*kL*SO2−*water* <sup>=</sup> 1.45 <sup>×</sup> <sup>10</sup>−<sup>4</sup> m/s). Moreover, the values for CO2 in above nanofluids were, respectively, 117%, 103% and 88% more than those of CO2 in water alone (*kL*CO2−*water* <sup>=</sup> 1.03 <sup>×</sup> <sup>10</sup>−<sup>4</sup> m/s). Finally, this study tries to offer a new comprehensive correlation for mass transfer coefficient and absorption rate prediction.

**Keywords:** nanofluids; absorption intensification; mass transfer coefficient; bubble column

#### **1. Introduction**

Combustion of fossil fuels led to deforestation and global warming by the emission of acidic gases such as SO2 and CO2 into the environment [1]. Hence, in 1992, the United Nation Conference on Environment and Development offered a new strategy for reducing the emission of acidic and other greenhouse gases to below the standard level until 2000 [1]. Consequently, the governments should finance researchers and scholars to apply new methods and techniques to reduce the amount of CO2 as well as the SO2 produced from large-scale industries and sources [2–6].

In order to remove acidic gases from the natural gas, the scrubbing with the amine solution is the main process in the gas refineries. In addition, various techniques including physical and chemical absorption, membrane technology and adsorption methods are applied for the high CO2/SO2 production industries such as metal forming plants and petrochemical companies [7–9]. One of new approaches for enhance the absorption process, is addition of nanomaterials to basefluids for obtaining novel solvent with ability to absorb gases efficiently [2,3,10,11]. This method were elucidated by several researchers due to its high efficiency, and it has received much more attention in recent years [12,13].

Krishnamurthy et al. fulfilled a comprehensive research on the application of nanoparticles for increasing of mass transfer rate within a basefluid environment. They revealed that Brownian motion of nanoparticles, leading to induce the micro-convections in nanofluids, has the most impact on mass transfer rate [14]. Ashrafmansouri et al. comprehensively studied previous research and reported an review to highlight the impacts of nanomaterials in heat and mass transfer processes [11]. They reported that much higher thorough studies are needed to disclose the impacts of main parameters including nanoparticles mean size and morphology on absorption rate by using nanofluids. They also exhibited that nanofluid reusing as well as absorption process modeling are the most important subjects for advancement of this technique. In addition, Kim et al. showed that mass transfer rate of ammonia is enhanced when a few nanoparticles are added to the basefluid. They exhibited that bubbles breaking by nanoparticles considerably enhances mass transfer through increasing interfacial area. They also reported that smaller bubbles were produced in nanofluid than in a base fluid, leading to intensification in mass transfer surface area [15].

Ma et al. declared that by adding CNTs to a basefluid, the localized micro-convection occurs due to the Brownian motion of nanotubes [16]. They reported that induced convection can intensify the ammonia molecular diffusion within the nanofluid. Moreover, they concluded that the grazing effect can be considered another mechanism enhancing the efficiency of NH3 by means of the bubble absorption process [16]. Absorption of gas molecules by means of the nanoparticle surfaces at the bubble interface and then removing the adsorbed gas components from the nanoparticles surface into the fluid is known as grazing effect [17]. Kang et al. also assessed the impact of Carbon nanotubes on gas absorption in a nanofluid [18]. They also revealed that the mass transfer rate of gaseous ammonia in 0.001 wt. % CNTs loaded in nanofluid was 20% higher than that of pure deionized water [18,19].

Numerous researchers have focused on the application of nanofluids as a potential absorbent for the removal of acidic gases [6,11,12,20–23]. Esmaeili-Faraj et al. exhibited that the removal rate of H2S enhanced up to 40% when 0.02 wt. % of EGO (Exfoliated Graphene Oxide) is added to deionized water as a basefluid. They showed that the main mechanism for enhanced absorption rate is the grazing effect [4].

Jung et al. performed an extensive research in which Al2O3 nanoparticles were scattered in methanol as with nanoparticles volume fractions range of 0.005–0.1 vol. % [24]. They observed that the maximum CO2 removal was 8.3% at 0.01 vol. % nanoparticles compared to the conditions that pure methanol was used as an absorbent. They concluded that the enhanced CO2 uptake is due to the mixing effect of Al2O3 nanoparticles, which were caused by the particle-laden flows induced by Brownian motion [24]. In addition, they observed that for the concentration above a critical value, insignificant Brownian motion can be seen since the inter-particle interactions declines this motion [24].

Darvanjooghi et al. studied the absorption of CO2 by means of Fe3O4/water nanofluid during the applied alternating and constant magnetic fields [3]. Their results declared that both CO2 solubility and mass transfer rate are increased when the strength of magnetic field is high. In addition, they found that the solubility of CO2 and its average molar flux into the nanofluid possess a maximum value by applying an AC magnetic field. Finally, they showed that with the increment of magnetic field strength, the mass diffusivity of carbon dioxide in the nanofluid and renewal surface factor increase, whereas the diffusion layer thickness diminishes.

Although, the impacts of different parameters on gas absorption, by means of nanofluids, are studied in previous works, there are no fully agreement and comprehensive results regarding the influence of nanoparticles types on mass transfer parameters in oxides nanoparticles loaded in nanofluids.

Thus, the aim of this study is to reveal the effect of different metal oxide nanoparticles on SO2 and CO2 mass transfer parameters in a single-bubble absorber. Hence, comprehensive experimental studies are done to investigate the molar flux, absorption rate, mass transfer coefficient and diffusivity coefficient. In addition, a new correlation encompassing nanofluid properties was developed in order to estimate mass transfer coefficients of the mentioned gases in nanofluids.

#### **2. Materials and Methods**

#### *2.1. Materials*

In this research, SiO2 nanoparticles with the purity of 99.99 wt. %, Al2O3 nanoparticles with the purity of 99.98 wt. % and Fe2O3 nanoparticles with the purity of 99.92 wt. % were purchased from U.S. Nano Company, United State (see Table 1) to prepare water based nanofluids. In order to perform reverse titration for measuring the quantity of CO2 and SO2 dissolved in nanofluids, pure NaOH pellets (99.99 wt. %) and HCl with the purity of 37 vol. % were purchased from Merck Company, Germany. Moreover, phenolphthalein and methyl orange obtained from Merck Company, Germany were used as indicators for determination of the equivalent points. Deionized water was used for the preparation and dilution of nanofluids as well as washing the laboratory glassware. All chemical materials are used as received without further purification.

**Table 1.** Physical properties of the nanoparticles (NPs) used in this study.


#### *2.2. Apparatus*

#### 2.2.1. Nanofluid Preparation Instruments

In this study, the transmission electron microcopy (TEM) and dynamic light scattering (DLS) were used to estimate the size distribution of dry and dispersed metal oxides nanoparticles in deionized water, respectively. The TEM images of SiO2, Al2O3 and Fe2O3 nanoparticles were obtained by using Hitachi, 9000 NA, Japan to characterize the size of nanoparticles and their agglomeration [25]. For preparing the sample of nanoparticles used in TEM analysis, a suspension of the nanoparticles dispersed in ethanol (0.001 wt. %) was sonicated by using an ultrasonic bath, Parsonic 30S-400W, 28 kHz, for 20 min and then was placed on the graphite surface. The samples were then put in a vacuum oven to remove the ethanol before being introduced into the TEM test device. DLS, Malvern, Zeta Sizer Nano ZS, United Kingdom, was applied to estimate the sizes of nanoparticles and the size distribution of the obtained metal oxides nanoparticles in deionized water [5,25,26]. The stability and surficial electrostatic charges of the metal oxides nanoparticles in deionized water were estimated by using Zeta Potential test (ELSZ-2000, Otsuka Electronics Co., Osaka, Japan). This analysis is a key indicator of the stability of metal oxides nanoparticles within deionized water [12]. Zeta potential accounts for the electrostatic charges on the surface of nanoparticles causing repulsive forces between dispersed particles. The negatively and positively larger magnitude of zeta potential exhibits a significant stability of nanoparticles in the basefluid, whereas a lower magnitude of maximum Zeta-potential declares the tendency of nanoparticles for agglomeration [27]. A Mass Flow Controller (MFC) model Brooks Instrument 1-888-554-flow, USA, was implemented for the injection of CO2 and SO2 gases into the nanofluids through the absorption apparatus. Furthermore, water based nanofluids were prepared by measuring and adding the required weight amounts of metal oxide nanoparticles. To do so, a precise electric balance (TR 120 SNOWREX, Taiwan) was implemented. A pH meter (PCE-PHD 1, PCE-Instruments holding, Southampton, UK) was used for recording the pH of solutions during the titration. Finally, an ultrasonic processor (QSONICA-Q700, NY, USA) was used in order to stop forming the agglomeration of SiO2, Al2O3 and Fe2O3 nanoparticles, after they were under a mechanical ball-mill (YKM-2L, Changsha Yonglekang Equipment Co., Changsha, China) for grinding the clustered nanoparticles. A syringe-pump (Viltechmeda Plus SEP21S, manufactured in Vilnius, Lithuania) was

also employed for injection of the titrant to the flask. Lastly, a magnetic stirrer (Model IKA-10038, Staufen, Germany) was used for stirring the solutions.

#### 2.2.2. Experimental Set-Up

The experimental set-up contained a bubble column absorber filled with metal oxide nanoparticles loaded in nanofluids. A certain volume of CO2 and SO2 was injected into the nanofluid within the absorption column. Figure 1 exhibits the schematic diagram of a bubble column absorber that consists of a 1 m high and 16.2 mm diameter poly-methyl-meta-acrylate (PMMA) tube used as a semi-batch instrument to examine the absorption of acidic gases by means of nanofluids. In addition, in order to control the rate of gas absorption in nanofluids, a syringe-pump was used for the injection of the aforementioned gases through the absorber column. The gases were continuously injected into nanofluids in the absorber column with the constant flow rate of 500 mL/h in each experiment. The average bubbles diameter ranged from 6.9 to 7 mm, and the time for the rising of bubbles was found to be 2.3 s. Finally, in order to measure the concentration of gases in nanofluids in the reverse titration method, the injection of HCl solution into the discharged nanofluid was performed by means of the syringe-pump.

**Figure 1.** Schematic diagram of experimental set-up.

#### *2.3. Methods*

#### 2.3.1. Nanofluid Preparation Procedure

At first, the nanoparticles were introduced to a ball-mill device for about 4 h to separate the agglomeration of nanoparticles. Then, water based nanofluids were prepared with the dispersing of 50 g SiO2, Al2O3 and Fe2O3 nanoparticles in 1000 mL deionized water, separately, to produce the main suspension with the nanoparticles concentration of 5.0 wt. %, (equal to 50,000 mg/L). After adding the nanoparticles to deionized water, the suspensions were kept under stirring condition of 800 rpm for 5 h. Finally, the nanoparticles were dispersed in the basefluid by using the sonication process under three sequences of 20 min. The amplitude and cycle time of sonication were set on 70% and 0.5 s, respectively. Also for the preparation of other suspensions with different nanoparticle concentrations of 0.005, 0.01, 0.1, 1.0, and 5.0 wt. %, the stock solutions were diluted with further deionized water.

#### 2.3.2. Experimental Procedure

#### Sample Analysis Procedure

The analysis for measuring the amounts of absorbed CO2 and SO2 in the nanofluids was carried out by using the reverse titration wherein the standard HCl and NaOH solutions were used as the titrant and reactant for producing Na2CO3 and Na2SO3, respectively [28]. Consequently, in order to determine CO2 and SO2 content by using the reverse titration, it is needed to convert H2SO3 and H2CO3 to Na2SO3 and Na2CO3, respectively, by the addition of a strong standard base. To do so, the nanofluids were discharged to the flask containing 15 mL of 0.1 M NaOH solution. The carbon dioxide and sulfur dioxide in the solution reacted with the sodium hydroxide and formed sodium bicarbonate or bisulfate as Equation (1) [5]:

$$\text{RO}\_2 + 2\text{NaOH} \rightarrow \text{Na}\_2\text{RO}\_3 + \text{H}\_2\text{O}, \text{R} = \text{C}(\text{Carbon}) \quad \text{or} \quad \text{S(Sulfur)}\tag{1}$$

The titration was then accomplished to neutralize the amount of remained NaOH, and then excess HCl (as a titrant) in the flask reacted with Na2SO3 and Na2CO3 during the titration according to the following reactions:

$$\text{Na2RO}\_3 + \text{HCl} \rightarrow \text{NaCl} + \text{NaHRO}\_2 \text{, } R = \text{C(Carbon)} \quad or \quad S \text{(Sulfur)}\tag{2}$$

$$\text{NaHPO}\_3 + \text{HCl} \rightarrow \text{NaCl} + \text{H}\_2\text{O} + \text{RO}\_2, \text{ } R = \text{C(Carbon)} \quad \text{or} \quad \text{S(Sulfur)} \tag{3}$$

According to Equation (2), the discharged samples were titrated with the standard acid solution, (0.1 M HCl), at first equivalent point. The titration with HCl then converted all the remained bicarbonate and bisulfate to SO2 and CO2 according to Equation (3). In this method, the difference of consumed HCl between two equivalent points represents the amount of CO2 or SO2 absorbed in the solution. Equation (4) was used for determining the value of absorbed gases by means of nanofluids [2,3,28]:

$$\text{C}\_{\text{RO}\_2} = \frac{(\text{V}\_2 - \text{V}\_1) \times \text{M}}{\text{V}} \times 10^3 \tag{4}$$

where CRO2 is the absorbed CO2 or SO2 concentration in the nanofluids or deionized water (mol/m3), M is HCl molarity (mol/lit), and V is the volume of absorbent used in the column, (equal to 100 mL), in all experiments. V1 and V2 are the volumes (mL) of standard acid solution consumed for neutralizing bicarbonate and bisulfate to SO2 and CO2 at two equivalent points, respectively (Figures 2 and 3).

**Figure 2.** Plot of pH and its differentiation versus volume of consumed titrant, (HCl), for the injection of 50 mL SO2 through deionized water.

**Figure 3.** Plot of pH and its differentiation versus volume of consumed titrant, (HCl), for the injection of 50 mL CO2 through deionized water.

In this work, the molar flux of absorbed CO2 and SO2 was calculated by means of the CO2 and SO2 concentration in the nanofluid according to the following equation (Equation (5)) [2,3,28]:

$$\text{CN}\_{\text{ave, RO}\_2} = \frac{\text{C}\_{\text{RO}\_2} \times \text{V}}{(4\pi \text{r}\_0^2 \text{n}) \times (\text{r})} \times 10^{-6} \tag{5}$$

Here, Nave,RO2 is the average molar flux transferred from gas, (pure CO2 or SO2), to liquid phase (mol/m2 s), τ is the total gas-liquid contact time of bubbles passing through the nanofluids (s), which is equal to multiply of the bubbles number by raising time of one single bubble (2.3 S), n is the number of bubbles passes through nanofluids within the absorber column and r0 is the average bubbles radius (3.5 mm) that assumed to be constant at all experiments.

#### Measurement of Mass Transfer Parameters

In order to obtain the mass transfer coefficient and diffusivity of CO2 or SO2 in a water based nanofluid, a set of experiments were performed in which the aforementioned gases were separately injected at the bottom of the column within the volumes of 20, 25, 30, 35, 40, 45 and 50 mL. The mass transfer parameters were then calculated by obtaining the absorption of CO2 and SO2 as well as the implementation of the model suggested by Zhao et al. [29].

#### Uncertainty Analysis

In this research, the uncertainty of the experimentations was calculated by the errors of measurements for parameters, incorporating time of raising bubbles, volume of liquid for the titration method and pH of solutions. The time of raising bubbles was measured by using a digital chronometer with the maximum accuracy of ±0.01 s, the pH of discharged nanofluids was measured during the titration by a pH meter with the maximum accuracy of ±0.1, and the volumes of liquids were measured by laboratories glassware with the maximum accuracy of ±0.1. According to the literature [2,3], the relative uncertainty of final experimental results was calculated as follows [30,31]:

$$\text{LU} = \pm \sqrt{\left(\frac{\Delta \text{V}}{\text{V}}\right)^2 + \left(\frac{\Delta \text{t}}{\text{t}}\right)^2 + \left(\frac{\Delta \text{pH}}{\text{PH}}\right)^2} \tag{6}$$

Consequently, by substituting the values in Equation (6) the relative uncertainty of the experimental results was found to be less than 5.2 %.

#### **3. Results and Discussion**

#### *3.1. Nanofluid Characterization*

Figure 4 exhibits the TEM images of SiO2, Al2O3 and Fe2O3 nanoparticles that used for the preparation of water based nanofluids. These images show that the diameter of SiO2 nanoparticles ranged from 20 to 60 nm (Figure 4a), the diameter of Al2O3 nanoparticles ranged from 30 to 80 nm (Figure 4b) and the diameter of Fe2O3 nanoparticles ranged from 20 to 60 nm (Figure 4c). In addition, the results presented in Figure 4 exhibit that all metal oxides nanoparticles have a semi-spherical morphology that no considerable agglomeration was observed [32].

**Figure 4.** Transmission electron microcopy (TEM) images of (**a**) SiO2, (**b**) Al2O3 and (**c**) Fe2O3 nanoparticles.

The results of DLS analysis for SiO2, Al2O3 and Fe2O3 nanoparticles dispersed in deionized water exhibited that the mean diameter of nanoparticles for SiO2 is 48.3 nm with Poly Dispersity Index, (P.D.I.), of 0.105 and the mean diameter of nanoparticles for Al2O3 and Fe2O3 is found to be 54.7 nm and 55.1 nm, respectively, with P.D.I.s of 0.145 and 0.138, respectively. These results confirm that the dispersion technique, which was used in this research, led to the well-dispersed nanoparticles diameter, with a narrow range of 48.3 to 55.1 nm. The results of this test indicate that the average size of nanoparticles is equal to that estimated by using TEM test declaring no significant agglomeration during the dispersion of nanoparticles in the basefluid.

Zeta-potential analysis can be implemented in order to quantify the stability of nanoparticles in the basefluid [33]. These results represent that nanofluids have high stability due to the fact that their zeta potential is lower than −45 mV [34]. In other words, the magnitude of the zeta potential determines the degree of electrostatic repulsion between similarly charged particles in colloidal dispersions. The large magnitude of the zeta potential for SiO2/water, Al2O3/water and Fe2O3/water nanofluids (−97.8 mV for Al2O3/water, 100.2 mV for SiO2/water and 79.5 mV for Fe2O3/water nanofluids) indicated high stability of nanoparticles representing high repulsive electrostatic forces [35].

#### *3.2. Absorption*

#### 3.2.1. Maximum Absorption

Figure 5 shows the average molar flux of CO2 into each of these three nanofluids: SiO2/water, Al2O3/water or Fe2O3/water. The mass fraction of each metal oxides nanoparticle varies from 0.005 to 5 wt. %. The experimentations were repeated four times at a fixed mass fraction of metal oxides nanoparticles and the standard deviations are shown in this figure as the error bars. According to the results presented in this figure, the average molar flux of CO2 increases about 21% with the increase of Al2O3 nanoparticles from 0.005 to 0.1 wt. % while the molar flux decreases for higher Al2O3 nanoparticles loads (0.1 to 5 wt. %). Moreover, the value of CO2 molar flux increases about 45% when the mass fraction of SiO2 nanoparticles increases from 0.005 to 0.01 wt. %. Moreover, for higher mass fractions of SiO2 nanoparticles, a remarkable declination on CO2 molar flux resulted. In addition, the value of CO2 molar flux enhances about 16% when mass fraction of Fe2O3 nanoparticles enhances from 0.005 to 1 wt. %, and a declination of CO2 molar flux resulted in the mass fraction range of up to 5 wt. %. Table 2 represents the mass fraction of nanoparticles where by the maximum value of CO2 molar flux obtained. It can be concluded from this table that CO2 absorption molar flux has a maximum value at 0.1, 0.01 and 1 wt. % for Al2O3/water, SiO2/water and Fe2O3/water nanofluids, respectively. For all nanoparticles types, the nanoparticles intensify the micro-convections, producing larger mass transfer rate in comparison to pure basefluid; thus, initial increase in CO2 absorption would be rationalizable with the aforementioned nanoparticles mass fractions. On the other hands, increasing a number of nanoparticles leads to enhance further the viscosity of nanofluids, thereby overcoming the nanoparticles micro-convection impacts together with diminishing the absorption of CO2 within the nanofluids [4,12]. Furthermore, Figure 5 clearly exhibits that CO2 absorption molar flux in metal oxides-based nanofluids is larger than that in deionized water for various nanoparticles mass loads.

**Figure 5.** Average molar flux of CO2 versus mass fraction of metal oxides nanoparticles (NPs).


**Table 2.** Maximum molar flux and relative absorption rate for SO2 and CO2.

Figure 6 displays the average molar flux of SO2 into each of these three nanofluids: SiO2/water, Al2O3/water or Fe2O3/water. The aforementioned metal oxides nanoparticles were dispersed in deionized water with different concentrations of 0.005, 0.01, 0.1, 1 and 5 wt. %. These experimentations were also repeated four times at a fixed mass fraction of each metal oxide nanoparticle, and the error bars express the standard deviation obtained from the measurements. According to the obtained results, the average molar flux of SO2 enhances about 28% with the Al2O3 nanoparticles enhancement from 0.005 to 0.1 wt. %, and for higher nanoparticles loads, a substantial decrease resulted in its molar flux. In addition, the value of SO2 absorption rate into SiO2/water nanofluid increases about 32% when the mass fraction of SiO2 nanoparticles in deionized water increases from 0.005 to 1 wt. %. After a further increase of mass fraction up to 5 wt. %, the absorption of CO2 declines. Moreover, the value of CO2 molar flux increases about 26% when mass fraction of Fe2O3 nanoparticles increases from 0.005 to 0.1 wt. %; and with a further increase of nanoparticles mass fraction from 0.1 to 5 wt. %, the value of CO2 absorption declines. According to the results presented in Table 2, the maximum molar flux of SO2 can be obtained with the nanoparticles mass fractions of 0.1, 1 and 0.1 wt. % for Al2O3/water, SiO2/water and Fe2O3/water nanofluids, respectively. Similar to the results achieved for CO2 absorption, the addition of nanoparticles into the deionized water enhances the micro-convections and intensifies the mass transfer rate of SO2 while increasing the nanoparticles load increases further the viscosity of nanofluids, declining the absorption rate of SO2 into the nanofluids [4,12]. The results presented in this figure show that SO2 absorption in metal oxides nanofluids is more than that in deionized water.

**Figure 6.** Average molar flux of SO2 versus mass fraction of metal oxides nanoparticles.

#### 3.2.2. Probing of Mass Transfer Rate

Volume loading rate (mL/mL s), can be attributed to the rate of gas injection divided to the total volume of gas equal to which is 50 mL. It actually represents the time which is passing during the mass transfer process and clearly shows what portion of gas is injected through the nanofluid. Therefore, this parameter can easily show the ability of nanofluid to absorb gas at the beginning of the injection or at the end of the process. Figure 7 presents the results of average CO2 absorption in each of these three nanofluids: SiO2/water, Al2O3/water or Fe2O3/water against the volume loading rate that was measured at the temperature of 25 ◦C and the optimum mass fractions of 0.1, 0.01 and 1 wt. % for SiO2, Al2O3 and Fe2O3 nanoparticles in deionized water, respectively. These findings reveal that the absorption rate increases with the enhancement in volume loading rate. Additionally, it is chiefly clear when Fe2O3/water is used as an absorbent, the maximum value of absorption rate is obtained at any volume loading rate. Moreover, these results indicate that the minimum value of CO2 absorption for the Al2O3/water nanofluid resulted in comparison to the other nanofluids assessed in this work. These findings indicated that type of the used nanoparticles had a major effect on mass transfer rate. In addition, it can be concluded from this figure that the mass transfer flux is low at lower volume loading rates, and it increases with the increment of loading rate due to having a higher driving force of mass transfer.

Figure 8 also shows the results of average SO2 absorption in each of these three nanofluids: SiO2/water, Al2O3/water or Fe2O3/water against the volume loading rate that was measured at the temperature of 25 ◦C and the concentrations of 0.1, 1 and 0.1 wt. % for Al2O3, SiO2 and Fe2O3 nanoparticles in deionized water, respectively. These results, which are similar to those obtained for CO2 absorption, show that the absorption rate increases with the growth in volume loading rate, and when SiO2/water is used as an absorbent, the maximum value of absorption rate is obtained at each gas volume loading rate; while for CO2 absorption by using Fe2O3/water nanofluid, a higher absorption rate achieved. In addition, it is chiefly evident that the minimum value of SO2 absorption for the Al2O3/water nanofluid resulted in comparison to the other nanofluids assessed in this work, that is similar to CO2 case. These findings declared that type of the used nanoparticles and their interactions with CO2 and SO2 had a major effect on mass transfer rate of the gas into the nanofluids. Moreover, the value of absorption rate is similar to the case of CO2 absorption.

**Figure 7.** Average molar flux of CO2 versus volume loading rate.

**Figure 8.** Average molar flux of SO2 versus volume loading rate.

#### 3.2.3. Mass Transfer Coefficient

For the calculation of mass transfer coefficient, in separate runs, various volumes of gases (20, 25, 30, 35, 40, 45 and 50 mL that are, respectively, equal to 7, 10, 12, 13, 15.6, 17.6 and 20 min total gas-liquid contact time) were injected into the column and then gas concentration and molar flux were measured. Figure 9 shows the average molar flux of CO2/SO2 against the dissolved concentration of CO2/SO2 in the liquid bulk. These results clearly exhibit that an increase in CO2/SO2 bulk concentration consecutively decreases the average value of molar flux due to the reduction of mass transfer driving force. Moreover this observation has approximately a linear behavior for all cases. In order to potpourri of this linear behavior, the principal mass transfer equation (Equation (7)) was used, and the experimental values for the absorption of CO2/SO2 by using different nanofluids were fitted to Equation (7):

$$N\_{\rm Av\%} = k\_L \left( \mathbf{C}\_{\rm RO\_2,Observed}^\* - \mathbf{C}\_{\rm RO\_2} \right) \tag{7}$$

where *kL* is the mass transfer coefficient at liquid phase, (m/s), CRO2 is the bulk concentration of CO2/SO2 within the nanofluids, and C∗ RO2,*Observed* is the observed concentration of CO2/SO2 at gas-liquid interface, (mol/m3). It is mentioned that observed value for gas concentration in the interface was calculated

from extrapolation of line fitted on experimental data. Since linear pattern was assumed for molar flux and gas concentration. According to the results obtained for the absorption of CO2 into each of these three nanofluids: SiO2/water, Al2O3/water or Fe2O3/water (Figure 9a–c), the model was fitted to the experimental data with the R<sup>2</sup> equal to 0.9753, 0.9755 and 0.9897 declaring high accuracy of the regression analysis and low deviation of the experimental data from the fitted model.

**Figure 9.** Average molar flux versus CO2 bulk concentration for (**a**) SiO2/water, (**b**) Al2O3/water and (**c**) Fe2O3/water nanofluids.

The average molar flux of SO2 versus the bulk concentration is shown in Figure 10. These results are also similar to those obtained for CO2 absorption declaring that an increase in SO2 bulk concentration leads to decrease the average value of molar flux, representing a significant declination in mass transfer driving force. In order to obtain the mass transfer coefficient and SO2 concentration at the bubbles-liquid interface, the regression analysis was also performed on Equation (7), and the equation was fitted to the values for SO2 absorption into each of these three nanofluids: SiO2/water, Al2O3/water or Fe2O3/water (Figure 10a–c) with the R2 equal to 0.9711, 0.9705 and 0.9788, respectively. These values confirm the high accuracy of the regression analysis.

According to the results obtained from Figures 9 and 10, it can be concluded that for all nanofluids used in this study, the vertical diagram (dashed line) shows the observed concentration of CO2 and SO2 at the bubble-liquid interface. Furthermore, the diagonal plot of average molar flux versus the bulk concentration of CO2 and SO2 represents the operating line for gas absorption into the nanofluids. It is clearly evident that by approaching the operating line to the equilibrium concentration of CO2 and SO2 in each of these three nanofluids, namely SiO2/water, Al2O3/water or Fe2O3/water, a lower molar flux resulted.

**Figure 10.** *Cont.*

**Figure 10.** Average molar flux versus SO2 bulk concentration for (**a**) SiO2/water, (**b**) Al2O3/water and (**c**) Fe2O3/water nanofluids.

Table 3 represents the values of relative mass transfer coefficient for SO2 and CO2 absorption by using SiO2/water, Al2O3/water or Fe2O3/water nanofluids with respect to water alone. These values are the slope of operating line in Figures 9 and 10. According to these results, the maximum value of relative mass transfer coefficient for CO2 absorption was achieved by Al2O3/water nanofluid while the value of relative mass transfer coefficient for SiO2/water was observed to possess a minimum value in comparison to the other nanofluids assessed in this work. Additionally, these findings exhibit that the maximum value of mass transfer coefficient for SO2 absorption was achieved for SiO2/water, and this parameter for Fe2O3/water was found to be less than the others. According to the results presented in this table, relative mass transfer coefficient intensively depend on type of the nanofluid. In fact, the absorption of SO2 by SiO2/water nanofluid and the absorption of CO2 by Fe2O3/water nanofluid demonstrate higher values for the relative mass transfer coefficient and relative gas concentration at the bubble-liquid interface.


**Table 3.** Relative mass transfer coefficient for CO2 and SO2 in the base fluid (BF) and various nanofluids (NF).

#### *3.3. Di*ff*usivity Coe*ffi*cient*

In general, diffusivity of gases into a fluid has a higher impact on mass transfer coefficient as well as rate of gas absorption. In this study, Equation (8) was used to obtain the diffusivity of SO2 and CO2 into each of these three nanofluids, namely SiO2/water, Al2O3/water or Fe2O3/water. This equation *Processes* **2019**, *7*, 393

indicates a bubble-liquid mass transfer model for raising a single bubble through a liquid based on Dankwert's theory [5,29].

$$N\_{\rm Axe} = \frac{D \sinh\left(\delta\sqrt{\frac{\delta}{\Delta}}\right) + D \, r\_0 \, \sqrt{\frac{\delta}{\Delta}} \cosh\left(\delta\sqrt{\frac{\delta}{\Delta}}\right)}{r\_0 \sinh\left(\delta\sqrt{\frac{\delta}{\Delta}}\right)} \left(\mathbf{C}\_{\rm RO\_2,i} - \mathbf{C}\_{\rm RO\_2}\right) \tag{8}$$

In this model, the main factors affecting on mass transfer rate are the surface renewal rate (*s*), bubbles radius (*r*0), diffusion layer thickness (δ) and the diffusivity of gases through a liquid (*D*). *NAve* is the molar flux *mol*/*m*<sup>2</sup> *s* , CRO2 and CRO2,i are the concentration of dioxide gases within the liquid bulk and at the bubble-liquid interface (mol/m3), respectively.

By comparing Equations (7) and (8), the mass transfer coefficient of a gas into the liquid by using a single bubble can be obtained from the following relation:

$$k\_L = \frac{D \sinh\left(\delta \sqrt{\frac{s}{D}}\right) + D \, r\_0 \sqrt{\frac{s}{D}} \cosh\left(\delta \sqrt{\frac{s}{D}}\right)}{r\_0 \sinh\left(\delta \sqrt{\frac{s}{D}}\right)}\tag{9}$$

This equation was used for estimating the diffusivity of SO2 and CO2 within the nanofluids. It has been reported by Darvanjooghi et al. that the effective parameters in Equation (9) (*s*, δ and *D*) intensively depend on the size of nanoparticles in the basefluid. They reported that the size of nanoparticles was about 40 to 50 nm, and the values of surface renewal rate, *s*, and the diffusion layer thickness, δ, were 6.85 and 0.201 mm, respectively [2]. In this research, the average mean diameter of nanoparticles ranges from 40 to 60 nm. Additionally, it can be assumed that the values of *s* and δ would be constant during the absorption of SO2 and CO2 and depend on just nanoparticles mean diameter. Additionally, the mass transfer coefficients for both SO2 and CO2 gases within the nanofluids studied here have been already calculated in Table 3. Therefore, Equation (9) can be simplified to the following relation:

$$F(D, \mathbf{s}, \delta) = \exp\left(2\delta\sqrt{\frac{\mathbf{s}}{D}}\right) \mp \frac{D - r\_0\sqrt{\mathbf{s}\cdot\mathbf{D}} - r\_0k\_L}{r\_0\sqrt{\mathbf{s}\cdot\mathbf{D}} - r\_0k\_L} = 0, \text{ s} = 6.85 \text{ and } \delta = 0.201 \tag{10}$$

where *F*(*D*,*s*, δ) must be equal to zero for certain values of mass transfer coefficient and gas diffusivity within the different nanofluids. By using the Newton-Raphson method, Equation 10 can be solved according to the following equation in which ∂*F*(*Dn*,*s*, δ)/∂*Dn* can be obtained by obtaining partial derivative of Equation (10). The initial value of diffusivity, *D*0, was set on 10<sup>−</sup>10.

$$D\_{n+1} = D\_n - \frac{F(D\_{n\prime}s, \delta)}{\partial F(D\_{n\prime}s, \delta) / \partial D\_n}, \ n = 0, 1, 2, 3, \dots \tag{11}$$

Table 4 presents the values of SO2 and CO2 diffusivities into SiO2/water, Al2O3/water or Fe2O3/water nanofluids. According to the results obtained from Table 4, it is evident that the maximum value of diffusivity for the absorption of CO2 is obtained when water/Fe2O3 is used as an absorbent, and the maximum diffusivity for the absorption of SO2 is achieved when being used water/SiO2 nanofluid. As can be seen in this table, for nanoparticles with the higher density (ρSiO2 = 2.196 g/cm3, ρAl2O3 = 3.980 g/cm3, ρFe2O3 = 5.242 g/cm3) more diffusivity of CO2 within the nanofluid is observed which is attributed to the nanoparticles Brownian motion inducing more diffusion of CO2 molecules at the bubble-liquid interface. It has been previously reported by Attari et al. that the momentum caused by Brownian velocity of nanoparticles leading to produce micro-convections, depending on nanoparticles density according to the following relation [20]:

$$
\Lambda o\_{\text{Brownian}} = \lambda \sqrt{\rho\_p} \tag{12}
$$


**Table 4.** Diffusion coefficient as well as *Re*, *Sh* and *Sc* for CO2 and SO2 absorption by using of nanofluids.

According to this equation by having an increase in nanoparticles density, more momentum can be transferred through the liquid phase; and consequently, a higher magnitude of micro-convections produces. Previous efforts declared that only two significant mechanisms including Brownian micro-convections and grazing effect (absorption of gas molecules by nanoparticles at the bubble-liquid interface and desorption of them into the liquid) can be involved during the gas absorption when a nanofluid is used as an absorbent [2–5,10,11,36]. For the absorption of CO2, Brownian mechanism has a major impact on gas molecules transfer due to the fact that CO2 molecules have not a very polar structure and asymmetric molecular configuration to produce high molecular charges (O=C=O) for being absorbed by nanoparticles surface charge; therefore, the Brownian mechanism indicates that water/Fe2O3 leads to a higher diffusivity of CO2 because of the larger micro-convections. Consequently, the minimum value of CO2 diffusivity in water/SiO2 nanofluid could be observed due to the lower density and lower magnitude of micro-convections produced by SiO2 nanoparticles.

On the other hands, due to the high polarity of SO2 molecules and formation of its Lewis structure during the absorption process [37] (Figure 11), it can be easily absorbed by means of nanoparticles surficial charge, which they are at the vicinity of the bubble-liquid interface. In addition, it is reported from the previous researches that SiO2 nanoparticles have a high value of surface charge due to the formation of silanol bonds (Si-O-H) at the nanoparticles surface [12], which has been confirmed by Zeta Potential test presented in this study. Therefore, the main mechanism for the absorption of SO2 is attributed to grazing effect by means of nanoparticles at the bubble-liquid interface resulting a high diffusivity of SO2 gas when water/SiO2 nanofluid is used (Figure 11).

**Figure 11.** Schematic diagram of grazing effect of SiO2 nanoparticles during the absorption of SO2.

#### *3.4. Correlation*

Froessling [38] estimated the mas transfer of a raising bubble in a liquid by using Equation (13):

$$Sh = 0.6(Re)^{1/2}(Sc)^{1/3} \tag{13}$$

*Processes* **2019**, *7*, 393

Equation (13) was found to be a suitable correlation for prediction of the absorption of different gases into wide ranges of liquids by means of single bubble absorber system [39]. In order to estimate *Sh* number for the gas absorption by nanofluids, other physical properties including dynamic viscosity, kinematic viscosity, and density of nanofluids were needed to obtain according to the following relations [40]:

$$
\mu\_{nf} = \mu\_{bf} (1 - q)^{2.5} \tag{14}
$$

$$
\rho\_{nf} = \varphi \rho\_{\mathcal{P}} + (1 - \varphi) \left. \rho\_{bf} \right| \tag{15}
$$

$$\nu\_{nf} = \mu\_{nf} / \rho\_{nf} \tag{16}$$

where ϕ is the volume fraction of oxides nanoparticles within the deionized water (can be obtained by using Equation (17) μ*b f* is the dynamic viscosity of the deionized water, ρ*<sup>p</sup>* is the bulk density of nanoparticles (presented in Table 1) and ρ*b f* is the density of the deionized water (1000 kg/m3).

$$\wp(\%vol) = \frac{w(\%uvt)}{w(\%uvt) + \frac{\rho\_p}{\rho\_{bf}}(100 - w(\%uvt))}\tag{17}$$

The values of *Re*, *Sc* and *Sh* can be calculated using the following equations:

$$Re\_b = \mathcal{U}\_b d\_b / \nu\_{nf} \tag{18}$$

$$\text{Sc}\_{nf} = \text{v}\_{nf} / \text{D}\_{nf} \tag{19}$$

$$Sh\_{nf} = k\_{\text{L,nf}} d\_b / D\_{nf} \tag{20}$$

In these equations, *Ub* means the bubble rising velocity in the column that was approximately found to be 0.21 m/s for all the experiments. Additionally, *db* is the bubble diameter that was measured as 7 mm for all cases. Table 4 also presents the values of *Reb*, *Sh* and *Sc* for the absorption of CO2 and SO2 by using the mentioned nanofluids.

According to Table 4 and Equation (18), the value of Reynolds number does not change significantly when either nanofluid or pure basefluid is applied during the absorption process by means of raising a single bubble absorber i.e., ν*n f* ≈ ν*b f* . Therefore, it can be assumed that the Reynolds number has no significant effect on relative Sherwood number and this parameter is found to be just as a function of relative Schmidt number according to below:

$$\frac{Sh\_{nf}}{Sh\_{bf}} = K \left(\frac{Sc\_{nf}}{Sc\_{bf}}\right)^m \tag{21}$$

*m* and *K* were calculated by using a two-dimensional regression analysis over the experimental data shown in Figure 12. According to this figure, the following equation was obtained for the mentioned parameters with the R<sup>2</sup> = 0.9919. Equation (22) can predict the Sherwood number for various gas-nanofluid absorption systems at Reb~1300, accurately:

$$\frac{Sh\_{nf}}{Sh\_{bf}} = 1.3643 \left(\frac{\text{Sc}\_{nf}}{\text{Sc}\_{bf}}\right)^{0.6125} \quad \text{for } \text{Re}\_b \cong 1300 \tag{22}$$

It is mentioned that *Shb f* can be calculated by the Froessling equation (Equation (13)).

**Figure 12.** Effect of relative Schmidt number on relative experimental Sherwood number.

#### **4. Conclusions**

In this research, the absorption of SO2 and CO2 was elucidated by using a single-bubble column absorption setup into water based nanofluids containing SiO2, Fe2O3 or Al2O3 nanoparticles. The results of this study clearly show that the aforementioned nanofluids have high stability since the zeta potential is lower than −45 mV. The results of TEM and DLS analysis also display that the average size of nanoparticles is within limit of 40–60 nm.

These results also declared that the maximum absorption of CO2 and SO2 could be obtained when water/SiO2 or water/Fe2O3 nanofluid is utilized as an absorbent. Moreover, our findings also showed that the maximum relative absorption for SO2 and CO2 in the studied nanofluids in comparison to base fluid occurs when a water/Fe2O3 or water/SiO2 nanofluid was used as the absorbent. Indeed, our results show that the type of gas molecules and nanoparticles determines the mechanism of mass transfer intensification of nanofluids. Therefore, both Brownian motion and grazing effect play crucial role for the increment of mass transfer in gas absorption by nanofluids. According to the type of gas and nanoparticles, the major mechanism can be distinguished.

In addition, mass transfer parameters incorporating diffusivity of gases into the oxides nanoparticles loaded in nanofluids, Sherwood number and Schmidt number were obtained. The results exhibit that the addition of nanoparticles (due to increment of Brownian momentum) increases diffusivity coefficient, and the maximum diffusivity for CO2 and SO2 absorption was obtained for water/Fe2O3 and water/SiO2 nanofluids, respectively.

Finally, a new correlation is offered for the prediction of Sherwood number versus Schmidt number in gas-nanofluid systems (for Reb about 1300) in which the experimental values are predicted with high accuracy.

**Author Contributions:** S.K.: Conceived and designed the analysis, Collected the data, Contributed data or analysis tools, Performed the analysis, Wrote the paper; F.E.: Conceived and designed the analysis, Contributed data or analysis tools, Performed the analysis, Wrote the paper; D.M.: Conceived and designed the analysis, Performed the analysis.

**Funding:** This research received no external funding.

**Acknowledgments:** The authors are grateful to the Shiraz University for supporting this research.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Nomenclature**


*p* Nanoparticles

*B* Bubble

#### **References**


© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Article* **Optimization of Post Combustion CO2 Capture from a Combined-Cycle Gas Turbine Power Plant via Taguchi Design of Experiment**

#### **Ben Alexanda Petrovic <sup>1</sup> and Salman Masoudi Soltani 2,\***


Received: 20 May 2019; Accepted: 6 June 2019; Published: 12 June 2019

**Abstract:** The potential of carbon capture and storage to provide a low carbon fossil-fueled power generation sector that complements the continuously growing renewable sector is becoming ever more apparent. An optimization of a post combustion capture unit employing the solvent monoethanolamine (MEA) was carried out using a Taguchi design of experiment to mitigate the parasitic energy demands of the system. An equilibrium-based approach was employed in Aspen Plus to simulate 90% capture of the CO2 emitted from a 600 MW natural gas combined-cycle gas turbine power plant. The effects of varying the inlet flue gas temperature, absorber column operating pressure, amount of exhaust gas recycle, and amine concentration were evaluated using signal to noise ratios and analysis of variance. The optimum levels that minimized the specific energy requirements were a: flue gas temperature = 50 ◦C; absorber pressure = 1 bar; exhaust gas recirculation = 20% and; amine concentration = 35 wt%, with a relative importance of: amine concentration > absorber column pressure > exhaust gas recirculation > flue gas temperature. This configuration gave a total capture unit energy requirement of 5.05 GJ/tonneCO2, with an energy requirement in the reboiler of 3.94 GJ/tonneCO2. All the studied factors except the flue gas temperature, demonstrated a statistically significant association to the response.

**Keywords:** CO2 capture; Aspen Plus; CCGT; Taguchi; Minitab; optimization

#### **1. Introduction**

Anthropogenic greenhouse gas (GHG) emissions in 2010 reached 49 ±4.5 GtCO2-eq/year, emissions of CO2 from fossil fuel combustion and industrial processes contributed approximately 80% of the total GHG emissions increase from 1970–2010 [1]. The mitigation of climate change and increasing global temperatures requires a combination of new, renewable technology and an improvement of the existing infrastructure to move towards a low and ideally zero-carbon society; in line with the Climate Change Act requirements of an 80% reduction in total emissions by 2050 [2]. The use of fossil-fueled power stations continues to grow due to their ability to respond to changes in demand [3] and offset the intermittency of current renewable technology. Coal and gas are the predominant fuels used in power generation; however, since the UK's 2016 consultation to end the use of unabated coal, its usage in power generation has declined from 22% in 2015 to 1.6% in the second quarter of 2018 [4]. Natural gas sees its share of generation at 42% and although often perceived as a much cleaner fuel at the point of use than coal [5], producing around 350 kgCO2/MWh [6], reducing the carbon intensity of this growing sector is vital for stabilizing global temperature increase to below 2 ◦C. Amine-based carbon capture and storage (CCS) is seen as one of the best CO2 abatement approaches [7]; the solvent monoethanolamine (MEA) is most commonly used due to its low material costs [8]; however, due to the energy requirements of solvent regeneration, there is a large energy penalty incurred on the power plant. To make post combustion CCS a viable option in mitigating the GHG emissions from power generation, optimization of such a plant is paramount.

The techno-economic analysis of a post combustion capture (PCC) and compression plant using MEA coupled to a 400 MW NG-CCGT conducted by Alhaja et al. [9] found that by studying the effect of the PCC unit's key operating parameters on the power plant's key performance indicators, an optimum lean loading of 0.31 molCO2/molMEA, which minimized the specific reboiler duty (SRD), could be found. This represents a balance between the sensible heat required to raise the temperature of the solvent to that of the reboiler and the latent heat to vaporize water and provide the stripping steam. By increasing the pressure within the stripping column within the limits of solvent degradation, a reduction in SRD was also seen. The inclusion of packing volume as a studied parameter illustrates the importance of studying the capture process as a whole system, especially due to the capital costs associated with such plants. An optimum lean loading of the solvent that minimized the SRD was also found by Masoudi Soltani et al. [10]; the MEA-based unit demonstrated that the SRD was dependent on the concentration of MEA within the solution. The 3.98 GJ/tonneCO2 SRD occurred with a 30 wt% MEA solution and a lean loading in the range of 0.19–0.21 molCO2/molMEA. The SRD was also seen to vary secondly as a function of EGR, owing to the change in CO2 partial pressure within the flue gas stream; employing a greater percentage EGR reduced SRD further. Another optimization of an MEA-based PCC system [11] determined strong links between the L/G ratio, lean loading, and reboiler duty. The lean solvent flow rate was determined by varying the lean CO2 loading to achieve 90% capture; with a lower L/G ratio, the requirement in the reboiler is primarily for stripping steam, whereas with a higher L/G ratio, there is a larger requirement for heat to increase the temperature of the rich stream; once more corroborating the balance of sensible and latent heats in the reboiler [9,10]. A 30 wt% MEA-employing model validated against the UK CCS research centre pilot plant was used to evaluate and optimize the performance of a PCC unit [12]; a lean loading of 0.23 gave a 15% reduction in SRD from 7.1 Mj/kgCO2 to 5.13 MJ/kgCO2. An increase in stripper pressure from 1.25–2.50 bar added a further 17% reduction in SRD, but in order to avoid thermal degradation of the solvent, a pressure of 1.80 bar was found to be most suitable, a similar phenomenon to that found by Lindqvist et al. [13]. With an optimum lean loading of 0.21 in line with the findings of Masoudi Soltani et al. [10], the SRD was 4.4 MJ/kgCO2. Packing material and heat exchanger logarithmic mean temperature difference (LMTD) were also studied, optimization of which could give SRD reductions of 40% and 5%, respectively. Xiaobo Luo [14] investigated the optimal operation of an MEA-based capture unit and found that for a 90% capture rate, a 9.58% net power efficiency decrease was seen in the NGCC using an optimum lean loading of 0.26–0.28, slightly higher than in the previous studies. The reason for this is that in the other studies, column sizing is minimized to reduce capital expenditure by reducing the L/G ratio, thus requiring a lower lean loading, whereas with this study [14], column sizing is fixed and the optimal operation is to reduce the operating expenditure, hence a higher lean loading can be exploited. The comparison between MEA and CESAR-1 (an aqueous solution of 2-amino-2-methyl-propanal and piperazine) [15] found that using MEA reduced the NGCC plant efficiency by 8.4%, with an energy requirement in the PCC unit of 3.36 GJ/tonneCO2. A parametric evaluation carried out by Kothandaraman et al. [16] on a 30 wt% MEA PCC system identified that for a DOC above 95%, there was a disproportionate increase in SRD. The temperature of the solvent was shown to have little effect on the system's performance; decreasing absorption temperature increases the driving force for reaction but the rate of reaction and diffusivity decreased, effectively cancelling each other out. For the lean loading used (0.22), the SRD was 4.5 GJ/tCO2 with a flue gas CO2 content of 4 vol%, i.e., without the use of EGR. An MEA-based PCC unit modelled by Arachchige et al. [17] concluded that the removal efficiency was proportional to the solvent concentration and temperature whilst being inversely effected by lean loading thanks to the reduction in MEA capacity; a similar phenomenon to that found by Kothandaraman et al. [16] was observed with respect to solvent temperature. Variation of absorber pressure saw a decrease in SRD due to the increased partial pressure of the CO2; 4.56 GJ/tCO2 to 4.38 GJ/tCO2 with an increase

in absorber pressure of 0.9 to 1.2 bar. The effect of implementing EGR on the integrated MEA-based CO2 capture plant when coupled to an 800 MW NGCC was studied exclusively by Ali et al. [18], something not considered comprehensively in other studies [12,13,15]. The use of EGR resulted in a 57% increase in CO2 molar composition in the flue gas stream (4.16–6.53 mol%), resulting in a 2.3% reduction in SRD; they also identified that the NGCC case with EGR is the most attractive for use with CCS due to its lowest reduction in plant net efficiency. Lars Erik Øi [19] simulated the operation of a simplified MEA-based PCC coupled to a 400 MW CCGT; he found that for a removal of 85% of the emitted CO2, the heat consumption was 3.7 GJ/tCO2. He identified that an increased solvent circulation rate would increase the removal grade of the CO2; an increase in the temperatures of the inlet streams to the absorber would improve CO2 absorption due to an increase in reaction rates and; operating the stripper close to the degradation limits of the solvent would give better removal efficiency and thus lower CO2 loading in the lean stream. Afkhamipour and Mofarahi [20] employed the methods of Taguchi to maximize the CO2 removal efficiency using a sophisticated multilayer-perceptual-neural-network model. Focusing on the controllable inlet conditions to the absorber, they identified that CO2 loading, amine flow rate, and amine concentration were the major factors in increasing the capture efficiency. The degree of capture (DOC), however, was not kept constant; this is dependent on amine flow rate. Instead, the response value used in the Taguchi analysis was the CO2 removal efficiency; the optimization of removal efficiency does not inherently mean a less intensive energy requirement for the system.

It is clear that there are myriad KOPs that impact the energy intensity of a PCC unit; the importance of solvent concentration, solvent type, and CO2 concentration were shown to be extremely influential on the energy requirements in a PCC unit; the sizing and packing of the columns were also important, but played a greater role in the economic analysis. Few studies have looked at the effect of varying absorber column pressure and its effect on the reaction kinetics within the column. Although solvent type can be seen as an optimization variable, to assess the influence of the other KOPs, this would need to be kept constant; the industry baseline solvent is MEA which has a number of advantages over other commercial solvents [21], its availability and relatively low cost also continue to make it one of the more viable options. The L/G ratio was shown to be dependent on the DOC; therefore, maintaining a DOC would require a variation of L/G ratio. It is also clear that the stripping column operates optimally near the degradation limits of the solvent, leaving little requirement for optimization; the inlet streams and conditions within the absorber were shown to be more influential in the energy demands of a PCC unit. The importance of operating the systems at near optimal configuration to mitigate the cost of capturing CO2 and improve the viability of CCS is obvious.

A description of the chemical absorption that takes place in a PCC system can be achieved by modelling, using either an equilibrium or rate-based mass-transfer, with a number of studies being validated against pilot plant data [9,12,22–24]. Rate-based simulations can provide a greater accuracy and allow a more informed evaluation of the process [12,14,17,23–26] but equilibrium approaches can still be employed for process assessment [11,19,27,28]. The relative simplicity of an equilibrium approach and the ability to improve the accuracy of such a model with stage efficiencies [16,19,29–31] is the justification for employing such a strategy here.

In this work, the optimization of an MEA-based PCC system is carried out using a Taguchi design of experiment. The PCC system is modelled in Aspen Plus to process the flue gas from a 600 MW CCGT power plant and capture 90% of the emitted CO2. The optimization parameters were: the inlet temperature of the flue gas to the absorber (FGT); the operating pressure of the absorber column (ACP); the amount of exhaust gas recirculation (EGR) so as to model the capture process using different molar CO2 concentrations in the flue gas; and the concentration of the amine (CONC) in the lean stream inlet to the absorber.

#### **2. Model Development**

The modelling framework for this study employs a combination of the electrolyte-nonrandomtwo-liquid (e-NRTL) [32] description of activity coefficients for the equilibrium ionic species in solution with the Soave–Redlich–Kwong (SRK) [33] cubic equation of state for the fugacities of the species in the vapor phase, a well-demonstrated combination [10,34,35]. The following sections describe briefly the thermodynamic framework that Aspen Plus uses for the calculations of chemical equilibrium, vapor-liquid equilibrium, liquid phase constitution, and regeneration energy [36].

The absorption and reaction mechanisms that occur in the MEA-CO2-H2O system are detailed in Equations (1)–(7) [10,21].

$$\text{CO}\_{2(g)} \leftrightarrow \text{CO}\_{2(aq)}\tag{1}$$

$$\mathrm{2H\_2O} \overset{\mathrm{K\_2}}{\leftrightarrow} \mathrm{H\_3O^+} + \mathrm{OH^-} \tag{2}$$

$$\text{CO}\_2 + 2\text{H}\_2\text{O} \overset{\text{K}\_3}{\leftrightarrow} \text{H}\_3\text{O}^+ + \text{HCO}\_3^- \tag{3}$$

$$\rm HCO\_3^- + H\_2O \overset{K\_4}{\leftrightarrow} H\_3O^+ + CO\_3^{2-} \tag{4}$$

$$\mathrm{MEAH^{+}} + \mathrm{H\_{2}O} \overset{\mathrm{K\_{5}}}{\rightsquigarrow} \mathrm{H\_{3}O^{+}} + \mathrm{MEA} \tag{5}$$

$$\text{MEAHCOO}^{-} + \text{H}\_{2}\text{O} \overset{\text{K}\_{\text{Q}}}{\leftrightarrow} \text{MEA} + \text{HCO}\_{3}^{-} \tag{6}$$

$$\text{YO}\_2 + \text{YMEA} \rightarrow \text{oxidation products} \tag{7}$$

The absorption process begins with the dissolution of the gaseous carbon dioxide molecules into the liquid MEA-H2O-CO2 where the dissolved CO2 undergoes a series of reactions described in Equations (2)–(6), resulting in the formation of a number of ionic species [10]. Reaction (2) describes the water hydrolysis resulting in the production of two ions, reaction (3) shows the formation of a bicarbonate in water, and reaction (4) demonstrates the dissociation of the bicarbonate salt into carbonate ions in the presence of liquid water. Reactions (5) and (6) describe the reactions between molecular MEA with CO2 in the aqueous solution, specifically the dissociation of MEAH + (pronated MEA) in (5) and the carbamate reversion to bicarbonate of MEACOO-. The equilibrium constants (*K*(*T*)) for reactions (2)–(6) are defined on a molar basis as [10,35]:

$$K(T) = \prod a\_i^{v\_i} \tag{8}$$

where α*<sup>i</sup>* is the activity of species *i*; the above equation can be rewritten in terms of mole fractions, *xi* and activity coefficients γ*i*, to give:

$$K(T) = \prod (\mathbf{x}\_{i}\boldsymbol{\gamma}\_{i})^{\text{v}} \tag{9}$$

where ν*<sup>i</sup>* is the stoichiometric constant of species *i*. It is worth noting that in the presence of oxygen, solvents such as MEA will react irreversibly to produce a number of oxidation products, and prediction of the accumulation of such products is limited by the incomplete knowledge of the interactions between oxygen and MEA [37]. The implication of these reactions on the energy requirements of the capture unit are insignificant but they do tend to influence the economics of the system. An economic analysis is not executed here and so the inclusion of these reactions in the model has been dismissed. The e-NRTL model is itself an excess Gibbs energy (*gex*\* ) expression comprised of three contributions [21]: (1) The long-range interactions due to the electrostatic forces between ions represented by the Pitzer–Debye–Hückel expression; (2) the ion-reference-state-transfer contribution represented by the Born expression; and (3) the short range forces between all species. The equation is given below [10,21,35]:

$$\mathbf{g}^{\varepsilon \mathbf{x}^\*} = \mathbf{g}^{\varepsilon \mathbf{x}^\*, PDH} + \mathbf{g}^{\varepsilon \mathbf{x}^\*, Born} + \mathbf{g}^{\varepsilon \mathbf{x}^\*, NRTL} \tag{10}$$

The activity coefficient, γ*<sup>i</sup>* for an ionic or molecular species, solute, or solvent is derived from the partial derivative of the excess Gibbs free energy with respect to the species mole number, *ni* [10,21]:

$$\ln \gamma\_i = \frac{1}{RT} \left[ \frac{\partial \left( n\_i \mathcal{g}^{c \chi^\*} \right)}{\partial n\_i} \right]\_{T, P, n\_{j \neq i}} \tag{11}$$

where *i*, *j* = molecule, cation, anionspecies. Finally, Equation (11) leads to:

$$\ln \gamma\_i^\* = \ln \gamma\_i^{\*PDH} + \ln \gamma\_i^{\*BORN} + \ln \gamma\_i^{\*NRTL} \tag{12}$$

The e-NRTL property method includes the temperature-dependent reaction equilibrium constants (*Ki*) listed in Equations (2)–(6) and are calculated using the Aspen Plus built-in equation [10,17,21,35]:

$$\ln(K\_i) = a\_i + \frac{b\_i}{T} + c\_i \ln(T) + d\_i T \tag{13}$$

The vapor–liquid thermodynamic system can be described using an extended Henry's law to represent the behavior of solutes such as CO2 [10,21,35,38]:

$$\chi\_i \cdot q\_i \cdot P = \chi\_i \cdot \gamma\_i \cdot H\_i^{P^0} \cdot \exp\left[\frac{V\_i^{\infty} \left(P - P\_s^0\right)}{RT}\right] \tag{14}$$

Similarly for the solvent species, an extended Raoult's law is employed [10,21,35,38]:

$$\mathbf{y}\_s \cdot \mathbf{q}\_s \cdot \mathbf{P} = \mathbf{x}\_s \cdot \mathbf{y}\_s \cdot \mathbf{P}\_s^0 \cdot \mathbf{q}\_s^0 \cdot \exp\left[\frac{V\_s \left(\mathbf{P} - P\_s^0\right)}{RT}\right] \tag{15}$$

where, *yi* and *ys* are the vapor phase mole fractions of species *i* and *s*; ϕ*<sup>i</sup>* and ϕ*<sup>s</sup>* are the fugacity coefficients for species *i* and *s* as estimated by the SRK equation of state; *P* is the total pressure; *xi* and *xs* are the liquid phase mole fractions of *i* and *s*; *yi* and *ys* are the liquid phase activity coefficients for *i* an *s*; *HP*<sup>0</sup> *<sup>i</sup>* is the Henry's Law constant of *<sup>i</sup>* in the solution at saturation pressure and *<sup>P</sup>*<sup>0</sup> *<sup>s</sup>* is the saturation pressure of *s*; ϕ<sup>0</sup> *<sup>s</sup>* is the fugacity coefficient of *s* under saturation pressure condition; *V*<sup>∞</sup> *<sup>i</sup>* is the partial molar volume of solute at infinite dilution; and *Vs* is the molar volume of solvent *s*. In Equations (14) and (15), the exponential terms are the Poynting factors of corrections for moderate pressure and are derived from integration forms by assuming *V*∞ *<sup>i</sup>* and *Vs* to be constant over the pressure range [10]. Figure 1 depicts the process flow sheet developed for this work and is detailed in the following section; block names are given in capitals and the Aspen Plus model names are given in parentheses. The model includes two columns: the ABSORBER and the STRIPPER (RadFrac); a water wash section, WATERWAS (SEP) where any residual solvent is removed from the clean flue gas; a make-up section, MAKE-UP (MIXER) which allows the addition of both H2O and MEA to ensure the lean amine stream inlet to the absorber is of the correct composition. The five-stage intercooled compression train [9,15,24] used to process the captured CO2 for storage is comprised of a series of four compressors, COM1-4 (COMPR), knock out drums, KO1-4 (FLASH) to remove residual water and intercoolers, IN-COOL1-4 (HEATER) to reduce the temperature of the CO2; this puts the CO2 into the supercritical fluid state where the 5th stage, a pump, CO2PUMP (PUMP) increases the pressure of the CO2 to 140 bar. The cross-heat exchanger, HEATX (HEATX) is used to heat the rich amine stream using the waste heat in the lean stream and the BLOWER (COMPR) is used to increase the flue gases pressure to overcome the pressure drop in the column. Table 1 Outlines the operating conditions for the individual units in the flow sheet.

**Figure 1.** Post combustion CO2 capture process flow sheet.



The flue gas stream flow rate inlet to the absorber is maintained at 825.31 kg/s, representing a NG-fired CCGT operating in a 1 × 1 configuration with a rated capacity of 592 MW and an efficiency of 56% on an LHV basis, modelled by Dutta et al. [27] using GT-PRO® operating at an ambient condition of 15 ◦C.

The Taguchi method is a statistical technique in the design of experiments (DOE), sensitivity analysis, and optimization [20]. Developed to overcome the limitations in a full factorial experiment design the method necessitates that all parameters be split into either control or noise factors which can take on a variety of preset levels; through the use of orthogonal arrays (OA) [39] a fractional-factorial sequence of experiments can be devised, upon which statistical analysis of the results can be carried out to find the optimum configuration of factors and their respective levels in order to maximize or minimize the objective function. An OA is abbreviated as LN; the subscript N refers to the number of required trials for a given experiment; the number of levels of factors are included in parentheses next to the abbreviation. For example, L4 (23) refers to a four-trial experiment to investigate the influence of three factors at two levels each on a given process [40]. The Taguchi methods serve as an offline tool for designing quality into products in a three-stage process [40,41]. The first being system design, where

the factors and their appropriate levels are determined, which requires a thorough understanding of the system, the second is parametric design, where the optimum condition is determined at specific factor levels, and the third is tolerance design, where fine tuning of the optimum factor levels found in the second stage takes place. The method employed here is similar to that employed by Yusoff et al. [40] and is shown in Figure 2.

**Figure 2.** Flow diagram of the Taguchi method.

The problem formulation step involves defining the objective function, factors, and levels. The four factors assessed in this work are: the temperature of the flue gas stream inlet to the absorber column (FGT); the operating pressure of the absorber column (ACP); the percentage exhaust gas recirculation (EGR); and the concentration of the lean amine stream inlet to the absorber (CONC). The values of which take one of five levels detailed in Table 2.

**Table 2.** Description of factors and levels for the post combustion capture (PCC) plant.


The composition of the flue gas stream is modelled so as to represent the various EGR ratios [42] and is illustrated in Figure 3.

**Figure 3.** Molar composition of the flue gas stream at various exhaust gas recirculation (EGR) ratios.

The objective function for optimization is the minimization of the parasitic energy demand of the capture unit, given as a function of captured CO2, *A*; calculated using Equation (16).

$$A\left(\frac{Gf}{t\_{CO\_2}}\right) = \frac{R(GW) + C(GW) + P(GW)}{F(ts^{-1})}\tag{16}$$

where *R* is the reboiler duty, *C* is the condenser duty, *P* is the total pumping duty for the unit including the compression train, and F is the mass flow rate of captured CO2 from the stripper column.

The second step involves designing and conducting the experiment employing Minitab, a complete statistical software package that provides a comprehensive set of methods for data analysis. For four factors at five levels, an appropriate L25 OA was selected and is given Table 3.


**Table 3.** Orthogonal array illustrating the configuration of the simulations.

The third step looks at the analysis of the results; two statistical tools are used in this work both of which are commonly applied in the Taguchi method: signal-to-noise ratios (SNR) and analysis of variance (ANOVA). The SNR can take three forms that are characteristic of the objective function i.e., smaller the better, nominal is best or larger the better; in this case the smaller the better SNR is used and is defined for each run, n as [20]:

$$SNR = -10\log\_{10}\left[\frac{1}{n}\sum\_{i=1}^{N}\frac{1}{Y\_i^2}\right] \tag{17}$$

where *N* is the number of runs and *Yi* is the response value in the *ith* experiment. The SNR is a single response which makes a trade-off between setting the mean to a desirable level while minimizing variance; the intention is to maximize the SNR regardless of its characteristic. The SNR values can be used to determine the relative importance of each factor on the objective function and can be plotted to identify the optimum levels for each factor. The mathematical technique of variance analysis (ANOVA) dissects the total variation into accounted sources and delivers a way to interpret the results from the simulations [43]. The ANOVA is conducted using the SNR values to assess the percentage contribution of each factor in minimizing the variation of the capture unit's energy demand. Both analyses are conducted using Minitab.

#### **3. Results**

The duties of each unit taken as outputs from Aspen Plus were manipulated to give specific energy requirements within the capture unit as a function of captured CO2 and are presented in Table 4.


**Table 4.** Energy duty per tonne of captured CO2 for each individual unit of the capture plant.

The primary contributor to the energy requirements is the reboiler duty which demonstrated the greatest variation throughout the simulations, with a standard deviation of 0.301 when compared to 0.216 and 0.008 for the condenser and pumping duties, respectively. From the 25 simulations, the minimum total energy requirement for the unit as a whole was 5.14 GJ/tCO2 with a reboiler requirement of 3.97 GJ/tCO2. These requirements were found in run 11 using the following factor configuration: FGT = 60◦C; ACP = 1 bar; EGR = 30% and; CONC = 35 wt%.

#### *3.1. Signal-to-Noise Ratio (SNR) Analysis*

The SNR values can be used to identify the factor levels that minimize the variability in the capture unit's energy requirements. Minitab was used to calculate the SNR for each configuration of the factors, the average of which is shown in the response Table 5. With the smaller-the-better SNR the target value is 0; as such, the values given in red/bold are the levels that minimized the energy requirement. The optimum values being: FGT = 50 ◦C; ACP = 1 bar; EGR = 20% and; CONC = 35 wt%.


**Table 5.** Response table of the signal-to-noise ratio (SNR) taken from Minitab.

The delta values represent the variation in the mean SNR values and permit a ranking of the factor's relative importance on the energy requirements when varied in the specified range. The sequence follows CONC > ACP > EGR > FGT. The importance of each factor can be seen graphically by plotting the SNR values for each factor as shown in Figure 4. The line connecting each SNR value exemplifies whether a main effect exists for that factor; a line that demonstrates a larger difference in vertical position such as in CONC shows that the magnitude of the main effect for that factor is greater. From Figure 4 it is clear that ACP and CONC had the greatest influence with FGT and EGR showing a smaller but still notable influence.

**Figure 4.** The main effects plot for the SNR.

#### *3.2. Confirmation Experiment*

After determining the optimum configuration of the factors, a confirmation experiment is required to validate the employed method for experimental design. The optimum factor levels were:


The energy requirements for the individual units within the capture unit are given in Table 6. A total requirement of 5.05 GJ/tCO2 was seen, 1.8% less than the value seen from simulation 11 and thus validating the results of the Taguchi analysis.


**Table 6.** Energy requirements within the capture unit during the confirmation experiment.

#### **4. Discussion**

#### *4.1. Analysis of Variance (ANOVA)*

Using a significance level of 5%, Table 7 exhibits the results of ANOVA using the SNR values from Table 5 The *p*-values can be used to evaluate the statistical significance of the factors influence on the capture unit's energy requirement; a *p*-value of less than 0.05 demonstrates a significant association of the factor to the mean of the quality characteristic value [44]. ANOVA also permits the identification of the percentage contribution of each factor. As in Figure 4 the greatest contributors to the variation in the capture unit's energy requirement are ACP and CONC, 31.07% and 35.24%, respectively. FGT and EGR demonstrated 6.92% and 18.14% contributions, respectively; the *p*-value for FGT, however, is 0.263, thus indicating that the flue gas temperature inlet to the absorber demonstrated no statistically significant influence on the energy requirements of the capture system. The other three factors did however show a statistically significant relationship, highlighting their importance in minimizing the parasitic energy demand of a capture unit.

**Table 7.** ANOVA table for the mean SNR.


#### *4.2. Main E*ff*ect of the Factors on the Mean Energy Requirement in the Capture Unit*

The influence of each factor on the energy requirement can be seen in Figure 5. An increase in ACP was seen to increase the energy requirements, potentially due to the increased energy demand to raise the pressure of the inlet streams to that of the absorber. Contradictory to this, however, the solubility of CO2 increases in aqueous solutions at higher pressures [45] which should act to reduce the reboiler duty as found in [17] owing to an increase in CO2 partial pressure. The equilibrium-based strategy used here may not have been able to quantify the effects of a greatly increased ACP. It is worth noting that in the aforementioned study, the variation in pressure was between 0.9 and 1.2 bar. When increasing EGR, the general trend is for SRD to decrease, owing to the increase in molar CO2 concentration in the gas phase in the absorber column; the higher the mole fraction the feed has, the easier the MEA reaches the required rich loading for 90% capture [46]. In the literature, the optimal EGR is often around 40%; the discrepancy in this project is likely due to the fact that the mass flow rate of the flue gas was kept constant. If employing EGR in a NG-CCGT, the recycled flue gas would act to reduce the total mass flow processed by the PCC unit and as such, a smaller solvent flow rate and energy requirement would be seen [47].

**Figure 5.** Main effects plot for the mean total energy requirement.

#### *4.3. Interaction Analysis*

Figure 4 illustrates the effect of each factor on the response; but, given that four variables were evaluated concurrently, an assessment of the interactions between the variables must be made so that the interpretation of the main effects is accurate. Figure 6 is the interaction plot for the four factors on the mean energy requirement in the capture unit; the optimum process parameters are illustrated by red circles. When the connecting lines are parallel, the interaction is small; the more nonparallel the lines are, the greater the level of interaction. All factors can be seen to show some level of influence on one another. It is worth noting that Figure 6 illustrates a minimum energy requirement at a FGT of 60 ◦C and not the 50 ◦C defined by the SNR analysis; but, due to the statistical insignificance of FGT, this can be disregarded and the initial interpretation can be upheld. When considering the other three factors (ACP, EGR, and CONC), the interaction plot corroborates the findings from the SNR analysis in that the minimum energy requirement was found with an ACP = 1 bar, EGR = 20%, and CONC = 35 wt%. The plot also makes evident a significant interaction between all four factors on the total energy requirement, highlighting the importance of conducting an optimization with all factors in concert.

**Figure 6.** Interaction plot for all factors on the specific energy requirement in the capture unit.

#### **5. Conclusions**

The use of the Taguchi method allowed an accurate assessment of the effect of four control variables in the operation of an MEA-based post combustion CO2 capture plant. The analysis of the results from the 25 simulations outlined by the Taguchi DOE demonstrated the importance of considering the capture unit as an entirety; the variation in energy demand when operating suboptimally clarifies the importance of deducing the optimal configuration to minimize the parasitic energy penalty incurred. Using the signal to noise ratios and analysis of variance for the four evaluated factors, the concentration of the amine was shown to be the greatest impetus in minimizing the energy demands; the operating pressure in the absorber and amount of exhaust gas recirculation also exhibit a significant influence on the total energy requirement, whereas the temperature of the flue gas was shown to have an insignificant effect on the specific energy requirements. The accuracy of designing experiments in this way allowed a more efficient assessment of the four factors and permitted the determination of a minimum energy requirement in the capture unit. The confirmation experiment, as outlined by the statistical analysis, further strengthens the rationale of employing a robust design of experiment. The minimum specific energy requirement for the PCC unit found with the defined optimum factor levels was 5.05 GJ/tonneCO2, corresponding to a 3.94 GJ/tonneCO2 requirement in the reboiler.

**Author Contributions:** Dr Masoudi Soltani was the project lead and the principal investigator on this research project. Mr Ben Petrovic was the active researcher in this work, responsible for the successful implementation of the research project, interpretation and analysis of the results from the start to the end.

**Funding:** This research received no external funding.

**Acknowledgments:** The authors would like to acknowledge the Chemical Engineering as well as the Mechanical and Aerospace Engineering Departments at Brunel University London, UK, to support conducting this research.

**Conflicts of Interest:** The authors declare no conflict of interest.



#### **References**

1. Intergovernmental Panel on Climate Change, (IPCC). *Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change*; IPCC: Geneva, Switzerland, 2014.


© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Article* **Highly Selective CO2 Capture on Waste Polyurethane Foam-Based Activated Carbon**

**Chao Ge 1, Dandan Lian 1, Shaopeng Cui 2, Jie Gao <sup>3</sup> and Jianjun Lu 1,4,\***


Received: 8 August 2019; Accepted: 28 August 2019; Published: 3 September 2019

**Abstract:** Low-cost activated carbons were prepared from waste polyurethane foam by physical activation with CO2 for the first time and chemical activation with Ca(OH)2, NaOH, or KOH. The activation conditions were optimized to produce microporous carbons with high CO2 adsorption capacity and CO2/N2 selectivity. The sample prepared by physical activation showed CO2/N2 selectivity of up to 24, much higher than that of chemical activation. This is mainly due to the narrower microporosity and the rich N content produced during the physical activation process. However, physical activation samples showed inferior textural properties compared to chemical activation samples and led to a lower CO2 uptake of 3.37 mmol·g−<sup>1</sup> at 273 K. Porous carbons obtained by chemical activation showed a high CO2 uptake of 5.85 mmol·g−<sup>1</sup> at 273 K, comparable to the optimum activated carbon materials prepared from other wastes. This is mainly attributed to large volumes of ultra-micropores (<1 nm) up to 0.212 cm3·g−<sup>1</sup> and a high surface area of 1360 m2·g−1. Furthermore, in consideration of the presence of fewer contaminants, lower weight losses of physical activation samples, and the excellent recyclability of both physical- and chemical-activated samples, the waste polyurethane foam-based carbon materials exhibited potential application prospects in CO2 capture.

**Keywords:** waste polyurethane foam; physical activation; high selectivity; CO2 capture; ultra-micropore

#### **1. Introduction**

CO2 emission from the combustion of coal and natural gas is mainly responsible for global warming [1,2]. The "least-cost" solution is to limit greenhouse gas emissions to meet the Paris Agreement pledges, wherein 60% of CO2 emissions are hoped to be reduced in 2030 relative to 2005 [3]. To reduce the emission of CO2, the selective and energy-efficient capture and storage of CO2 is considered to be a satisfactory approach. Until now, three main CO2 capturing strategies, namely pre-combustion, post-combustion, and oxy-combustion, have been discussed. For pre-combustion, the coal must be gasified first; the typical technology used is called an integrated gasification combined cycle. Post-combustion involves capturing CO2 from the flue gases produced after fossil fuels are burned. Both of these methods have been widely accepted and used in gas-stream purification in industry. Oxy-combustion can produce a relatively pure CO2 stream in emissions, but it is still in development due to the high cost of the technology involved [4]. In addition to these, several technologies have also been investigated to separate and store CO2, such as membrane separation, solution absorption, cryogenic refrigeration, and adsorption approaches [5–7]. Although these CCUS (Carbon Capture, Utilization and Storage) technologies are intended to reduce CO2 emission, reaching

the Paris Agreement targets is still a serious challenge. Nevertheless, in these techniques, adsorption is considered to be the approach with the most potential, since it involves simple operation and has low-cost and energy-saving benefits [8].

Many adsorbents have been intensively studied for CO2 capture, including zeolites [9], metal-organic frameworks [10], metallic oxide [11], graphene-based adsorbents [12], and porous carbon materials [13]. For future commercialization application, the selection of adsorbents is strongly dependent not only on CO2 capture capacity but also the cost, including the availability of the raw materials, the preparation of the adsorbent, and the operating costs. Porous carbons (PCs) are considered to be the most competitive candidates due to their controlled pore structure, low cost, stable physicochemical properties, ease of chemical modification, and regeneration [14]. Micropore sizes smaller than 1 nm are beneficial to high-density CO2 filling at ambient conditions [15,16]. In addition, a high surface area (>1000 m2·g<sup>−</sup>1) and a rich nitrogen environment can improve the CO2 adsorption capacity.

The route of preparation, especially activation, will significantly affect the performance of CO2 absorption. The porous carbons can be activated either by physical or chemical methods. Physical activation is usually achieved by carbonization in an inert atmosphere followed by oxidizing in CO2 [17], steam [18], or air. In this way, activated carbons with a narrower pore size distribution can be obtained [19]. Generally, chemical activation takes advantage of oxidizing or dehydrating agents, such as KOH [1,15,20], NaOH, H3PO4, or CaCl2, and being subjected to calcination under N2 between 773 and 1223 K. Chemical activation contributes to the formation of pores and lead to carbons with higher textural development. However, it is an energy-consuming process and is also associated with the corrosion of equipment and environmental problems [21].

To achieve green and sustainable development, the production of low-cost porous carbons from original waste materials is a good, established technique [22]. Polyurethane foams (PUFs), as one of the most important thermoset polymers, are widely used in the chemical industry, electronics, textiles, and medical and other fields due to their high strength, excellent wear resistance, and wide hardness range. The total annual production of polyurethane products in the Asia Pacific region was about 11.5 million tons in 2014, and it is predicted to be over 15.5 million tons by 2019 [23]. As a result, large amounts of useless waste and spent product were generated. Since the processes used for recycling polyurethane foams, like mechanical recycling [24] or chemical depolymerization [25,26], are highly time- and energy-consuming [27], most of the wastes are discarded in landfills or directly burnt, leading to serious environmental issues [28]. However, the ease of availability of the raw materials helps to make waste PUFs a potential activated carbon adsorbent. This would not only alleviate pollution and protect the environment, but it could also convert PUF into a high-value-added product.

Herein, low-cost activated carbons were prepared from waste PUF materials using physical activation with CO2 for the first time, and chemical activation with metal hydroxide. The synthetic procedure is simple and clear, and production costs are low. The activation conditions, like physical activation temperature and chemical activating agent, were discussed and optimized to produce porous carbons with high CO2 adsorption capacity and CO2/N2 selectivity. The richness of N content and narrower microporosity produced by physical activation may be responsible for the higher CO2/N2 selectivity of up to 24. Furthermore, physical activation leads to less contaminant and lower weight losses but also lower CO2 uptake. Porous carbons obtained by chemical activation with KOH possess large volumes of micropores (<1 nm) and high surface areas of up to 0.212 cm3·g−<sup>1</sup> and 1360 m2·g<sup>−</sup>1, respectively. This material exhibits a high CO2 adsorption capacity of 5.85 mmol·g−<sup>1</sup> at 273 K and excellent recyclability. The easy regeneration of the PUF-based carbon adsorbent requires minimum energy input, thus reducing operational costs. Therefore, the waste PUFs have the potential to be utilized for the production of activated carbon adsorbents on an industrial scale.

#### **2. Materials and Methods**

Prior to carrying out chemical or physical activation, a batch of waste PUFs were carbonized in a porcelain boat inside a horizontal tube furnace at 673 K for 1 h under nitrogen flow (60 mL·min−1). The obtained material was designated as PUF/C. Chemical activation was conducted by treating the carbonized PUFs in alkaline solutions. First, 0.8 g PUF/C was dipped into 10 mL of separate aqueous solutions containing 1.6 g of Ca(OH)2, NaOH, and KOH; was stirred uniformly for 1 h at room temperature; and was subsequently dried at 383 K for 12 h to remove water. Then, the mixture was heated up to 973 K under N2 flow (60 mL·min<sup>−</sup>1) for 2 h. The samples were thoroughly washed in diluted HCl and distilled water until pH = 7. The products were dried at 373 K for 6 h and denoted as PUF/C-Ca(OH)2-973, PUF/C-NaOH-973, and PUF/C-KOH-973, respectively.

Physical activation of PUFs was accomplished by one-step carbonization and CO2 activation. Certain amounts of PUFs were carbonized in the same procedure followed above, and then N2 was switched to CO2; the obtained PUF/C char was heated up to 1073–1273 K for 2 h under CO2 flow (15 mL·min<sup>−</sup>1). After physical activation, the sample was cooled to ambient temperature under nitrogen protection and was labeled as PUF/C-CO2-x, where x represents the CO2 activation temperature.

Nitrogen sorption isotherms and the textural properties of the porous carbons were measured at 77 K on an ASAP 2020 apparatus. Before N2 adsorption measurements, all samples were degassed at 493 K for 4 h. The specific surface area was obtained using the BET method (*p*/*p*<sup>0</sup> = 0–0.5). The total pore volume was estimated with the amount of N2 adsorbed at *p*/*p*<sup>0</sup> = 0.99. The morphology and structure of the obtained samples were investigated by scanning electron microscopy (SEM, JEOL JSM-700) and transmission electron microscopy (TEM, JEOL JEM-2001F). The amount of N in this paper was determined by elemental analysis using an Elemntar Vario EL Cube microanalyzer. X-ray photoelectron spectra (XPS) were collected on a Krato AXIS Ultra DLD spectrometer. CO2 temperature-programmed-desorption (CO2-TPD) was measured on a Micromeritics Autochem II 2920 chemisorption analyzer.

The CO2 adsorption capacity and adsorption–desorption cycles were investigated at 273 K and ordinary pressure on a BEL-SORP-max instrument. The activated carbon materials were degassed at 453 K for at least 2 h before the measurement.

#### **3. Results and Discussion**

#### *3.1. Pyrolysis of Wasted PUF and Morphologies of Activated Carbons*

Figure 1 shows the pyrolysis of the waste polyurethane foam at a heating rate of 5 K·min−<sup>1</sup> in a nitrogen atmosphere. Water adsorbed on the surface is released at about 373 K. The weight loss of the raw materials mainly occurs between 523–773 K, which include three differentiated weight loss peaks. The first peak nears 593 K, resulting from the formation of amines, methylene methyl, and methane species. The second peak around 653 K is due to the decomposition of isocyanate, and the third one near 733 K results from the production of quaternary N, aromatics, CH4, and other species [15]. It is clear that the carbonized precursor mainly results from the former two peaks. Therefore, the carbonization temperature was subsequently set to 673 K.

Figure 2 shows the SEM and TEM images of carbonized and chemical and physical activation samples. A slightly rough surface appeared when the carbonized precursor was activated by Ca(OH)2, but the activation degree was so limited that only few macropores existed on the surface (Figure 2b). In comparison, as it was activated by NaOH and KOH, the smooth surface changes to a multi-hole morphology to a large extent (Figure 2a,c,d). In addition, NaOH activation resulted in more macropores on the surface, while KOH activation led to the plentiful generation of micropores. These observations indicate that the carbonized precursor was etched increasingly severely by the chemical activation of Ca(OH)2, NaOH, and KOH (Figure 2a–d). Furthermore, in the physical activation process, the activation temperature determines the development of porosity. It can be observed that the surface of the carbonized precursor became rugged after physical activation by CO2 at 1173 K and formed

many micropores during the gasification reaction between CO2 and carbon and the following diffusion processes of the generated products (Figure 2e,g). Micropore generation would possibly benefit the fast, dynamic CO2 adsorption-desorption. Nevertheless, much higher physical activation temperatures should be avoided as they can broaden the average micropore width, producing few mesopores (Figure 2f,h), which is not good for CO2 capture.

**Figure 1.** Pyrolysis of polyurethane foam measured at the heating rate of 5 K·min−<sup>1</sup> in N2 atmosphere.

**Figure 2.** SEM images of the PUF/C (**a**), PUF/C-Ca(OH)2-973 (**b**), PUF/C-NaOH-973 (**c**), PUF/C-KOH-973 (**d**), PUF/C-CO2-1173 (**e**), PUF/C-CO2-1273 (**f**), and TEM images of PUF/C-CO2-1173 (**g**) and PUF/C-CO2-1273 (**h**). The inset in (**a**) is a photo showing the morphology of waste polyurethane foams.

#### *3.2. Surface Oxygen and Nitrogen Species Analyses*

Figure 3 exhibits the FT-IR spectra of PUF-based activated carbons. The broad band around 3440 cm−<sup>1</sup> could be ascribed to the N–H/O–H symmetric stretching vibration [29]. The intense bands at 1090 cm−<sup>1</sup> and 1584 cm−<sup>1</sup> are related to C–O stretching [29] and N–H in-plane deformation. The relatively weak band around 1383 cm−<sup>1</sup> is attributed to the C–N stretching vibration. Bands at 2927 and 2851 cm−<sup>1</sup> could be ascribed to the C–H stretching vibrations of –CH2 and –CH3 [15]. The presence of oxygen and nitrogen species were further confirmed by XPS. The O 1s spectra show the presence of three main peaks at 531, 532.1, and 533.5 eV, which are attributed to –C=O, C–O–C/C–OH, and O–C=O, respectively (Figure 4) [30]. The results confirm that plentiful amounts of oxygen-rich functional groups are incorporated onto the surface of the PUF-based activated carbons, which play an important role in enhancing the overall CO2 adsorption.

**Figure 3.** FT-IR spectra of the obtained samples prepared by physical and chemical activation.

**Figure 4.** XPS spectra O 1s of PUF/C-KOH-973 (**a**), PUF/C-NaOH-973 (**b**), PUF/C-Ca(OH)2-973 (**c**), and PUF/C-CO2-1173 (**d**).

It is well-known that the type and content of N-containing species of carbon adsorbents can greatly affect CO2 adsorption capacity. As shown in Figure 5, pyridinic (N-6), pyrrolic (N-5), quaternary N (N-Q), and pyridine N-oxide (N-X) species with binding energies of 398.5, 399.7, 400.9, and 403.0 eV, respectively, can be identified by deconvolving the N 1s signal of the activated carbons [31]. The specific components depend on the physical activation temperature or the degree of chemical activation with R(OH)x (R = Ca, Na, K; x = 1/2). The relative amount of pyridine N-oxide species increased with increasing CO2 activation temperature. This can also apply to the quaternary N species (Figure 5a–c). Conversely, upon increasing the CO2 activation temperature from 1073 to 1273 K, the contents of pyridinic and pyrrole N species dropped from 35% and 24% to 18% and 21%, respectively (Table 1), indicating that elevating the CO2 activation temperature can decrease the total amount of N species and also convert the pyridinic nitrogen and pyrrole nitrogen to quaternary N and pyridine N-oxide species (Figure 5a–c). This trend was also observed upon increasing the degree of chemical activation using Ca(OH)2, NaOH, and KOH as activated reagents (Figure 5d–f). The pyridinic and pyrrole-type N declined from 28% and 72% to 21% and 41%, respectively. Quaternary-N and pyridine N-oxide species are considered less effective in CO2 capture than pyridinic and pyrrolic nitrogen [32], since basic N species are superior at capturing CO2. Therefore, CO2 uptake on the activated carbons would increase with decreasing the physical activation temperature or the degree of chemical activation under the conditions of similar porous structures. Actually, CO2 uptake and physical activation temperature or the degree of chemical activation show a volcano relationship or a positive relationship, respectively, implying that the porous structure of the activated carbons may greatly affect their CO2 capture.


**Table 1.** Surface nitrogen contents of polyurethane foam-based activated carbons prepared by physical and chemical activation.

<sup>1</sup> Determined by element analysis. <sup>2</sup> Relative content of different types of N species, as determined by XPS analysis results. pyridinic (N-6), pyrrolic (N-5), quaternary N (N-Q), and pyridine N-oxide (N-X) species.

Figure 6 shows the CO2-TPD profiles of the porous carbons prepared by physical and chemical activation from wasted polyurethane foam. In general, physical and chemical adsorption are the two main routes for CO2 capture. An intense peak was present between 343 and 358 K, indicating that most of the CO2 molecules physically adsorbed on the activated carbons due to their developed pore structures. In contrast, a very weak peak was also observed around 448 K, implying that there exists a weak chemical interaction between CO2 and N species. Thus, physical adsorption occupies the dominant position during the CO2 capture process in this studied case.

**Figure 5.** XPS N 1s spectra of PUF/C-CO2-1073 (**a**), PUF/C-CO2-1173 (**b**), and PUF/C-CO2-1273 (**c**), PUF/C-Ca(OH)2-973 (**d**), PUF/C-NaOH-973 (**e**), and PUF/C-KOH-973 (**f**).

**Figure 6.** CO2- temperature-programmed-desorption (TPD) profile of PUF/C-KOH-973 and PUF/C-CO2-1273.

#### *3.3. E*ff*ect of Physical and Chemical Activation on the Textural Properties of PUF-Based Porous Carbon Materials*

Figure 7 shows the N2 adsorption isotherms and pore size distributions (PSDs) of carbon materials after physical and chemical activation. Clearly, the prepared samples exhibit a typical micropore structure that is mainly responsible for CO2 capture at ambient conditions [33]. Meanwhile, the hysteresis loops of the adsorption isotherm of PUF/C-KOH-973 or PUF/C-NaOH-973 are of type-IV, representing slit-shaped pores and implying the generation of mesopores (Figure 7a). It is well-known that carbon materials with narrow micropores can be obtained by physical activation [18]. It can be observed from Table 2 that, upon increasing CO2 activation temperature from 1073 to 1273 K, the surface area and pore volume of the samples significantly rose from 15 m2·g−<sup>1</sup> and 0.04 cm3·g−<sup>1</sup> to 865 m2·g−<sup>1</sup> and 0.42 cm3·g−1, respectively, and the PSDs (<1 nm) of samples widened from 0.58 to 0.87 nm (Figure 7b). In particular, the ultra-micropore (<1 nm) volume increased from 0.085 to 0.122 cm3·g−<sup>1</sup> and then decreased to 0.119 cm3·g−<sup>1</sup> upon elevating the activation temperature (Table <sup>2</sup> and Figure 8). A much higher physical activation temperature will make the reaction rate between carbon and CO2 faster than the diffusion rate, which is not helpful for the formation of micropores. From the above, we can conclude that the pore structure of activated carbons could be controlled by adjusting the CO2 activation temperature. In this work, the developed ultra-micropore can be obtained at 1173 K with a CO2 flowrate of 15 mL·min<sup>−</sup>1.

**Figure 7.** Adsorption isotherms of N2 at 77 K (**a**) and pore size distributions determined by the Dubinin–Radushkevich equation applied to Table 2 adsorption data at 273 K (**b**) of the obtained samples prepared by physical and chemical activation.


**Table 2.** Textural parameters of prepared samples from adsorption isotherms of N2 and CO2.

<sup>1</sup> Total pore volume at *p*/*p*<sup>0</sup> = 0.99. <sup>2</sup> Micropore volume determined by the *t*-plot method. <sup>3</sup> The cumulative volume of pores smaller than 1 nm determined using CO2 adsorption data at 273 K. <sup>4</sup> D is the maximum value of the PSDs. The yield of the prepared sample was calculated by the mass ratio of activated carbon and dry PUF.

**Figure 8.** Cumulative volumes of ultramicropores (≤1 nm) calculated by the nonlocal density functional theory (NLDFT) method of PUF-based activated carbons. Adapted with permission from [15]. Copyright 2016 American Chemical Society.

In addition, the textural parameters of chemical activation samples are also shown in Table 2. The carbonized precursor was activated by chemical reagents Ca(OH)2, NaOH, and KOH at 973 K. In addition to the sharp increase of surface area and pore volume from 39 m2·g−<sup>1</sup> and 0.04 cm3·g−<sup>1</sup> to 1360 m2·g−<sup>1</sup> and 0.59 cm3·g−1, respectively, and the volumes of ultra-micropores (<1 nm) increased from barely detectable to 0.212 cm3·g−<sup>1</sup> (Table <sup>2</sup> and Figure 8). It can be inferred that the pore structure of the carbon adsorbents could also be controlled by adjusting the chemical activation degree, and that increasing the degree of chemical activation is helpful for the formation of pores, especially the ultra-micropores, although a small number of mesopores were generated simultaneously.

Comparing physical activation with chemical activation, it can be concluded that physical activation mainly develops micropores and results in porous carbon materials with lower textural properties, which may affect the adsorption performance and lead to lower CO2 uptake. However, it involves less contaminant, which avoids the emission of metal ions and the corrosion of equipment.

By increasing the activation temperature from 1073 to 1273 K (Table 2), the yield of porous carbons with physical activation decreased from 16% to 9.3%, as a result of CO2 gas reacting with the walls of the pores (Equation (1)) [34]. In addition, the higher the burn-off temperature, the wider the pore size distribution becomes, as mentioned above. Regardless, higher carbon yield can be produced by physical activation due to lower burn-off degrees compared with effective chemical activation.

$$\text{2CO}\_2 + \text{C} \to \text{2CO} \tag{1}$$

Similarly, the yield of carbon materials, as expected, decreased from 25.5% to 12.3% when enhancing the chemical activation degree using Ca(OH)2, NaOH, or KOH as activated reagents (Table 2), which can be explained by graphitic C being oxidized by KOH beginning at 673 K, while for NaOH the temperature needs to be higher than 843 K, not even mentioning Ca(OH)2 [35].

The porosity formed upon KOH, NaOH, and Ca(OH)2 activation is considered to be the intercalation of metallic K, Na, and Ca in the carbon matrix, causing the deformation and expansion of the carbon lattice [36]. CO2, H2O, and H2 generated in the redox reaction (take KOH and C for example) can also contribute to the development of pores. As shown in Equations (2)–(4) [35].

$$4\text{KOH} + \text{C} \to 4\text{K} + \text{CO}\_2 + 2\text{H}\_2\text{O} \tag{2}$$

$$2\text{4KOH} + 2\text{CO}\_2 \rightarrow 2\text{K}\_2\text{CO}\_3 + 2\text{H}\_2\text{O} \tag{3}$$

$$2\text{6KOH} + 2\text{C} \to 2\text{K} + 3\text{H}\_2 + 2\text{K}\_2\text{CO}\_3\tag{4}$$

#### *3.4. CO2 Adsorption Performances*

CO2 adsorption performances of samples with physical and chemical activation at 273 K are shown in Figure 9. For physically-activated samples, CO2 adsorption capacities increased from 2.4 to 3.4 mmol·g−<sup>1</sup> upon increasing activation temperature from 1073 to 1173 K. This cannot be simply attributed to surface area and pore volume (Table 2), since PUF/C-CO2-1273 possesses larger surface area and pore volume but a lower CO2 adsorption capacity than that of PUF/C-CO2-1173. It was reported recently that small micropores are dominant in CO2 capture for activated carbon materials [9]. This is attributed to the fact that the interaction between CO2 and carbon adsorbents can be enhanced in small pores, especially at elevated temperatures and low pressures. The correlation of CO2 uptake of PUF-based activated carbons at 273 K and 1 bar with the volumes of micropores (<1 nm) is presented in Figure 10a. Clearly, the CO2 adsorption capacity shows a perfect linear correlation with the volumes of micropores less than 1 nm. Severe activation at higher temperature results in widening the pore size distribution, reducing the amount of ultra-micropores, which is responsible for the lower CO2 uptake of PUF/C-CO2-1273 compared to PUF/C-CO2-1173.

**Figure 9.** Adsorption isotherms of CO2 at 273 K for the studied samples.

**Figure 10.** Correlation of CO2 adsorption capacity with ultra-micropore volume (**a**) and with sample N content (**b**) at 273 K.

This trend can also be applied to the CO2 adsorption performances of samples after chemical activation. Figure 8 shows that the adsorption capacity of CO2 at 273 K and 1 bar increases as follows: PUF/C-Ca(OH)2-973 < PUF/C-NaOH-973 < PUF/C-KOH-973. The PUF/C-KOH-973 sample exhibits the highest CO2 uptake at 5.85 mmol·g<sup>−</sup>1. This is mainly due to it having the largest ultra-micropore volume (<1 nm) of 0.212 cm3·g−<sup>1</sup> coupled with the largest surface area, pore volume, and abundance of basic N species. Chemical activation can be described by two steps: The formation of micropores, which is beginning with the redox reaction between activated agents and carbonized precursors; and the broadening of pores inside the opened porous channel [37]. Hence, the stage of creating micropores is primary when enhancing the activation degree from Ca(OH)2 to KOH activation at 973 K.

N content in the prepared carbon materials may also contribute to the CO2 adsorption capacity, wherein the correlation coefficient of CO2 uptake with the N content (3.5–10.5 wt.%) of PUF-based activated carbons was only 0.29 (Figure 10b). This indicates that N content in the samples should not be the primary effect in this experiment. Thus, the superior CO2 adsorption capabilities of the studied activated carbons predominantly result from the ultra-micropores (<1 nm).

Table 3 shows a comparation of CO2 uptakes of carbon adsorbents prepared from PUF and other different waste materials at 273 K. It is worth mentioning that samples prepared with the waste polyurethane foams under CO2 activation at 1173 K and KOH activation at 973 K exhibit comparable or better CO2 adsorption capacities than those adsorbents obtained from many types of waste materials, such as ocean pollutant, sawdust, and coal fly ash (see Table 3). Whereas, some new derived activated carbons from biomass porous materials, like *Arundo donax* [29] or lotus seed [38], show a relatively higher adsorption capacity. The results mentioned above suggest that activated carbon prepared from waste PUF is a potential candidate for use in capturing CO2.


**Table 3.** CO2 adsorption capacities at 273 K and 1 bar of activated carbons prepared from different waste materials.

#### *3.5. CO2 Adsorption Recyclability and Selectivity*

CO2 and N2 adsorption behaviors of PUF/C-CO2-1173 and PUF/C-KOH-973 at 273 K were compared, as shown in Figure 11. The N2 adsorption capacity is 0.59 mmol g−1, which is almost one-tenth of the CO2 uptake of chemically-activated sample PUF/C-KOH-973. While, the selectivity of CO2/N2 of PUF/C-CO2-1173 outperforms PUF/C-KOH-973 with a lower N2 adsorption of only 0.14 mmol g−<sup>1</sup> at 273 K, which is much higher than CO2 adsorbents from other waste materials, such as mangosteen peel waste (24 vs. 12) [45]. This can be ascribed to the rich and narrower microporosity [39], as well as the nitrogen content of 6.84 wt% and the presence of other functional groups [46].

**Figure 11.** CO2 and N2 adsorption isotherms at 273 K on the PUF/C-CO2-1173 (**a**) and PUF/C-KOH-973 (**b**).

The CO2 adsorption performances of PUF/C-CO2-1173 at 273 K and PUF/C-KOH-973 at 298 K are completely the same in the four repeated runs, as shown in Figure 12a,b, indicating that the physically-activated sample has as high of a recyclability as the chemically-activated sample. Hence, in comparison with the environmental pollution and cumbersome process associated with chemical activation, CO2 sorbent from waste polyurethane foam by physical activation is encouraged, due to the higher CO2/N2 selectivity and the relatively higher carbon yield.

**Figure 12.** CO2 adsorption performances on the PUF/C-CO2-1173 at 273 K (**a**) and PUF/C-KOH-973 at 298 K (**b**) within four repeated cycles with regeneration.

#### **4. Conclusions**

In this work, waste polyurethane foam was physically activated by CO2 for the first time and chemically activated with Ca(OH)2, NaOH, or KOH, and was compared for the preparation of the low-cost activated carbons with a highly-developed porosity. To achieve high CO2 adsorption capacity and CO2/N2 selectivity, activation conditions of the waste polyurethane foam were optimized. The low CO2 flowrate (15 mL·min<sup>−</sup>1) and temperature of 1173 K for CO2 activation are beneficial to the formation of a narrower microporosity. This, combined with relatively high nitrogen content, resulted in the high CO2/N2 selectivity of up to 24, much higher than samples that underwent chemical activation. However, physical activation samples showed inferior textural properties compared to chemical activation samples and led to lower CO2 uptake. The sample activated with KOH possessed a high volume of ultramicropores (<1 nm) and surface area up to 0.212 cm3·g−<sup>1</sup> and 1360 m2·g<sup>−</sup>1, respectively. A high CO2 adsorption capacity of 5.85 mmol·g−<sup>1</sup> was obtained at 273 K and 1 bar, which was better than the physical activation sample with 3.37 mmol·g<sup>−</sup>1, and is comparable to the best reported carbon materials prepared from other waste materials. Moreover, in consideration of the decreased presence of the contaminant, the lower weight losses of physical activation samples, and the excellent recyclability of both physical and chemical activated samples, the waste polyurethane foam-based carbon materials exhibited potential application prospects in CO2 capture.

**Author Contributions:** Writing—original draft, C.G.; data curation, D.L.; investigation, S.C.; formal analysis, J.G.; supervision, J.L.

**Funding:** This research was funded by the National Natural Science Foundation of China (No. 21802101), Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi, and Shanxi Province Science Foundation for Youths (No. 201801D221129).

**Conflicts of Interest:** The authors declare no conflicts of interest.

#### **References**


© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Article* **Modeling and Simulation of the Absorption of CO2 and NO2 from a Gas Mixture in a Membrane Contactor**

#### **Nayef Ghasem**

Department of Chemical and Petroleum Engineering, UAE University, Al-Ain 15551, United Arab Emirates; nayef@uaeu.ac.ae; Tel.: +971-3-713-5313

Received: 9 June 2019; Accepted: 8 July 2019; Published: 11 July 2019

**Abstract:** The removal of undesirable compounds such as CO2 and NO2 from incineration and natural gas is essential because of their harmful influence on the atmosphere and on the reduction of natural gas heating value. The use of membrane contactor for the capture of the post-combustion NO2 and CO2 had been widely considered in the past decades. In this study, membrane contactor was used for the simultaneous absorption of CO2 and NO2 from a mixture of gas (5% CO2, 300 ppm NO2, balance N2) with aqueous sodium hydroxide solution. For the first time, a mathematical model was established for the simultaneous removal of the two undesired gas solutes (CO2, NO2) from flue gas using membrane contactor. The model considers the reaction rate, and radial and axial diffusion of both compounds. The model was verified and validated with experimental data and found to be in good agreement. The model was used to examine the effect of the flow rate of liquid, gas, and inlet solute mole fraction on the percent removal and molar flux of both impurity species. The results revealed that the effect of the liquid flow rate improves the percent removal of both compounds. A high inlet gas flow rate decreases the percent removal. It was possible to obtain the complete removal of both undesired compounds. The model was confirmed to be a dependable tool for the optimization of such process, and for similar systems.

**Keywords:** global warming; chemical absorption; membrane contactor; removal of NO2 and CO2

#### **1. Introduction**

Harmful gases are emitted into the atmosphere from industrial plants, because of the increase in the human population and the associated economic development, energy consumption, and the requirement of burning fossil fuels for water desalination and power generation purposes. Nitrogen dioxide (NO2) is believed to be one of the gases that contributes to smog and acid rain and which is harmful to human and animal well-being. Accordingly, there is an obligation to capture and eliminate nitrogen dioxide and other harmful gases, such NOx, SO2, and CO2 from industrial emission streams, proceeding to discharge into the atmosphere [1–3]. Various methods have been established for capturing the impurity of compounds such as physical and/or chemical absorption, adsorption, membrane technology, conversion to another compound, and condensation. There are various technologies available to remove CO2 and NOx [3–5]. Physical absorption incorporates mass transport within the phases and mass transfer at the liquid–gas boundary. Operating conditions and gas solubility are the main factors affecting physical absorption. An example of physical absorption is the capture of CO2 into liquid water using industrial absorption towers or gas–liquid membrane contactors. Chemical absorption is based on a chemical reaction between the absorbed substances and the liquid phase, such as the capture of CO2 in amine solutions [4,6]. The most widely used commercial and economical method is the chemical absorption technique, used in the conventional absorption packed bed towers employed in the absorption of CO2, H2S, and NO2 from chimney

gas and natural gas via alkanolamine solutions, where the flue gas and the absorbent liquid are in direct contact. The conventional scrubbing method requires a huge absorption column with excess liquid absorbent and a large cross-sectional area in order to prevent foaming and channeling. The large amount of absorbent liquid utilized in the absorption process (i.e., rich solvent) increases the operating and regeneration cost as more heat is required for the regeneration and pumping of the recycled lean solvent. The main disadvantages of conventional chemical absorption processes are channeling, foaming, corrosion, and a large space area required and hence high operating and capital cost. The idea of using membrane contactor was first proposed for the absorption of carbon dioxide in sodium hydroxide as a liquid absorbent utilizing a non-dispersive microporous membrane where gas and liquid phases are not dispersed in each other [7,8]. A hollow fiber membrane contactor (HFMC) provides a greater surface contact area per unit volume, more than that of a conventional absorption column [9–13]. In most cases of membrane contactor operation, gas flows in the shell side, and liquid absorbent flows in the tube side; vice versa is also possible [14]. The performance of a HFMC declines when the micropores of the membranes are wetted with a liquid solvent [3,11,15–17]. The advantages of membrane processes are as follows: Gas and liquid flow rates are independent, high ratio of surface area per volume, easy scale up and down, and no worry about flooding and channeling [18]. The membrane acts as an obstacle between gas and liquid and delivers an exchange surface zone for the two phases, without the dispersion of the gas phase in the liquid phase. In the non-wetted mode, the membrane pores are filled with gas, and by contrast, in the wetted mode, part of the membrane pores are filled with the liquid absorbent. The pollutant gas compounds' amputation process occurs when the gas filling the membrane micropores diffuses from the gas stream and is absorbed by the liquid absorbent running in the membrane lumen ide [1,16,19]. The removal of the contaminant gas compounds from the gas stream depends on the solubility of the acid gas molecules in the absorbent liquid, and on the concentration incline among the gas stream absorbent solution. The interaction between the selected gas solute and the selective absorbent liquid defines the performance of the pollutant gas removal rate [20]. Membrane fouling and membrane wettability are the main drawbacks of the membrane contactor. The wetted portion of the membrane adds an additional mass transfer resistance. Accordingly, in order to avoid membrane wettability, researchers focused on the use of hydrophobic polymeric membranes, such those made from polypropylene (PP), polytetrafluoroethylene (PTFE), polyvinylidene fluoride (PVDF), and polyethylene (PE) [21–24]. The Gas Liquid Hollow Fiber Member Contactor (GLMC) processes were used for the removal of CO2 from nitrogen, CO2 from natural gas, and the instantaneous removal of CO2 and SO2 from combustion released gas [17,19,20,25–33]. The experimental simultaneous capture of CO2 and NO2 from a pretended flue gas mixture containing CO2/NO2/N2 was first investigated using a commercial PTFE membrane by Ghobadi et al. [23]. The effects of the gas and liquid cross-flow velocity on the percent removal of these gasses were investigated. Various mathematical models were developed for the removal of carbon dioxide, sulfur dioxide, and hydrogen sulfide from simulated flue gas and natural gas streams [7,9,15,17,25,32,34–39]. To the extent of the author's knowledge, so far, there is no mathematical model published to designate the synchronized capture of CO2/NO2 from a mixture of gas consisting of CO2/NO2/N2.

This study focusses on the development of a numerical model for the capture of CO2/NO2 from a gas containing: 5% CO2, 300 ppm NO2, and the balance is N2. The principal model equations were solved using COMSOL Multiphysics (Version 5.4, Comsol Inc., Zürich, Switzerland). The developed model was used to predict the influence of various operating parameters on the percent removal and molar flux of the acid gas components. The model was verified and validated with experimental data from literature [23].

#### **2. Model Development**

The gas-liquid membrane process consists of many hollow fibers assembled in a module, where the liquid solvent flows inside the membrane lumen, and the gas flows in the shell sideways, or vice versa, in a co-current or countercurrent parallel flow. The pollutant gas compounds diffuse

through the fiber walls towards the absorbent membrane–tube interface, as a result of the concentration gradient. Other gases are retained in the membrane pores because of their low diffusivity and low solubility in the liquid solvent. Figure 1 shows a schematic simplified geometry of the model domains representing the HFM module grounded on Happel's free surface [40]. The model considers the following three separate domains: the liquid phase in the tube side, the non-wetted membrane, and the gas phase in the shell side. The system is steady state, described by cylindrical coordinates, angular concentration gradients are neglected, and an asymmetrical approach is considered.

**Figure 1.** Simplified geometry of membrane module based on Happel's free surface method.

The sizes of the membrane used in the simulation are presented in Table 1.

**Table 1.** Membrane module dimensions [23].


As seen from Table 1, the fiber length is 588 times longer than the fiber radius (effective module length is 200 mm and radius are 0.34 mm). Accordingly, the membrane length is scaled up so as to avoid an excessive number of elements and nodes and for a better appearance of the module in the simulation; therefore, a new scaled length is introduced by dividing the length by 100. The following assumptions were considered:


The blended gas (CO2, NO2, and N2) is transported in the shell side by convection and diffusion, whereas, in the membrane section, the only transport mechanism is diffusion. The liquid phase (NaOH + H2O) is transported in the lumen by diffusion and convection. The following mass transport equations are formulated to describe the chemical absorption system in the model domains (tube, membrane, and shell). The developed mass transport equations are presented as follows.

#### *2.1. Tube Side*

The mass balance equations for the gas components of CO2, NO2, and N2 in the tube side can be stated, as per Equation (1), as follows:

$$D\_{i,t} \frac{1}{r} \left(\frac{\partial}{\partial r} r \left(\frac{\partial C\_{i,t}}{\partial r}\right)\right) + D\_{i,t} \frac{\partial^2 C\_{i,t}}{\partial z^2} = \upsilon\_{z,t} \left(\frac{\partial C\_{i,t}}{\partial z}\right) + R\_i \tag{1}$$

The subscripts in the material balance the following equations: *t* refers to tube side, *m* refers to membrane, and *s* refers to shell side, where *Ci*,*<sup>t</sup>* refers to the concentration of component *i* in liquid moving in the tube side, *i* refers to the pollutant gas components: CO2, NO2, and *Ri* is the rate of reaction of the species *i*. The length of the fiber is scaled to avoid excessive computation and to make the simulation result in a better profile. The scaling is performed as follows: let ξ = *z*/*scale*. The scaling factor is substituted in Equation (1). Consequently, the equation becomes Equation (2), as follows:

$$D\_{i,t} \frac{1}{r} \left(\frac{\partial}{\partial r} r \left(\frac{\partial \mathbf{C}\_{i,t}}{\partial r}\right)\right) + \frac{D\_{i,t}}{\text{scale}^2} \frac{\partial^2 \mathbf{C}\_{i,t}}{\partial \xi^2} = \frac{\upsilon\_{z,t}}{\text{scale}} \left(\frac{\partial \mathbf{C}\_{i,t}}{\partial \xi}\right) + R\_i \tag{2}$$

where the velocity of liquid inside the hollow fiber (*vz*,*t*) is described by the following parabolic equation:

$$v\_{z,t} = \frac{2Q\_t}{n\pi r\_1^2} \left(1 - \left(\frac{r}{r\_1}\right)^2\right) \tag{3}$$

where *Qt* is the volumetric liquid flow rate in the tube side, and *n* is the number of hollow fibers. The appropriate set of boundary conditions are specified as follows:

$$\begin{array}{llll}\mbox{at } z = 0, & \mathbb{C}\_{i,t} = 0 & (\mbox{a})\\ \mbox{at } z = H, & \frac{\partial^2 \mathbb{C}\_{i,t}}{\partial r^2} = 0 & (\mbox{b})\\ \mbox{at } r = 0, & \frac{\partial \mathbb{C}\_{i,t}}{\partial r} = 0 & (\mbox{c})\\ \mbox{at } r = r\_1, & \mathbb{C}\_{i,t} = m\_i \mathbb{C}\_{i,m} & (\mbox{d})\end{array} \tag{4}$$

where *mi* is the dimensionless physical solubility of CO2 and NO2 in solvent, 0.82, 0.17, respectively. The values of the dimensionless physical solubility of CO2 and NO2 were calculated from Henry's constant (H): 0.034 kmol/m<sup>3</sup> atm, 0.007 kmol/m3 atm [23], respectively, using the relation *mi* = RTxH. The liquid phase reactions between NO2 and NaOH took several steps. First, NO2 dissolved in the aqueous NaOH was reacted with H2O, then neutralized with sodium hydroxide [41]. The controlling liquid phase reactions are as follows:

$$2\text{NO}\_2 + \text{H}\_2\text{O} \to \text{HNO}\_2 + \text{HNO}\_3\tag{5}$$

$$\text{HNO}\_2 + \text{NaOH} \rightarrow \text{NaNO}\_2 + \text{H}\_2\text{O} \tag{6}$$

$$\text{HNO}\_3 + \text{NaOH} \rightarrow \text{NaNO}\_3 + \text{H}\_2\text{O} \tag{7}$$

The overall reaction is designated, as per Equation (8), as follows:

$$2\text{ NaOH} + 2\text{NaOH} \rightarrow \text{NaNO}\_2 + \text{NaNO}\_3 + \text{H}\_2\text{O} \tag{8}$$

The general reaction rate is expressed in Equation (9), as follows:

$$r\_{NO\_2-NaOH} = k\_{r,1} [NO\_2][NaOH] \tag{9}$$

The reaction is the second order with a rate constant, *kr*,1 m<sup>3</sup> mol<sup>−</sup>1s−<sup>1</sup> = 1.0 <sup>×</sup> <sup>10</sup>5, [1] The overall reaction of CO2 and NaOH is represented by the following reaction [42].

$$\rm{CO\_2} + 2NaOH \rightarrow \rm{Na\_2CO\_3} + \rm{H\_2O} \tag{10}$$

The rate of the reaction is determined, as per Equation (11), as follows:

$$r\_{\text{CO}\_2-\text{NaOH}} = k\_{r,2}[\text{CO}\_2][\text{NaOH}] \tag{11}$$

The reaction rate constant (in Equation (11)) is *kr*,2 = 8.37 (m<sup>3</sup> mol<sup>−</sup>1s−1) [23].

#### *2.2. Membrane Side*

The transport of the solute gas (CO2 and NO2) components in the membrane section confined between *r*<sup>1</sup> and *r*<sup>2</sup> can be described by the steady state material balance equation (Equation (12)), where diffusion is the only transport mechanism in the membrane phase [34], as follows:

$$D\_{i,m} \frac{1}{r} \left(\frac{\partial}{\partial r} r \left(\frac{\partial C\_{i,m}}{\partial r}\right)\right) + \frac{D\_{i,m}}{\text{scale}^2} \frac{\partial^2 C\_{i,m}}{\partial \xi^2} = 0 \tag{12}$$

The proper boundary settings are specified, as per Equation (13), as follows:

$$\begin{array}{llll}\text{at } z = 0, & \frac{\partial \mathbf{C}\_{i}m}{\partial z} = 0 & (\text{a})\\ \text{at } z = H, & \frac{\partial \mathbf{C}\_{i}m}{\partial z} = 0 & (\text{b})\\ \text{at } r = r\_{1}, & D\_{i,m} \frac{\partial \mathbf{C}\_{i}m}{\partial r} = D\_{i,t} \frac{\partial \mathbf{C}\_{i}t}{\partial r} & (\text{c})\\ \text{at } r = r\_{2} & \mathbf{C}\_{i,m} = \mathbf{C}\_{i,s} & (\text{d}) \end{array} \tag{13}$$

#### *2.3. Shell Side*

The steady state mass transport of solute gas (CO2 and NO2) components in the shell side (no chemical reaction occurs in the module shell zone) is expressed in Equation (14), as follows:

$$D\_{i,s} \frac{1}{r} \left(\frac{\partial}{\partial r} r \left(\frac{\partial \mathcal{C}\_{i,s}}{\partial r}\right)\right) + \frac{D\_{i,s}}{\text{scale}^2} \frac{\partial^2 \mathcal{C}\_{i,s}}{\partial \xi^2} = \frac{\upsilon\_{z,s}}{\text{scale}} \left(\frac{\partial \mathcal{C}\_{i,s}}{\partial \xi}\right) \tag{14}$$

The velocity profile in the shell side is described by Happel's free surface [40] and can be calculated as per Equation (15), as follows:

$$\begin{array}{rcl} v\_{\overline{z},s} & = & v\_{\overline{z},\max} \left\{ 1 - \left(\frac{r\_2}{r\_3}\right)^2 \right\} \left\{ \frac{\left(\frac{r}{r\_3}\right)^2 - \left(\frac{r\_2}{r\_3}\right)^2 - 2\ln\left(\frac{r}{r\_2}\right)}{3 + \left(\frac{r\_2}{r\_3}\right)^4 - 4\left(\frac{r\_2}{r\_3}\right)^2 + 4\ln\left(\frac{r\_2}{r\_3}\right)} \right\} \end{array} \tag{15}$$

The applicable boundary conditions are specified as follows:

$$\text{at } z = H, \qquad \qquad \mathcal{C}\_{i\_{\mathcal{C}}^s} = \mathcal{C}\_{i,0} \qquad \qquad (\text{a})$$

$$\begin{array}{llll}\texttt{at}\,\,\,\,\,\mathbf{z}=\,\,\,\mathbf{0}, & \frac{\stackrel{\partial^2\mathbb{C}\_{i}\,\,\mathbf{s}}{\partial\mathbf{z}^2}}{\stackrel{\partial\mathbf{c}\_{i}}{\partial\mathbf{r}}}=\,\,\mathbf{0} & \quad \text{(b)}\\\texttt{at}\,\,\,\mathbf{r}=\,\,\mathbf{r}\_{2}, & \frac{\stackrel{\partial\mathbf{C}\_{i}\,\,\mathbf{s}}{\partial\mathbf{r}}}{\stackrel{\partial\mathbf{C}\_{i}\,\,\mathbf{s}}{\partial\mathbf{r}}}=\,\,\mathbf{D}\_{i,\mathbf{u}}\,\frac{\stackrel{\partial\mathbf{C}\_{i}\,\,\mathbf{u}}{\partial\mathbf{r}}}{\stackrel{\partial\mathbf{C}\_{i}\,\,\mathbf{s}}{\partial\mathbf{r}}} & \quad \text{(c)}\\\texttt{at}\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\end{array} \end{array} \tag{16}$$

The radius of the free surface (*r*3) can be determined as per Equation (17), as follows:

$$r\_3 = r\_2 \left(\frac{1}{1-\wp}\right)^{0.5} \tag{17}$$

The void fraction of the membrane module (ϕ) is calculated as per Equation (18):

$$
\rho = \frac{R^2 - n\,r\_2^2}{R^2} \tag{18}
$$

where *R* is the module inner radius, *n* is the number of fibers *r1*, and *r2* is the fiber outside radius. The parameters used in the model are shown in Table 2.

**Table 2.** Physical properties used in the model.


#### **3. Numerical Solution**

The model governing the partial differential and algebraic equations was solved simultaneously using COMSOL software version 5.4. The software uses a finite element method to solve the model equations.

#### **4. Results and Discussion**

The accuracy of the mathematical model was checked prior to using the model for studying the effect of the various operating parameters on the percent deletion of CO2 and NO2 from the imitated flue gas. The model was authenticated with experimental data for the simultaneous absorption of an NO2 and CO2 from gas mixture in a PTFE polymeric gas–liquid hollow fiber membrane [23]. The percent removal of CO2 and NO2 was calculated as per Equation (19), as follows:

$$\% \text{Removal} = \frac{F\_{\text{g,in}} \text{ C}\_{i,in} - F\_{\text{g,out}} \text{ C}\_{i,out}}{F\_{\text{g,in}} \text{ C}\_{i,in}} \times 100 \tag{19}$$

where *Fg*,*in*, *Fg*,*out*, *Ci*,*in*, and *Ci*,*out* are the inlet gas flow rate, outlet gas flow rate, inlet gas concentration of component *i*, and outlet gas concentration of component *i*, respectively. The 2D surface plot of the CO2 and NO2 concentration profile throughout the model domains are shown in Figure 2. The figure reveals that, even though the solubility of CO2 (0.75) is higher than NO2 (0.17) in the aqueous NaOH solution, the removal rate of nitrogen dioxide is much higher than that of carbon dioxide, which is attributed to the high reaction rate of NO2-NaOH.

**Figure 2.** The 2D surface plot for the concentration profile of CO2 (left) and NO2 (right) at other fixed parameters (velocity of gas: 1.05 m/s; liquid rate: 0.02 m/s; 2% CO2; 300 ppm NO2; the balance is N2, initial concentration of CO2 and NO2, 0.832 mol/m3, 0.0125 mol/m3, respectively).

Figure 3. illustrates the association of this model's predictions relative to the experimental results for the effect cross-flow velocity of the feed gas on the simultaneous percent removal of CO2 and NO2 with fixed other parameters. A comparison of the percent removal of NO2 and the experimental data obtained from the literature was in good agreement; by contrast, there is a slight variance in the case of CO2. The removal flux decreased with the increased inlet gas velocity, attributable to the low residence time.

**Figure 3.** A comparison of this model's predictions and experimental data [23] for the influence of the inlet gas velocity on the simultaneous percent removal of CO2 and NO2 (velocity of liquid: 0.05 m/s; solvent concentration: 0.5 M NaOH; inlet gas composition: 2% CO2; 300 ppm NO2; the balance is N2).

The predicted results were also authenticated with the experimental investigations for the case of the effects of the variable liquid velocities on the percent removal of NO2 and CO2 (Figure 4) at a fixed gas cross-flow velocity of 2.11 m/s, 0.5 M NaOH, 2% CO2, 300 ppm NO2, and the balance was nitrogen. The simulation results matched the experimental data, to a certain extent [23]. The results revealed that the increase in solvent velocity increased the percent removal of CO2 and NO2 sharply at low liquid velocities (below 0.05 m/s); as the liquid velocity increased further, there was a slight increase in the percent simultaneous removal of CO2 and NO2 from the gas mixture. The insignificant increase in

liquid velocity higher than 0.05 m/s was attributed to a decrease in the residence time. The removal flux was calculated as per Equation (20):

$$J\_i = \frac{(y\_{i, \text{in}}Q\_{\text{in}} - y\_{i, \text{out}}Q\_{\text{out}}) \times 273.15 \times 1000}{22.4 \times T\_\% \times A} \tag{20}$$

where *Ji* is the removal molar flux of component *i* (mol/m2·s), *yi,in, yi,out* are the inlet and outlet molar fraction of component *i* in the gas phase, *Qin*, *Qout* are the inlet and outlet gas volumetric flow rate (m3/s), respectively, in gas molar volume (liter/mol) at standard conditions (1 atm and 0 ◦C) is 22.4; A is the membrane surface area (m2); 1000 is the conversion factor (liter/m3); *Tg* is the gas temperature in K. The 273.15 is the temperature at 0 ◦C (273.15 K).

**Figure 4.** Effect of flow rate of absorbent on the percent removal of undesired gas (gas flow rate = 2.11 m/s, solvent concentration: 0.5 M NaOH; inlet gas composition: 2% CO2; 300 ppm NO2; the balance is N2).

The influence of the speed of the gas on the molar flux of CO2 and NO2 is illustrated in Figure 5. The figure reveals that there was noticeable increase in the removal flux of CO2 with the gas velocity; by contrast, the removal flux of NO2 was insignificant because of its lower inlet concentration in the gas stream (300 ppm), compared with the CO2 inlet concentration (2%). When the velocity of gas was increased from 1.05 m/s to 2.11 m/s, the removal flux increased from 0.003 to 0.0038 mol/m2·s; at a high gas velocity, the increase was insignificant, from example, with the increase in gas velocity from 4.21 to 6.32 m/s, the increase in molar flux was very small. This was attributed to a decrease in residence time, as well as the insufficient amount of solvent available for the excess amount of CO2 and NO2 components associated with the increase in gas stream volumetric feed rate.

**Figure 5.** Effect of the variable gas velocities on the NO2 and CO2 removal flux along the membrane length with fixed other parameters (concentration of NaOH: 0.5 M; velocity of solvent: 0.05 m/s; gas composition: 2% CO2; 300 ppm NO2; the balance is N2).

Figure 6 demonstrates the effect of the change in the inlet CO2 mole fraction at a fixed inlet concentration of NO2 (300 ppm) on the component's molar flux. The CO2 molar flux increased significantly when its concentration increased, which was expected, because as the amount of CO2 increased in the inlet gas stream, more CO2 was being absorbed, and hence the CO2 removal molar flux increased (molar flux: moles gas removed per area per time). By contrast, because of the fixed low concentration of NO2 in the feed stream, its removal flux was insignificant compared to that of CO2.

**Figure 6.** Impact of the inlet CO2 feed concentration on the NO2 and CO2 capture flux along the membrane length at other fixed parameters (aqueous NaOH: 0.5 M; velocity of solvent: 0.05 m/s; gas velocity: 2.11 m/s; gas composition: (2% to 10%) CO2; 300 ppm NO2; the balance is N2).

Figure 7 explains the effect of change in the inlet NO2 mole fraction in the feed gas stream at a fixed concentration of CO2 (2%) on the removal flux of CO2 and NO2. The predicted results are in the range of the experimental data [23] under the same conditions. The effect of change in the inlet mole fraction of NO2 on the CO2 removal flux was insignificant, the CO2 removal flux was kept around 0.004 mol/m2·s and was not influenced by the change of the NO2 inlet mole fraction. By contrast, there was a slight increase in the removal flux of NO2 which caused an increase form 3 <sup>×</sup> 10−<sup>5</sup> to <sup>15</sup> <sup>×</sup> <sup>10</sup>−<sup>5</sup> mol/m2·s. This was attributed to the low inlet concentration of NO2 (in ppm) compared with the CO2 inlet concentration (2%), and consequently, the amount absorbed from CO2 and NO2 did not change significantly.

**Figure 7.** Effect of NO2 mole fraction in the feed gas stream on the removal molar flux of CO2 (left) and NO2 (right) at other fixed parameters (liquid velocity: 0.05 m/s; gas velocity: 2.11 m/s; 0.5 M NaOH; 100 to 500 ppm NO2; 2% CO2; the balance is N2).

#### **5. Conclusions**

Model equations based on material balance were utilized to describe and study the simultaneous detention of NO2 and CO2 with aqueous NaOH solution in a membrane module. The hollow fiber membranes were fabricated from PTFE polymer. The model equations were solved, and the model predicted results were compared with data from experimental investigation available in literature. The model was found to be in good agreement with the experimental findings. The mathematical model was then employed to study the influence of the inlet flow rate of gas and liquid, concentration of CO2 and NO2 in the feed stream on their percent removal and molar flux. The results revealed that the increase in CO2 inlet mole fraction and gas cross-flow velocity shows a strong impact on the molar flux. By contrast, the change in the NO2 inlet concentration showed insignificant influence on the CO2 removal flux.

**Conflicts of Interest:** The author declare no conflict of interest.

#### **References**


© 2019 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Article* **Adsorption of NO Gas Molecules on Monolayer Arsenene Doped with Al, B, S and Si: A First-Principles Study**

#### **Keliang Wang 1,\* , Jing Li 1,\* , Yu Huang 2, Minglei Lian <sup>1</sup> and Dingmei Chen <sup>1</sup>**


Received: 20 July 2019; Accepted: 9 August 2019; Published: 15 August 2019

**Abstract:** The structures and electronic properties of monolayer arsenene doped with Al, B, S and Si have been investigated based on first-principles calculation. The dopants have great influences on the properties of the monolayer arsenene. The electronic properties of the substrate are effectively tuned by substitutional doping. After doping, NO adsorbed on four kinds of substrates were investigated. The results demonstrate that NO exhibits a chemisorption character on Al-, B- and Si-doped arsenene while a physisorption character on S-doped arsenene with moderate adsorption energy. Due to the adsorption of NO, the band structures of the four systems have great changes. It reduces the energy gap of Al- and B-doped arsenene and opens the energy gap of S- and Si-doped arsenene. The large charge depletion between the NO molecule and the dopant demonstrates that there is a strong hybridization of orbitals at the surface of the doped substrate because of the formation of a covalent bond, except for S-doped arsenene. It indicates that Al-, B- and Si-doped arsenene might be good candidates as gas sensors to detect NO gas molecules owning to their high sensitivity.

**Keywords:** arsenene; doping; first principles study; gas adsorption; two-dimensional

#### **1. Introduction**

Owing to the adequate preparation of single-layer graphene, the research on two-dimensional (2D) materials has been increasing. Graphene, silicene, germanene, hexagonal boron nitride (h-BN), phosphorene, transition metal dichalcogenide and stanine [1–3] have attracted more and more attention in recent years. Especially, because of their ultrathin thickness and high surface-to-volume ratio, atomically thin 2D nanomaterials have been proven to be prospected nanoscale in catalyst, gas sensors and energy storage [4–6].

Good gas sensors for the detection of toxic gas plays a crucial role in industries, chemical detection and environment protection [7–9]. Due to the excellent structural properties of 2D materials, it has attracted wide attention in the field of gas sensors. Ma et al. [10] studied the small molecules (CO, H2O, NH3, N2, NO2, NO and O2) adsorbed on the InSe monolayer and found that 2D InSe nanomaterials is a potential candidate for fabricating gas sensors. The study of Liang et al. [11] showed that the affinity between Ga-doped graphene and gas molecules is stronger than that of pristine graphene. For the adsorption of NOx gases, there have been some experimental studies. MoS2 nanosheets, prepared by the micromechanical exfoliation method, show a high selectivity for NOx and NH3 gas molecules at the ppb level [12,13]. Schedin et al. [14] have researched the adsorption of NO2, NH3, H2O and CO gas molecules on graphene-based gas sensors and found that graphene was electronically quiet enough to be used as a single electronic detector at room temperature.

Recently, monolayer arsenene was predicted to be indirect semiconductors based on its excellent properties of high stability and wide band gaps [15,16]. Similar to silicene, arsenene possesses buckled honeycomb structures [17]. Liu et al. [18] have researched the adsorption of six kinds of small gas molecules on the pristine arsenene monolayer and found that in gas sensing, arsenene can be a potential candidate applied for NO and NO2 molecules with electrical and magnetic methods. Khan and his coworkers [19] have reported NH3 and NO2 molecules show a significant affinity for arsenene leading to strong physisorption.

Generally, doping can improve the adsorption ability of 2D materials to gas molecules [20]. For arsenene doping, the adsorption energy of NH3 can be enhanced by Ge- and Se-doping [21]. Bai et al. [22] have investigated the structures and properties of monolayer arsenene doped with Ge, Ga, Sb and P, and the results demonstrated that monolayer arsenene doped with Ga changes into the direct band gap. Chen et al. [23] have calculated the adsorption properties of NO2 and SO2 on different types of pristine, boron- and nitrogen-doped arsenene, and found that N-doped arsenene is more suitable for SO2 gas sensors as well as that P-doped arsenene has more potential application in NO2 gas sensors.

In this paper, we investigated the structures and electronic properties of monolayer arsenene doped with Al, B, S and Si theoretically based on the first-principles calculation. After doping, NO adsorbed on four kinds of substrates were investigated. The adsorption energy, adsorption distance and Mulliken charge transfer were calculated. These results can support a theoretical foundation to design gas sensors using 2D arsenic materials.

#### **2. Computational Methods**

The first-principles study, based on density functional theory (DFT), was calculated through the DMol<sup>3</sup> code. The generalized gradient approximation (GGA) with the Perdew–Burke–Ernzerhof (PBE) functional [18,19,22] was adopted in the simulation. However, GGA ignores the long-range electron effects, which led to the overestimation of van der Waals forces [24–27]. Therefore, the Grimme custom method was used to describe tiny van der Waals interaction [23,28]. Double numerical atomic orbital plus polarization (DNP) was selected as the basis set to expand electronic wave function. A 4 × 4 × 1 supercell containing 32 atoms was adopted with a vacuum space of 20 Å to guarantee there was no interaction between adjacent layers. An 8 × 8 × 1 and 16 × 16 × 1 k-points in the Brillouin zone were adopted to optimize the configurations and calculate the electronic properties, respectively [19,21]. The convergence tolerance for energy minimizations was 1.0 <sup>×</sup> 10−<sup>6</sup> Ha, for maximum force it was 0.002 Ha/Å and for geometry optimizations it was 0.005 Å, respectively. The flow chart of the computational process is shown in Figure 1.

**Figure 1.** The flow chart of the computational process.

#### **3. Result and Discussion**

The structure and electronic properties of pristine monolayer arsenene is firstly tested to check the accuracy of the calculation procedure. The full relaxed lattice constant of monolayer arsenene is 3.624 Å, which is very close to the experimental data [29]. It can be seen from Figure 2a that the bond length of As–As and the bond angle are 2.524 Å and 91.751◦, respectively. The thickness of monolayer arsenene is 1.412 Å. The monolayer arsenene with an indirect band gap of 1.718 eV shows a semiconductor property, in which the valence band maximum (VBM) displays at Γ point and the conduction band minimum (CBM) displays between M and Γ point. The results agree well with the previous results [18,22,30,31]. These results verify the accuracy of the calculation procedure and the characterization of the material. Simultaneously, it can be seen from Figure 2b that the partial density of states (PDOS) of the supercell of arsenene, which is mainly dominated by s and p orbitals of As atoms, but the influence on the total density of states (DOS) of the p orbital is greater than that of the s orbital. Similar to the feature of blue phosphorene and silicene [22,32], the energy region near the Fermi level is mainly due to the p orbital of As atoms.

**Figure 2.** (**a**) Optimized structure of monolayer arsenene. The blue and green boxes in the top view (top panel) show the primitive cell and the 4 × 4 supercell, respectively. The buckling height (*h*) is indicated in the side view (bottom panel). (**b**) Electronic band structure (left panel) and partial density of states (PDOS) (right panel) of the 4 × 4 supercell of monolayer arsenene. The Fermi energy was set to zero.

Substitutional doping can effectively improve the adsorption ability of 2D materials to gas molecules. So, the structures and electronic properties of X-doped monolayer arsenene (X = Al, B, S, Si) have been firstly optimized and calculated. The binding energy (*E*b) is calculated and defined as *E*<sup>b</sup> = [*E*X-As − (n − 1)*E*As − *E*X]/n, where *E*X-As is the total energy of substitutional systems, *E*As and *E*<sup>X</sup> are the energies of the isolated atom As and the dopant atom X, respectively, and n is the number of As atoms in arsenene supercell. It can be seen from the above equation that the greater the binding force between doping elements and the substrate, the more negative the value of *E*b. As shown in Table 1, the bond length *l*X-As between the dopant X and the nearest As element is in the range of 2.064 to 2.459 Å, and *l*B-As is the shortest with 2.064 Å. The binding strength increases in the order of S < Al < Si < B with high binding energy of −4.065 eV, −4.085 eV, −4.108 eV and −4.137 eV, respectively. It indicates that S, Al, Si and B can interact strongly with its neighboring As atoms. The above results show that the dopants can effectively affect the binding energy for the doped monolayer arsenene.

**Table 1.** The binding energy (*E*b), energy gap (*E*g), Mulliken population (*Q*) and the bond length of X-As (*l*X-As). A positive *Q* value means the electrons move from the dopant to the substrate. X denotes the dopant atom.


Meanwhile, Mulliken analysis is used to calculate the atomic charge near the dopant X and the calculated results are listed in Table 1. The Mulliken population of Al, B, S and Si atoms are 0.807, 0.175, −0.120 and 0.665 e, respectively. The results show that a large amount of electrons transfer occurs between the dopant and the substrate, which implies that there are strong interactions between the dopant and the substrate. The electrons transfer from the dopant to the substrate except for the S atom, because the S atom has a higher electronegativity than that of the As atom. Among these structures, the values of the Mulliken population of Al, B and Si atoms are positive. This leads to the formation

of a huge electron depletion layer on the substrate, which is conducive to improving the adsorption properties for NO gas molecules.

The optimized adsorption configurations of NO adsorbed on four doped systems are demonstrated in Figure 3. The corresponding parameters are listed in Table 2, including the adsorption energy (*E*ad), adsorption distance (*d*) and Mulliken charge (*Q*). It can be seen from Figure 3 that the N atom is toward the substrate and the O atom is away from the substrate in four doped systems. *E*ad is defined as *E*ad = *E*NO/X-As − (*E*X-As + *E*NO), where *E*NO/X-As, *E*X-As and *E*NO denote the total energies of the NO molecule adsorbed on the doped system, the isolated doped substrate and the NO molecule with the same lattice parameters, respectively. The more negative *E*ad is, the more stable the structure is. B-doped arsenene has the largest adsorption energy of −1.884 eV and the shortest adsorption distance of 1.428 Å with the NO molecule. For Al-, S- and Si-doped arsenene, the adsorption energy values are −1.157 eV, −0.469 eV and −1.586 eV, and the adsorption distance values are 1.942 Å, 2.548 Å and 1.864 Å, respectively. It is clear that the adsorption of NO on S-doped arsenene is physical adsorption and NO on Al-, B- and Si-doped arsenene is chemical adsorption. Mulliken population analysis is performed and the negative *Q* value indicates electron transfer from the substrates to the NO molecule. It shows that the NO molecule is an electron acceptor with four substrates.

**Figure 3.** Top view and side view of the most energetically favorable adsorption configurations for NO adsorbed on (**a**) Al-, (**b**) B-, (**c**) S- and (**d**) Si-doped monolayer arsenene. The O and N atoms are labeled red and blue, respectively.

**Table 2.** Adsorption energy (*E*ad), the shortest distance of the As-dopant atom (*d*) and Mulliken charge (*Q*) for the optimized stable configurations of gas molecules on Al-, B-, S- and Si-doped arsenene. A negative *Q* value indicates electron move from the doped substrates to NO molecule.


Furthermore, the band structures of Al-, B-, S- and Si- doped monolayer arsenene are also calculated to investigate the effects introduced by the dopant. As shown in Figure 4a,b, the dopants of Al and B change the 2D material to the direct band gap from the indirect band gap. Both CBM and VBM are displayed on the Γ point in Brillouin Zone with the values of the band gap 1.538 and 1.370 eV, respectively. The Fermi level of S- and Si-doped arsenene systems enter into the conduction band (see Figure 4c,d) and a semiconductor-metal transition is realized. Through the band structures comparison

of four doped systems, it can be concluded that the electronic properties of 2D materials are effectively tuned by substitutional doping.

**Figure 4.** Band structures of (**a**) Al-, (**b**) B-, (**c**) S- and (**d**) Si-doped arsenene As31X systems, as well as NO adsorbed on (**e**) As31Al, (**f**) As31B, (**g**) As31S and (**h**) As31Si monolayers, respectively. The Fermi level is set to zero with the black dashed line.

As can be seen from Figure 4e–h, the band structures of four systems have great changes after the adsorption of the NO molecule. Interestingly, the energy gap values of NO/Al-doped arsenene and NO/B-doped arsenene both change to 0.372 and 0.407 eV, and the energy gap values of NO/S-doped arsenene and NO/Si-doped arsenene change to 0.294 and 0.521 eV. The adsorption of the NO molecule reduces the energy gap of Al- and B-doped arsenene and opens the energy gap of S- and Si-doped arsenene.

To explore the electronic properties of the four systems, the total density of states (DOS) of four systems before and after absorbing the NO molecule were analyzed and shown in Figure 5. Because of the adsorption of NO, the DOS of Al- and B-doped arsenene are shifted to the lower energy level, but the DOS of S- and Si-doped arsenene are shifted slightly to the higher energy level, which are in accordance with the changes of band structures.

To further investigate the NO adsorption on X-doped monolayer arsenene, the charge density differences of NO adsorbed on four kinds of substrates were calculated and shown in Figure 6. The charge density difference can be expressed as Δρ = ρNO/X-As − (ρX-As + ρNO), where ρNO/X-As, ρX-As and ρNO denote the total charge densities of the optimized NO adsorption system, isolated substrate and NO molecule, respectively. The purple and green parts correspond to the charge accumulation and the charge depletion, respectively. It can be clearly seen that charge redistribution is generated between the NO molecule and the dopant atoms due to the adsorption. The large charge depletion between the NO molecule and the dopant demonstrates that there is a strong hybridization of orbitals at the surface of the doped substrate because of the formation of a covalent bond except for S-doped arsenene. The results are consistent with the Mulliken population analysis.

**Figure 5.** The density of states (DOS) of four systems. Blue and green lines present the DOS of substrates before and after NO adsorption, respectively.

**Figure 6.** Charge density difference plots for NO adsorbed on (**a**) Al-, (**b**) B-, (**c**) S- and (**d**) Si-doped monolayer arsenene, respectively. The purple (green) distribution denotes the charge accumulation (depletion) with the isosurface of 0.03 e/Å3 for (**a**) and (**d**); 0.044 e/Å<sup>3</sup> for (**b**) and 0.05 e/Å<sup>3</sup> for (**c**).

#### **4. Conclusions**

On the basis of first-principles calculation, the structures and electronic properties of monolayer arsenene doped with Al, B, S and Si were investigated. The dopants have great influences on the properties of the monolayer arsenene. The electronic properties of the substrate are effectively tuned by substitutional doping.

After doping, NO adsorbed on four kinds of substrates have been investigated. The NO molecule is an electron acceptor with four substrates. The adsorption energy, adsorption distance and Mulliken charge transfer have been calculated. The results demonstrate that NO exhibits a chemisorption character on Al-, B- and Si-doped arsenene, while a physisorption character on S-doped arsenene with moderate adsorption energy.

Due to the adsorption of NO, the band structures of the four systems have great changes. It reduces the energy gap of Al- and B-doped arsenene and opens the energy gap of S- and Si-doped arsenene. The large charge depletion between the NO molecule and the dopant demonstrates that there is a strong hybridization of orbitals at the surface of the doped substrate because of the formation of a covalent bond except for S-doped arsenene. It indicates that Al-, B- and Si-doped arsenene might be good candidates as gas sensors to detect NO gas molecules owing to their high sensitivity.

**Author Contributions:** K.W. and J.L. conceived and designed this case-study as well as wrote the paper; M.L. and D.C. reviewed the paper; all authors interpreted the data; and Y.H. substantively revised the work and contributed the process simulation.

**Funding:** This work is financially supported by Guizhou Province United Fund (Qiankehe J zi LKLS[2013]27), Excellent engineers education training plan (LPSSY zyjypyjh201702), Guizhou Solid Waste Recycling Laboratory of Coal Utilization ([2011]278), the Scientific and Technological Innovation Platform of Liupanshui (52020-2018-03-02) and Academician Workstation of Liupanshui Normal University (qiankehepingtairencai [2019]5604).

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Article* **Study of Various Aqueous and Non-Aqueous Amine Blends for Hydrogen Sulfide Removal from Natural Gas**

#### **Usman Shoukat , Diego D. D. Pinto and Hanna K. Knuutila \***

Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway; usman.shoukat@ntnu.no (U.S.); diego.pinto@hovyu.com (D.D.D.P.) **\*** Correspondence: hanna.knuutila@ntnu.no

Received: 8 February 2019; Accepted: 8 March 2019; Published: 15 March 2019

**Abstract:** Various novel amine solutions both in aqueous and non-aqueous [monoethylene glycol (MEG)/triethylene glycol(TEG)] forms have been studied for hydrogen sulfide (H2S) absorption. The study was conducted in a custom build experimental setup at temperatures relevant to subsea operation conditions and atmospheric pressure. Liquid phase absorbed H2S, and amine concentrations were measured analytically to calculate H2S loading (mole of H2S/mole of amine). Maximum achieved H2S loadings as the function of pKa, gas partial pressure, temperature and amine concentration are presented. Effects of solvent type on absorbed H2S have also been discussed. Several new solvents showed higher H2S loading as compared to aqueous N-Methyldiethanolamine (MDEA) solution which is the current industrial benchmark compound for selective H2S removal in natural gas sweetening process.

**Keywords:** H2S absorption; amine solutions; glycols; desulfurization; aqueous and non-aqueous solutions

#### **1. Introduction**

Natural gas is considered one of the cleanest forms of fossil fuel. Its usage in industrial processes and human activities is increasing worldwide, providing 23.4% of total world energy requirement in 2017 [1]. Natural gas is half of the price of crude oil and produces 29% less carbon dioxide than oil per unit of energy output [2]. Methane is a major energy providing component in natural gas. However, it also contains other hydrocarbons and a variety of impurities like acid gasses (CO2 and H2S) and water. Besides reducing the gas energy value, the impurities can cause operational problems such as corrosion in the pipeline and other equipment [3]. Mercury, mercaptans and other sulfur components are also often found in natural gas and must be removed. Sulfur components can produce SO2 during combustion which ultimately leads to acid rain. Therefore, it is necessary to remove acid gases, water vapors, and other impurities before the usage of natural gas.

H2S is an extremely poisonous component, and it can cause instant death when concentrations are over 500 parts per million volume (ppmv) [4,5]. H2S exposure limits by the Norwegian Labour Inspection Authority are 5 ppmv for an eight-hour time-weighted average (TWA) and 10 ppmv for 15 min short-term exposure limit (STEL) [6]. The most commonly used method for H2S removal is liquid scavenging. These processes usually employ non-regenerative chemicals such as triazine or aldehydes, and because of costs and operational issues (e.g., chemicals disposal), scavengers are not used for gases with high H2S concentrations. Alkanolamines, in particularly N-Methyldiethanolamine (MDEA), are generally used for regenerative H2S removal processes [7].

Natural gas is commonly saturated with water increasing the chances of solid gas hydrates formation with methane at high pressure and low temperatures potentially causing plugging in gas transport pipelines. One common way to avoid hydrate formation and to achieve problem-free continuous gas transportation operations is to add hydrates inhibitors like monoethylene glycol (MEG) or triethylene glycol(TEG) in gas pipelines [8].

A system which could selectively remove H2S and control hydrate simultaneously would not only reduce equipment footprint but also help to reduce the installation and operational costs for both subsea and platform operations. This type of system was initially proposed by Hutchinson [9]. The idea of combined H2S and water removal was presented in 1939 by using amine glycol solution as a solvent. 2-ethanolamine (MEA) and diethylene glycol (DEG) solution in water solution was the tested solvent for the concept. McCartney [10,11] and Chapin [12] built upon Hutchinson concept and presented the idea of both absorption and regeneration process in two-stages. They discussed various arrangements to get higher efficiency and lower energy requirement. Later on, this process development discontinued due to lower selectivity of H2S compared to CO2, higher amine degradation and corrosion rate of MEA [7]. However, tertiary amine systems could be very interesting for this type of operations as they are known for their high selectivity to H2S. Tertiary amine systems, like a blend of methyl diethanolamine (MDEA) with glycols (MEG/TEG), have, additionally, higher amine stability and reduce corrosion rates [7,13,14].

In the literature, there is limited data available for the tertiary amine-glycols blends and most of the data is available for aqueous Triethanolamine (TEA), diisopropanolamine (DIPA), and MDEA. TEA was the first commercially used alkanolamine for gas treating process [7]. It is now being replaced with other amines like monoethanolamine (MEA), diethanolamine (DEA), diisopropanolamine (DIPA), methyl diethanolamine (MDEA), 2-amino-2-methyl-l-L-propanol (AMP), ethyl diethanolamine (EDEA) and 2-(2-aminoethoxy) ethanol (DGA) due to its low capacity and high circulation rate [15]. MDEA based system offered advantages like selective hydrogen sulfide removal over carbon dioxide, low vapor pressure, higher thermal stability, less corrosion, lower heat of reactions and specific heat [7,13]. Equations (1)–(6) show the mechanism and overall reactions of H2S with aqueous secondary and tertiary amines. These reactions are instantaneous and involve a proton transfer.


The solubility of H2S in aqueous solutions of MDEA from 11.9 wt.% to 51 wt.% in the temperature range from 10 ◦C to 120 ◦C and H2S partial pressure from 0.141 kPa–6900 kPa were studied by various authors [16–27]. All the previous studies of aqueous MDEA showed similar trends like increasing the partial pressure of H2S (pH2S) increases H2S loading at given concentration and temperature, while the increase in amine concentration at a given temperature and pH2S decreases H2S loading. Surplus to MDEA data, TEA from 0.09–6.32 kPa H2S and DIPA at a pressure range of 19–1554 kPa has been reported [28,29].

Xu et al. [24] also studied H2S absorption in 30 wt.% MDEA in MEG and MEG-H2O solutions over a range of partial pressures of H2S from 0.34 to 38.8 kPa and found that increasing the water content in solution increases the H2S loading at a given temperature (40 ◦C). Also, the increase in temperature decreases the H2S loading for a given concentration (30 wt.% MDEA—65 wt.% MEG— 5 wt.% H2O). Most of the previous studies were conducted using static cell apparatus and higher liquid phase H2S loading can be obtained by using total gas pressure (>101.3 kPa) with higher amine

concentration. Therefore, very few H2S absorption studies are available for low amine concentrations at low temperatures and low acid gas inlet partial pressure range in literature.

The objective of this study is to identify blends where the solute (amine) can give higher H2S removal capacity as compared to MDEA in the presence of glycol. The overall goal for this process is to absorb H2S and water simultaneously at the subsea level in two-steps. In the first step, absorption can take place at the subsea level, potentially using a co-current contactor for absorption and flash drum to separate the natural gas from solvent at subsea levels. In the second step, loaded solution can be sent to a platform for regeneration and natural gas will be transported directly from subsea allowing a system where the natural gas will not enter the platform at all. The current work focuses on the identification of amine-glycol blends with high H2S absorption capacity. The amines for this work were chosen systematically so that insight into the influence of its structure, like amine alkanol groups, alkyl chain length, and a hydroxyl group, can be obtained. In total twelve amines were studied, one secondary sterically hindered amines (diisopropylamine), one tertiary sterically hindered amine (N-tert-butyldiethanolamine), and ten tertiary amines. The list of amines along their chemical structure used in the study is given in Figure 1.

**Figure 1.** List of amines with chemical structures.

#### **2. Material and Methodology**

#### *2.1. Materials*

2-Dimethylaminoethanol (DMAE), 2-(Diethylamino) ethanol (DEEA), 2-(Dibutylamino) ethanol (DBAE), Diisopropylamine (DIPA), 3-Dimethylamino-1-propanol (3DEA-1P), N-Methyldiethanolamine (MDEA), Triethanolamine (TEA), Ethylene glycol (MEG), and Triethylene glycol (TEG) were bought from Sigma-Aldrich (Oslo, Norway), while 3-(Diethylamino)-1,2-propanediol (DEA-1,2-PD), 2-[2-(Diethylamino) ethoxy] ethanol (DEAE-EO), 6-Dimethylamino-1-Hexanol (DMAH), N-tert-Butyldiethanolamine (t-BDEA), and 3-Diethylamino-1-propanol (3DEA-1P) were bought from TCI Europe (Zwijndrecht, Belgium) in available maximum commercial purity. Additionally, premixed 1500 ppmv (0.15 vol.%) Hydrogen Sulphide (H2S) in Nitrogen (N2), 10,000 ppmv (1 vol.%) Hydrogen Sulphide (H2S) in Nitrogen (N2) and pure Nitrogen (N2) (99.998 vol.%) were purchased from AGA Norway, Oslo. All chemicals were used without further purifications. Chemicals with their abbreviation, CAS number, purity, molecular weight, and pKa are given in Table 1 except hydrogen sulfide and deionized water.


**Table 1.** Name, abbreviation, CAS, purity (wt.%), and pKa of chemicals.

All amine solutions were prepared by weighing the required amount of the amines using the Mettler Toledo MS6002S Scale, with an uncertainty of ±10−<sup>5</sup> kg. Aqueous solutions were made with deionized water produced by ICW-3000 Millipore water purification system, while for non-aqueous solutions MEG/TEG was used as a solvent. All amines were miscible in DI water, MEG and TEG except DBAE which made visible two phases with DI water but less visible two phases with MEG and TEG. DBAE solutions appeared homogeneous while stirring.

#### *2.2. Methodology and Equipment*

A custom-built apparatus, as shown in Figure 2, was used to screen amine solutions for hydrogen sulfide absorption study. The apparatus is designed to operate at atmospheric pressure and temperatures up to 80 ◦C and is similar to apparatus previously used for CO2 absorption and desorption studies by Ma'mun et al. and Hartono et al. [31]. The apparatus consisted of the water-jacketed reactor with volume of ~200 cm3 (NTNU, Trondheim, Norway) with a magnetic stirrer, Alicat MCS series Mass flow controllers (Tucson, AZ, USA), thermocouple (Omega Engineering Limited, Nærum, Denmark), Hubor® water bath (Huber Kältemaschinenbau AG, Offenburg, Germany), and sodium hydroxide (NaOH) vessel for caustic wash. LabVIEW (National Instruments Norway, Drammen, Norway) was used to control and record gases flowrates and both reactor and water bath temperatures. The apparatus and H2S gas bottles were installed in a closed fume cabinet equipped with an H2S sensor, alarm and fail-safe system; which shut down the whole apparatus automatically in case of any H2S leakage (limit >10 ppmv) or electrical failure. Personal protective equipment and personal H2S sensor were used during experiments.

**Figure 2.** Schematic flow diagram of the screening apparatus.

Since the overall goal is to develop a solvent system that could be used at the subsea level, where the total gas pressure is high and H2S content is from 50 ppm and up, higher partial pressure of H2S up to 1 vol.% was used to achieve similar H2S quantity during these experiments at atmospheric pressure.

At the start of each experiment 150 g of the solution was filled in the reactor and cooled/heated to the required experiment temperature after purging it with nitrogen to remove any air present. Pre-mixed H2S and N2 were used to achieve the required inlet hydrogen sulfide partial pressure (pH2S) with the help of MFCs. The reactor was continuously stirred with a magnetic stirrer at isothermal and isobaric condition during the whole experiment. Hubor® water bath was used to maintain the temperature constant. A thermocouple was placed in the liquid phase and used for continuous monitoring of reactor temperature. The exit gas from the reactor was sent to a series of three 10 wt.% NaOH solution vessels in order to remove residual H2S present in it. All experiments were run for 120 min to give sample time to reach close to equilibrium between acid gas and amine solution. To ensure that 120 min is enough, experiments with 20 wt.% MDEA were performed until 240 min at 5 ◦C with sampling after every 15 min. The data showed that H2S stopped absorbing after 45 min. This is in line with Lemoine et al. [20] and confirms that 120 min is enough time to reach close to equilibrium. Also, several parallel experiments for both aqueous and non-aqueous solutions of various

amines were run and repeatability the data were confirmed. Different solutions were tested at different temperatures (5 ◦C, 25 ◦C and 40 ◦C) and inlet H2S partial pressures (0.03 kPa, 0.5 kPa, 0.75 kPa and 1 kPa). For inlet pH2S = 0.5 kPa to 1 kPa, 10,000 ppm H2S gas mixture at total flow rate of 200 mL/min and for inlet pH2S = 0.03kPa, 1500 ppm H2S gas mixture at total flow rate of 1000 mL/min were used. The uncertainty of the inlet partial pressure of H2S was estimated to be 2% including both the uncertainty of the ready H2S gas mixture and the pre-calibrated mass flow controllers.

After the experiment, liquid samples were stored at <4 ◦C in the fridge and later on delivered to the analytical lab (St. Olav's Hospital Laboratory, Trondheim, Norway) for total sulfur analysis with inductivity coupled plasma mass spectrometry (ICP-MS). The samples were transported in ice box along with ice to keep sample temperature <5 ◦C. To ensure no amine loss during the experiments, amine concentration was determined by with Mettler Toledo G20 compact titrator [36] using a liquid sample of 0.2 mL that was diluted with 50 mL deionized water and titrated with 0.1 mol/L H2SO4. Each liquid sample was analyzed twice for total sulfur and amine concentration. The standard deviations between the duplicates of each solution were <2.5% for total sulfur and <1.5% for amine concentration. The differences in the amine concentration were less than 2% found in initial and final amine concentrations for all the solutions indicating that there was no significant amine loss during the experiments. The hydrogen sulfide loadings calculated by Equation (7), given in this work are based on the analyzed values for H2S and amine in the liquid phase.

$$\approx\_{H\_2S} = \frac{mole\ of\ H\_2S}{mole\ of\ amine} \tag{7}$$

#### **3. Results**

This screening apparatus was validated with a benchmarking 30 wt.% aqueous monoethanolamine (MEA) for CO2 absorption before using it for H2S absorption. Inlet CO2 partial pressure was 10 kPa and absorption was done at 40 ◦C until 95% CO2 absorption. Rich loading was found 0.54 mol CO2/mol MEA after titration which was in good agreement with Hartono et al. [31] with an average deviation of 1.9%. 23.8 wt.% aqueous MDEA has been mostly used to study H2S absorption. Therefore, the same amine concentration was used to verify the screening equipment and experimental parameters at 40 ◦C and pH2S = 1 kPa. The liquid phase of H2S loading was measure 0.14 (mol/mol) with the deviation of 4.6% from Jou et al. [16]. The experimental data are shown in with experimental uncertainties at the end is shown in Table 2.


**Table 2.** Experimental data.


**Table 2.** *Cont.*

#### *3.1. Effect of pKa*

Effect of pKa on H2S loading in 20 wt.% aqueous amine solutions atT=5 ◦C ± 0.1 ◦C and pH2S = 1 kPa is shown in Figure 3. In the reaction between H2S and aqueous amine solution, H2S acts as weak acid whereas aqueous amine acts as a strong base, therefore, an increase in pKa increases the hydration of H2S subsequently increasing the H2S loading. This is also evident in tertiary amines aqueous solutions with DEEA, t-BDEA, and DBAE acting like outliers. DEEA shows lower absorption capacity than its closest pKa tertiary amine (DEA-1,2-PD), which can be due to short molecular chain of DEEA. t-BDEA. Sterically hindered amine shows the highest loading of all amines whereas DBAE shows the lowest loading, and it makes two phases with almost all the H2S absorbed in the upper phase, i.e., amine (solute). If the amount of H2S absorbed only in the amine phase (solute) is used to calculate H2S loading in DBAE aqueous solutions, these solutions also start to follow the trend. The amount of H2S absorbed in DBAE amine phase is ≈4.6 ± 0.2 times of absorbed H2S in the whole solution both in aqueous and non-aqueous solutions.

**Figure 3.** Effect of pKa on H2S loading in aqueous amine solutions; T = 5 ◦C ± 0.1 ◦C; pH2S = 1 kPa; amine concentration = 20 wt.% (unloaded); DBAE solutions make two phases.

Aqueous solutions of amines highlighted in the circle in Figure 3 presented higher H2S loading as compared to MDEA and can be potential amines for further studies. t-BDEA showed the highest H2S loading, but in-house data show it also degraded a lot in the presence of CO2 and caused higher corrosion rates leading to damages in steel pipelines and equipment as compared to MDEA [37].

When looking into the amine structure, the results show that an increase in alkyl group decreases the H2S loading in an amino-ethanol group, i.e., DMAE > DEAE (DEEA) > DBAE. It can be due to reduction in activity of nitrogen group due to increase in chain length of alkyl group in ethanol amine, a similar trend was previously observed in carbon dioxide capture studies [38,39]. Structure wise it would have been interesting to test 2-Dipropylaminoethanol (DPAE). Unfortunately, we were unable to purchase the chemical since it is commercially unavailable in Norway as it is being used in the weapon industry. A reverse trend was seen in an amino-propanol group where an increase in the alkyl group increases the H2S loading, i.e., 3DMA-1P < 3DEA-1P. Hydroxyl group attracts electrons therefore, addition of more hydroxyl group reduces the activity of nitrogen atom of amine resulting in decreased H2S loading in aqueous amine solutions, i.e., DMAE > MDEA > TEA and 3DEA-1P > 3DEA-1,2-PD. Also, an increase in the length of chain for hydroxyl group from -N- decreases the H2S loading as seen when comparing DEAE-EO and DMAH (DEAE-EO shows higher capacity). Moreover, by adding the ethoxy group in DEEA, (DEAE-EO) increases the H2S loading significantly.

Effects of pKa on H2S loading in 20 wt.% amine solutions in MEG and TEG at T = 5 ◦C ± 0.1 ◦C; pH2S = 1 kPa are shown in Figures 4 and 5 respectively. In each of the figures, a weak trend with respect to pKa is observed. H2S loading in (DEAE-EO)-MEG solutions is higher than all other amine-MEG solutions and is even higher than aqueous MDEA solution shown in Figure 3. Increase in alkyl group in amine-TEG solutions and amine-MEG solutions are in line with each other, but not with the trend seen in aqueous solutions. However, adding an ethoxy group in DEEA has a similar effect in all three solutions.

**Figure 4.** Effect of pKa on H2S loading in amine-MEG solutions; T = 5 ◦C ± 0.1 ◦C; pH2S = 1 kPa; amine concentration = 20 wt.% (unloaded); DBAE solutions makes two phases.

**Figure 5.** Effect of pKa on H2S loading in amine-TEG solutions; T = 5 ◦C ± 0.1 ◦C; pH2S = 1 kPa; amine concentration = 20 wt.% (unloaded); DBAE solutions makes two phases.

#### *3.2. Effect of Solvent*

Each aqueous amine solution gives more H2S absorption capacity than its non-aqueous counterpart when compared on weight bases and having same system temperature, inlet partial pressure of gas and residence time of gas in the reactor as shown in Figure 6. Change of solvent from water to ethylene glycol or tri-ethylene glycol has a similar effect on all the amine solutions. Replacing the solvent from water to monoethylene glycol decrease the H2S loading significantly, the maximum decrease was observed in DEEA solutions while minimum has observed DBAE solutions. H2S absorption decreased more rapidly when TEG had used as a solvent compared to MEG or water. Visual inspection also showed TEG solutions become more viscous as compared to MEG and H2O solutions in respective amines. Furthermore, MEG shows more reactivity than TEG due to the autoprotolyses. However, the H2S absorption capacity in TEG solutions is expected to increase significantly if water is present even at relatively low amounts [40].

**Figure 6.** Effect of solvent on absorbed H2S; T = 5 ◦C ± 0.1 ◦C; pH2S = 1 kPa; mine concentration = 20 wt.% (unloaded); DBAE solutions makes two phases.

#### *3.3. Effect of H2S Partial Pressure*

Hydrogen sulfide loading as the function of inlet H2S partial pressure (pH2S) at T = 5 ◦C ± 0.1 ◦C for 20 wt.% amine solutions is shown in Figure 7. The rise in inlet H2S partial pressure (pH2S) increases the H2S loading at given temperature and amine concentration for both aqueous and non-aqueous solutions except DEAE-EO by providing more reaction sites for reaction between H2S and amine solutions. The same trend was seen in previous studies. However, in aqueous DEAE-EO solution, H2S loading starts to decrease with increases in pH2S from 0.5 kPa to 1.0 kPa for an unknown reason. It is not possible to explain the behavior with the current data.

**Figure 7.** Effect of inlet H2S partial pressure on H2S loading; T = 5 ◦C ± 0.1 ◦C; amine concentration = 20 wt.% (unloaded); DBAE solutions makes two phases.

#### *3.4. Effect of Temperature*

The effect of temperature on H2S loading on various 20 wt.% amine solutions at pH2S = 1 kPa is shown in Figure 8. As the screening temperature increases from 5 ◦C to 40 ◦C H2S loading decreases for all solutions except DEAE-EO and DEEA. The decrease in loading is as expected since the final loading in the experiments is almost in equilibria with the gas phase [16,20]. For DEEA, the loading difference between 5 ◦C and 40 ◦C is 0.01 mol H2S/mol DEEA indicating that loading capacity is not as dependent on temperature as for some of the other amines. In the case of DEAE-EO, the changes are larger: The loading difference between 5 ◦C and 40 ◦C is 1.8% which is within our analytical uncertainty. However, the reason for the increase in loading seen at 25 ◦C, is unknown. We believe this is due to uncertainties in the analysis of H2S and amine concentrations in the liquid samples.

**Figure 8.** Effect of temperature on H2S loading; pH2S = 1 kPa; amine concentration = 20 wt.% (unloaded); DBAE solutions makes two phases.

#### *3.5. Effect of Amine Concentration*

Figure 9 shows the effect of amine concentration on hydrogen sulfide loading in aqueous solutions at 5 ◦C and inlet H2S partial pressure of 1 kPa. The increase in amine concentration from 20 wt.% to 50 wt.% at given temperature and pressure decreases the H2S absorption (mole/mol) subsequently decreasing H2S loading. The trends are similar to those reported for MDEA as seen in the figure. In case of MDEA, the absorption capacity decreases by 40–50% when the MDEA concentration increases from 2.5 mol/kg to 4.2 mol/kg and it is similar to the reduction seen for DBEA. For 3DEA-1P and DEAE-EO a higher reduction in the absorption capacity is seen. Overall, the results indicate that increase in amine concentration changes the vapor-liquid equilibria behavior of the system [16,27,41,42].

**Figure 9.** Effect of amine concentration on H2S loading; T = 5 ◦C ± 0.1 ◦C; pH2S = 1 kPa for all amines except MDEA; DBAE solutions makes two phases.; MDEA is at T = 40 ◦C and MDEAa pH2S = 0.3 kPa [27]; MDEA<sup>b</sup> pH2S = 0.5 kPa [27], MDEA<sup>c</sup> pH2S = 1 kPa [16].

The data at 50 wt% allows us to compare the absorption capacity of 3DEA-1P, DEAE-EO and DBEA in aqueous and MEG solutions with similar mole fraction (mole amine/mole solution). The mole fraction of 3DEA-1P in 3DEA-1P.MEG solution (0.13) is similar to that of aqueous 50 wt% DEA-1P (0.15). Likewise, DEAE-EO and DBEA have similar mole fraction for 50 wt% aqueous solutions and 20 wt.% MEG solutions. For these three amines, the absorption capacity is 60–80% higher in the presence of MEG as compared to water (Figure 10). Further studies will be required to explain the performance differences between water and MEG based solvents.

**Figure 10.** Effect of amine (mole fraction) on H2S loading; T = 5 ◦C ± 0.1 ◦C; pH2S = 1 kPa; DBAE solutions makes two phases.

#### **4. Conclusions**

In this study, various new aqueous and non-aqueous amine blends have been tested for H2S absorption. The results show that an increase in hydroxyl group and addition of ethoxy group in amines increases the H2S absorption in aqueous amine solutions. In general, the H2S absorption increases also with increasing pKa. Also, increase in alkyl group enhances the H2S loading in aqueous ethanol amines and vice versa for aqueous propanol amines. Several of the tested amines show higher H2S absorption capacity compared to MDEA in aqueous solutions. Even though replacing water with TEG or MEG significantly decreased the H2S loading in all tested solvents, the non-aqueous solution of (DEAE-EO)-MEG showed higher loading than aqueous MDEA at same weight concentration.

**Author Contributions:** Conceptualization, H.K.K., U.S. and D.D.D.P.; methodology, U.S. and H.K.K.; writing—original draft preparation, U.S.; writing—review and editing, H.K.K. and D.D.D.P.; supervision, H.K.K. and D.D.D.P.

**Funding:** This work was carried out under funding provided by NTNU-SINTEF Gas Technology Centre (GTS) and Faculty of Natural Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.

**Acknowledgments:** We would like to thank Department of Chemical Engineering (IKP) at Faculty of Natural Sciences and Technology, Norwegian University of Science and Technology (NTNU), Trondheim, Norway for their support.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Article* **Formation and Evolution Mechanism for Carbonaceous Deposits on the Surface of a Coking Chamber**

#### **Hao Wang 1,2, Baosheng Jin 1,\*, Xiaojia Wang 1,\* and Gang Tang <sup>3</sup>**


Received: 12 July 2019; Accepted: 29 July 2019; Published: 3 August 2019

**Abstract:** This work aimed to investigate the carbonaceous deposits on the surface of the coking chamber. Scanning electron microscopy (SEM), X-ray fluorescence spectrum (XRF), Fourier transform infrared spectrometer (FTIR), Raman spectroscopy, X-ray diffraction spectrum (XRD), and X-ray photoelectron spectroscopy (XPS) were applied to investigate the carbonaceous deposits. FTIR revealed the existence of carboxyl, hydroxyl, and carbonyl groups in the carbonaceous deposits. SEM showed that different carbonaceous deposit layers presented significant differences in morphology. XRF and XPS revealed that the carbonaceous deposits mainly contained C, O, and N elements, with smaller amounts of Al, Si, and Ca elements. It was found that in the formation of carbonaceous deposits, the C content gradually increased while the O and N elements gradually decreased. It was also found that the absorbed O2 and H2O took part in the oxidation process of the carbon skeleton to form the =O and –O– structure. The oxidation and elimination reaction resulted in change in the bonding state of the O element, and finally formed compact carbonaceous deposits on the surface of the coking chamber. Based on the analysis, the formation and evolution mechanisms of carbonaceous deposits were discussed.

**Keywords:** coke oven; carbonaceous deposits; spectral analysis; mechanism

#### **1. Introduction**

As an important chemical raw material, coke plays an indispensable role in the fields of metallurgy and energy. China is the largest coke supplier in the world and accounts for more than 70% of global production [1,2]. In 2016, China produced 449.1 million tons of coke [3]. Coke-making contains many processes, in which the coking chamber is the key carrier for coking. Thus, the operating status of the coking chamber significantly influences the production and quality of the coking process [4–6].

The coke-making process is a complex physical–chemical process [7–10]. The coal is pyrolyzed into many polycyclic aromatic hydrocarbon compounds: methane (CH4), hydrogen (H2), ammonium (NH3), sulfur dioxide (SO2), and so on. At the same time, the mineral composition, which contains many metal ions, also takes part in the coking process [11]. With the increase in coking operations, a compact carbonaceous deposit forms on the surface of the coking chamber, affecting its stable operation and shortening the lifetime of coke oven batteries, which not only decreases coking production, but also deteriorates the quality of the coking products [12]. Thus, it is important to investigate the formation and evolution processes of carbonaceous deposits on the surface of the coking chambers, which will benefit the enhancement of stable operations and prolong the lifetime of coke oven batteries [13].

In fact, carbonaceous deposits are significantly influenced by the temperature of the coke oven chamber, the gas phase, residence time of volatiles in the hot zone, and the surface on which the deposition takes place. A series of works have reported on the carbonaceous deposits on the surface of coke oven chambers. Furusa et al. investigated the influence of coal moisture and fine coal particles on carbonaceous deposits and clarified the formation mechanism of the carbonaceous deposits [14]. Uebo et al. researched the temperature and water presence on carbon depositions in laboratory tests, and tested brick pieces in the pilot plant oven [13]. Dumay et al. investigated the cracking conditions in the coke oven free space to better assess the parameters for the control of carbonaceous deposits. They also reported on a special device that could measure the growth of carbonaceous deposits in situ [15]. Krebs et al. investigated the influence of coal moisture content on carbonaceous deposits' yield and microstructure in detail [16]. Additionally, some strategies were applied to remove the carbonaceous deposits including manual or mechanical removal by spearing, burning-off by nature, air flow from the door or charring hole, and decomposition by blowing exhaust gas into the top space [17]. Furthermore, some methods have been proposed to prevent carbonaceous deposits. Nakagava et al. reported that injecting atomized water into the free space of the coke oven chambers could significantly decrease carbonaceous deposits [18]. Ando et al. reported the chamber wall being coated with glassy products containing 18–70 wt% of SiO2, 10–60 wt% of Na2O, 2–14 wt% of BaO, 0.5–25 wt% of SrO, and 0.5–20 wt% of Fe2O3, which could significantly inhibit carbonaceous deposits [19].

However, most of the above-mentioned studies have focused on the influencing factors of carbonaceous deposits and most of the methods used to remove the carbon depositions were based on macro experiments, with few studies focused on the composition of carbonaceous deposits. Furthermore, the formation and evolution mechanism of carbonaceous deposits need to be further investigated, which are pivotal to effectively prevent carbonaceous deposits. Thus, this paper aimed to systematically investigate the difference of the carbonaceous deposits on the surface of the coking chamber. Scanning electron microscope (SEM), X-ray fluorescence spectrum (XRF), Fourier transform infrared spectrometer (FTIR), Raman spectroscopy, X-ray diffraction spectrum (XRD), and X-ray photoelectron spectroscopy (XPS) were used to research the morphological structure, elemental composition, and bonding states of different carbonaceous deposit layers. Furthermore, the formation and evolution mechanism of carbonaceous deposits on the surface of coking chambers were discussed. The above work will provide a theoretical basis for effectively inhibiting carbonaceous deposits on the surface of coke oven chambers, which will benefit the stable operation of coke ovens.

#### **2. Materials and Methods**

#### *2.1. Sample Preparation*

The carbonaceous deposit bulk was collected from the coking chamber of the No. 3 coking plant at the Ma'anshan Iron and Steel Co. Ltd. The coals used for coking were produced by the Huainan Mining Group. The bulk sample was obtained from the inner surface of the coking chamber, which had run for 16 months. As shown in Figure 1, the bulk carbonaceous deposit sample presented a length of 25–30 cm, a width of 10–15 cm, and a thickness of 3–4 cm. Small carbonaceous deposit samples with a length of 1 cm, a width of 1 cm, and a thickness of 0.3 cm were obtained from the bulk sample for further characterization, which were marked as #1, #2, #3, and #4. Figure 2 shows the position of the four carbonaceous deposit samples in the coking chamber and bulk sample, where sample #1 was close to the coking chamber and sample #4 was connected with the coking chamber wall.

**Figure 1.** Dimensions of the bulk sample: (**a**) length and width; (**b**) thickness.

**Figure 2.** Distribution of each carbonaceous deposit layers: (**a**) schematic diagram; (**b**) digital photo.

#### *2.2. Measurement and Characterization*

Black carbonaceous deposit powders were obtained by the milled bulk sample in a planetary ball mill (XQM-4L, Kexi Laboratory Instrument Co Ltd., Nanjing, China) for 2 h at 300 rpm. X-ray fluorescence spectroscopy (XRF, ARL ADVANT'X Intellipower™ 3600, Thermo Scientific Nicolet, Waltham, MA, USA) was applied to investigate the elemental composition of the carbonaceous deposit powders with a working voltage of 60 kV, working current of 60 mA, and resolution of 0.01◦.

Scanning electron microscopy (SEM, JSM-6490LV, JEOL Ltd., Tokyo, Japan) was applied to observe the morphology of small carbonaceous deposit samples with an accelerating voltage of 20 kV and resolution of 3 nm. Prior to observation, the sample surface was coated with a thin conductive layer.

The small carbonaceous deposit samples were ground into powders. Fourier-transformed infrared spectra spectroscopy (FTIR, Nicolet MAGNA-IR 750, Nicolet, Madison, WI, USA) was applied to characterize the powders of small carbonaceous deposit samples using a thin KBr disk. The transition mode was used and the wavenumber range was set from 4000 to 400 cm−<sup>1</sup> with a resolution of 4 cm<sup>−</sup>1.

The powders of small carbonaceous deposit samples were investigated by Laser Raman spectroscopy (LRS, inVia, Renishaw, London, UK). The excitation wavelength was 514 nm with a wavenumber range set from 800 to 2000 cm−<sup>1</sup> with a resolution of 1 cm<sup>−</sup>1.

The powders of small carbonaceous deposit samples were investigated by X-ray diffractometer (D8ADVANCE, Bruker, Karlsruhe, Germany) equipped with a Cu Kα tube and a Ni filter (λ = 0.154178 nm). The samples were scanned from 2θ = 10◦ to 80◦ with a step size of 0.02◦.

X-ray photoelectron spectroscopy (XPS) with a VG Escalab Mark II spectrometer (Thermo-VG Scientific Ltd. Waltham, MA, USA) using Al Kα excitation radiation (hν = 1253.6 eV, resolution of 0.45 eV) was used to analyze the powders of small carbonaceous deposit samples.

#### **3. Results and Discussion**

#### *3.1. Elemental Composition*

Table 1 shows the XRF test results of the carbonaceous deposits on the surface of the coking chamber. It was found that the carbonaceous deposits mainly contained 34.51% of SO2, 30.54% of SiO2, 19.22% of Al2O3, and 5.6% of Fe2O3 (except the C element). Furthermore, small amounts of CaO, ZnO, MnO, and Cl with an abundance of 1.05–1.74% were detected in the carbonaceous deposits. All of the above data indicated that the carbonaceous deposits contained abundant S, Si, Al, Fe, and others, where S, Fe, Cr, and Al could significantly enhance the condensation reaction of polycyclic aromatic hydrocarbon compounds that result from the pyrolysis of coal, thus promoting the formation of carbonaceous deposits on the surface of the coking chamber.


**Table 1.** XRF data of carbonaceous deposits on the surface of the coking chamber.

#### *3.2. Morphology*

Scanning electron microscopy (SEM) was applied to investigate the morphology of different carbonaceous deposit layers on the surface of the coking chamber. Figure 3a presents sample #1 at low magnification, which presented a loose structure with many holes. Figure 3b shows sample #1 at high magnification, from which we found combined particles of 3–5 μm, which may have come from the condensation of polycyclic aromatic hydrocarbon in the coke-making process. Figure 3c shows sample #2 at low magnification, which presented a cluster structure. Figure 3d reveals that the cluster structure was composed of carbon particles with diameters of 0.5–2 μm. It was seen that the compactness of sample #2 was significantly enhanced when compared with #1. This may be due to the primary carbon particles possessing poor stability, which can split into smaller carbon particles. These carbon particles reacted with each other to form more compact carbonaceous deposit layers. Furthermore, there were obvious gaps between the clusters, which may be due to the carbon particles having many hydroxyls, carboxyls, and carbonyls on the surface. These groups reacted and released CO2 in the existence of metal ions (Fe, Al, Si) and high temperature. This phenomenon indicated that the primary carbon particles presented a metastable state, which can further split and combine with each other to form a compact carbonaceous deposit layer. Figure 3e,f display the morphology of sample #3, where the gap between the clusters disappeared, indicating the chemical reaction between the clusters at high temperature. Figure 3g,h displays the morphology of #4, which presented enhanced compactness compared with #3. The high magnification of sample #4 in Figure 3h showed a sponge structure with many holes, which may have resulted from the release of small molecules such as CO2 at high temperature.

**Figure 3.** SEM images of each carbonaceous deposit layer in the coking chamber: sample #1 carbonaceous deposit layer, (**a,b**); sample #2 carbonaceous deposit layer, (**c,d**); sample #3 carbonaceous deposit layer, (**e,f**); and sample #4 carbonaceous deposit layer, (**g,h**).

#### *3.3. X-ray Di*ff*raction (XRD)*

Figure 4 shows the XRD patterns of different carbonaceous deposit layers. A diffraction peak was found around 25.76◦ and 42.5◦. The peaks at around 25.7◦ can be ascribed to (002), which was attributed to a hexagonal graphite structure. The peaks at around 42.5◦ corresponded to (100) peaks. As shown in Equations (1)–(4), the Bragg equation and Scherrer formula were introduced to calculate the structure parameter of different carbonaceous deposit layers [20–23]. In the equations, θ<sup>002</sup> and θ<sup>100</sup> are the diffraction angle of (002) and (100) peaks; β<sup>002</sup> and β<sup>100</sup> are the half-peak width of (002) and (100) peaks; *d*<sup>002</sup> is the layer spacing; *La* is the diameter of the micro crystallite; *Lc* is the height of the layers; and N is the layer number of the aromatic structure. λ is the wavelength of the X-ray, and *k*1, *k*<sup>2</sup> are the shape factors, where *k*<sup>1</sup> = 1.84, *k*<sup>2</sup> = 0.94 [24]. The calculated data are listed in Table 2.

$$d\_{002} = \lambda \% 2 \sin \Theta\_{002} \tag{1}$$

$$La = k\_1 \lambda / \beta\_{100} cos \theta\_{100} \tag{2}$$

$$L\mathcal{L} = k\_2 \lambda / \beta\_{002} \cos \theta\_{002} \tag{3}$$

$$LN = Lc/d\_{002} + 1\tag{4}$$

It can be seen from Table 2 that the #1, #2, #3, and #4 carbonaceous deposit layers presented *d*<sup>002</sup> values from 0.3437 nm to 0.3482 nm, indicating little difference in the layer spacing between the different carbonaceous deposits. Additionally, sample #1 presented a *La* value of 30.81 nm, while #2 showed a decreased *La* value of 27.84 nm. This may be because the lamellar structure based on polycyclic aromatic compounds was not stable. Part of the lamellar was linked by chemical bonds such as ethers, esters, and aliphatics, which were destroyed at high temperature. We also found that samples #3 and #4 presented increased *La* values of 35.18 nm and 36.47 nm, respectively. This may have resulted from the edges of the lamellar structure reacting with each other at high temperature. Samples #1, #2, #3, and #4 presented *Lc* values of 9.97 nm, 9.85 nm, 8.93 nm, and 8.82 nm, respectively, indicating a decrease in the packing height of the lamellar structure from #1 to #4. This can be explained by the exfoliation of the out layered graphite lamellar by strong thermal radiation, which was consistent with the change in the *N* value.

**Figure 4.** XRD spectra of each carbonaceous deposit layer in the coking chamber.


**Table 2.** XRD structure parameters of each carbonaceous deposit layer in the coking chamber.

#### *3.4. Fourier Transform Infrared Spectrometer (FTIR)*

The FTIR spectra of samples #1, #2, #3, and #4 are shown in Figure 5. As seen in the FTIR spectrum, the peak at the range of 3200–3700 cm−<sup>1</sup> can be ascribed to the stretching vibration of –OH and –NH. The peak at 1631 cm−<sup>1</sup> was assigned to the C=O stretching vibration and the absorption peak at 1086 cm−<sup>1</sup> was assigned to the stretching vibration of the C–O band. The peaks at 671 cm−<sup>1</sup> corresponded to the bending vibration of C–H in the benzene ring structures [21]. The peak at 471 cm−<sup>1</sup> confirmed the existence of the Fe–O and Al–O band [25]. The FTIR test confirmed the existence of –COOH, –NH, and –OH structure in the carbonaceous deposit layers. It was also found that the peaks at 3433 cm−<sup>1</sup> in #2 and #3 were stronger than that in #1, while #4 displayed weaker absorption at 3433 cm−1. This may be because there are few O2 and H2O entrained with polycyclic aromatic hydrocarbons to form carbonaceous deposit layers in the coke-making process. O2 and H2O could oxidize the carbonaceous deposits and form –COOH and –OH structures at high temperature and in the existence of metal ions (Fe, Al, etc.). However, the formed –COOH and –OH structures were not stable, and were eliminated from the carbon particle. Similar phenomena also existed at the characteristic peaks at 1631 cm−<sup>1</sup> and 1086 cm<sup>−</sup>1, indicating the coexistence of the formation and elimination reaction of –COOH and –OH groups during the coke-making process. In this process, the high temperature in the coking chamber promoted the oxidation reaction, and also enhanced the elimination of –COOH and –OH groups, which will consume the absorbed O2 and H2O, thus promoting the formation of a compact carbonaceous deposit layer.

**Figure 5.** FTIR spectra of each carbonaceous deposit layer in the coking chamber.

#### *3.5. Raman Spectroscopy*

Figure 6 shows the Raman spectra of samples #1#, #2, #3, and #4. As shown in Figure 6, the Raman spectra of the different carbonaceous deposit layers exhibited the G band at 1591 cm−<sup>1</sup> and the D band at 1347 cm−<sup>1</sup> [26]. The G band corresponded to the E2g mode of graphite related to the vibration of the sp2-bonded carbon atoms in two-dimensional carbon materials, while the D band related to the defects and disorder in the hexagonal graphitic layers. The result confirmed that the carbonaceous deposits contained crystalline carbon and amorphous carbon.

Furthermore, Raman spectroscopy was used to analyze the graphitization degree of the carbonaceous material by integrating the intensity ratio of the D to G bands (ID/IG). A lower ratio of ID/IG indicates a higher graphitization degree [27]. As shown in Figure 7, the ID/IG values followed the sequence of #1 (2.34) > #2 (2.07) > #3 (1.79) ≈ #4 (1.84), indicating that the high temperature improved the graphitization degree of the carbonaceous deposits.

**Figure 6.** Raman spectra of each carbonaceous deposit layer in the coking chamber.

**Figure 7.** Raman spectra of each carbonaceous deposit layer in the coking chamber: (**a**) #1 carbonaceous deposit layer; (**b**) #2 carbonaceous deposit layer; (**c**) #3 carbonaceous deposit layer; and (**d**) #4 carbonaceous deposit layer.

#### *3.6. X-ray Photoelectron Spectroscopy (XPS)*

The chemical components of different carbonaceous deposit layers were also investigated by XPS, and the corresponding spectra are shown in Figure 8. The strong peak at around 284 eV was the characteristic peak of C1s, while the peaks at about 532 eV can be ascribed to the characteristic peak of O1s [28]. Furthermore, spots of N, Al, Si, Ca, Fe, S, and P were detected from #1 to #4. The C, O, S, and P may have come from the polycyclic aromatic hydrocarbons compounds, and Al, Si, Fe, and Ca resulted from the ore composition in coal. The above metal ions can take part in the formation of carbonaceous deposits in the form of dust particles. The test results were consistent with the XRF test.

Furthermore, the quantitative content of the above elements were conducted, which are listed in Table 3. It was found that Al, Si, Ca, Fe, S, P, etc., presented small fluctuations in samples #1, #2, #3, and #4. However, C, O, and N showed significant change from #1 to #4. The four carbonaceous deposit layers presented enhanced C elemental content of 82.41%, 89.43%, 89.69%, and 91.51% from #1 to #4, respectively. At the same time, the O element showed a decreased content of 10.91%, 6.20%, 6.32%, and 5.14%, respectively. Furthermore, the N element displayed a similar change law with a 2.20% content of #1, 1.49% content of #2, 1.17% content of #3, and a 0.76% content of #4. The C/O ratio and C/N ratio were also introduced to research change in the four carbonaceous deposit layers. It was found that samples #1–4 presented increased C/O of 7.55, 14.42, 14.19, and 17.80 with increased C/N of 37.46, 60.02, 76.66, and 120.39, respectively, indicating the O and N element release process in the formation of carbonaceous deposit layers in the coking chamber. This may be explained by many of the structures of the phenols, alcohols, ethers, and amines not being stable enough, and were eliminated at high temperature and the metal ions. The elimination of N containing and O containing groups can increase the C/O and C/N values in the carbonaceous deposits, and significantly enhance the compactness of the carbonaceous deposit layer.

**Figure 8.** XPS spectra of each carbonaceous deposit layer in the coking chamber.

**Table 3.** XPS test data of each carbonaceous deposit layer in the coking chamber.


To further investigate the existing state and change law of C and O elements in the carbonaceous deposit layers, the peaks were resolved using peak analysis software (XPSPEAK4.1, kindly provided by Raymund W.M. Kwok, Department of Chemistry, the Chinese University of Hong Kong, Hong Kong, China). Figure 9 presents the peak separation fitting results of C1s for different carbonaceous deposits in the coking chamber. The peaks at 284.7 eV could be attributed to the C–C/C–H bond in aliphatic and aromatic species, which mainly resulted from a graphite carbon structure. The band at around 285.5 eV was assigned to the C–O/C–N bond in the structural formation of phenols, alcohols, ethers, and amines. The peaks at 287.4 eV were ascribed to the C=O/C=N bond in the formation of carbonyl, quinonyl, carboxylate, ester, and heterocyclic aromatic structures [28,29]. Table 4 lists the bond state content of the C element. It can be seen that #1 presented C–C/C–H, C–O/C–N, C=O/C=N contents of 69.73%, 21.68%, and 8.60%, respectively. In the carbonaceous deposit layer of #2, the content of C–C/C–H decreased to 64.59%, and the content of C–O/C–N and C=O/C=N were increased to 25.87% and 9.54%, respectively. This may be because the absorbed O2 and H2O in the primary carbonaceous deposits can oxidize the carbon skeletons to form C–O/C–N and C=O/C=N structures as well as decrease the C–C/C–H content. The carbonaceous deposit layer in sample #3 presented the three structure content of 66.60%, 24.51%, and 8.89%, while the carbonaceous deposit layer of sample #4 showed contents of 69.46%, 21.67%, and 8.87%, respectively. This may be due to the limited amount of absorbed O2 and H2O, which was consumed in the oxidation process. Furthermore, the formed C–O/C–N and C=O/C=N structures showed poor stability, which were eliminated at high temperature, resulting in the increase of C–O/C–N and C=O/C=N structures and enhanced C–C/C–H content in the carbonaceous deposits.

**Figure 9.** C1s spectra of each carbonaceous deposit layer in the coking chamber: (**a**) #1 carbonaceous deposit layer; (**b**) #2 carbonaceous deposit layer; (**c**) #3 carbonaceous deposit layer; and (**d**) #4 carbonaceous deposit layer.


**Table 4.** Bonding state content of the C element of each carbonaceous deposit layer.

Figure 10 shows the separate fitting results of O1s for different carbonaceous deposit layers in the coking chamber. The peak at 531.6 eV was assigned to the =O structure of carbonyl, quinonyl, carboxylate, and esters in the carbonaceous deposits. The bond at around 532.8 eV was ascribed to the –O– structure in the form of alcohols, phenols, and ethers in the carbonaceous deposits. The peaks at 533.8 eV can be assigned to the absorbed O2 and H2O in the carbonaceous deposits [30,31]. Table 5 lists the bond state content of the O element. As shown in Table 5, #1 presented =O, –O–, and O2/H2O contents of 30.10%, 42.10%, and 27.80%, respectively. In Sample #2, the =O and –O– contents increased to 35.16% and 43.39%, while the O2/H2O content decreased to 21.44%. This may be due to the consumption of the absorbed O2 and H2O in the primary carbonaceous deposit to form the =O and –O– structure, thus increasing the content of the =O and –O– structure in the carbonaceous deposits. Sample #3 presents the three bonding states of 33.48%, 43.19%, and 23.33%, while #4 showed the three bonding states of 32.43%, 44.89%, and 22.68%. It was found that the –O– content was almost invariant when compared with the increase in the O2/H2O content and decrease of the =O content in #3 and #4. This may be because the =O structure products such as carboxylate and esters presented weaker stability when compared with typical –O– structure products, which were eliminated to release H2O, thus resulting in the decrease in the =O structure content and increase of the O2/H2O structure. The above results confirmed the coexistence of the oxidation and elimination process in the formation of carbonaceous deposits, which resulted in the change of contents for the O2/H2O, –O–, and =O structures.

**Figure 10.** O1s spectra of each carbonaceous deposit layer in the coking chamber: (**a**) #1 carbonaceous deposit layer; (**b**) #2 carbonaceous deposit layer; (**c**) #3 carbonaceous deposit layer; and (**d**) #4 carbonaceous deposit layer.


**Table 5.** Bonding state content of the O element of each carbonaceous deposit layer.

#### *3.7. Mechanism Consideration*

On the basis of the above results, the possible formation and evolution mechanism of the carbonaceous deposits on the surface of the coking chamber are presented in Figure 11. In the coke-making process, many polycyclic aromatic hydrocarbon (such as anthracene and naphthalene) molecules associate with each other to form primary carbon particles with diameter of 3–5 μm. The primary carbon particles absorb O2 and H2O molecules combined with dust particles (containing metal ions) to form loose primary carbonaceous deposits. The primary carbon particle was not stable, and split into smaller pieces of intermediate carbon particles with diameters of 0.5–2 μm. The intermediate carbon particles reacted with each other to form a cluster structure with a diameter of 5–20 μm. The cluster structure further reacted to form compact intermediate carbonaceous deposits. There were many carboxyl, hydroxyl, and carbonyl groups on the surface of the primary and intermediate carbon particles, which can be eliminated from the particles at high temperatures and metal ions to finally form terminal carbonaceous deposits with a more compact structure at high temperature.

**Figure 11.** Schematic illustration for the formation and evolution mechanism for carbonaceous deposits on the surface of the coking chamber.

In the formation of carbonaceous deposits, the absorbed O2 and H2O can oxidize carbon skeletons to form –O– (such as alcohol, phenol, and ether) and =O (such as carbonyl, carboxyl, and esters) structures, resulting in the change of the bonding state of the O element. At the same time, the =O containing structure and –O– containing structure in the carbonaceous deposits conduct an elimination reaction in the condition of high temperature and metal ions. The oxidation and elimination reaction

resulted in the change of the bonding state of the O element, and formed compact carbonaceous deposits on the surface of the coking chamber.

#### **4. Conclusions**

In this work, carbonaceous deposits on the surface of the coking chamber were investigated by scanning electron microscopy (SEM), X-ray fluorescence spectroscopy (XRF), Fourier transform infrared spectrometry (FTIR), Raman spectroscopy, X-ray diffraction spectroscopy (XRD), and X-ray photoelectron spectroscopy (XPS). FTIR revealed the existence of carboxyl, hydroxyl, and carbonyl groups in the carbonaceous deposits. Raman spectroscopy confirmed the decreased ID/IG values from the #1 carbonaceous deposit layer to the #4 carbonaceous deposit layer, indicating an enhancement in the graphitization degree of the carbonaceous deposits. SEM showed that the carbonaceous deposits resulted from polycyclic aromatic hydrocarbons, which can react to form unstable primary carbon particles with diameters of 3–5 μm. The primary carbon particles can split into intermediate carbon particles with diameters of 0.5–2 μm. The intermediate carbon particles can further react to form compact secondary carbonaceous deposits and finally form compact terminal carbonaceous deposits. XRF revealed that the carbonaceous deposits mainly contained C, O, and N elements, with a spot of Al, Si, and Ca elements. It was found from the XPS that the C content gradually increased while O and N content gradually decreased in the formation of carbonaceous deposits. The peak fitting of the XPS results revealed that absorbed O2 and H2O took part in the oxidation process of the carbon skeleton to form =O and –O– structures. The oxidation and elimination reaction resulted in the change in the bonding state of the O element, and formed compact carbonaceous deposits on the surface of the coking chamber. Based on the analysis, the formation and evolution mechanism of carbonaceous deposits were discussed, which provides a theoretical basis for inhibiting the formation of carbonaceous deposits on the surface of the coking oven chamber and a significantly enhanced stable operation of a coke oven.

**Author Contributions:** Conceptualization, H.W. and B.J.; Methodology, H.W. and B.J.; Software, G.T.; Validation, H.W., B.J., X.W., and G.T.; Formal analysis, B.J.; Investigation, H.W. and G.T.; Resources, H.W.; Data curation, H.W. and G.T.; Writing—original draft preparation, H.W. and G.T.; Writing—review and editing, H.W. and G.T.; Visualization, G.T.; Supervision, H.W.; Project administration, H.W.; Funding acquisition, H.W., B.J., and X.W.

**Funding:** This work was financially supported by the National Natural Science Foundation of China (51806035, 51676038), the Natural Science Foundation of Jiangsu Province (BK20170669), and the Key Research and Development Projects of Anhui Province (1804a0802195).

**Conflicts of Interest:** The authors declare no conflicts of interest.

#### **References**


© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Article* **Energy Consumption and Economic Analyses of a Supercritical Water Oxidation System with Oxygen Recovery**

#### **Fengming Zhang 1,2,\*, Jiulin Chen 1, Chuangjian Su <sup>1</sup> and Chunyuan Ma <sup>3</sup>**


Received: 15 October 2018; Accepted: 14 November 2018; Published: 16 November 2018

**Abstract:** Oxygen consumption is one of the factors that contributes to the high treatment cost of a supercritical water oxidation (SCWO) system. In this work, we proposed an oxygen recovery (OR) process for an SCWO system based on the solubility difference between oxygen and CO2 in high-pressure water. A two-stage gas–liquid separation process was established using Aspen Plus software to obtain the optimized separation parameters. Accordingly, energy consumption and economic analyses were conducted for the SCWO process with and without OR. Electricity, depreciation, and oxygen costs contribute to the major cost of the SCWO system without OR, accounting for 46.18, 30.24, and 18.01 \$·t <sup>−</sup>1, respectively. When OR was introduced, the total treatment cost decreased from 56.80 \$·t <sup>−</sup><sup>1</sup> to 46.17 \$·<sup>t</sup> <sup>−</sup>1, with a reduction of 18.82%. Operating cost can be significantly reduced at higher values of the stoichiometric oxygen excess for the SCWO system with OR. Moreover, the treatment cost for the SCWO system with OR decreases with increasing feed concentration for more reaction heat and oxygen recovery.

**Keywords:** supercritical water oxidation; high-pressure separation; oxygen recovery; energy recovery; economic analysis

#### **1. Introduction**

Supercritical water (SCW) (*P* > 22.1 MPa, *T* > 374 ◦C) has unique thermo-physical characteristics [1], which can dissolve organic compounds and gases to form a homogeneous mixture without mass transfer resistance [2,3]. SCW has been widely used to treat organic waste by supercritical water oxidation (SCWO) or supercritical water gasification (SCWG) for high efficiency and low residence time [3–5]. In the SCWO process, no SO2 or NOX by-products during organic waste degradation emit [6–8]. Although SCWO has many unique advantages in treating wastewater, some technical problems, such as corrosion and salt plugging, have hindered its development for years [9,10]. Inorganic acids (e.g., HCl and H2SO4), combined with high temperature and high oxygen concentration, can cause severe corrosion of the reactor and other devices [11]. Inorganic salt is hardly soluble in SCW, which leads to the plugging of the reactor, as well as the preheating and cooling sections [12]. At present, an effective solution for corrosion and salt plugging is the use of a transpiring wall reactor (TWR). A TWR typically consists of a dual shell with an outer pressure-resistant vessel and an inner porous tube. Transpiring water at subcritical temperatures passes through the porous pipe to form a protective film on its inner surface. This water film can prevent reactants from spreading to the porous wall and dissolve inorganic salt. Numerous studies have proven that TWR plays an effective role in preventing corrosion and salt plugging [13–15].

A high treatment cost, which is due to material input (such as oxidants and additives) and energy consumption during the pressurization and heating steps, is another obstacle that hinders the SCWO application. Treatment cost is determined according to the adopted equipment, treatment capacity, concentrations, and types of organic matter, operating conditions, and staff costs. At present, the treatment cost for an SCWO system with 1000 kg/h wet organic waste and an organic matter content of 10 wt% typically ranges from tens to hundreds of dollars [16]. Energy recovery is the leading method for reducing energy consumption and operating cost. An autothermal operation with a certain feed concentration (>2 wt%) can be achieved under ideal conditions [17–19]. Power generation is another potential application that uses high-pressure and high-temperature reactor effluent [20–22]. However, the reactor effluent in an SCWO system with TWR is cooled to subcritical temperature (<360 ◦C) for transpiring water injection at a low temperature to avoid salt plugging [13]. Accordingly, feedstock preheating and hot water/steam production may be more realistic and effective choices [23,24].

Oxygen, the most popular oxidant in SCWO systems, is another major source of cost. Results have indicated that a stoichiometric oxygen excess (*R*) of 1.05 may be sufficient for complete oxidation of organic wastewater [16]. However, a higher amount of oxygen is required in the pilot or industrial plant, which is mainly due to the local heterogeneous state in the reactor. Thus, twice the value of *R* (or even higher) is obtained, which leads to oxygen loss. Xu et al. [25] conducted an economic analysis of urban sludge via SCWO using a 2.5 t/day demonstration device. The operating cost was approximately 83.1 \$·t <sup>−</sup>1, with oxygen cost accounting for 25% of the total amount. Zhang et al. [26] analyzed a 100 t/day SCWO system for high-concentration printing and dyeing wastewater; the operating cost of the system was 10.3 \$·t <sup>−</sup>1, with oxygen cost accounting for 37% of the total amount. Shen et al. [27] conducted an economic analysis of an SCWO system with TWR. The feed was 300 m3/day, with an initial concentration of 40,000 mg/L chemical oxygen demand; the cost was 4.99 \$·t <sup>−</sup>1, with oxygen cost accounting for 71.8% of the total amount. Thus, oxygen consumption control will be an important solution for reducing operating cost.

In addition, CO2 is another primary gas in reactor effluent. However, it is low in purity due to excess oxygen consumption, which is the main obstacle that inhibits CO2 recovery and utilization. Thus, recovering CO2 with high purity may be another solution for reducing the operating cost of SCWO systems. The low operating cost calculated by Shen [27] is mainly attributed to the benefit of CO2 recovery. Abeln [28] reported that the operating cost of a 100 kg/h SCWO–TWR plant is approximately 659 €/t, and by-product income, such as surplus heat energy and CO2, must be ensured to obtain a comparably low operating cost.

Species recovery can considerably reduce operating cost for less input and additional income. However, only a few studies have focused on this issue, and a simple operation process with low energy consumption is urgently required for species recovery. In the current work, a species recovery process based on high-pressure water absorption was proposed to separate and recover oxygen and CO2. A two-stage gas–liquid separation process was established using Aspen Plus V8.0. Reasonable thermodynamic models for high-pressure separation were evaluated to identify the optimized separation parameters. Accordingly, SCWO processes with and without oxygen recovery (OR) were simulated, and energy consumption and economic analyses were conducted.

#### **2. Proposal of OR for SCWO Systems**

Baranenko et al. [29] tested the solubility of oxygen and CO2 in high-pressure water at temperatures ranging from 0 ◦C to 360 ◦C and pressures from 1 MPa to 20 MPa. The solubility of oxygen (Figure 1a) and CO2 (Figure 1b) increases with increasing pressure, but the effect of temperature on solubility does not exhibit a distinct trend. At low pressures, an evident reduction in solubility is observed as temperature increases. At high pressures, solubility initially decreases, then increases, and finally decreases with increasing temperature. Thus, two solubility extremes occur in the wave curve of the high-pressure zone. Moreover, the solubility of CO2 is nearly one order of magnitude higher than that of oxygen under the same conditions. Given that reactor effluent is mostly composed

of oxygen, CO2, and water, the ratio of oxygen to CO2 in the gaseous phase under different conditions is calculated using the typical effluent composition in our previous pilot plant.

**Figure 1.** The solubility of oxygen (**a**) and carbon dioxide (**b**) in the high-pressure water.

Over 80% of oxygen cannot be dissolved in high-pressure water and occurs in gaseous phase at *P* < 9 MPa and 20 ◦C < *T* < 360 ◦C, as shown in Figure 2a. In addition, Figure 2b shows that CO2 can be dissolved completely in water under certain conditions. Moreover, low temperatures are conducive to dissolving CO2 in water. CO2 can dissolve completely in water at temperatures below 20 ◦C when *P* = 2 MPa; however, temperature can be increased to 280 ◦C when *P* = 10 MPa. Thus, the temperatures for completely dissolving CO2 in water can be increased at high pressures. Figure 2c shows the releasing ratio difference in gaseous phase between oxygen and CO2. The temperature zone gradually widens with increasing pressure to obtain a high releasing ratio, but the releasing ratio difference slowly decreases. The temperature zone between 20 ◦C and 60 ◦C can reach a releasing ratio difference of 80% at 2 MPa. However, when pressure is increased to 8 MPa, the temperature zone can be widened to a range of 20 ◦C to 240 ◦C. These results have motivated us to develop a solution for separating oxygen and CO2 by adjusting reactor effluent parameters. Thus, a new process for improving oxygen utilization rate in SCWO systems [30] is proposed, as shown in Figure 3.

In the proposed SCWO process, excess oxygen and preheated organic waste are injected from the top of the TWR, which initiates the oxidation reaction. Simultaneously, two branches of transpiring water with different temperatures are injected from the side of the TWR to protect the reactor. The reactor effluent first enters a high-pressure gas–liquid separator after heat exchange and depressurization. Most of the oxygen is released in gaseous phase, whereas most of the CO2 is dissolved in aqueous phase for the solubility difference between oxygen and CO2 in water, thereby achieving the separation of oxygen and CO2. Subsequently, oxygen is reused through the oxygen circulation pump. The aqueous fluid from the high-pressure gas–liquid separator is adjusted further and injected into a low-pressure separator, whereas CO2 is released and collected. Therefore, oxygen and CO2 are separated and recovered.

**Figure 2.** The releasing ratio difference between oxygen and carbon dioxide at different pressures and temperatures based on our previous experimental results, in the reactor effluent, water flow: 46.044 kg/h, oxygen flow: 0.448 kg/h, carbon dioxide flow: 0.836 kg/h, (**a**) O2 ratio in the gas, (**b**) CO2 ratio in the gas, (**c**) the ratio difference between O2 and CO2.

**Figure 3.** The simplified diagram of a SCWO system to increase the oxygen utilization rate.

#### **3. High-Pressure Separation for Reactor Effluent**

#### *3.1. High-Pressure Separation Process*

To identify optimized parameters for OR, a simulation flow of a two-step separation process for reactor effluent based on high-pressure water absorption was first established using Aspen Plus V8.0 (Figure 4). High- and low-pressure gas–liquid separators were introduced to separate and recover oxygen and CO2.

**Figure 4.** The simulation flow of the high-pressure water absorption for oxygen recovery.

#### *3.2. Definition of Process Parameters*

The OR ratio (*γ*O2) is defined as the oxygen in the gaseous phase of the high-pressure separator divided by the oxygen in the reactor effluent:

$$\gamma\_{\rm O\_2} = \frac{F\_{\rm O\_{2\cdot \xi}}'}{F\_{\rm O\_{2\cdot \xi}}' + F\_{\rm O\_2J}'} \tag{1}$$

where *F* O2,*<sup>g</sup>* and *F* O2,*<sup>l</sup>* are the oxygen mass flows in the gaseous and aqueous phases, respectively, of the high-pressure separator.

Oxygen purity (*β*O2) is defined as the oxygen ratio in the gaseous phase of the high-pressure separator, which can be calculated as follows:

$$\beta\_{\rm O\_2} = \frac{F\_{\rm O\_{2,\xi}}'}{F\_{\rm O\_{2,\xi}}' + F\_{\rm CO\_2,\xi}' + F\_{\rm H\_2O,\xi}'} \tag{2}$$

where *F* CO2,*<sup>g</sup>* and *F* H2O,*<sup>g</sup>* are the mass flows of CO2 and water in the gaseous phase, respectively. Water can be typically disregarded when its content is small.

Similarly, the CO2 recovery ratio (*γ*CO2) is defined as the CO2 in the gaseous phase of the low-pressure separator divided by the CO2 in the reactor effluent:

$$\gamma\_{\text{CO}\_2} = \frac{F'' \text{CO}\_{2,\text{g}}}{F'\_{\text{CO}\_{2,\text{g}}} + F'\_{\text{CO}\_2,l}} \tag{3}$$

where *F* CO2,*<sup>g</sup>* is the CO2 mass flow in the gaseous phase of the low-pressure separator, and *F* CO2,*<sup>l</sup>* is the CO2 mass flow in the aqueous phase of the high-pressure separator.

CO2 purity (*β*CO2) is defined as the CO2 ratio in the gaseous phase of the low-pressure separator, which can be calculated as follows:

$$\beta\_{\rm CO\_2} = \frac{F'' \, \_{\rm CO\_2 \, \rm g}}{F' \, \_{\rm O\_2 \, \rm g} + F'' \, \_{\rm CO\_2 \, \rm g} + F'' \, \_{\rm H\_2O\_4 \, \rm g}} \tag{4}$$

where *F* O2,*<sup>g</sup>* and *F* H2O,*<sup>g</sup>* are the mass flows of oxygen and water, respectively, in the gaseous phase of the low-pressure separator, and water can be typically disregarded when its content is small.

The mass flow rate of oxygen is calculated using a constant *R* [18], which is defined as follows:

$$R = \frac{F\_{O\_2}}{1.5 F\_f \omega} \tag{5}$$

where *FO*<sup>2</sup> (kg/h) and *F*<sup>f</sup> (kg/h) are the mass flow rates of oxygen and the feed, respectively; and *ω* (wt%) is the methanol concentration in the feed.

#### *3.3. Thermodynamic Property Models*

The selection of an appropriate model for estimating the thermodynamic properties of reactor effluent is one of the most important steps that can affect the simulation results. To date, no model has been adopted for all the components and processes. Moreover, the same model cannot be used under all operating conditions, especially at wide ranges of pressure (0.1–23 MPa) and temperature (20–360 ◦C). Therefore, an appropriate method for estimating the separation process should be carefully selected. Aspen Plus includes a large databank of thermodynamic properties and transport models with the corresponding mixing rules for estimating mixture properties. Several potential thermodynamic models recommended by Aspen Plus were selected and tested (as listed in Table 1) based on the composition of our reactor effluent (i.e., water, CO2, and oxygen) and the range of the operating conditions. The selected models were simulated with default interaction parameters for the preliminary assessment due to the lack of component interaction coefficients within a large range. The *γ*O2 and *γ*CO2 values at different pressure and temperature values with 10 recommended thermodynamic models were plotted in Figure 5. Additionally, the ideal results calculated from the experimental solubility data of Baranenko et al. [29] were also plotted for the comparison and verification of the thermodynamic models. In the ideal results calculation, the reactor effluent was assumed to conduct an ideal separation in the high-pressure and low-pressure separators, and the interaction between O2 and CO2 has been ignored.

Identifying an accurate thermodynamic model that can fulfill the standard for CO2 and oxygen within a wide range of temperature and pressure values is difficult, as shown in Figure 5. The *γ*O2 (Figure 5a,c,e,g,i,k) calculated using the BWR-LS, PR-BM, SR-POLAR, SRK, PSRK, RKS-BM, and LK-Plock models agree well with the ideal results calculated from the experimental solubility data (red curves) of Baranenko et al. [29]. By contrast, the comparison of *γ*CO2 between the thermodynamic models and the ideal results present more complex information. At 0.1 MPa (Figure 5b), all the models can accurately predict *γC*O2. At 2 MPa (Figure 5d) and 4 MPa (Figure 5f), only the PSRK, RKS-BM, and RKS-MHV2 models exhibit accurate prediction performance in terms of trend and value. At higher pressures (i.e., 6, 8, and 10 MPa), only the PSRK model (magenta curves) can achieve good prediction performance, with a maximal deviation of less than 20% (Figure 5h,j,l). Thus, PSRK is selected as the thermodynamic model for the high-pressure separation process in this study under the comprehensive consideration of *γ*O2 and *γ*CO2. A detailed model expression for PSRK is available in the literature [31].



**Figure 5.** Comparisons of the ideal results calculated from the experimental solubility data and simulation results at different pressures and temperatures, (**a**) *γ*O2 at *P* = 0.1 MPa, (**b**) *γ*CO2 at *P* = 0.1 MPa, (**c**) *γ*O2 at *P* = 2 MPa, (**d**) *γ*CO2 at *P* = 2 MPa, (**e**) *γ*O2 at *P* = 4 MPa, (**f**) γCO2 at *P* = 4 MPa, (**g**) γO2 at *P* = 6 MPa, (**h**) *γ*CO2 at *P* = 6 MPa, (**i**) *γ*O2 at *P* = 8 MPa, (**j**) *γ*CO2 at *P* = 8 MPa, (**k**) *γ*O2 at *P* = 10 MPa, (**l**) *γ*CO2 at *P* = 10 MPa.

#### *3.4. Effects of Operating Parameters*

#### 3.4.1. Stoichiometric Oxygen Excess

The interaction between the high- and low-pressure separators typically results in different recovery ratio and purity values for oxygen and CO2. For convenience, the separation parameters of the low-pressure separator are set under ambient conditions (*P* = 0.1 MPa, *T* = 20 ◦C) and, thus, we focus only on the separation parameters of the high-pressure separator.

Figure 6(a1–a4,b1–b4) show that a temperature increase or a pressure decrease is favorable for increasing *γ*O2 but unfavorable for increasing *β*O2. *R* = 1.5 is used as an example. *γ*O2 is 89.3% at *P* = 8 MPa and *T* = 20 ◦C, and it increased to 92.8% when pressure decreased to 5 MPa. *γ*O2 increased further to 96.4% when pressure and temperature were modified to 5 MPa and 90 ◦C, respectively (Figure 6(a1)). Similarly, *β*O2 is 78.5% at *P* = 8 MPa and *T* = 20 ◦C. It decreased to 70.1% when pressure was reduced to 5 MPa and to 56.5% when pressure and temperature were adjusted to 5 MPa and 90 ◦C, respectively (Figure 6(b1)).

*Processes* **2018**, *6*, 224

**Figure 6.** The effect of *R* on the performance of the high-pressure and low-pressure separators, (**a1**) *γ*O2 at *R* = 1.5, (**a2**) *γ*O2 at *R* = 2, (**a3**) *γ*O2 at *R* = 2.5, (**a4**) *γ*O2 at *R* = 3, (**b1**) *β*O2 at *R* = 1.5, (**b2**) *β*O2 at *R* = 2, (**b3**) *β*O2 at *R* = 2.5, (**b4**) *β*O2 at *R* = 3, (**c1**) *γ*CO2 at *R* = 1.5, (**c2**) *γ*CO2 at *R* = 2, (**c3**) *γ*CO2 at *R* = 2.5, (**c4**) *γ*CO2 at *R* = 3, (**d1**) *β*CO2 at *R* = 1.5, (**d2**) *β*CO2 at *R* = 2, (**d3**) *β*CO2 at *R* = 2.5, and (**d4**) *β*CO2 at *R* = 3.

The input of the low-pressure separator came from the aqueous mixture of the high-pressure separator. Thus, *γ*CO2 and *β*CO2 in the low-pressure separator are dependent on the separation parameters of the high-pressure separator. A standard for the high-pressure separator is first defined with high values of *γ*O2 (>80%) and *β*O2 (>70%) to narrow down the parameter range. The separating pressure and temperature values that can fulfill the standard can then be obtained. Subsequently, *γ*CO2 and *β*CO2 are analyzed based on the high-pressure separation results. Figure 6(c1–c4) show that a temperature increase or a pressure decrease in the high-pressure separator decreases *γ*CO2, which is contrary to the effects of pressure and temperature on *γ*O2. *R* = 1.5 is used as an example. *γ*CO2 is 78.9% at 8 MPa and 30 ◦C, and it decreased to 42.1% at 5 MPa and 90 ◦C (Figure 6(c1)). Moreover, Figure 6(d1–d4) show that a decrease in temperature and pressure are beneficial for *β*CO2. *β*CO2 is 83.3% at 8 MPa and 90 ◦C, and it increased to 86.7% when pressure decreased to 5 MPa. Moreover, *β*CO2 increased further to 88.7% when pressure and temperature were decreased to 5 MPa and 30 ◦C, respectively (Figure 6(d1)).

Figure 6 shows that an increase in *R* contributes to an increase in *γ*O2 and *β*O2, but decreases the values of *γ*CO2 and *β*CO2. *γ*O2, *β*O2, *γ*CO2, and *β*CO2 at *P* = 5 MPa and *T* = 90 ◦C are 92.8%, 56.5%, 42.1%, and 88.9%, respectively, at *R* = 1.5. *γ*O2 and *β*O2 increased to 98.2% and 78.5%, respectively, whereas *γ*CO2 and *β*CO2 decreased to 15.7% and 75%, respectively, when *R* increased to 3. An increase in *R* increases the amount of oxygen in reactor effluent, whereas the amount of CO2 remains constant (constant (Table 2(A1−A4))). An increase in *R* is conducive to OR, but reduces CO2 recovery and purity. The optimized parameters are provided in Table 2(A1−A4). pressure range of 6 MPa to 7 MPa and a temperature range of 30 ◦C to 40 ◦C are appropriate for the high-pressure separator.

#### 3.4.2. Feed Concentration

The effects of pressure and temperature at different feed concentrations on species recovery and purity (Figure 7(a1−a5,b1−b5,c1−c5,d1−d5)) are similar to those discussed in the previous section. The values of *γ*CO2 and *β*O2 will be lower at higher feed concentrations, but the values of *γ*O2 and *β*CO2 will be higher. Although an increase in feed concentration will increase the amounts of oxygen and CO2 in the reactor effluent with the same proportion, the solubility difference between oxygen and CO2 in the water achieves the following results. The *γ*O2, *β*O2, *γ*CO2, and *β*CO2 at *P* = 5 MPa and *T* = 90 ◦C are 88.9%, 80.0%, 50.0%, and 66.7% at *ω* = 2 wt%, respectively (Figure 7(a1,b1,c1,d1)). When *ω* increased to 10 wt%, *γ*O2 and *β*CO2 increased to 98.2% and 85.7%, respectively, but *β*O2 and *γ*CO2 decreased to 68.2% and 19.3%, respectively (Figure 7(a5,b5,c5,d5)).

Therefore, an increase in feed concentration is also conducive to OR, but oxygen purity will be lower. Moreover, an increase in feed concentration is unfavorable for CO2 recovery, but high CO2 purity will be obtained. The optimized parameters at different feed concentrations are provided in (B1−B4) in Table 2. A pressure range of 5 MPa to 7 MPa and a temperature range of 30 ◦C to 70 ◦C are appropriate for the high-pressure separator.

**Figure 7.** The effect of feed concentration on the performance of the high-pressure and low-pressure separator, (**a1**) *γ*O2 at *ω* = 2 wt%, (**a2**) *γ*O2 at *ω* = 4 wt%, (**a3**) *γ*O2 at *ω* = 6 wt%, (**a4**) *γ*O2 at *ω* = 8 wt%, (**a5**) *γ*O2 at *ω* = 10 wt%, (**b1**) *β*O2 at *ω* = 2 wt%, (**b2**) *β*O2 at *ω* = 4 wt%, (**b3**) *β*O2 at *ω* = 6 wt%, (**b4**) *β*O2 at *ω* = 8 wt%, (**b5**) *β*O2 at *ω* = 10 wt%, (**c1**) *γ*CO2 at *ω* = 2 wt%, (**c2**) *γ*CO2 at *ω* = 4 wt%, (**c3**) *γ*CO2 at *ω* = 6 wt%, (**c4**) *γ*CO2 at *ω* = 8 wt%, (**cd**) *γ*CO2 at *ω* = 10 wt%, (**d1**) *β*CO2 at *ω* = 2 wt%, (**d2**) *β*CO2 at *ω* = 4 wt%, (**d3**) *β*CO2 at *ω* = 6 wt%, (**d4**) *β*CO2 at *ω* = 8 wt%, and (**d5**) *β*CO2 at *ω* = 10 wt%.


**Table 2.** Detailed parameters of the high-pressure and low-pressure parameters.

originated from the injection of transpiring water with a transpiring intensity of 0.06 [13] and methanol oxidation product. ε A2 = B3.

#### **4. Aspen Model for SCWO System Simulation with Energy and Species Recovery**

In this section, our pilot plant was amplified similar to an SCWO industrial plant with a treatment capacity of 1000 kg/h based on the optimized parameters for energy and species recovery. The simulation process can be established without considering the complex equipment structure in Aspen Plus, which is a 1D simulation software based on mass and energy conservation.

#### *4.1. TWR*

A TWR is the most important equipment of an SCWO system, and Figure 8a shows the diagram of the TWR in our pilot plant [13]. Five streams were introduced into the reactor. The oxygen and the feed were injected into the reactor via a coaxial nozzle from the top of the reactor, with oxygen in the central tube and the feed in the outer tube. The transpiring tube is divided into three zones using two retaining rings to ensure that the transpiring streams can pass through the porous tube at different temperatures and flow rates. The transpiring water (tw) is divided into three branches, namely, the upper (tw1), middle (tw2), and lower (tw3) branches of transpiring water.

Considering the complicated flow, transpiring heat, and reaction characteristics, the reactor was separated into three sections for simplicity, namely, the mixing, adiabatic reacting, and cooling sections. A simplified model was proposed to simulate the TWR (Figure 8b) in Aspen Plus. The mixing section provides a sufficient mixing space for the reactants. Among the three branches of transpiring water, the upper branch is the only one that can directly influence the reaction [11]. For simplicity, the feed, oxygen, and upper branch of transpiring water will first flow into a mixer to fully mix the reactants. The adiabatic reacting section is simulated by a plug flow reactor (PLUG). When reaction is done, the product flows into the cooling section where the middle and lower branches of transpiring water are injected sequentially into the reactor, and the two mixers are used to simulate the mixing process. Finally, hot effluent flows out of the reactor.

**Figure 8.** (**a**) The experimental diagram of the TWR and (**b**) the simplified model for the TWR in Aspen plus.

#### *4.2. Reaction*

A desalinated water–methanol mixture is also used as artificial wastewater in Aspen Plus. Previous experimental results [13,32] have proven that CO is the major intermediate during the

SCWO of methanol. Thus, a two-step mechanism based on Arrhenius law is created and implemented in the simulation, as shown in Equations (6) to (9):

$$\text{CH}\_3\text{OH} + \text{O}\_2 = \text{CO} + \text{H}\_2\text{O} \tag{6}$$

$$\text{CO} + 0.5\text{O}\_2 = \text{CO}\_2 \tag{7}$$

$$\sigma\_{\rm CH\_3OH} = -\frac{\rm d[CH\_3OH]}{dt} = 2.0 \times 10^{21} \times \exp\left(\frac{-303.85 \text{ kJ/mol}}{\rm RT}\right) [\rm CH\_3OH] \tag{8}$$

$$r\_{\rm CO} = -\frac{\rm d[CO]}{\rm dt} = 3.16 \times 10^6 \times \exp\left(\frac{-88 \rm kJ/mol}{\rm RT}\right) [\rm CO] \tag{9}$$

The kinetic data used in the present study were based on the literature [33–36], and the reaction order of oxygen was assumed zero because of the large excess amount.

#### *4.3. Process Flow*

The simulation process, including energy recovery and OR, was developed and presented in Figure 9. After the feed is pressurized by pump 1 (P1), it first flows into heat exchanger 1 (HE1) to be heated by one branch of the final products (FINAL), and then it flows into electric heater 1 (EH1) for further heating. Simultaneously, oxygen is pressurized by the air compressor (AC), and then flows into mixer 1 (M1) to fully mix with the feed and tw1. After transpiring water is pressurized by pump 2 (P2), it splits into three branches (tw1, tw2, and tw3). Before tw1 reaches M1, it first flows into heat exchanger 2 (HE2) to be preheated, and then flows to electric heater 2 (EH2) for further heating. tw2 is preheated by heat exchanger 3 (HE3), and then it mixes with the effluent in mixer 2 (M2). tw3 mixes with the effluent in mixer 3 (M3) to form the final products (FINAL). Oxygen and tw3 are injected into the reactor at room temperature.

**Figure 9.** The Aspen Plus diagram of supercritical water oxidation system with oxygen recovery (lines and equipment with red color are specially for OR).

FINAL is split into two branches in split 2, and these branches are treated as hot streams to preheat the feed and tw1. The two branches of FINAL then reunite in mixer 4 (M4) and are cooled down in heat exchanger 3 (HE3). Moreover, the effluent was further cooled in heat exchangers 4 (HE4) and 5 (HE5) by cooling water before gas–liquid separators 1 (S1) and 2 (S2), respectively. The recovered oxygen from S1 is pressurized by pump 3 (P3) and mixed with the supplement oxygen in mixer 5 (M5).

#### **5. Energy and Economic Analysis**

#### *5.1. Equipment Investment Calculation*

Several alternatives are available to estimate the cost of a major piece of equipment, such as obtaining a quotation from a suitable vendor, using the cost data of a previously purchased equipment of the same type, or utilizing available summary graphs for various types of common equipment. Considering that no similar SCWO industrial plant exists, the last option may be more accurate for our preliminary cost estimation. This methodology allows the estimation of equipment and installation costs according to certain base conditions (e.g., low pressure and construction materials with the lowest cost) and a particular year. Deviations from the base conditions are corrected by a factor that depends on working pressure and construction materials. The obtained cost is then translated into the current time by using an index that considers the time variation of equipment cost.

On the basis of the results obtained for the pilot plant under typical conditions (Table 3, B3, and D3), economic analyses for the 1000 kg/h SCWO plant with and without OR were performed. The investment costs for the TWR, high-pressure pumps, compressors, electric heaters, and gas–liquid separators can be calculated as follows [37]:

$$C\_{\rm PM} = C(B\_1 + B\_2 F\_{\rm M} F\_{\rm P}) \tag{10}$$

$$\text{lgC} = K\_1 + K\_2 \text{lgX} + K\_3 \text{(lgX)}^2 \tag{11}$$

$$\lg F\_{\rm P} = \rm C\_1 + \rm C\_2 \lg P + \rm C\_3 \left(\lg P\right)^2 \tag{12}$$

where *C* is the equipment investment that uses carbon steel under environmental conditions, and *X* is the design parameter (e.g., pump power and reactor volume). *P* is the design pressure, which is set as 30 MPa. *K*1, *K*2, *K*3, *C*1, *C*2, *C*3, *B*1, and *B*<sup>2</sup> are constant for each piece of equipment. *F*<sup>P</sup> and *F*<sup>M</sup> are the pressure and material correction coefficients, respectively. Detailed data are provided in Table 4.



temperature of the cooling water is set as 60 ◦C by adjusting flow for hot water production. c The FSP2-1/FSP2-2 is kept at 1.5 for energy recovery optimization [24]. d *T*EH1, *T*EH2,*T*EH3 are the outlet temperatures of feed, tw1, and tw2 in EH1, EH2, and EH3, respectively. *T*tw1, *T*f, and *T*tw2 are the reactor inlet temperatures for tw1, feed, and tw2, respectively.*T*out is the temperatures of the reactor effluent. *T*SP2-1, and *T* SP2-2 are the temperatures of the reactor effluent after cooling by HE2 and HE1, respectively. *T*M1, and *T*M4 are the mixingtemperatures after M1 and M4, respectively. e A2 = B3, C2 = D3. f The *T*w for reaction initiation is usually higher (380–420 ◦C) than that of the steady state for reaction heat releasing.


**Table 4.** The coefficient for each equipment.

Directly estimating the cost of the TWR is difficult because no similar reactor is available for comparison. The cost of a plug flow reactor was first estimated with the same volume for sufficient residence time, and then the cost of the TWR was calculated based on our empirical relationship. The reactor was divided into three sections according to our previous TWR design [24]. The total required volume of the reactor is 570 L. Thus, the actual reactor volume is 695 L when a loading coefficient of 0.82 is considered [38].

Shell and tube heat exchangers were selected in the SCWO system, and the cost of the regular heat exchanger can be calculated as follows [39]:

$$\mathbf{C\_{HE}} = 3.28 \times 10^4 \left(\frac{A}{80}\right)^{0.68} \delta\_\mathrm{M} \delta\_\mathrm{P} \delta\_\mathrm{T} \tag{13}$$

where *A* is the heat exchanger area. Considering that the heat exchanger was used in high-pressure and high-temperature conditions, *δ*M, *δ*P, and *δ*<sup>T</sup> are the material, pressure, and temperature correction coefficients (Table 5), respectively, which were used to modify cost estimation.


**Table 5.** The coefficients for heat exchanger.

The obtained cost is then translated into the present time by using an index that considers the time variation of equipment cost for the process industries, which was calculated using the following equation [37]:

$$\text{Cost}\_{2016} = \text{Cost}\_{2001} \left( \frac{\text{CEPCI}\_{2016}}{\text{CEPCI}\_{2001}} \right) \tag{14}$$

Given the aforementioned considerations, the total equipment cost for the SCWO pilot plant with and without OR in 2016 was calculated as \$2,592,096 and \$2,522,654, respectively. Details on equipment sizing assumptions, construction materials, and estimated cost per piece of equipment are presented in Table 6.



#### *Processes* **2018** , *6*, 224

#### *5.2. Treatment Cost Calculation and Distribution*

The treatment cost of an SCWO system includes investment and operating costs. The basic operating costs were determined using the procedure parameters in Table 3 (B3) and (D3), which were estimated under the assumption that the plant operates 330 days a year and 24 h a day. The operating cost includes energy consumption, raw material, labor, and capital-related costs [36]. Energy consumption cost includes the cost of electricity required to operate the process equipment and the plant. Raw material cost, which includes the costs of oxygen, cooling water, and transpiring water, was estimated from the amount of required raw materials. Labor cost includes the salaries of operation and supervisory employees. The depreciation time of the system is 10 years, and the maintenance cost is 3% of the equipment cost.

Figure 10 shows the treatment cost comparisons of the SCWO systems with and without OR. In the SCWO system without OR, electricity, depreciation, and oxygen contribute to the primary treatment cost, accounting for 46.18, 30.24, and 18.01 \$·t <sup>−</sup>1, respectively, of the total cost. Although the heat of the reactor effluent has been recovered, energy (electricity) consumption remains high. This phenomenon is attributed to the low-grade heat of the reaction effluent (<370 ◦C) due to the injection of transpiring water at a low temperature to avoid salt plugging. Hot water, which comprises the major income of the system, was calculated as a negative value in the treatment cost and accounted for −56.72 \$·t <sup>−</sup>1. Thus, the total treatment cost for the SCWO system without OR is 56.80 \$·<sup>t</sup> <sup>−</sup>1, with electricity and oxygen cost accounting for 81.30% and 31.69% of the total treatment cost, respectively.

**Figure 10.** The treatment cost comparisons for SCWO systems with and without OR, the prices for electricity, oxygen, transpiring water and cooling water, are 0.079 \$/kW·h, 100 \$·t <sup>−</sup>1, 0.8 \$·<sup>t</sup> <sup>−</sup>1, and 0.24 \$·t <sup>−</sup>1, respectively; the manpower is 6000 \$/man·year; the income for hot water and CO2 are 2.7 \$·t <sup>−</sup><sup>1</sup> and 71.4 \$·<sup>t</sup> <sup>−</sup>1, respectively.

Electricity, depreciation, and oxygen still contribute to the primary treatment cost of the SCWO system with OR. Electricity consumption slight decreases from 46.18 \$·t <sup>−</sup><sup>1</sup> to 45.88 \$·<sup>t</sup> <sup>−</sup><sup>1</sup> due to OR, but oxygen cost significantly decreased from 18.01 \$·t <sup>−</sup><sup>1</sup> to 9.77 \$·<sup>t</sup> <sup>−</sup>1. Additionally, the additional income of CO2, which accounted for −5.65 \$·t <sup>−</sup>1, was obtained due to OR. Treatment cost considerably decreased from 56.80 \$·t <sup>−</sup><sup>1</sup> to 46.17 \$·<sup>t</sup> <sup>−</sup>1, with a reduction rate of 18.82%. Thus, OR considerably contributes to reducing the treatment cost of an SCWO system.

#### *5.3. Effect of Stoichiometric Oxygen Excess*

On the basis of the previously designed system, this section investigates the effects of the operating parameters on energy consumption and treatment cost. Similar to the previous analysis, several episodes of actual oxygen consumption may be necessary for complete feed degradation. Thus, the effect of *R* on the treatment cost of the SCWO systems with and without OR is analyzed in this section, and the operating parameters and detailed results are listed in Table 3(A1–A4, C1–C4) and Table 7(A1–A4, C1–C4). Electricity consumption and hot water income increase slightly with an increase in *R* in both SCWO systems (Figure 11a,e). Oxygen consumption increases linearly with an increase in *R* in the SCWO system without OR. When *R* increased from 1.5 to 3, oxygen consumption considerably increased from 13.5 \$·t <sup>−</sup><sup>1</sup> to 27 \$·<sup>t</sup> <sup>−</sup><sup>1</sup> (Figure 11b). Furthermore, a slight increase in cooling water consumption (Figure 11d) occurs with an increase in *R.* An increase in *R* has minimal effect on depreciation, repair (Figure 11c), transpiring water consumption, manpower (Figure 11d), and CO2 income (Figure 11e). Thus, the treatment cost of the SCWO system without OR can increase from 53.89 \$·t <sup>−</sup><sup>1</sup> to 65.25 \$·<sup>t</sup> <sup>−</sup><sup>1</sup> (Figure 11f) when *R* increased from 1.5 to 3. In the SCWO system with OR, oxygen consumption in the start-up stage is equal to that of the SCWO system without OR. However, the supplemental oxygen content is gradually reduced to a value that is slightly higher than the actual oxygen consumption after attaining OR equilibrium (Table 3). Thus, an increase in *R* exerts minimal effect on oxygen consumption (Figure 11b). Moreover, high-purity CO2 can be recovered as an income due to OR (Figure 11e). In addition, equipment repairs and depreciation (Figure 11c), cooling water, transpiring water, and manpower consumption (Figure 11d) also exhibit minimal differences with varying *R* values. Figure 11f shows that the treatment cost of the SCWO system with OR slightly increased from 46.63 \$·t <sup>−</sup><sup>1</sup> at *<sup>R</sup>* = 1.5 to 48.89 \$·<sup>t</sup> <sup>−</sup><sup>1</sup> at *R* = 3, which motivates us to operate the SCWO system with a high *R* value for complete feed degradation.


**Table 7.** Electricity consumption for the SCWO system.

**Figure 11.** The effect of *R* on the treatment cost for the SCWO system with and without OR, (**a**) electricity consumption, (**b**) oxygen consumption, (**c**) equipment repairs and depreciation, (**d**) cooling water, transpiring water, and manpower consumption, (**e**) CO2 and hot water income, and (**f**) total treatment cost.

#### *5.4. Effect of the Feed Concentration*

The treatment cost for feed concentration between 2 wt% and 8 wt% is tested in this section under operating conditions, and the detailed results are listed in Table 3(B1–B4, D1–D4) and Table 7(B1–B4, D1–D4). When feed concentration increases, oxygen and transpiring water flow rates will also increase for feed degradation and reactor protection, and consequently, the electricity consumption of the pumps will also increase. However, reaction heat linearly increases with increasing feed concentration, and more heat can be recovered from the reactor effluent. Moreover, the preheating temperature of the feed at the starting and steady states can be reduced at a high feed concentration [40]. Thus, the total electricity consumption of the systems with and without OR decreased from 49.51 \$·t <sup>−</sup><sup>1</sup> and 49.80 \$·t <sup>−</sup><sup>1</sup> to 44.35 \$·<sup>t</sup> <sup>−</sup><sup>1</sup> and 43.91 \$·<sup>t</sup> <sup>−</sup>1, respectively, when feed concentration was increased from 2 wt% to 8 wt% (Figure 12a).

Oxygen consumption significantly increased from 6.00 \$·t <sup>−</sup><sup>1</sup> at *<sup>ω</sup>* = 2 wt% to 24.00 \$·<sup>t</sup> <sup>−</sup><sup>1</sup> at *ω* = 8 wt% (Figure 12b), and hot water income considerably increased from 48.6 \$·t <sup>−</sup><sup>1</sup> to 60.75 \$·<sup>t</sup> −1 (Figure 12e) in the SCWO system without OR. Thus, treatment cost can increase from 54.82 \$·t −1 at *ω* = 2 wt% to 57.93 \$·t <sup>−</sup><sup>1</sup> at *ω* = 8 wt% (Figure 12f). In the SCWO system with OR, when feed concentration was increased from 2 wt% to 8 wt%, the supplemental oxygen content increased from 3.26 \$·t <sup>−</sup><sup>1</sup> to 13.05 \$·<sup>t</sup> <sup>−</sup>1, respectively (Figure 12b), and hot water and CO2 income increased from 47.25 \$·t <sup>−</sup><sup>1</sup> and 1.57 \$·<sup>t</sup> <sup>−</sup><sup>1</sup> to 58.05 \$·<sup>t</sup> <sup>−</sup><sup>1</sup> and 7.22 \$·<sup>t</sup> <sup>−</sup>1, respectively (Figure 12e). Figure 12f shows that the treatment cost of the SCWO system with OR decreased from 54.27 \$·t <sup>−</sup><sup>1</sup> at *ω* = 2 wt% to 42.06 \$·t <sup>−</sup><sup>1</sup> at *ω* = 8 wt%. Thus, an increase in feed concentration is conducive to reducing both the energy consumption and the treatment cost of the SCWO system with OR.

**Figure 12.** The effect of feed concentration on the treatment cost for a SCWO system with and without OR, (**a**) electricity consumption, (**b**) oxygen consumption, (**c**) equipment repairs and depreciation, (**d**) cooling water, transpiring water, and manpower consumption, (**e**) CO2 and hot water income, and (**f**) total treatment cost.

#### **6. Conclusions**

In this work, a species recovery process for an SCWO system with a TWR was first proposed based on the solubility difference between oxygen and CO2 in high-pressure water. Thus, oxygen and CO2 can be separated and recovered from the reactor effluent to reduce operating cost.

A two-step separation process was first established using Aspen Plus software to increase species recovery rate. Then, 10 potential thermodynamic models for high-pressure separation were evaluated and selected. The detailed recovery rates of oxygen and CO2 were compared with the ideal results calculated from the experimental solubility data. The PSRK model was proven to be an appropriate thermodynamic model for predicting the separation process of the reactor effluent under a wide range of conditions. Accordingly, the detailed optimized parameters for species separation were obtained.

The SCWO processes with and without OR were simulated and economic analyses were conducted. Electricity, depreciation, and oxygen costs contribute to the major treatment cost of the SCWO system without OR, accounting for 46.18, 30.24, and 18.01 \$·t <sup>−</sup>1, respectively. When OR was introduced, oxygen cost decreased from 18.01 \$·t <sup>−</sup><sup>1</sup> to 9.77 \$·<sup>t</sup> <sup>−</sup>1, and additional CO2 income, which amounted to −5.65 \$·t <sup>−</sup>1, was gained due to OR. The total treatment cost considerably decreased from 56.80 \$·t <sup>−</sup><sup>1</sup> to 46.17 \$·<sup>t</sup> <sup>−</sup>1, with a reduction rate of 18.82%. Thus, OR contributes to reducing the treatment cost of an SCWO system. In addition, *R* and feed concentration increased and contributed to reducing the operating cost of the SCWO system with OR.

As a preliminary study of new SCWO system with OR, more experiments are needed to obtain more accurate results based on the simulation results in the future.

**Author Contributions:** F.Z. and C.M. put forward the idea of this work, F.Z. wrote this paper, J.C. conducted the simulation and calculation, F.Z., J.C., and C.S. contributed to the results analysis and post-processing.

**Funding:** This work is supported by National Natural Science Foundation (no. 51706049), Youth Innovation Promotion Association CAS (no. 2017412), and Science research project of Guangzhou City (201707010407).

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Nomenclature**


*Processes* **2018**, *6*, 224

#### *Greek letters*


#### *Subscripts*


#### **References**


© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Communication* **Analysis of the Excess Hydrocarbon Gases Output from Refinery Plants**

#### **Jerzy Szpalerski <sup>1</sup> and Adam Smoli ´nski 2,\***


Received: 8 March 2019; Accepted: 25 April 2019; Published: 1 May 2019

**Abstract:** The article presents the ideas of maximizing recovery of flare gases in the industrial plants processing hydrocarbons. The functioning of a flare stack and depressurization systems in a typical refinery plant is described, and the architecture of the depressurization systems and construction of the flares are shown in a simplified way. The proposal to recover the flare gases together with their output outside the industrial plant, in order to minimize impact on the environment (reduction of emissions) and to limit consumption of fossil fuels is presented. Contaminants that may be found in the depressurization systems are indicated. The idea presented in the article assumes the injection of an excess stream of gases into an existing natural gas pipelines system. A method of monitoring is proposed, aiming to eliminate introduction of undesirable harmful components into the systems.

**Keywords:** refinery plants; industrial gas streams; petrochemical processes; waste gases

#### **1. Introduction**

There are many technologies applied in processing of so widely understood charges [1–5] in the industrial plants dealing with crude oil or individual hydrocarbons processing. Typical production processes carried out in the refineries are: fractional distillation process, catalytic cracking process, gasoline reforming process, diesel/oil/hydro desulfurization, hydrocracking, gasoline isomerization, asphalt oxidation, and storage of raw materials, semi-finished products and finished products [1–5].

As a part of the refinery, many auxiliary processes are carried out, without which a modern refinery could not function, particularly the refinery in which extended are conversion processes that lead to greater destruction of the hydrocarbon chain. Among these processes, the following should first and foremost be distinguished [5–7]:


Refineries produce engine fuels, lubricating oils, other commercial fluids, and some refinery products are used as raw materials for petrochemical processes. This is particularly important when the refinery plant is integrated with a petrochemical plant [8]. Among the petrochemical processes, above all should be mentioned [9–12] production of olefins, butadiene, aromatic hydrocarbons, cumene, or ethylene oxide.

These processes aim at obtaining products that comply with the relevant technical specifications (technical conditions, company standards, national standards, etc.). During almost every production process various types of by-products (gaseous, liquid) are formed. Great efforts are made to achieve a situation in which the by-products are utilized within the production plants. In the case of gas products, the aim is also to direct them to a further process. However, there is always a certain amount of gas products that for various reasons cannot be utilized. Some of these streams are directed to the flare stacks. These are not only streams coming from production but also streams from the preparation of entire production units to stop (e.g., repairs), from commissioning, and finally from the preparation of individual apparatus for repair, revisions provided for by law, system switching, etc.

It happens that industrial gas streams, directed to depressurization systems, are full-value products which should be used outside the plants as a fuel for energy combustion whether they cannot be used in the industrial plant at the moment.

The aim of this paper is to analyze the possibilities of maximizing recovery of the flare gases in the hydrocarbon processing plants.

#### **2. Flare Gases in the Industrial Plants**

Some of the gas streams coming from various types of operations, carried out on production installations, are directed to the flare stacks. However, it is not possible to consider the flare stack as isolated from the entire depressurization system that is understood as a system of pipelines and collectors with assigned devices supplying the flare gases to the flare [13,14]. The flare stacks are applied and used most often in the crude oil and natural gas mining facilities, refineries, coke plants, chemical plants, and garbage dumps. The flare stack (utilization flare) is a device, usually in a shape of a stack, which burns the not utilized gas (generally), or which excess is impossible (or inexpedient) to be managed or stored at a given moment. Gas combustion in flares is mainly used in order to:


In the most commonly used open flares, gas combustion takes place at their outlet [14]. In the closed flares, gas combustion takes place in the inlet (lower) part of the flare. In view of construction, it is usually a stack supported by a lattice tower supporting system with a triangle or a square base. What distinguishes it from a typical stack it is the use of a different technological solution. In the typical solutions, the stack serves for removal of waste gases that remain after the process, e.g., combustion. In case of the flare stack, it is used to transport the waste gases coming from the technological processes that will be burnt with open fire at its outlet, by the burner installed there. For this reason, the entire technological system of the flare consists of a knockout drum with liquid seal drum located at the base, constituting the beginning of the stack pipe, which can be a self-supporting element, self-supporting with guy-ropes or can be supported on the tower lattice structure. In the upper part of the stack pipe there is a head part with a molecular seal, protecting against the penetration of fire inside the stack and equipped with a main burner in which the waste gases are burnt. The discharge gases ignite from the "pilot" burner, on which a burning flame is continuously maintained. The flares are devices classified to the first class of reliability. Properly functioning discharge and flare systems guarantee safe operation of production plants, they can be described as the most important safety valve. The unavailability of the flare stack disqualifies the work of the installation connected to it. For economic reasons and space saving, in practice, a single flare is usually dedicated to many installations. Therefore, the failure of one flare generates the need to stop not only the flare itself, but also all installations associated with it. To avoid similar situations, the principle of combining discharge systems is used—several flares are connected into one system. It is possible after performing appropriate calculations of hydraulics for the discharge systems and proper design of water closures. In case of emergency, the flares with such a system are provided with the appropriate counter pressure at the outlet of the discharge gases of the individual installations. Figure 1 shows the flares most commonly applied in the hydrocarbon

processing plants. The flares serving for the largest amounts of discharges are most often supported by a lattice construction.

**Figure 1.** Division of flares in view of their construction: (**A**) self-supporting flare, (**B**) flare with guy-ropes, and (**C**) flare with a lattice construction.

The flares make the end of the entire depressurization systems. The beginning of such systems starts at the individual production installations from safety valves, built into the individual apparatus that are the equipment of a production installation in order to protect them against the uncontrolled pressure increase, flow increase, etc. The safety valves are connected by individual pipelines to gathering collectors that remove discharge gases beyond the installation. The collectors outputs the gases out of the installation usually pass through the tanks—knockout drums built into the battery limit of the installation. Most often, the liquid caught in the separators consists of valuable hydrocarbons or hydrocarbon fractions. Usually these hydrocarbons are recycled back to the process. Separation of liquid hydrocarbons takes place in the tanks, then they are returned for recycling by means of sloping systems. Collectors removing gases out of the installations without a liquid phase can send them directly to the flare from a single installation. An example of this are the acid gases, the so-called discharges with oxygen or other dedicated discharges, or they can be connected to the collective depressurization collectors with larger diameters, located in the direct vicinity of the flares. These collectors are connected to the water seal, whose role is to maintain the appropriate pressure in the installation line to the water seal and protect for back of flame. As a part of the water seal, there may be a separation chamber (KO) in which the liquid, entrained by the flare gas flowing through the closure, is separated again from the gas. Then, the flare gas leaves the separation chamber and is directed by a pipeline to the flare socket on which the flare stack is set. The flare gases are transported to the end (up) of the stack, on which the molecular seal is built, the task of which is to protect the discharge system against backing of the flame and aspiration of the ambient air into the system. The fire closure (with protect the system from fire flashback) is a part of the molecular closure. The molecular closure is made by the purging gas that can be fed into the flare gases line to the battery limit, to the discharge line behind the water closure or to the flare tube. Fuel gas or inert gas—nitrogen—may be used as a purge gas. The gas transported to the depressurization line or the flare tube is fed continuously in the appropriate projected quantity.

At the molecular closure, a main burner is installed in which the flare gases are burnt. The discharge gases ignite from the pilot burners installed on the main burner. The pilot burners burn all the time and are fed with external fuel gas or the fuel gas coming from the internal heating gas systems of a given refinery or another production plant, where waste flares are located. The fire closure as a part of the molecular closure is shown in Figure 2. The red line illustrates the flow direction of the discharge gases. The blow-through gas passes the same route. At the top of the fire closure there is a main burner of the flare with a flame atomization system, a pilot burner system and a fuel gas distribution system. In turn, a typical water closure is shown in Figure 3.

**Figure 2.** A fire closure as a part of the molecular closure.

**Figure 3.** A typical water closure in horizontal arrangement.

The flare stacks allow for utilization of a few up to several thousand Mg of discharge gases per hour, depending on the production systems for which needs they are designed [14]. In the case of very large hydrocarbon processing plants as well as for economic reasons and savings of investment areas (to a large extent also an economic effect in the aspect of infrastructure is achieved) multi-stack flares are designed and built. The economics of these solutions is that one lattice structure holds two, three, or a maximum of four stacks.

#### **3. Results and Discussion**

#### *3.1. Existing Solutions for Utilization of Discharge Gases*

Installation of flare gases disposal systems usually has a very good rate of return on the invested funds, because on the other side of the bill is unproductive burning of hydrocarbon gas, generating a physical loss for a given plant. On the other hand, the costs of recovery systems for discharge gases consist mainly of supply in energy media (electricity, circulating water, costs of servicing by operators, costs of ongoing repairs, costs of major repairs, costs of technical supervision, etc.). The legitimacy of the construction of systems recovering the flare gases is meaningful first of all when there is a large number of production installations operating within a given production plant. This results in more frequent preparation of the installation for repairs, commissioning of these installations, larger number of equipment which should be periodically prepared for overhaul, and a larger number of safety valves (sometimes safety valves allow a certain amount of gases to pass, with such situations appear particularly at the end of the period between scheduled repairs). The use of such systems is especially justified in the situations:


Figure 4 presents a simplified scheme of the installation recovering the discharge gases, recycled to the internal gas network system of the production plant.

**Figure 4.** *Cont.*

**Figure 4.** Diagram of the installation recovering the discharge gases, which are "recycled" to the internal heating gas network of the production plant.

In every case, the aim is to recover a large amount of flare gases. The analyzed configurations revealed wide differences in the profitability of gas disposal due to economic effects and the discussed conditions. For each process in the given installation, the most desirable condition is a durable, stable technological situation that does not generate the discharge gases. Without loss of a part of the load, or a part of the streams at various stages of the process in the installation, the profits are maximized, because the high-value products always have higher price than the discharge gases streams.

Practical design solutions in the field of recovery of the discharge gases are almost always identical. The differences are small and first of all come from structural differences. These differences are primarily visible in the construction of water closures (which can be oriented horizontally or vertically), in solutions regarding the used compressors, preparation of gas for compression, etc. Functioning of the gas recovery can be presented in several stages:


The problem is the quality of recovered streams, which can be very variable over time. By controlling individual streams through the use of existing pipelines or construction of dedicated pipelines, it is possible to control and maintain the stream of recovered gases with constant and more stable quality.

The discharge gases, after a few, usually two, stages of separation and compression liquid hydrocarbon, are directed to the internal fuel gas system, which is used for heating of the process furnaces [15,16].

#### *3.2. The Use of Surplus of Discharge*/*Flare Gases beyond Manufacturing Plants*

In production practice, there are technological situations during which it is not possible to use all the gas streams in the existing networks of fuel gas. In manufacturing plants, one type or several types of fuel gas networks, in the aspect of system-work pressure, can be used, which may differ in terms of gas pressure as well as its quality. During normal stable operation of the refinery, these systems are mostly largely balanced. The balancing can also take place in a different way, appropriate for a given refinery or a given production plant. There are refineries which do not have connection to an external gas source, e.g., natural gas. Then balancing may be made, for example, by evaporation of a portion of LPG and directing it to the main fuel gas collector. In the fuel gas system, the most common is participation of light hydrocarbons: C1, C2, and lower amounts of C3 and C4 hydrocarbons and H2. Introduction of a larger part of C3 and C4 hydrocarbons into the fuel gas network causes a number of difficulties in the functioning of such a system, especially in periods of low ambient temperatures. In this case, there is a risk of condensation of heavier hydrocarbons and problems with burners built on technological furnaces. There may also be problems on pilot burners of flares. However, there are also situations in which the excess fuel gas is unproductively burnt in flares—the system is imbalanced. An example of such a case is the failure of the production installation, which is a large consumer of the fuel gas. The average composition of the flare gas and natural gas is presented in Table 1 [17,18].


**Table 1.** The average composition of the flare gases and natural gas [17,18].

\* mg/m3; \*\* g/m3.

Based on the results presented in Table 1 it is possible to conclude that the amount of energy contained in the flare gases is 63.14 MJ/m3, while in the case of the natural gas stream it is 35.8 MJ/m3, whereas the calorific values are equal 48.33 MJ/kg and 46.09 MJ/kg, respectively. In case of simple injection of 1 Mg/h of the flare gas into the natural gas pipeline (with flow equals 30 Mg/h), the methane content will decrease to 94.97%, nitrogen slightly decrease to 1.77%, while hydrogen sulphide content increase to 8 ppm/kg. Gas density increase from 0.740 kg/m3 to 0.748 kg/m3. Based on that simple calculation it is possible to conclude that injection of the flare gases to the natural gas system is technically feasible. Thus, it is possible to inject the non-balancing flare gasses produced in the refinery plats into existing methane pipeline system. Moreover for the presented in Table 1 composition of flare gases the amount of produced CO2 in the combustion of 1 Mg of this gas is equal 2.91 Mg.

There are also refineries that are connected to the natural gas system. Then, during the shortage of own fuel gas caused by stopping for repair or emergency stop of the installation, which is a large producer of fuel gas, the refinery complements the shortages by supplying gas from the external natural gas network.

In periods in which the current technological system in the refinery generates surplus fuel gas, or surpluses of other gas streams—technological streams and hydrogen gas streams (as mentioned, these are situations related to the retention of individual production installations or their groups for renovation or when we are dealing with situations Emergency systems that generate such streams).

A solution that would eliminate the need for unproductive combustion of the discharge gas in utilization flares is to take them out of production plants and take them, for example, to an existing natural gas transmission pipeline or to a nearby heating plant or a combined heat and power plant burning gas for energy.

Before leaving the industrial plant, the gases have to be properly identified, catalogued and prepared. Among the most important activities that should be performed before implementing such solution, first of all it is necessary to specify the following:


Knowing the gas streams (based on the processes carried out) that can be found in a given depressurization system, one may initially determine the quality of these gases. Based on this knowledge, one may decide whether a given stream should be sent for recovery or it should be burnt in a flare stack. Knowledge about the gas streams makes possible determination of the composition of these gases (the content of individual hydrocarbons) by applying analytical methods dedicated for this purpose. As a consequence, it is possible to select carefully gases that should undergo recovery. Determination of the number of discharge gas streams will allow determining the output capacity of the discharge gas recovery installation. In the refinery plants located in Europe, this is not a problem as a result of provisions of Commission Regulation (EU) No 601/2012 of 21 June 2012 on the monitoring and reporting of greenhouse gas emissions pursuant to Directive 2003/87/EC of the European Parliament and of the Council. The most commonly used measurement method is ultrasonic measurement. The measuring systems were installed on the flare stacks, i.e., at the end of the depressurization system. This solution indicates all the flare gases (excluding inert gases). A better way to determine accurately the amount of discharge gases is to use the plant balance. This method is much more precise. Knowing the quantity and quality of these streams, you can determine their variability. This parameter allows to determine the impact of a given industrial gas stream on the quality of natural gas, transported by a specific pipeline system in the event, when the industrial gas was pumped into the transmission gas pipeline. The necessary condition is knowing the quality and quantity of natural gas flowing through the pipeline. It is also important to identify impurities in the industrial stream. In the case of high-methane natural gas, the content of CH4 is very stable and basically does not decrease below 95%. The other components are methane homologs—as their molecular weight increases, their share in the natural gas stream decreases. From the point of view of natural gas utility, it is very important to ensure in the stream of industrial gas, which could be directed to the transmission system, that there is as little as possible heavier hydrocarbons, nitrogen, and sulfur compounds.

A larger amount of heavier hydrocarbons is a threat of their condensation and subsequent problems during transport and use. Nitrogen is an inert gas which is unnecessary from the point of view of transport and use—it significantly reduces the calorific value of fuel. Sulfur compounds can be very toxic (e.g., hydrogen sulfide) and could pose hazards related to the safety of use, they also constitute a significant corrosion hazard. The above mentioned components are always undesirable from the point of view of the industrial use of natural gas, for example for the production of hydrogen and heating industrial furnaces.

#### **4. Conclusions**

The recovery and output of excessive flare gases (in order to use them for energetic purpose) beyond the production plants is a solution possible to introduce in case of refinery and petrochemical plants. There are situations in which there is a surplus of gases: heating, technological, or discharge ones, characterized by good quality. This solution is possible for production plants in the vicinity of which natural gas transmission pipelines exist.

The following are facts confirming the applicability of the assumptions described in the paper:


The demonstrated effects for industrial plants would at the same time limit the negative impact on the natural environment—the use of less fuels for steam production and a significant reduction in the flare gas burnt in the flares.

**Author Contributions:** Conceptualization, J.S. and A.S.; Methodology, J.S.; Investigation, J.S.; Writing—Original Draft Preparation, J.S. and A.S.; Writing—Review and Editing, J.S. and A.S.; Supervision, A.S.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Article* **Theoretical and Experimental Insights into the Mechanism for Gas Separation through Nanochannels in 2D Laminar MXene Membranes**

**Yun Jin 1, Yiyi Fan 1, Xiuxia Meng 1, Weimin Zhang 1,\*, Bo Meng 1, Naitao Yang 1,\* and Shaomin Liu 2,\***


Received: 4 September 2019; Accepted: 10 October 2019; Published: 15 October 2019

**Abstract:** Clarifying the mechanism for the gas transportation in the emerging 2D materials-based membranes plays an important role on the design and performance optimization. In this work, the corresponding studies were conducted experimentally and theoretically. To this end, we measured the gas permeances of hydrogen and nitrogen from their mixture through the supported MXene lamellar membrane. Knudsen diffusion and molecular sieving through straight and tortuous nanochannels were proposed to elucidate the gas transport mechanism. The average pore diameter of 5.05 Å in straight nanochannels was calculated by linear regression in the Knudsen diffusion model. The activation energy for H2 transport in molecular sieving model was calculated to be 20.54 kJ mol<sup>−</sup>1. From the model, we can predict that the gas permeance of hydrogen (with smaller kinetic diameter) is contributed from both Knudsen diffusion and molecular sieving mechanism, but the permeance of larger molecular gases like nitrogen is sourced from Knudsen diffusion. The effects of the critical conditions such as temperature, the diffusion pore diameter of structural defects, and the thickness of the prepared MXene lamellar membrane on hydrogen and nitrogen permeance were also investigated to understand the hydrogen permeation difference from Knudsen diffusion and molecular sieving. At room temperature, the total hydrogen permeance was contributed 18% by Knudsen diffusion and 82% by molecular sieving. The modeling results indicate that molecular sieving plays a dominant role in controlling gas selectivity.

**Keywords:** MXene; gas separation; Knudsen diffusion; molecular sieving; transport mechanism

#### **1. Introduction**

Gas separation techniques using membranes have many merits such as high efficiency, facile operation, and low energy cost. [1,2]. Traditionally, polymeric membranes are often used for this application while they suffer from the well-known problem of permeability–selectivity trade-off (e.g., the Robinson upper bound) [3]. Recently, two-dimensional (2D) material membranes have been developed for gas separation because it exhibits the promising potential to overcome the permeability–selectivity trade-off problem. Therefore, in the past decade, they have gained enormous attention [1,2]. Typically, the 2D nanosheets assembled lamellar microporous inorganic membranes have interlayer galleries which can provide abundant molecular pathways [4,5]. For this attractive property, a variety of the 2D materials have been exploited to assemble lamellar membranes for gas separation. From the current published studies, the 2D materials include layered double hydroxides (LDH) [6], graphene (GA) [7,8], MXene 2D materials [2,9], graphene oxide (GO) [10,11], tungsten disulfide (WS2) [12], molybdenum disulphide (MoS2) [13,14], and metal-organic frameworks (MOFs) [15,16] have been receiving particular interest.

Generally, MXene 2D materials are a large family of 2D carbides and nitrides with the general formula of M*n*+1X*n*T*x*, where M represents a transition metal, X is carbon and/or nitrogen, and T is referred to the surface termination [17,18]. Very recently, MXene-based 2D materials have been introduced to fabricate 2D lamellar membranes because of their tunable nanochannel width, excellent mechanical strength, and easy fabrication and integration [2,19,20]. It has been reported that the assembled MXene 2D membranes exhibit a range of attractive characters in separation, e.g., precise ion sieving [21], ultrafast water permeation [22], and gas separation [19,20].

Even a promising potential for highly efficient gas separation has been proved experimentally, to fully clarify the gas transportation mechanism in 2D lamellar membranes, the insight into the theoretical and simulation clarification is scarce and the corresponding investigation is still needed to be conducted. Especially, there are only a few reports on theoretical simulation of gas separation through 2D lamellar nanochannels [11,23,24] while the mechanism of transportation and separation of gas through 2D nanochannels is far away to be fully clarified. For example, Li et al. [24] studied various gas transportations in MXene nanogalleries with molecular dynamic (MD) simulations and activated and Knudsen diffusion being observed for gas diffusion in through MD simulations. Nevertheless, Fan et al. [20] found the gas transportation in MXene nanogalleries via the molecular sieving mechanism. Therefore, in order to optimize and promote the performance and the efficiency of the 2D MXene lamellar membranes, the gas transport mechanisms are still needed to be further clarified because they can efficiently provide the guidance for tuning the channel width of 2D lamellar membranes and gas molecule–channel wall interactions in gas transportation. That is due to the gas transportation mechanism in porous media is primarily related with pore diameter, pore geometry, and interconnectivity of the interlayer distance and defects in the 2D lamellar membranes structure [25].

In this work, the related parameters were firstly determined theoretically and then the gas transport modeling to permeate through different nanochannels was developed to reveal the diffusion of different gas molecules (H2 and N2) in 2D MXene lamellar membranes. The simulation results are well consistent with the experimental results and their significance to the gas diffusion (e.g., permeance and selectivity) was discussed. The structural effects from MXene nanochannels formed during MXene nanosheets assembling on the transportation of different gas molecules (e.g., size and mass) were studied. Moreover, we provided the effects of temperature, pore diameter of structural defects, and the MXene thickness of the lamellar membrane on hydrogen and nitrogen permeances.

#### **2. Experimental**

The MXene lamellar membrane was prepared using the similar method illustrated previously [20]. Briefly, Ti3C2Tx was synthesized by etching Ti3AlC2 powders using 50 wt% HF solution at a certain temperature followed with DMSO intercalation. The interlayer interaction became weak due to the removal of Al atom between layers, leading to a facile exfoliation to form MXene nanosheets under sonication. The suspended MXene nanosheets were deposited by filtration on the anodic aluminum oxide (AAO) support with a pore diameter of 200 nm (Whatman Co., Maidstone, UK) by a vacuum pump. The resultant MXene membrane was dried at 120 ◦C for 8 h in a vacuum oven to remove the water molecular between interlayers.

H2 and N2 gas permeances were derived from their gas mixture separation performance measurement through the MXene membrane supported on AAO [20]. The gas mixture permeance was carried out in a home-made device as reported previously [20]. The gas mixture containing 50 vol% H2 and 50 vol% N2 as the feed gas was supplied to the membrane feed side with a flow rate of 50 mL min<sup>−</sup>1, while the argon sweep gas with a flow rate of 40 mL min−<sup>1</sup> at a standard pressure was supplied to the sweep side. The exit gas from the membrane sweep side was transferred to an online GC (6890N, Agilent Technologies, Inc, Waldbronn, Germany) with TCD to measure the permeated H2 or N2 concentration. The permeance and mixture selectivity or separation factor were defined by:

$$F\_i = \frac{N\_i}{P\_{i1} - P\_{i2}} \tag{1}$$

where *Fi* is the permeance of the derived gas (*i*) (mol m−2s−1Pa−1), *Ni* is the molar flux of the gas (*i*) (mol m−<sup>2</sup> s<sup>−</sup>1), *Pi*<sup>1</sup> and *Pi*<sup>2</sup> are the partial pressures of gas (*i*) at the feed side and sweep side.

$$S = \frac{y\_{i2} / y\_{j2}}{y\_{i1} / y\_{j1}} \tag{2}$$

where *S* is the mixture selectivity or separation factor y*i*1, *yi*2, *yj*<sup>1</sup> and *yj*2, the volumetric fraction of the gas component *i* or *j* in the feed or permeated side gas mixtures, respectively. Experimental results are summarised in Table 1.

**Table 1.** Experimental results of the supported 800-nm-thick MXene lamellar membrane for temperature dependent nitrogen and hydrogen permeances measured from gas mixture separation performance tests [20].


The crystalline characteristics of MXene nanosheets and composite membranes were studied by XRD (Bruker D8 Advance with Cu-Kα radiation λ = 0.154 nm at 40 kV and 40 mA, in the 2 θ range 20–80◦ with a sacn step of 0.01◦). The surface topology, cross-section, and microstructures of the MXene lamellar membrane were investigated by using a field emission scanning electron microscopy (SEM, FEI Sirion 200, Philips, The Netherlands).

#### **3. Theoretical Models of Transport Mechanism**

We propose the presence of two kinds of gas transport nanochannels as illustrated in Figure 1. One is straight channels from structural defects and its width is assumed between 2 nm and the mean free path of transport gases (λ). Another nanochannel consists of randomly distributed nanoscale wrinkles and interlayer spacing between stacked MXene sheets and its width is between the kinetic diameter of gas molecular (Φk) and 2 nm. Accordingly, two mathematical models of the gas transport mechanism for the permeation through the MXene lamellar membrane can be proposed based on the different nanochannels width using the following assumptions:


(6) Gas mixture (hydrogen and nitrogen) transport through the membrane is under ideal conditions on which the actual separation factor is equal to the ideal selectivity and pure gas permeance is equal to the mixture permeance.

**Figure 1.** Schematic diagram of the two kinds of gas transport model through different nanochannels within the MXene lamellar membranes.

The relevant schematic transport models for gas permeation through the MXene lamellar membranes are shown in Figure 1. Here, H2 (kinetic diameter of 2.89 Å) and N2 (3.64 Å) diffuse through the MXene lamellar membrane based on our experimental results [20], so the geometrical structure of nanochannels derived from the structural defects and interlayer spacing plays a significant role in the gas transportation. For gas permeation, two transport models were proposed corresponding to the nanochannels in order to explain the experimental results. The Knudsen diffusion is assumed to occur within the straight nanochannels with the lager pore diameter (2 nm < *dp* < λ). The tortuous nanochannels are mainly composed of randomly distributed nanoscale wrinkles, inter-galleries, and interlayer spacing between stacked MXene sheets. Therefore, the gas transportation within tortuous nanochannels containing the interlayer spacing (Φ<sup>k</sup> < *dp* < 2 nm) is mainly related to the molecular sieving.

Therefore, for MXene lamellar membranes, both Knudsen diffusion and molecular sieving contribute to the total mass transportation. The total permeance can be written by Equation (3) as follows:

$$F\_{\text{total}} = F\_{\text{defracts}} + F\_{\text{interlayer}} = F \kappa\_n + F\_{\%} \tag{3}$$

Here, *Ftotal*, *Fdefects*, and *F*int *erlayer* are the total gas permeance, the gas permeance through straight nanochannels of structural defects, and tortuous nanochannels containing interlayer spacing, respectively. *FKn* and *Fg* are the permeances contributed by the Knudsen diffusion and the molecular sieving, respectively.

#### *3.1. Knudsen Di*ff*usion (KD) Model*

Typically, Knudsen diffusion dominates in the mesoporous nanochannels with the size range between 2 nm and the mean free path of transport gases (2 nm < *dp* < λ), i.e., the average distance a molecule traversed by collisions, which is comparable or larger than transport channels, transport falls in Knudsen regime [27]. The mass transport of gas may be described by Fick's first law as Equation (4).

$$J\_{K,i} = -qD\_K \frac{dc}{dz} = -\frac{qD\_K}{RT} \frac{dP}{dz} \tag{4}$$

where ϕ is the factor for structure geometrical effects by Equation (16). *R* is the ideal gas constant, *T* is the absolute temperature. Thus, the expression of Knudsen diffusion flux (*JK,i*) for gas can be obtained in terms of pressure gradient. In this case, *DK* is the Fick diffusion coefficient (Knudsen

diffusivity), which may be expressed as the product of a geometric factor by diffusion pore diameter and the velocity of gas molecules by Equation (5):

$$D\_{K,i} = \frac{d\_p}{3} (\frac{8RT}{\pi M})^{\frac{1}{2}} \tag{5}$$

where *dp* is the diffusion pore diameter, the velocity of diffusing molecules is given by the kinetic theory of gases, and *M* is the molecular weight of the diffusing gas. The geometric factor is 1/3 since only these molecules moving in the considered direction will be taken into account [28]. The expression for Knudsen diffusion flux (*JK,i*) obtained by combining Equations (4) and (5) is expressed as Equation (6).

$$J\_{K,i} = -\frac{q\alpha d\_p}{3} (\frac{8}{\pi MRT})^{\frac{1}{2}} \frac{dP}{dz} \tag{6}$$

The Knudsen diffusion permeance through a porous membrane can be determined after integration of Equation (6) over the membrane thickness (δ):

$$F\_{K,i} = \frac{f\_K}{\Delta P} = \frac{qd\_p}{3\delta} \left(\frac{8}{\pi MRT}\right)^{\frac{1}{2}}\tag{7}$$

In order to obtain the diffusion pore diameter (*dp*), Equation (7) was transformed to Equation (8) to reveal the gas permeance dependence on the temperature. Thus, the gas permeance in Knudsen regime is pressure-independent and decreases with temperature as indicated by Equation (8).

$$F\_{K,i}(\frac{8}{\pi MRT})^{\frac{1}{2}} = \frac{8\rho d\_p}{3\delta\pi MR}(\frac{1}{T})\tag{8}$$

#### *3.2. Molecular Sieving (MS) Model*

The molecular sieving model was firstly used for zeolite, which can be illustrated by the kinetic theory of gases [28]. In the small channels with the size range between kinetic diameter of gas molecular and 2 nm (Φ<sup>k</sup> < *dp* < 2 nm), channel size changes into the molecular dimensions and molecules are no longer as free as these in Knudsen diffusion. For simplification purpose, the individual gas molecular adsorption difference is not considered. This is a reasonable assumption for these gases with less adsorption like He, H2, and N2 than CO2. The molecular sieving flux can be expressed in terms of pressure gradient. As a result, the molecular sieving flux (*Js,i*) can be written as:

$$J\_{s,i} = -qD\_{sj} \frac{dc}{dz} = -\frac{qD\_{sj}}{RT} \frac{dP}{dz} \tag{9}$$

where *Ds,i* is the molecular sieving coefficient in the MXene laminates, which is given by Equation (10).

$$D\_{s,i} = \frac{l\_s}{Z} (\frac{8RT}{\pi M})^{\frac{1}{2}} \exp\left(-\frac{E\_{a\_s\xi}}{RT}\right) \tag{10}$$

where *ls* is the diffusion distance (the distance between two adjacent sites of the low energy regions), *Z* is the number of adjacent sites [28], and *E*a,*<sup>g</sup>* is the activation energy, which is required for molecules to surmount the attractive constrictions imposed by the nanochannels structure. However, the geometrical factor (1/*Z*) is the probability of a molecule moving in the direction under consideration. The expression of molecular sieving flux (*Js,i*) obtained by combining Equations (9) and (10) is shown as the following.

$$J\_{s, \dot{l}} = -\frac{q l\_s}{Z} (\frac{8}{\pi M R T})^{\frac{1}{2}} \exp(-\frac{E\_{a, \text{g}}}{R T}) \frac{dP}{dz} \tag{11}$$

The gas permeance for molecular sieving through a microporous membrane is obtained after integration of Equation (12) over the membrane thickness δ:

$$F\_{s,i} = \frac{I\_{s,i}}{\Delta P} = \frac{q l\_{s,i}}{Z \delta} \left(\frac{8}{\pi MRT}\right)^{\frac{1}{2}} \exp\left(-\frac{E\_{a\_s g}}{RT}\right) \tag{12}$$

Equation (12) reveals an exponential dependence of gas permeance on the temperature, which is different from that of the Knudsen diffusion model. Taking logs of the both sides of Equation (12) can obtain the activation energy *E*a,*<sup>g</sup>* and the diffusion distance.

$$\mathrm{Lnn}(F\_{s,i}T^{\frac{1}{2}}) = (-\frac{E\_{a,\emptyset}}{R})\frac{1}{T} + \mathrm{Ln}\left[\frac{qI\_{s,i}}{Z\delta}\left(\frac{8}{\pi MR}\right)^{\frac{1}{2}}\right] \tag{13}$$

The gas permeance tests at different temperatures were carried out, the activation energy *E*a,*<sup>g</sup>* and the diffusion distance in the logarithmic plots can be regressed based on experimental data by Equation (13).

#### *3.3. Di*ff*usion Contribution to Total Transport*

The fractional diffusion of Knudsen diffusion and molecular sieving, respectively can be expressed as the following Equations (14) and (15).

$$f\_{\rm Kn} = \frac{F\_{\rm Kn}}{F\_{\rm Kn} + F\_{\rm s}} = \frac{Zd\_p}{Zd\_p + 3l\_s \exp\left(-\frac{E\_{\rm af}}{RT}\right)}\tag{14}$$

$$f\_s = \frac{F\_s}{F\_{Kn} + F\_s} = \frac{3l\_s \exp\left(-\frac{E\_{x\_s}}{RT}\right)}{Zd\_p + 3l\_s \exp\left(-\frac{E\_{x\_0}}{RT}\right)}\tag{15}$$

The relative individual diffusion contribution to total transport from Equations (3), (7), and (12) obtained using Equations (14) and (15) can be used to determine the rate-dominated diffusion process.

#### **4. Results and Discussion**

#### *4.1. Morphology and Structure*

The scanning electron microscopy (SEM) images of the cross-section of MXene (Ti3C2Tx), high magnification over the cross-section of MXene membrane, external surface of MXene membrane, and low magnification of the cross-section of MXene membrane are displayed as Figure 2a–d, respectively.

As shown in Figure 2a, the MXene membrane was assembled by the stacked 2D MXene nanosheets. Additionally, the nanosheets exhibit plicate feature on their surface. We can find there are structure defects and interlayer spacing in the bulky membrane, which can provide channels for the gas transportation. It is worth noting that the inner-sheet structural defect is assumed to be correlated with straight channels. The tortuous nanochannels consist of randomly distributed inter-galleries and nanoscale wrinkles between the stacked nanosheets. Here, the 2D MXene laminar membrane with 800-nm-thickness was assembled and supported on the AAO substrate (Figure 2b), which shows similar morphology to that of other laminar materials such as GO membranes [11]. MXene (Ti3C2Tx) nanosheets were deposited as an outer layer on top of a porous AAO support with a pore diameter of 200 nm using the vacuum impregnation method to form the supported MXene membrane (Figure 2c,d).

**Figure 2.** SEM images of the cross-section of MXene (Ti3C2Tx) (**a**), the cross-section of MXene membrane (**b**), external surface of MXene membrane (**c**), and the cross-section of MXene membrane (low magnification) (**d**).

From the XRD patterns in Figure 3, the (002) plane at 6.6◦ can be used to determine the *d*-spacing between the MXene nanosheets and the monolayer thickness, which were calculated to be ~13.4 Å and 10 Å, respectively. These data are inconsistent with the previous publications [2,20]. Moreover, the interlayer spacing of MXene membrane is estimated to be ~3.4 Å (Figure 1), which is favorable to sieve small molecules such as hydrogen (kinetic diameter of 2.89 Å) and helium (2.60 Å).

**Figure 3.** XRD patterns of the MAX (Ti3AlC2) and MXene (Ti3C2Tx) membrane.

The determination of geometrical effects in nanochannel structure is quite critical for the gas transport mechanisms model, and which can be described by [16]:

$$\varphi = \frac{\varepsilon}{\tau} = \frac{\left(1 - \frac{a}{d}\right)}{\tau} \tag{16}$$

where ϕ is the geometrical effect of the porous structure (i.e., the ratio of the membrane porosity ε to the tortuosity factor τ), *a* is the thickness of monolayer thickness MXene lamellar which is ~10 Å, *d* is

T

the *d*-spacing of MXene laminates which is about ~13.4 Å, and τ is the tortuosity factor which can be approximated as the ratio of diffusion length to the MXene laminar thickness (Equation (17)).

$$
\pi = \frac{l\_s}{\delta} \tag{17}
$$

In our work, the thickness of the MXene laminar membrane is 800 nm. The tortuosity factor τ can be estimated to be ~1 for straight nanochannels of inner-sheet structural defects. Moreover, the tortuosity factor τ (τ > 1) in tortuous nanochannels must be calculated using molecular sieving model.

#### *4.2. The Experimental Nitrogen Permeance and the Parameters Regression of Knudsen Di*ff*usion (KD) Model*

There are two transport nanochannels in the MXene lamellar membrane, which are straight nanochannels from inner-sheet structural defects and tortuous nanochannels in wrinkles and inter-galleries contained interlayer spacing between stacked MXene sheets. Additionally, the gases transport mechanisms for the diffusion in porous membrane are primarily dependent on the transport channel width, geometry, and interconnectivity [2,25,28]. Therefore, it is extremely critical to figure out the dimension of the nanochannels before we perform the modeling. To calculate the interlay spacing, the XRD measurement was conducted.

We can determine from the XRD results (Figure 3) for MXene membranes that the interlayer spacing between the MXene sheets is 3.4 Å, which is smaller than the kinetic diameter of N2 (3.64 Å), CH4 (3.84 Å), C3H6 (4.30 Å), and C3H8 (4.50 Å) (Figure 4). Since we assume that the width of straight nanochannels are larger than the kinetic diameters of these gases, therefore, for the transport permeance of N2, CH4, C3H6, and C3H8, Knudsen diffusion is the main process in the gas transportation in the straight channels. Since straight nanochannels flow dominates gas permeation, the Knudsen diffusion modeling was performed by the ordinary least squares method using MATLAB 7.0 (The Math Works Inc., Natick, MA, USA) [29] to obtain the parameters in Equations (7) and (8) and the regression results were shown in Figure 5. We can observe that the calculations using the Knudsen diffusion model with the obtained parameters can fit the experimental data well with the resultant correlation coefficient up to 0.9966.

**Figure 4.** The kinetic diameter of gases (Φk) of different gases.

**Figure 5.** Comparison of the experimental data and the model predictions of the temperature dependent nitrogen permeance through the MXene lamellar membrane.

The values for the model parameters ϕ and *dp* can be obtained from the regression. Since the tortuosity factor τ in Equation (17) can be estimated to ~1 in inner-sheet structural defects because of the straight diffusion channels. The geometrical effects (ϕ) of the porous structure derived from Equation (16) is 0.25, and the average diffusion pore diameter *dp* is 5.05 Å which is larger than the kinetic diameter of N2 (3.64 Å), CH4 (3.84 Å), C3H6 (4.30 Å), and C3H8 (4.50 Å). It can be concluded that the Knudsen diffusion through straight nanochannels for nitrogen is reasonable.

#### *4.3. The Experimental Hydrogen Permeance and the Parameters Regression of Molecular Sieving (MS) Model*

The interlayer spacing width between the MXene sheets is 3.4 Å which is larger than the kinetic diameter of He (2.60 Å) and H2 (2.89 Å) (Figure 4), thus it is favorable to separate them by molecular sieving diffusion. Therefore, hydrogen transport mechanism models in the MXene membrane are the combined Knudsen diffusion and molecular sieving, which correspond to the diffusion through straight nanochannels of structural defects, and tortuous nanochannels contained interlayer spacing between stacked MXene sheets, respectively. Hence, H2 permeance is higher than N2 permeance in the temperature range of 295~593 K in experimental (Table 1 and Figure 6). For example, H2 and N2 permeances were 2.34 and 0.174 <sup>×</sup> 10−<sup>7</sup> mol m−<sup>2</sup> s−<sup>1</sup> Pa−1, respectively, at 295 K. The calculated separation factor or selectivity of H2/N2 was ~13 based on the gas permeance, and it greatly exceeded the Knudsen selectivity of 3.74 for H2/N2 pairs. It indicates the promising potential application of the 2D MXene membrane to separate H2 from its gas mixture. The modeling results show that molecular sieving plays a dominant role in the selectivity of gas separation.

**Figure 6.** Comparison of the experimental data and the model predictions of temperature dependent hydrogen permeance through the MXene lamellar membrane.

Hydrogen permeances through the MXene lamellar membrane were obtained via the gas permeation test as a function of temperature between 22 ◦C (295.15 K) and 320 ◦C (593.15 K) using the mixture of hydrogen and nitrogen with a flow rate of 50 mL (STP) min−<sup>1</sup> in the feed side of the membrane, and an argon sweep gas with a flow rate of 40 mL (STP) min−<sup>1</sup> on the permeate side to remove the permeated hydrogen and nitrogen. The values for the model parameters ϕ*, ls*, and *Ea,g* can be obtained by Equations (12) and (13).

The calculated activation energies (*Ea,g*) for H2 permeance displayed in Figure 6 is 20.54 kJ mol<sup>−</sup>1. Although the geometrical effects of the porous structure (ϕ) and diffusion distance (*ls*) are difficult to be determined in Equation (12), by performing the ordinary least squares method using MATLAB 7.0 (The Math Works Inc., Natick, MA, USA) [29] for regression, the value of multiple (*l*<sup>s</sup> × ϕ) can be calculated to be 1.73 <sup>×</sup> <sup>10</sup><sup>−</sup>12. In addition, <sup>ϕ</sup> is the ratio of the membrane porosity <sup>ε</sup> to the tortuosity factor τ (see Equation (16)) and the tortuosity factor τ is difficult to be determined in Equation (17) as transport nanochannels from wrinkles and inter-galleries between stacked MXene sheets. The calculations using the model incorporating the obtained parameters fit the experimental data well with the resultant correlation coefficient of 0.9966.

Molecular sieving mechanism is also generally characterized by the activated diffusion [24,30]. Thus, the hydrogen permeance contributed from molecular sieving increased while that from Knudsen diffusion decreased with the temperature increment, so leading to the total permeance change with temperature variation.

#### *4.4. Temperature Dependent Permeance and Relative Contribution to Total Gas Transport from Knudsen Di*ff*usion and Molecular Sieving*

To simulate the hydrogen or nitrogen gas permeance through the MXene lamellar membrane under the same conditions, we calculated the permeance using the models (Equations (7) and (12)) with the related regressed parameters and analyzed fractional diffusion to determine the rate dominates diffusion descripted by Equations (14) and (15) at different temperatures.

Figure 7 displays the gas permeance of hydrogen and nitrogen, and selectivity (H2/N2) between 260 and 700 K. We can see that the hydrogen permeances are higher than nitrogen. For example, the hydrogen and nitrogen permeance through the MXene lamellar membrane is 2.11 and 0.11 <sup>×</sup> 10−<sup>7</sup> mol m−2s−1Pa−<sup>1</sup> at 363 K, respectively. Moreover, the corresponding selectivity of H2/N2 is ~19 based on the gas permeance calculation. These results can be explained by the gas diffusion mechanism. The nitrogen transport is dominated by Knudsen diffusion through straight nanochannel of structural defects with the width of ~5.05 Å, while hydrogen transports based on Knudsen diffusion and molecular sieving through straight nanochannels of structural defects and tortuous nanochannels from interlayer spacing.

**Figure 7.** The gas permeance and selectivity (H2/N2) through the MXene lamellar membrane as a function of temperature.

Meanwhile, we can also see that in Figure 7 the nitrogen permeance decay upon the temperature increased in the range between 260 and 700 K. This trend can be ascribed to its Knudsen diffusion mechanism via which the permeance decreased with temperature as illustrated by Equation (7). Compared with nitrogen, the hydrogen permeance displays a different trend and reaches its maximum value of 2.12 <sup>×</sup> 10−<sup>7</sup> mol m<sup>−</sup>2s−1Pa−<sup>1</sup> at 408 K. However, the selectivity of H2/N2 is always enhanced. Such results can be interpreted by the joint effects of molecular sieving and Knudsen diffusion. From molecular sieving (Equation (12)), it reveals an exponential dependence of gas permeance on the temperature, which is obviously different from that of Knudsen diffusion. However, these trends are ascribed to the coverage of functional groups such as −OH and −O which will affect the adsorbed amount of hydrogen [31,32].

The effect of temperature on the fractional diffusion of Knudsen and molecular sieving for hydrogen permeance through the MXene membrane is depicted in Figure 8. It is clear that the molecular sieving permeances are always higher than these of Knudsen diffusion between 260 and 700 K. Meanwhile, the fraction of molecular sieving increases steadily from ~80% to ~88%, and the Knudsen diffusion fraction slightly decreases from ~20% to ~12%. On average, the molecular sieving diffusion is about four times higher than the Knudsen diffusion, which indicates tortuous nanochannels containing interlayer spacing that dominates the whole transport channels.

Figure 8 also reveals that the increase of temperature will result in the decrease of Knudsen diffusion permeance which is consistent with Equation (7). However, the molecular sieving permeance increases steadily upon the increase of temperature which reveals that temperature is more readily affected in the exponent than those in pre-exponential coefficients in Equation (12).

**Figure 8.** Temperature dependent fractional diffusion of H2 to total transport from Knudsen diffusion and molecular sieving. Note: Data point represents experimental data while the continuous line indicates model predictions.

#### *4.5. The E*ff*ect of the Di*ff*usion Pore Diameter of Straight Nanochannels on the Gas Permeance and Relative Contribution from Knudsen Di*ff*usion and Molecular Sieving to Total Gas Transport*

The change of the gas permeance and the transport fractions of Knudsen diffusion and molecular sieving with the pore diameter of the straight nanochannels can be calculated from Equations (7) and (12). According to the relationship between the gas permeance and the average pore diameter of straight nanochannels at 295 K as shown in Figure 9, the hydrogen and nitrogen permeances increase with the average diffusion pore diameter of straight nanochannels in the MXene lamellar membrane. We can see the hydrogen permeance increased from 1.955 to 9.0 <sup>×</sup> <sup>10</sup>−<sup>7</sup> mol m<sup>−</sup>2s−1Pa−<sup>1</sup> and nitrogen permeance 0.73 to 18.6 <sup>×</sup> <sup>10</sup>−<sup>8</sup> mol m<sup>−</sup>2s−1Pa−<sup>1</sup> with the pore diameter of straight nanochannel alternation between 0.364 and 1.0 nm, respectively. The discrepancy between them becomes more pronounced with the increasing average pore diameter of straight nanochannels. This reflects that the larger nanochannel of structural defects will enhance Knudsen diffusion more significantly for permeance through the MXene lamellar membrane. Therefore, the selectivity of H2/N2 decreases from 26 to 4.87 (close to 3.74 of the Knudsen selectivity). This indicates that it is important to reduce the average pore diameter of straight nanochannels resulting from structural defects to ensure the good gas selectivity.

**Figure 9.** The effect of the average pore diameter of straight nanochannels on gas permeance at 295 K.

Figure 10 shows the fractions of Knudsen diffusion and molecular sieving contribution as a function of the average pore diameter of straight nanochannels at 295 K. As can be seen, the increase of the average pore diameter of straight nanochannels from 0.364 to 1.0 nm enlarges the fractions of Knudsen diffusion permeance from 14% to 82%, while the fractions of molecular sieving decreases from 86% to 18%. Moreover, the characteristic pore diameter (*dc*) of 2.32 nm (23.2 Å) was also obtained from Figure 10, at which the Knudsen diffusion and molecular sieving equally share the transport. in addition, increasing the average diffusion pore diameter of defects more than such characteristic value (*d*c) will lead to the higher proportion of Knudsen diffusion than molecular sieving in the total permeance (Figure 10).

**Figure 10.** Average pore diameter of the straight nanochannels dependent fractional diffusion to total transport of Knudsen diffusion and molecular sieving for H2 at 295 K.

#### *4.6. The E*ff*ect of MXene Layer Thickness on the Gas Permeance and Fractional Di*ff*usion of Knudsen Di*ff*usion and Molecular Sieving*

The dependence of the gas permeance and the fractions of Knudsen diffusion and molecular sieving on the effect of the thickness was determined by Equations (7) and (12), and Figure 11 reveals the effect of the MXene membrane thickness on gas permeance at 295 K. From Figure 11 we can see that the decrease of the thickness of the MXene layer leads to the increase for both hydrogen and nitrogen permeances. For example, the hydrogen and nitrogen permeances increase from 1.60 to 13.7 and 0.081 to 0.675 <sup>×</sup> 10−<sup>7</sup> mol m−2s−1Pa−1, respectively, when the MXene thickness is reduced from 1000 to 20 nm. On the other hand, the H2/N2 selectivity maintains at 13.5. It indicates that the thinner MXene layer can lead to higher gas permeance for hydrogen or nitrogen (Equations (7) and (12)) but maintain the gas selectivity unaltered.

Figure 12 presents the transport fractions of Knudsen diffusion and molecular sieving to the total permeance as a function of the MXene membrane thickness (operated at 295 K). As can be seen, the decrease of the thickness of the MXene layer results in the substantial increase in the hydrogen permeance. However, the fractions of Knudsen diffusion and molecular sieving keep constant at around 18% and 82%, respectively. This can be explained from Equations (14) and (15), where the MXene membrane thickness does not affect the fractional diffusion to total transport from Knudsen diffusion and molecular sieving.

**Figure 11.** The effect of MXene membrane thickness on gas permeance at 295 K.

**Figure 12.** Effects of the MXene membrane thickness on the transport fractions from Knudsen diffusion and molecular sieving to total gas transport operated at 295 K.

#### **5. Conclusions**

In conclusion, in order to determine the diffusion mechanism of gases in the MXene lamellar membrane, we evaluated the hydrogen and nitrogen permeation properties as a function of temperature using the prepared membrane. We proposed Knudsen diffusion and molecular sieving through the respective straight nanochannels that stemmed from structural defects and tortuous nanochannels formed by interlayer spacing. Furthermore, we performed linear regression on the experimental data to obtain the model parameters values for the MXene lamellar membrane, which were further applied to explain the Knudsen diffusion and molecular sieving mechanisms for hydrogen and nitrogen transport. Based on the modeling results, we simulated the effects of temperature, the pore size of the structural defects, and the thickness of the lamellar MXene membranes on hydrogen and nitrogen permeances. The relative contribution of Knudsen diffusion and molecular sieving to the total hydrogen permeance was also investigated. The model provides insights into the dominant diffusion at different operational condition and geometry variables. The results of theoretical and experimental study show that molecular sieving through tortuous nanochannels plays a dominant role in controlling the gas selectivity.

**Author Contributions:** Conceptualization, X.M., B.M. and S.L.; methodology, Y.J.; investigation, Y.F.; writing—original draft preparation, Y.J.; writing—review and editing, W.Z. and S.L.; supervision, N.Y.; funding acquisition, Y.J., W.Z., X.M., B.M., N.Y. and S.L.

**Funding:** The authors gratefully acknowledge the research funding provided by the National Natural Science Foundation of China (No. 21878179, 21776165, and 21776175), Project of Shandong Province Higher Educational Science and Technology Program (J18KA095) and Natural Science Foundation of Shandong Province (ZR2019MB056). S. Liu acknowledges the financial support provided by the Australian Research Council through the Discovery Program (DP180103861).

**Acknowledgments:** The authors would thank the facilities support from Analysis & Testing Center of Shandong University of Technology.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Nomenclature**



#### **References**


© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Article* **Simulation Study on the Influence of Gas Mole Fraction and Aqueous Activity under Phase Equilibrium**

#### **Weilong Zhao 1, Hao Wu 1, Jing Wen 2, Xin Guo 1, Yongsheng Zhang 1,\* and Ruirui Wang <sup>2</sup>**


Received: 28 December 2018; Accepted: 17 January 2019; Published: 22 January 2019

**Abstract:** This work explored the influence of gas mole fraction and activity in aqueous phase while predicting phase equilibrium conditions. In pure gas systems, such as CH4, CO2, N2 and O2, the gas mole fraction in aqueous phase as one of phase equilibrium conditions was proposed, and a simplified correlation of the gas mole fraction was established. The gas mole fraction threshold maintaining three-phase equilibrium was obtained by phase equilibrium data regression. The UNIFAC model, the predictive Soave-Redlich-Kwong equation and the Chen-Guo model were used to calculate aqueous phase activity, the fugacity of gas and hydrate phase, respectively. It showed that the predicted phase equilibrium pressures are in good agreement with published phase equilibrium experiment data, and the percentage of Absolute Average Deviation Pressures are given. The water activity, gas mole fraction in aqueous phase and the fugacity coefficient in vapor phase are discussed.

**Keywords:** gas mole fraction; activity; UNIFAC; phase equilibrium; threshold value

#### **1. Introduction**

Gas hydrate is a non-stoichiometric crystalline compound, which consists of a lattice formed by hydrogen bonds of water molecules as the host under high pressure and low temperature. Some gas molecules, such as methane, nitrogen, carbon dioxide and propane, are as the guest firmly surrounded by the crystal network formed by hydrogen bond of water molecules. The ice-like structure enables and stabilizes the existence of gas hydrates at higher temperatures and elevated pressures. The guest molecules must be of correct size to fit inside and stabilize the crystal lattice via weak van der Waals forces with the host water molecules [1,2].

Gas hydrate technology can be applied in many applications, such as gas storage and transportation [3], gas separation [4], refrigeration [5,6], etc. Meanwhile, in response to increasing carbon emissions, the hydrate-based gas separation (HBGS) has attracted the interest of researchers as an effective CO2 capture and storage (CCS) technology [7]. In the last couple of years, vast quantities of natural gas hydrate in the permafrost and deep seabed was found, which is twice as much as the amount of the other fossil fuels combined under a conservative estimate [8]; it makes natural gas hydrate as a kind of potential energy possible. However, gas hydrates can block oil and gas pipelines with high pressure and low temperature inside subsea oil and gas flow line [9]. Furthermore, the methane trapped in gas hydrates is a potent greenhouse gas [10]. In order to solve these problems, scholars conducted a lot of studies and found that adding thermodynamic inhibitors can effectively change the conditions of hydrate formation into higher pressure and lower temperature. On the contrary, the formation conditions of hydrate can be changed to lower pressure and higher temperature by adding thermodynamic promoters. Regardless of whether inhibiting or promoting hydrate formation, it is necessary to predict phase equilibrium conditions for the above-mentioned applications, and it is important to use reliable and accurate predictive models for predicting hydrate phase equilibria [11].

Among the gas hydrate predictive models of three-phase equilibrium, there are two common thermodynamic prediction models for calculating the phase equilibrium conditions. One is the classical statistical thermodynamic model of van der Waals and Platteeuw [12]. Some improved models designed for the phase equilibrium conditions in distilled water and aqueous solutions were proposed by Nasrifar et al. [13], Haghighi et al. [14], and Martin and Peters [15]. Another hydrate model is the Chen-Guo model [16,17], based on equality of the fugacity in the hydrate and vapor phases, which assumed the activity of water to be in unity and neglected the influence of gas solubility in aqueous phase. However, the changes in water activity caused by the solubility of gases, especially acid gases (e.g., carbon dioxide and hydrogen sulfide), and the addition of inhibitors/promoters should not be ignored [18,19]. Therefore, a better precision activity of water in aqueous phase is the key to improving the Chen-Guo model. Ma et al. [20,21] used the Patel–Teja equation of state (PT EoS), coupled with the Kurihara mixing rule and the one-fluid mixing rule to calculate the water activity in aqueous phase. Sun and Chen [18] combined the modified method introducing the Debye–Hückel electrostatic contribution term with the PT EoS to predict the nonideality of aqueous phase including ionic components. Liu et al. [22] built a simple correlation to calculate the activity of water in methanol–water solutions of sour gases (CH4/CO2/H2S/N2), in which parameters were determined from the phase equilibrium data. Moreover, among the models of aqueous phase activity, the UNIQUAC model [23] and the modified UNIFAC model [24] were constantly used to calculate aqueous phase. Delavar and Haghtalab [25,26] used the Chen-Guo and UNIQUAC models, referring the Soave-Redlich-Kwong-Huron-Vidal equation of state (SRK-HV EoS) conjunction with the Henry's law, to calculate the gas hydrate formation conditions. Dehaghani and Karami [27] employed the predictive Soave-Redlich-Kwong equation of state (PSRK-EoS) along with the modified Huron-Vidal (MHV1) missing rule and UNIQUAC model to calculate fugacity and activity coefficient of water in equilibrated fluid phases. Klauda and Sander [19,28] applied the modified UNIFAC model and PSRK-EoS coupled with the classical mixing rules, and the results obtained were in good agreement with the experimental data.

However, regardless of using UNIFAC or UNIQUAC model to calculate aqueous phase activity, the gas mole fraction in aqueous phase must be obtained first. Thus, in previous literature [19,25–28], Henry's law was used to describe the gas solubility in aqueous phase, and the gas mole fraction in aqueous phase relied on simultaneous Henry constants, the partial molar volume at infinite dilute (presented by Heidemann and Prausnitz [29]) and vapor phase fugacity calculated by the equation of state. It should be noted that both Henry's law and the partial molar volume at infinite dilution are defined on the basis of an imaginary ideal system. Furthermore, some parameters used in calculating the Henry's law constants and the partial molar volume at infinite dilution are also obtained by regression under the assumed ideal state. As a result, when acid gases exist in the actual system, there is a deviation in the water activity and the gas mole fraction in aqueous phase. Therefore, the gas mole fraction in aqueous phase as a function of temperature and pressure is considered one of the factors that influence the phase equilibrium conditions in this work.

In order to minimize the adverse impacts of the Henry's law constants and infinite dilution partial molar volumes on the hydrate equilibrium conditions prediction, we fitted a correlation of gas mole fraction in aqueous phase according to experimental data. Moreover, the UNIFAC model [30–32] and the correlation proposed in this work were employed to calculate the activity coefficient of aqueous phase; PSRK [33–35] was used to calculate vapor phase fugacity, and the Chen-Guo model was used to calculate the fugacity of the hydrate phase. These models not only alleviate empirically fitting the intermolecular parameters required in the van der Waals and Platteeuw model but also avoid the calculation error of water activity caused by Henry's law and the infinite dilution partial molar volume. It is noteworthy that the framework proposed in this work only applied in the pure gas (such as CH4, CO2, N2 or O2) and distilled water system; the mixed gas system and the additive system will be further studied in future work. Finally, the results calculated are compared with the experimental data in literatures, and the calculated fugacity coefficient of vapor phase and water activity are given.

#### **2. Thermodynamic Framework**

To predict the phase equilibrium conditions of gas hydrate, the iso-fugacity rule is used in the three phases (vapor, water, and hydrate) and a fugacity approach is considered for both gas and water as:

$$f\_i^H(z, T, P) = f\_i^L(x, T, P) = f\_i^V(y, T, P) \tag{1}$$

where *f <sup>H</sup> <sup>i</sup>* , *<sup>f</sup> <sup>L</sup> <sup>i</sup>* and *<sup>f</sup> <sup>V</sup> <sup>i</sup>* are the fugacity of component *i* including water in the hydrate, liquid and vapor phases, respectively; *z*, *x* and *y* represent the mole fraction of component *i* in the hydrate, liquid and vapor phases, respectively. The fugacity of water in hydrate phase, *f <sup>H</sup> <sup>w</sup>* , is expressed as:

$$f\_w^H(T, P) = f\_w^{MT}(T) \times \exp\left(\frac{-\Delta\mu\_w^{MT-L}}{RT}\right) \tag{2}$$

where *f MT <sup>w</sup>* represents the fugacity of water in the hypothetical empty hydrate lattice and is assumed equal to the saturated vapor pressure of the empty hydrate lattice [36]; <sup>−</sup>Δ*μMT*<sup>−</sup> *L w* is the chemical potential difference calculated by the method of Holder et al. [37]; and *R* is the universal gas constant.

#### *2.1. Thermodynamic Model of Vapor Phase*

The PSRK group-contribution method is based on the SRK equation of state [38], which is used to calculate the fugacity of components in vapor phase, as:

$$P = \frac{RT}{v\_m - b} - \frac{a}{v\_m(v\_m - b)}\tag{3}$$

where *P* and *T* are the system pressure and temperature, respectively; *vm* represents the mole volume, which is obtained by solving the cubic equation derived from Equation (3), and the value is the same as the largest real root of the equation [35]; *a* and *b* are parameters of PSRK.

The parameters *ai* and *bi* of pure component *i* can be calculated from the critical properties *Tc,i* and *Pc,i*.

$$a\_i = \frac{0.42748 R^2 T\_{c,i}^2}{P\_{c,i}} f(T) \tag{4a}$$

$$b\_i = \frac{0.08664RT\_{c,i}}{P\_{c,i}}\tag{4b}$$

$$f(T) = \left[1 + c\_1 \left(1 - T\_r^{0.5}\right)\right]^2 \tag{5}$$

where *Tr* = *T*/*Tc*; the pure fluid parameter *c1* is taken from the study of Holderbaum and Gmehling [35]. The PSRK mixing rule is written as:

$$a = b \left[ \frac{RT \sum y\_i l n \gamma\_i}{A\_1} + \sum y\_i \frac{a\_i}{b\_i} + \frac{RT}{A\_1} \sum y\_i l n \frac{b}{b\_i} \right] \tag{6}$$

$$b = \sum y\_i b\_i \tag{7}$$

where *γ<sup>i</sup>* stands for the activity coefficient of component *i* calculated by UNIFAC model; the recommended value of *A*<sup>1</sup> = −0.64663 in PSRK model. The activity coefficient *γ<sup>i</sup>* is a correction factor that accounts for deviations of real systems from that of an ideal solution, which can be estimated from chemical models (such as UNIFAC). Thus, the fugacity coefficient is given by:

$$\ln \varrho\_i = \frac{b\_i}{b} (z - 1) - \ln \left[ z \left( 1 - \frac{b}{v\_m} \right) \right] - \sigma \ln(1 + \frac{b}{v\_m}) \tag{8}$$

$$
\sigma = \frac{1}{A\_1} \left( \ln \gamma\_i + \ln \frac{b}{b\_i} + \frac{b\_i}{b} - 1 \right) + \frac{a\_i}{b\_i RT} \tag{9}
$$

where *ϕ<sup>i</sup>* is the fugacity coefficient of component *i*; *z* = *Pvm/RT*.

#### *2.2. Thermodynamic Model of Hydrate Phase*

Chen and Guo [16,17] proposed a two-step hydrate formation mechanism for gas hydrate formation. The following two processes are considered simultaneously in the nucleation process of hydrate: (1) A quasi-chemical reaction process to form basic hydrate, and (2) an adsorption process of smaller gas molecules in the linked cavities of basic hydrate, which leads to the non-stoichiometric property of hydrate. The model is expressed as:

$$\,\_{1}f\_{i}^{H} = z\_{i}f\_{i}^{0} \left(1 - \sum\_{j} \theta\_{j}\right)^{\alpha} \tag{10}$$

where *zi* denotes the mole fraction of the basic hydrate formed by gas component *i,* and *zi* = 1 for pure gas; θ*<sup>j</sup>* represents the fraction of the linked cavities occupied by the gas component *j*; *α* is the ratio of linked cavities and basic cavities [39], which equals 1/3 for sI hydrates and 2 for sII hydrates, respectively.

$$\sum\_{j} \theta\_{j} = \frac{\sum\_{j} f\_{j} c\_{j}}{1 + \sum\_{j} f\_{j} c\_{j}} \tag{11}$$

where *fj* denotes the fugacity of component *j* in vapor phase calculated by PSRK method; *cj* stands for the rigorous Langmuir constant, which is calculated from the Lennard-Jones potential function.

In Equation (10), *fi <sup>0</sup>* represents the fugacity of component *i* in vapor phase in equilibrium with the unfilled pure basic hydrate *i* (∑*θ<sup>j</sup>* = 0) [21]. According to the Chen-Guo model, it can be calculated as:

$$f\_i^0 = \exp\left(\frac{-\sum\_j A\_{ij}\theta\_j}{T}\right) \left[A\_i^\prime \exp\left(\frac{B\_i^\prime}{T - C\_i^\prime}\right)\right] \exp\left(\frac{\beta P}{T}\right) a\_w^{\frac{-1}{\beta\_2}}\tag{12}$$

where *Aij* is the binary interaction coefficient which stands for the interplays between gas molecule *i* in the basic hydrate and gas molecule *j* in the linked cavities. *Ai* , *Bi* and *Ci* are the Antoine constants, as reported by Chen and Guo [17]. *β* is the function of water volume difference between that in the unfilled basic hydrate phase and the water phase, and the large cavity number per water molecule [20], *β* = 0.4242 K/bar for sI hydrates, *β* = 1.0224 K/bar for sII hydrates. *aw* is the activity of water in aqueous phase, which is calculated by the UNIFAC method. For sI and sII hydrates, λ<sup>2</sup> = 3/23 and λ<sup>2</sup> = 1/17, respectively.

#### *2.3. Thermodynamic Model of Aqueous Phase*

In order to calculate the activity coefficient of components in aqueous phase, the mole fraction of each component in aqueous phase should be obtained first. Therefore, gas is also considered to be a component in aqueous phase. The pressure-corrected Henry's law is employed to calculate the mole fraction of gas component in aqueous phase exclude water as:

$$f\_i^L(\mathbf{x}\_i, T, P) = \mathbf{x}\_i^L H\_i \exp\left(\frac{P \nabla\_i^{\infty}}{RT}\right) \tag{13}$$

where subscript *i* represents the gas component in aqueous phase; *Hi* is the Henry's constant of component *i*, given by the Krichevsky-Kasarnovsky correlation [36,40]; *Vi* <sup>∞</sup> is the infinite partial molar volume of the component *i* in water, given by Heidemann and Prausnitz [29].

With the phase equilibrium, the gas mole fraction in aqueous phase can be calculated by the correlation as:

$$x\_i^L = \frac{f\_i^V(y\_{i\prime}, T, P)}{H\_i \exp\left(\frac{p\overline{V\_i^{\infty}}}{RT}\right)}\tag{14}$$

Considering the presence of the additive, whether it is a promoter that raises the phase equilibrium temperature (pressure) or an inhibitor that lowers the temperature (pressure), the components in the liquid phase should be recalculated. Furthermore, Delavar and Haghtalab [25,26] point out that the mole fraction of each component in aqueous phases can be calculated as:

$$m\_i = \frac{\mathfrak{x}\_i^L (1 - wt\mathfrak{\ell}\mathfrak{\ell})}{M\_i} \tag{15}$$

$$m\_p = \frac{wt\%}{M\_p} \tag{16}$$

where *i* represents water and gas component; *p* represents the promoter (inhibitor); *Mi* and *Mp* are the molecular weight of component *i* and the promoter (inhibitor); *wt*% stands for the weight percentage of the promoter (inhibitor) in aqueous phase.

$$\mathbf{x}\_{i} = \frac{n\_{i}}{\sum n\_{i}} \tag{17}$$

where *i* represents water, gas components and the promoter (inhibitor); *ni* represents the mole fraction of water, gas component and the promoter (inhibitor) in aqueous solutions of a unit mass.

The activity coefficient of the components in aqueous phase is calculated by UNIFAC model [30–32], which consisting of the combinatorial and residual terms, as:

$$
\ln \gamma\_i = \ln \gamma\_i^{\mathbb{C}} + \ln \gamma\_i^{\mathbb{R}} \tag{18}
$$

where *γ<sup>C</sup> <sup>i</sup>* and *<sup>γ</sup><sup>R</sup> <sup>i</sup>* stand for the combinatorial term and residual term of component *i*, respectively. The combinatorial term takes the different sizes and shapes of the molecules into account.

$$\ln \gamma\_i^{\mathbb{C}} = \ln \frac{\psi\_i}{\chi\_i} + 1 - \frac{\psi\_i}{\chi\_i} - \frac{1}{2} Z q\_i (\ln \frac{\varrho\_i}{\theta\_i} + 1 - \frac{\varrho\_i}{\theta\_i}) \tag{19}$$

$$\psi\_i = \frac{\mathbf{x}\_i r\_i^{\frac{2}{3}}}{\sum\_j \mathbf{x}\_j r\_j^{\frac{2}{3}}} \tag{20a}$$

$$\varphi\_{i} = \frac{\mathbf{x}\_{i}r\_{i}}{\sum\_{j} \mathbf{x}\_{j}r\_{j}} \tag{20b}$$

$$\theta\_i = \frac{\mathbf{x}\_i \boldsymbol{q}\_i}{\sum\_j \mathbf{x}\_j \boldsymbol{q}\_j} \tag{20c}$$

where Z = 10; *j* represents all components in aqueous phase; *ϕ<sup>i</sup>* and *θ<sup>i</sup>* are the volume and surface area fraction of component *i*, respectively; *ri* and *qi* are the volume and surface area parameters of component *i*, respectively. They are calculated by the van der Waals volumes *Rk* and surface areas *Qk* of the individual group *k* using equations as follows:

$$r\_i = \sum\_k V\_k^{(i)} R\_k \tag{21a}$$

$$q\_i = \sum\_k V\_k^{(i)} Q\_k \tag{21b}$$

where *V*(*i*) *<sup>k</sup>* is the number of group *k* in component *i*. The volume *Rk* parameters and surface areas parameters *Qk* of group *k* are listed in Table 1.


**Table 1.** The UNIFAC group volume and surface-area parameters.

The residual term of component *i* in Equation (18) is replaced by the solution-of-groups concept [32] as:

$$\ln \gamma\_i^R = \sum\_k V\_k^{(i)} \left( \ln \Gamma\_k - \ln \Gamma\_k^{(i)} \right) \tag{22}$$

where *k* represents all groups in aqueous phase, including water; ln*Γ<sup>k</sup>* stands for the residual activity coefficient of functional group *k*; ln*Γ*(*i*) *<sup>k</sup>* is the residual activity coefficient of group *k* in the reference solution containing only component *<sup>i</sup>*. Both ln*Γ<sup>k</sup>* and ln*Γ*(*i*) *<sup>k</sup>* are calculated as:

$$\ln \Gamma\_k = Q\_k \left[ 1 - \ln \left( \sum\_m \theta\_m \Psi\_{mk} \right) - \sum\_m \frac{\theta\_m \Psi\_{km}}{\sum\_n \theta\_n \Psi\_{nm}} \right] \tag{23}$$

$$\theta\_{\rm nl} = \frac{Q\_{\rm m} X\_{\rm m}}{\sum\_{n} Q\_{n} X\_{n}} \tag{24}$$

$$X\_{ml} = \frac{\sum\_{\text{ll}} V\_{\text{m}}^{(j)} \mathbf{x}\_{i}}{\sum\_{j} \sum\_{k} V\_{k}^{(j)} \mathbf{x}\_{j}} \tag{25}$$

where *m* and *n* are the summations cover different groups of all components in aqueous phase; *i* in Equation (25) is the same as component *i* in Equation (22); *θ<sup>m</sup>* and *Xm* are the surface area fraction and the mole fraction of group *m* in the mixture, respectively. The group interaction parameter *Ψnm* proposed by Sander et al. [30] is described as:

$$\Psi\_{nm} = \exp\left(-\frac{u\_{nm} - u\_{mm}}{T}\right) \tag{26}$$

where *unm* and *umm* are the adjustable group interaction parameters (energy parameters). For each group–group interaction, the two parameters have the relation of *unm* = *umn*. The gas–gas group interaction-energy parameters *unm* and temperature range are given in Table 2.

**Table 2.** Gas–gas group interaction-energy parameters unm and temperature range.


<sup>a</sup> 56, 57, 58 and 60 are the group numbers of CO2, CH4, O2 and N2 in the UNIFAC group parameter list, respectively.

In order to properly describe the temperature dependence gas solubility, the correlation proposed by Sander et al. [30] has been used as follows:

$$
\mu\_{\text{gas}-water} = \mu\_0 + \frac{\mu\_1}{\left(\frac{T}{\mathcal{K}}\right)}\tag{27}
$$

where *u*<sup>0</sup> and *u*<sup>1</sup> are temperature-independent parameters, shown in Table 3.

**Table 3.** Constants for the calculation of gas–water interaction-energy parameters in the temperature range 273–348K.


#### **3. Calculation Procedure**

The equations described above were solved using codes generated by MATLAB 2014b (MathWork, Beijing, China). The calculation procedure to obtain the phase equilibrium conditions of gas hydrates is summarized in the schematic flow diagram shown in Figure 1.

**Figure 1.** Calculation procedure for the prediction of phase equilibrium pressures at given temperatures.

The percentage of the Average Absolute Deviation in Pressure (AADP) is calculated as:

$$AADP(\%) = 100 \sum\_{i=1}^{N} \left| \frac{P\_i^{\text{exp}} - P\_i^{\text{cal}}}{P\_i^{\text{exp}}} \right| / N \tag{28}$$

where *N* is the number of experimental points; *Pexp <sup>i</sup>* and *<sup>P</sup>cal <sup>i</sup>* stand for the experimental and calculated pressure, respectively.

#### **4. Results and Discussion**

When using the UNIFAC model to calculate the activity of gas components, the mole gas fraction in aqueous phase needs to be obtained first. Thus, the Henryconstant *Hi* and the infinitely diluted partial molar volume *Vi* <sup>∞</sup> are employed to calculate the gas mole fraction in aqueous phase. Heidemann and Prausnitz [29] provided a correlation for solving *Vi* <sup>∞</sup> as follows:

$$\frac{P\_{c,i}\overline{V\_i}^{\infty}}{RT\_{c,i}} = 0.095 + 2.35 \left(\frac{TP\_{c,i}}{c\_{11}T\_{c,i}}\right) \tag{29}$$

$$\omega\_{11} = \frac{\left(h^0 - h^s - P\_w^s \upsilon\_w^s + RT\right)}{\upsilon\_w^s} \tag{30}$$

where *c*<sup>11</sup> represents the cohesive energy for water, which was evaluated at each temperature from thermodynamic properties tabulated; *h*<sup>0</sup> is the molar enthalpy at the given temperature but at zero pressure, and *v<sup>s</sup> <sup>w</sup>* is the molar volume of the saturated liquid [29].

However, it is not convenient to get the infinitely diluted partial molar volume of gas in the actual system by the parameters regressed from the assumed ideal state system, especially in the industrial application. Moreover, the parameters are not available for a liquid of unknown components. Therefore, as one of the factors affecting the formation of gas hydrate, the gas mole fraction in aqueous phase cannot be obtained accurately.

As seen from Equation (14), when gas type was given, the gas mole fraction in aqueous phase is only a function of temperature and pressure in phase equilibrium. Furthermore, the gas mole fraction in aqueous phase should be a fixed value in the three-phase equilibrium, which is related to the phase equilibrium temperature and pressure. Therefore, when the equilibrium temperature and pressure are given, the gas mole fraction in aqueous phase can be found by interval search using the framework mentioned in this work. Remarkably, in the process of numerical calculation, due to the unreasonable search step length and the inevitable error of experimental data, a set of phase equilibrium temperature and pressure may correspond to multiple gas mole fraction values. In this case, the average of these values can be taken as the gas mole fraction in aqueous phase under the phase equilibrium. As a result, the correlation of the gas mole fraction in aqueous phase is fitted by temperature and pressure, which is determined by the experimental data and defined as:

$$\mathbf{x}\_i = a + b \times T + c \times P \tag{31}$$

where *a*, *b* and *c* are constants for gas component *i* + water system, given in Table 4.


**Table 4.** Parameters for the correlation of the gas mole fraction in aqueous phase.


**Table 5.** The phase equilibrium pressure and temperature range of experimental data and the Average Absolute Deviation in Pressure (AADP) for predicted results.

Although the correlation of the gas mole fraction in aqueous phase is simple and is multivariate linear form, its precision and calculation accuracy are satisfactory. The number of data points used in fitting Equation (31) and the R-Square are given in Table 4.

As shown in Figure 2, there are two kinds of tendencies for the fugacity coefficient in methane system with the increase of temperature. First, when the temperature is lower than 286.5 K, the fugacity coefficient decreases with the increase of temperature, which has the same tendency with the fugacity coefficient in the carbon dioxide system, although the reduction rate is smaller. On the other hand, when the temperature is higher than 286.5 K, the fugacity coefficient increases exponentially with the temperature increment, which can also be seen in nitrogen and oxygen systems, as displayed in Figure 2. It should be pointed out that the fugacity coefficient decreases with the temperature increment in carbon dioxide system. This indicates that carbon dioxide is more likely to yield to pressure when the temperature is below the critical temperature. Moreover, the exponential growth of the fugacity coefficient in the N2 and O2 systems is mainly related to the temperature increment.

**Figure 2.** The fugacity coefficient of CH4, CO2, N2 and O2 in vapor phase.

The mole fraction of CH4, CO2, N2 and O2 in aqueous phase under phase equilibrium condition is shown in Figures 3–5. In this work, the gas mole fraction was considered as one of the factors affecting the phase equilibrium. It represents the ratio of the number of gas molecules maintaining the three-phase equilibrium to the number of all molecules in aqueous phase.

×10−3

In Figures 3–5, under phase equilibrium condition, the gas mole fraction in aqueous phase decrease with temperature increment, and all the changing ranges are less than 1 × <sup>10</sup>−3. Therefore, there may exist a threshold value for the gas mole fraction in aqueous phase. In other words the hydrate will form when the gas mole fraction in aqueous phase reaches a certain threshold value. Furthermore, for methane hydrate, the results in this work are in agreement with the views of Walsh et al. [47] and Guo and Rodger [48]. Walsh et al. suggested that the threshold value of gas mole fraction triggering hydrate formation calculated by the molecular dynamics (MD) simulation was 1.5 × <sup>10</sup>−3. The threshold value is a reasonable explanation for reducing the temperature or increasing the pressure, which could effectively promote the formation of hydrate. This is because lowering the temperature or increasing the pressure will enhance the gas dissolution, which in turn causes the gas mole fraction in aqueous phase exceeding the threshold value, then hydrate forms.

**Figure 3.** The mole fraction of CH4 and water activity in aqueous phase.

×10−3 **Figure 4.** The mole fraction of CO2 and water activity in aqueous phase. In particular, although carbon dioxide has a large solubility in water, the mole fraction of carbon dioxide calculated in this work is not very large under phase equilibrium, as shown in Figure 4. It implied that the total number of carbon dioxide gas molecules in aqueous phase for maintaining the three-phase equilibrium is not large, and it may be much smaller than the sum of the gas molecules dissolved in the water. This is mainly because part of carbon dioxide gas molecules dissolved in water turn into carbonic acid, thus reducing the amount of carbon dioxide gas molecules existing in water. Meanwhile, the pH of aqueous phase will be changed, and affecting the activity of the water and phase equilibrium conditions.

**Figure 5.** The mole fraction of N2/O2 and water activity in aqueous phase.

In Figure 3, water activity increases with the increase of temperature, in the methane system, reaching a maximum value of 0.985 when the temperature is about 278 K, and then decreases rapidly with the increase of temperature. The general variation trend of the water activity in carbon dioxide system is similar to that of methane system, as seen in Figure 4. In addition, the water activity of the CO2 system reached its maximum value at about 279.5 K. However, the maximum water activity in the CO2 system is only about 0.5658, which is probably because of the effect of the carbonic acid. Nevertheless, the activity of water in aqueous phase decreases almost linearly with temperature increase in nitrogen and oxygen systems, as shown in Figure 5.

Figures 6 and 7 show the experimental and predicted phase equilibrium conditions for the single gas hydrate systems. The temperature range, pressure range and AADP (%) are listed in Table 5.

**Figure 6.** Experimental and predicted phase equilibrium conditions for CH4/CO2 + water systems. Sloan [41], squares -; Ma et al. [42], triangles Δ; this work, circles -.

Figure 6 shows the experimental and predicted phase equilibrium pressures for CH4 and CO2. It can be seen the predicted results for all the gas systems are in excellent agreement with the experimental data. It should be noted that the type of carbon dioxide hydrate structure was set to sI, and, because the carbon dioxide gas molecule is too big to be encaged in the linked cavities, the filling rate of the gas molecules in the linked cavities, *θj*, was set to 0, as described by Chen and Guo [16].

**Figure 7.** Experimental and predicted phase equilibrium conditions for N2/O2 + water systems. van Cleeff and Diepen [43], squares -, ; Mohammadi et al. [44], triangles ,; Duc et al. [45], stars ; van Cleeff and Diepen [46], circles -.

The experimental and predicted phase equilibrium pressures for N2 and O2 are displayed in Figure 7. The predicted phase equilibrium pressures are in good agreement with the experiment. It is especially noteworthy that, when calculating oxygen and nitrogen hydrate, the hydrate structure was set to sII, which was based on the ideas proposed by Chen and Guo [16]. This is because the gas molecules of N2 and O2 are small and have a high filling rate in the connected cavities.

The gas mole fraction in Figure 8 was obtained by inverse phase equilibrium data using the framework proposed in this work. The gas mole fraction threshold value for maintaining the three-phase equilibrium state is different to the critical gas concentration. The gas mole fraction threshold value calculated in this work does not contradict the critical gas concentration proposed by Zhang et al. [49]. They pointed out that there is a critical gas concentration in aqueous phase that can spontaneously nucleate in the induction period, and the critical gas concentration is calculated by the total amount of carbon dioxide consumed in vapor phase until hydrate nucleation.

**Figure 8.** The experimental data and the mole fraction of carbon dioxide in aqueous phase for CO2 + water systems under the phase equilibrium. The experimental data were reported by Ma et al. [42].

However, the phase equilibrium data cited in this work were recorded at the end of decomposition rather than in the preliminary stage of nucleation. Gas molecules entrapped in hydrate cage cannot be released totally, which was owing to memory effect [50]. Moreover, there theoretically exists a concentration difference as a force in mass transfer during hydrate nucleation and decomposition. Therefore, the gas mole fraction threshold value calculated in this work is less than the critical gas concentration.

In Figure 8, the threshold value of gas mole fraction achieves a maximum of 5.61 × <sup>10</sup>−<sup>3</sup> at 0 ◦C. A possible reason is that part of carbon dioxide molecules in aqueous phase react with water to form carbonic acid. When the temperature is above 0 ◦C and below 0 ◦C, the pressure increment and the temperature decrement become a dominant factor that results in more stability for the carbonic acid and less solubility of carbon dioxide, respectively. However, this analysis should be proved by further study.

Furthermore, since the correlation of gas mole fraction fitted in this work is a multivariate linear form, the trend of the gas mole fractions in Figures 4 and 8 are different. Therefore, a large number of accurate and reliable experiment data can effectively improve the prediction accuracy of the model in this work.

#### **5. Conclusions**

In this work, the Chen-Guo model coupled with the PSRK method were employed to predict phase equilibrium conditions of CH4, CO2, N2 or O2 in pure water systems. The gas mole fraction in aqueous phase is one of the factors that affect the phase equilibrium of gas hydrate proposed in this work. The gas mole fraction threshold value maintaining the three-phase equilibrium was obtained by reversed phase equilibrium data. Meanwhile, in order to obtain the water activity in aqueous phase, the correlation of the gas mole fraction threshold value in aqueous phase was fitted though UNIFAC model. The calculated water activity can effectively improve the accuracy of the prediction results, and the predicted results of this work are in good agreement with the experimental data reported in the references.

**Author Contributions:** conceptualization, W.Z. and Y.Z.; methodology, W.Z. and H.W.; software, H.W.; data curation, X.G.; project administration, Y.Z.; resources, Y.Z., writing—original draft preparation, W.Z..; writing—review and editing, J.W. and R.W.

**Funding:** This research was funded by the Young Teacher Capacity Improvement Fund of Henan Polytechnic University, grant number TM2017/02.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Article* **Calculation Model and Rapid Estimation Method for Coal Seam Gas Content**

#### **Fakai Wang 1, Xusheng Zhao 2, Yunpei Liang 1,\*, Xuelong Li 1,\* and Yulong Chen 3,\***


Received: 27 September 2018; Accepted: 12 November 2018; Published: 14 November 2018

**Abstract:** Coalbed gas content is the most important parameter for forecasting and preventing the occurrence of coal and gas outburst. However, existing methods have difficulty obtaining the coalbed gas content accurately. In this study, a numerical calculation model for the rapid estimation of coal seam gas content was established based on the characteristic values of gas desorption at specific exposure times. Combined with technical verification, a new method which avoids the calculation of gas loss for the rapid estimation of gas content in the coal seam was investigated. Study results show that the balanced adsorption gas pressure and coal gas desorption characteristic coefficient (*K*t) satisfy the exponential equation, and the gas content and *K*t are linear equations. The correlation coefficient of the fitting equation gradually decreases as the exposure time of the coal sample increases. Using the new method to measure and calculate the gas content of coal samples at two different working faces of the Lubanshan North mine (LBS), the deviation of the calculated coal sample gas content ranged from 0.32% to 8.84%, with an average of only 4.49%. Therefore, the new method meets the needs of field engineering technology.

**Keywords:** gas pressure; gas content; gas basic parameters; rapid estimation technology

#### **1. Introduction**

The basic parameters of coalbed gas are the foundation for preventing and controlling coal and gas outbursts [1]. As one of the most important basic parameters [2], coalbed gas content is employed to calculate the coal seam gas reserves, predicting gas emission from mines, and evaluating coal and gas outburst risk. Measuring coalbed gas content accurately is required to reduce the occurrence of mine disasters and the cost of mine gas hazard prevention [3]. At present, several methods have been proposed to determine the coalbed gas content, which can be roughly divided into direct and indirect methods. The indirect method is primarily used to calculate the gas content of coal through the Langmuir equation. However, this method has the disadvantages of in situ measurement process complexity and poor accuracy. Therefore, downhole direct measurement methods [4] have been widely used.

Many works have been conducted on theoretical and experimental studies of the estimation of gas content directly in the downhole. Jin and Firoozabadi [5] have studied phase behavior and flow in nanopores using density functional theory and various molecular simulations. Zhao et al. [1] present adsorption and desorption isotherms of methane, ethane, propane, n-butane, and isobutane, as well as carbon dioxide, for two shales and isolated kerogens determined by a gravimetric method. Bertard et al. [6] found that the early adsorption diffusion process of gas was proportional to the square of the time and formed a direct measurement method of coal seam gas content. This method predicts

the gas loss through the gas desorption data and derivation equation, thus laying the foundation for determining the gas loss in the sampling process. McCulloch et al. [7] simplified the Bertard method and proposed a United States Bureau of Mines (USBM) direct gas content estimation method that is the square root calculations of desorption time (√*t*), and is proportional to the cumulative desorption. Ulery and Hyman [8] proposed a modified determination method (MDM) based on the measurement of various gas pressures, then the ideal gas law is used to calculate the desorption of gases under standard temperature and pressure conditions (STP). Mavor et al. [9,10] established a process for the estimation of gas content based on the USBM direct assay. Saghafi et al. [11] showed that the initial desorption of coal gas has an exponential relation with time. Smith and Williams [12] proposed a technique for the direct estimation of coal sample gas content from exposed rotary boreholes. Chase [13] determined the coal gas content by plotting the gas desorption rate curve and the cumulative desorption curve using the least squares method. Sawyer et al. [14] found that it is difficult to obtain more accurate gas desorption amounts and the residual gas volume by prematurely breaking the coal sample during desorption. Chen et al. [15] found that using an equation or method that does not have a large correlation with the gas desorption feature to calculate the gas content is usually more error-prone. Chen et al. [16] concluded that the negative exponential equation method is more consistent with the gas desorption law in the initial stage of tectonic coal. Lei et al. [17] proposed a new method of improvement based on the Barrer equation method to improve the accuracy of gas content measurement in coal seams. Zhang et al. [18] determined the gas desorption law of coal samples in different gas pressure conditions by experiments and proved that the <sup>√</sup>*<sup>t</sup>* method is more consistent with the gas desorption law in the initial stage. Li and Yang [19] calculated and compared the gas loss in the sampling process using the graphic method and least squares method. The "Direct Gas Content Measurement Device (DGC)" [20–27], which was developed based on an empirical equation, can determine the gas content of coal. The calculation of the gas content is based on the law of gas desorption. The empirical equations of gas desorption in coal, which have been proposed by scholars throughout the world, are listed in Table 1.


**Table 1.** Coal gas desorption equations.

Barrer [28] concluded that adsorption-desorption is a reversible process, and the cumulative amount of gas absorbed or desorbed is proportional to the square root of time. Winter et al. [29] found that the change in the amount of desorbed gas over time can be expressed as a power equation. Wang et al. [30] believed that the gas desorption of coal with time is consistent with the Langmuir adsorption equation. Sun [31] confirmed that gas desorption in coal is mainly a diffusion process, and the change of desorption gas content with time can be expressed by a power equation. Others [32] believe that the decay process of coal gas desorption with time conforms to the exponential equation. Due to the extensive variety of research objects, the empirical equation has both reasonable and unreasonable components in revealing the law of gas desorption in coal [33]. Compared with the equations in Table 1, calculating the gas compensation amount is difficult due to three shortcomings. First, the definition of the gas desorption start time (zero time, as shown in Figure 1) is ambiguous. Second, the percentage of lost gas and original gas in the coal sample varies with different degrees of metamorphism and damage types. The model that is established by the derivation equation and the basic theory of the model have different assumptions, and the existence of these hypotheses may be

very contradictory to the real environment. Therefore, the best method for measuring the gas content is to avoid the calculation of the gas loss.

**Figure 1.** Diagram of gas desorption loss estimation.

In this paper, an analysis of the simulation results in gas desorption was performed, and a combination of numerical analysis and field tests were conducted. In order to improve the coalbed gas content measuring accuracy, a rapid method for determining the gas content in coal seams to avoid the calculation of gas loss was proposed for on-site measurement of the gas content in coal seams, which can accurately provide the basic parameters for mine safety production.

#### **2. Experimental Study**

#### *2.1. Experimental Apparatus*

To simulate the gas desorption process of a coal sample, a set of simulation equipment was designed and developed. A schematic of the experimental setup is shown in Figure 2.

**Figure 2.** Schematic of the experimental setup.

The experimental device consisted of four systems, the details of each system are listed as follows:


To eliminate the influence of temperature on the simulation results, the device could achieve a constant ambient temperature in the coal sample gas adsorption-desorption process.

#### *2.2. Coal Sample Preparation*

The coal sample was taken from the No. 3 coal seam in the Lubanshan North Mine (LBS), which is located in Yibin City of Sichuan Province, and primarily consisted of lean coal. The location of the mine is shown in Figure 3. The No. 3 coal seam of LBS is located in the lower part of the Shanxi Formation, with an average distance of 58.04 m from No. 9 and an average thickness of 6.71 m. Figure 4 shows a general sketch of the coal seams and a diagram of the coal seam gas pressure measurement. The roof of the coal seam is mudstone and sandy mudstone, and the bottom slab is sandy mudstone and dark gray sandstone.

**Figure 3.** Geographical location of the LBS.

**Figure 4.** General sketch of the coal seam and a diagram of coal seam gas pressure measurement.

Coal seam No. 3 was sampled and marked as N-3. According to the "Sampling of coal seams" [34], coal samples were taken from the same coal seam at the same location. Five samples of coal with different damage types were collected, each with a mass of 5 kg, and were sealed and sent to a laboratory for the preparation of experimental coal samples. According to the experimental requirements, the parameters, such as the hardiness coefficient, true relative density, and proximate analysis of the coal, need to be separately determined. Therefore, the coal sample collected at the site must be processed into a sample that satisfies these requirements. In addition, the gas pressure (1.2 MPa) and coal seam temperature (35 ◦C) in the N-3 coal seam were measured on site, as shown in Figure 4.

According to "Methods for determining coal hardiness coefficient" [35], a sample for the estimation of the coal hardiness coefficient was prepared as follows: A coal sample of 1000 g was crushed and screened using standard sieves with apertures of 20 mm and 30 mm. Next, 50 g of the prepared sample was weighed into 1 part, with one set for every 5 parts, and a total of 3 groups were weighed. The coal hardiness coefficient to be measured was applied.

According to "Proximate analysis of coal" [36], samples for proximate analysis and estimation were prepared as follows: 500 g of raw coal was crushed and sieved to create samples of coal with a size less than 0.2 mm, and placed in a ground jar for sealing. Three samples were prepared; each sample's weight exceeded 50 g.

According to "Methods for determining the block density of coal and rock" [37], samples for density analysis and determination were prepared as follows: 50 g of raw coal was crushed and sieved to create samples of granular coal with a size less than 0.2 mm, and kept in a grinding jar sealed for use. Three samples were prepared; each sample's weight exceeded 2 g. Figure 5 shows the coal sample processing flow and related test equipment.

**Figure 5.** Coal sample preparation processes.

The preparation process of the desorption coal sample is described as follows: The raw coal was crushed and sieved to create 200 g of granules with sizes that ranged between 1 mm to 3 mm sieves. All samples were placed in a dryer at 105 ◦C for 3 h. After cooling, the coal samples were placed in a container isolated from air and sealed for subsequent use. Details of the coal samples are listed in Table 2.


**Table 2.** Preparation of coal samples with different specifications.

The preparation of coal samples with different specifications was used to determine relevant parameters and the coal sample gas desorption characteristic coefficient (*K*t). Based on these indicators, the coal seam outburst risk assessment and coal seam classification could be carried out, and the index *K*t could be calculated.

#### *2.3. Experimental Procedure*

The coal sample gas desorption process simulation was conducted by employing the experimental device shown in Figure 2. Since the gas desorption environment of the sample was always maintained at a temperature of 30 ± 1 ◦C and a gas outlet pressure of 0.1 MPa during the measurement process, the gas desorption of the sample could be considered to be an isothermal and isostatic desorption process. Dried coal samples with a weight and particle size ranging from 60 g to 80 g and 1 mm to 3 mm, respectively, were firstly loaded into the coal sample tank. After loading the coal sample, the sample tank was sealed and vacuumed with a water bath temperature of 35 ◦C until the pressure was less than 20 Pa. Then, methane with a purity of 99.9% was inlet into the diffusion tank with a defined pressure. After that, the sample tank and diffusion tank were connected to permit methane gas flow into the sample tank and begin the adsorption process. The adsorption process was considered

finished only when the pressure in both the diffusion and sample tanks stayed constant, and this process usually continued for nearly 48 h. The gas desorption process began after the system pressure remained constant. The amount of desorption gas should be recorded every 30 s, and the test should be stopped after desorption for 30 min. The gas desorption capacity needs to be converted to the standard condition volume, and the conversion equation [38] is as follows:

$$\mathcal{W}\_{\rm t} = \frac{273.2}{101325(273.2 + t\_{\rm W})} (P\_{\rm atm} - 9.81h\_{\rm W} - P\_{\rm S}) \cdot \mathcal{W}\_{\rm t}^{\prime} \tag{1}$$

where *W*t is the total amount of gas desorption in the standard state (mL), *W*t' is the total gas desorption measured in the experimental environment (mL), *t*w is the water temperature in the tube (◦C), *P*atm is atmospheric pressure (Pa), *h*<sup>w</sup> is the height of the water column in the measuring tube (mm), and *PS* is the saturated water vapor pressure (Pa).

#### **3. Experimental Results**

#### *3.1. Related Parameter*

Proximate analysis is the main indicator for understanding the characteristics and the basic basis for evaluating the metamorphism of coal [36]. Elemental analysis is an important indicator for studying the degree of metamorphism of coal and estimating its carbonized product. It is also the basis for calorific calculation for coal as a fuel in industry. The proximate analysis indexes and elemental analysis indexes of the coal samples are listed in Table 3.

**Table 3.** Result of proximate analysis indexes and elemental analysis indexes.


Notes: *M*ad is the air dry basis moisture (%). *A*ad is the air dry basis ash (%). *V*daf is the dry ash-free basis of volatile content (%). *S*t,d is the true relative density (g/cm3). *Q*b,d is the calorific value (MJ/kg). *G*R,I is the clean coal bond index (dimensionless). *C*daf is the fixed carbon content (%). *H*daf is the dry ash-free basis hydrogen content (%). *O*daf is the dry ash-free basis oxygen content (%). *N*daf is the dry ash-free basis nitrogen content (%).

In practice, the outstanding predictive index is an important indicator for the identification of outburst-prone coal seams [39]. The characteristic indicator and the adsorption constants are key indicators for quantifying the adsorption-desorption characteristic of coal. Table 4 lists the measured data of the relevant outstanding indicators of N-3 coal, the characteristic indexes, and the coal gas adsorption constants.

**Table 4.** Relevant indicator measured data.


Notes: *f* is the coal hardiness coefficient (dimensionless). Δ*P* is the initial velocity of diffusion of coal gas (mmHg). *D*cf is the degree of coal fracturing (dimensionless), as shown in Table 5. *P* is the measured coal seam gas pressure (MPa). *TRD* is the true relative density of the coal sample (g/cm3). *ARD* is the apparent relative density of the coal sample (g/cm3). *n* is the ratio of the total volume of tiny voids to the total volume of coal (%). *a*ac and *b*ac are Langmuir adsorption constants; *a*ac is the maximum gas adsorption capacity (cm3/g) and *b*ac is the adsorption constant (MPa−1).

**Table 5.** Classification of the degree of coal fracturing.


The coal hardiness coefficient (*f*) reflects the ability of coal to resist damage, and can be employed to predict the ability to resist breakage and it's stability after drilling. When the index (*f*) exceeds 0.5, the coal has a strong ability to resist outburst. A comparison of Tables 4–6 reveals that *f* is 0.395 < 0.5, which indicates that coal N-3 is relatively easily destroyed under the gas pressure of 0.74 MPa.

**Table 6.** Thresholds of four indicators for the identification of outburst-prone coal seams.


The index (Δ*P*) of the initial velocity of the diffusion of coal gas is also one of the indicators for predicting the risk of coal and gas outburst [22,39], which can reflect the degree of gas emission from gas-filled coal bodies. Δ*P* is 29.1 mmHg > 10 mmHg, which indicates that the coal seam has a rapid dispersion and a strong destruction capability.

Porosity (*n*) refers to the ratio of the mass of a certain substance contained in a certain volume to the mass and volume of the same substance at a specified temperature. The porosity of coal is not only an important index for measuring the development status of pores and cracks in coal, but also an important factor that affects the adsorption and infiltration capacity of coal.

The adsorption constants *a*ac and *b*ac were measured using a high-pressure volumetric method to determine the coalbed methane adsorption constants *a*ac and *b*ac. The adsorption constants *a*ac and *b*ac are calculated from the Langmuir adsorption equation [32,33] as follows:

$$Q = \frac{a\_{\text{ac}} b\_{\text{ac}} P}{1 + b\_{\text{ac}} P} \tag{2}$$

where *Q* is the adsorption gas quantity (mL/g), *P* is the adsorption equilibrium gas pressure (MPa), and *a*ac and *b*ac are the Langmuir adsorption constants.

*a*ac and *b*ac are determined by the amount of coal gas sample adsorbed under different pressures. Therefore, the gas adsorption constant of the coal is an indicator of coal gas adsorption capacity. The physical meaning of *a*ac is the maximum gas adsorption capacity of coal.

#### *3.2. Gas Desorption Process*

The gas adsorption capacity of the coal samples was calculated based on the gas adsorption equilibrium pressures and the results for the related parameters, by using the Langmuir adsorption equation. The simulation results are shown in Figure 6.

**Figure 6.** Desorption curves under different adsorption equilibrium gas pressures.

As shown in Figure 6, with an increase in the gas pressure, the coal samples with a unit mass are located at the same time point. The amount of desorption gas also gradually increases, but its increase decelerates, which mainly resulted from the primary limitation of the internal pores of the coal body. In addition, the slope of the gas desorption curve decreases and gradually flattens as the exposure times of the coal samples increase, which can be attributed to the decrease of adsorption gas and the complexity of the pore structure of coal [14,16]. During the initial stage of exposure, the gas concentration in the large pores of the coal body was released outward, and the resistance to gas migration was large; whereas in the later stage, the primary factor for determining the gas release rate was the large number of pores and fractures in the coal. The diffusion coefficient of gas will be reduced by one to two orders of magnitude.

Figure 7 shows the relationship between the gas desorption amount and the time for different gas pressures.

**Figure 7.** Relationship between the gas desorption amount and the square root of desorption time.

As shown in Figure 7, the gas desorption amount and the square root of the gas desorption time are linear. However, with the extension of the exposure time of the coal sample, the slope of the straight line will slightly decrease. The square root of the distinct turning point at the gas desorption time is 1 min0.5 and 4.5 min0.5; since the start of desorption, the slope of the line gradually increased and then decreased. The square root of the gas desorption time exceeds 4.5 min0.5. The slope of the straight line is less than the slope of the straight line at the initial stage and gradually decreases; the square root of the gas desorption time falls between 1 min0.5 and 4.5 min0.5. As the gas pressure in the coal seam increases, the slope of the straight line increases. According to the calculation model of Winter [29], the change in the gas desorption rate with time can be expressed by an exponential equation [31] for certain other conditions as follows:

$$V\_{\mathbf{t}} = V\_{\mathbf{a}} \left(\frac{\mathbf{t}}{t\_{\mathbf{a}}}\right)^{K\_{\mathbf{t}}} \tag{3}$$

Then, Equation (4) can be derived as follows:

$$K\_{\rm t} = \frac{L\mathbf{n}V\_{\rm a} - L\mathbf{n}V\_{\rm t}}{L\mathbf{n}t - L\mathbf{n}t\_{\rm a}} \tag{4}$$

Here, *K*t is the gas desorption characteristic coefficient whose exposure time ranges from 1 min to 5 min (mL/(g·min0.5)). *<sup>V</sup>*<sup>t</sup> and *<sup>V</sup>*<sup>a</sup> are the gas desorption speed of coal samples with unit mass at the time *t* and *t*a, respectively (cm3/min). *t* and *t*<sup>a</sup> are the gas desorption time and time in min, respectively.

The results were converted to the amount of gas desorbed from the coal sample, and the index (*K*t) was determined by the least squares method. The results are listed in Table 7.


**Table 7.** Gas adsorption capacity and gas desorption characteristic coefficient (*K*t).

Notes: *P* is the gas pressure (MPa). *Q* is the adsorption gas quantity (mL/g). *K*<sup>t</sup> is the gas desorption characteristic coefficient whose exposure time ranges from 1 min to 5 min (mL/(g·min0.5)).

As shown in Table 7, under the same adsorption-balanced gas pressure, different desorption characteristic coefficient values gradually decrease from *K*<sup>1</sup> to *K*<sup>5</sup> with the exposure time of the experimental coal sample; that is, *K*<sup>1</sup> > *K*<sup>2</sup> > *K*<sup>3</sup> > *K*<sup>4</sup> > *K*5. First, this result is attributed to the gradual decrease of adsorbed gas and the decrease of the amount of available desorption gas. Second, with the accumulation of the amount of desorption gas in fixed space, the gas pressure in the fixed space and the pressure gradient in the coal gas gradually decrease. For the same gas desorption characteristic coefficient, *K*<sup>t</sup> gradually increases with an increase in the adsorption equilibrium gas pressure. The larger the adsorption equilibrium gas pressure, the larger the amount of gas adsorbed by the coal sample under the larger adsorbed gas pressure gradient. When the gas is desorbed into the fixed space, the larger the gas pressure gradient between the fixed space and the coal sample, the larger the amount of gas desorption per unit time.

As the exposure time increases, the gas pressure gradients between adsorbed-balance gas and fixed-space cumulative gas gradually decrease, and the amount of desorption gas will gradually decrease during the unit exposure time. Therefore, *K*<sup>t</sup> can be considered to be a reflection of the physical quantity of the gas desorption speed at different times.

#### **4. Discussion**

#### *4.1. Relationship between Gas Pressure and K*t

The relationships between gas pressure and gas desorption characteristic coefficients are shown in Figure 8.

**Figure 8.** Relationships between gas pressure and gas desorption characteristic coefficients.

As shown in Figure 8, the gas desorption characteristic coefficient (*K*t) also increases, and the increase in amplitude is gradually increased with gas pressure. Because coal is a natural adsorbent, the larger the adsorption pressure, the larger the amount of gas adsorption and the larger the amount of gas that is desorbed [31]. For the same gas desorption characteristic coefficient, the index (*K*t) gradually increases with an increase in the adsorption equilibrium gas pressure. According to the adsorption theory of Langmuir [33], under the action of the larger adsorption equilibrium gas pressure, the coal sample absorbs a larger amount of gas. When the gas is desorbed into the fixed space, a larger pressure gradient of desorption gas is generated between the fixed space and the coal sample to promote coal adsorption equilibrium gas desorption.

For the coal seam adsorption equilibrium gas pressure and the different *K*t respectively, trend fitting is available. The adsorption equilibrium gas pressure and the different *K*t are exponential equation relations and have good correlation, the coefficient of determination (*R*2) being higher than 0.97.

A comparison of the correlation curve of the gas pressure and *K*<sup>t</sup> at different exposure times is shown in Figure 8. The results indicate that *R*<sup>2</sup> decreases to a minor extent with the exposure time of the coal sample due to the deviation of the gas desorption amount error caused by the increase in the exposure time [16,40]. However, the *R*<sup>2</sup> of the coal sample gas desorption regression fitting curve remains greater than 0.97, the correlation of regression fitting curve is higher, and the result is reliable. Therefore, the gas pressure can be expressed as follows:

$$P = A\_{\mathbb{G}} \mathfrak{e}^{R\_{\mathbb{G}} K\_{\mathbb{G}}} \tag{5}$$

where *A*<sup>c</sup> and *B*<sup>c</sup> are the constants that correspond to different desorption times, which are dimensionless; *P* is the adsorption equilibrium gas pressure (MPa); and *K*<sup>t</sup> is the gas desorption characteristic coefficient that corresponds to different desorption times (mL/(g·min0.5)).

#### *4.2. Relationship between Gas Content and K*t

The relationships between gas content and gas desorption characteristic coefficients are shown in Figure 9.

**Figure 9.** Relationships between gas content and gas desorption characteristic coefficients.

As shown in Figure 9, the gas content increases as *K*t increases. Coal is a natural adsorbent with double pores and fissures. The larger the gas content, the larger *K*<sup>t</sup> is. According to the adsorption theory equation of Langmuir [32], the larger the gas content, the larger the gas pressure, and the larger the amount of adsorbed coal gas. The larger the amount of adsorption gas in the coal sample, the larger the index *K*t.

The coal seam gas content and *K*t have a linear equation relationship and an excellent correlation. The correlation coefficient of the regression fitting curve showed a slight decrease with the exposure time. With an increase in desorption time, the deviation of the desorption amount of the coal sample gas gradually increases. However, *R*<sup>2</sup> remains greater than 0.98, which means the regression fitting curve has higher correlation and the result is reliable. Therefore, the relationship between the gas content and *K*t can be expressed by Equation (6) as follows:

$$\mathcal{W} = \partial \mathcal{K}\_t + \mathcal{J} \tag{6}$$

where *∂* and *β* are the constants that correspond to different desorption times (dimensionless), *W* is the gas adsorption amount (mL/g), and *K*t is the gas desorption characteristic coefficients that correspond to different desorption times (mL/(g·min0.5)).

According to the adsorption theory equation of Langmuir [32], the gas pressure and gas content are not linear. To analyze the accuracy of the relationship between the coal seam gas pressure, and gas content and *K*<sup>t</sup> for different exposure times, *R*<sup>2</sup> at different times is compared and listed in Table 8.


**Table 8.** Comparison of the coefficient of determination (*R*2) under different exposure times.

As shown in Table 8, for *R*2, the *K*<sup>t</sup> used to describe the coal sample gas content and gas pressure at different exposure times can reach a high accuracy, especially when *K*<sup>t</sup> is used to describe the gas content. The maximum *R*<sup>2</sup> is 0.99146.

Although, with the extension of exposure time, the *K*<sup>t</sup> to describe coal gas pressure and gas content has a certain decrease; the decrease range is very small within 5 min, and the effect on the accuracy of the results is negligible.

#### *4.3. Technical Verification*

#### 4.3.1. Experimental Verification

Based on the experimental data, the method proposed in this paper was used to calculate and revise the gas content of the coal samples under two different pressures after exposure for 1 min, 2 min, 3 min, and 5 min. The calculation results of the gas content and the calculation deviation are listed in Table 9.


**Table 9.** Calculated values and deviation of gas content.

According to Table 9, the gas content calculated deviation range was from −9.14% to 0.6%, with an average of only 3.23%, which indicates that the new method can accurately calculate the gas content of coal samples. In addition, for the conditions of different adsorption equilibrium gas pressures, the calculated and measured gas content values are the smallest when the coal sample is exposed for 5 min, which indicates that the longer the exposure time, the closer the calculated gas content is to the measured value.

#### 4.3.2. Field Verification

To test the reliability of the new method, the gas content of the N-3 coal seam in the LBS was measured.

The test sites were the E2305 inlet and the northern inlet of the LBS. The downhole gas desorption apparatus was used to directly measure the downhole desorption section of the coal sample. The underground gas desorption operation flow chart is shown in Figure 10. A total of six groups were tested, of which E2305 entered the air in 5 samples and the north inlet entered the air in 1 sample. The gas content measurement and calculation results are listed in Table 10.

**Figure 10.** On-site gas desorption flow chart.


**Table 10.** Comparison of measured and calculated values of gas content.

Note: coal sample exposure time is 5 min.

As shown in Table 10, using the new method to measure and calculate the gas content of the coal seam at two different working faces of the LBS, the deviation of the calculated gas content ranged from 0.32% to 8.84%, with an average of only 4.49%. The main reasons for this finding are as follows: The coal sample is impure when it is collected by the method of powdered coal through holes due to coal expansion. Therefore, as the exposure time of the shallow coal bodies increases and the desorption rate of gas decreases, the *K*<sup>5</sup> calculated from the desorption law is also smaller than the actual *K*5, which directly causes the calculated gas content of the new method to be smaller than the real gas content. When the indirect method is used to determine the gas content, the desorption rate that is measured at the site is less than the real desorption rate and will only affect the calculation of the loss and a part of the desorption amount, and the influence on the gas content of the raw coal is small.

Temperature has a significant effect on the gas adsorption and desorption of coal. The isothermal adsorption-desorption experiment is performed in the condition of the coal seam temperature, which is easily controlled. In field applications, there is a temperature difference between the coal seam and the roadway due to the cooling effect of the roadway; that is, the temperature of the coal sample changed when it was removed from the coal seam. This weak temperature change will have a certain influence on the coal gas content measurement results. From the point of view of field applications, the temperature difference between the coal seam and the roadway has a minimal effect on the final result of the new method.

The difference between the measured value and the calculated value using the new method of the gas content of coal sample exposure within 5 min is not distinct. The results conclude that the new method can accurately estimate the gas content of the coal seam in a field application, and the accuracy satisfies engineering needs. Therefore, the "calculation model and rapid estimation method of coal seam gas content" can be implemented in the field.

#### **5. Conclusions**

This paper is based on the analysis of simulation results in gas desorption and applies a field application for the investigation. A set of independently developed experimental apparatus was used to measure the gas desorption process of coal with a particle size of 1–3 mm in the N-3 coal seam of the LBS to research the relationship between the gas desorption law and the gas content. According to the specific exposure time of the gas desorption, the eigenvalues of the rules, and the establishment of a method for the rapid estimation of gas content in coal seams, the following main conclusions are obtained:


In this paper, the method for the calculation model and the rapid estimation of the gas content is simple and concise with respect to the operation and measuring accuracy. Changes in the ambient temperature of the test site will have an impact on the accuracy of the final result during field application, but this effect is negligible.

**Author Contributions:** Conceptualization, Y.L.; Methodology, X.Z.; Validation, F.W.; Formal Analysis, X.L.; Data Curation, Y.C.; Writing-Original Draft Preparation, F.W.; Writing-Review and Editing, X.L.; Funding Acquisition, Y.L.

**Funding:** This research received no external funding.

**Acknowledgments:** This study is financially supported by the National Science and Technology Major Project of China (Grant No. 2016ZX05043005), the State Key Research Development Program of China (Grant No. 2016YFC0801404 and 2016YFC0801402), and the National Natural Science Foundation of China (51674050), which are gratefully acknowledged. The authors also thank the editor and anonymous reviewers for their valuable advice.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Nomenclature**


#### **References**


© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Article* **The Influence of Sorption Pressure on Gas Di**ff**usion in Coal Particles: An Experimental Study**

**Xin Yang 1,2,3, Gongda Wang 2,3,4,\*, Junying Zhang 2,3 and Ting Ren <sup>4</sup>**


Received: 10 February 2019; Accepted: 9 April 2019; Published: 16 April 2019

**Abstract:** Gas pressure changes during the process of coal mine gas drainage and CBM recovery. It is of great importance to understand the influence of sorption pressure on gas diffusion; however, the topic remains controversial in past studies. In this study, four samples with different coal ranks were collected and diffusion experiments were conducted under different pressures through the adsorption and desorption processes. Three widely used models, i.e., the unipore diffusion (UD) model, the bidisperse diffusion (BD) model and the dispersive diffusion (DD) model, were adopted to compare the applicability and to calculate the diffusion coefficients. Results show that for all coal ranks, the BD model and DD model can match the experimental results better than the UD model. Concerning the fast diffusion coefficient *Dae* of the BD model, three samples display a decreasing trend with increasing gas pressure while the other sample shows a V-type trend. The slow diffusion coefficient *Die* of BD model increases with gas pressure for all samples, while the ratio β is an intrinsic character of coal and remains constant. For the DD model, the characteristic rate parameter *k*Φ does not change sharply and the stretching parameter α increases with gas pressure. Both *Dae* and *Die* are in proportion to *k*Φ, which reflect the diffusion rate of gas in the coal. The impacts of pore characteristic on gas diffusion were also analyzed. Although pore size distributions and specific surface areas are different in the four coal samples, correlations are not apparent between pore characteristic and diffusion coefficients.

**Keywords:** gas diffusion; gas pressure; unipore diffusion model; bidisperse diffusion model; dispersive diffusion model

#### **1. Introduction**

During the process of coal mine gas drainage and CBM recovery, the gas flow process can be divided into two stages. First, driven by the concentration gradient force, the gas adsorbed on the surface of coal matrix desorbs and then diffuses into the fracture/cleat system of coal. Second, the dissociative state gas permeates to the surface well or the underground borehole driven by the pressure gradient force. Therefore, two key factors that affect net gas movement result are the gas diffusion coefficient and the gas permeability in the fracture. The diffusion coefficient represents the essential parameter of diffusibility and related studies show that it could be affected by temperature [1,2], moisture [3], pressure [4,5], gas type [6–8], sample size [6,9,10], and coal sample features [11,12]. It should be noted that the coal seam gas pressure is in the dynamic condition during gas extraction. Hence, it is of great significance to understand the impact of pressure on the gas diffusion coefficient. Several research

papers on this topic have been conducted but arguments can be found on how gas pressure impacts the diffusion coefficients.

For example, some scholars believe that the diffusion coefficient is in direct proportion to gas pressure. Charrière et al. [2] used CH4 and CO2 to conduct the adsorption kinetics experiments when the pressure is equal to 0.1 MPa and 5 MPa respectively. They found that the diffusion coefficient increases with gas pressure. Pan et al. [3] performed CH4 adsorption/desorption diffusion test within 0~4 MPa pressures range, and results show a direct ratio between diffusion coefficient and gas pressure. Jian et al. [13] carried out the desorption experiments within 0~4.68 MPa pressure range and the conclusion remains the same. However, some scholars reckon that the diffusion coefficient decreases with the increase in pressure. Cui et al. [8] found that the diffusion coefficient of CO2 reduces when gas pressure is smaller than 3.6 MPa. Staib et al. [4] conducted the adsorption kinetics experiments and analyzed the results using the BD model. It was found that the diffusion coefficient *Da* lowers when the pressure increases. Shi et al. [14] tested the influence of CO2 injection on microporous diffusion coefficient after the adsorption of CH4 was balanced. Findings show that the increasing injection pressure of CO2 would cause the reduction of micropore diffusion coefficient. There are also a few scholars who concluded that gas pressure has small effects on the diffusion coefficient. Nandi et al. [15] conducted CH4 adsorption/desorption experiments on bituminous and anthracite coals and they did not find an apparent relationship between pressure and gas sorption rate. To summarize, the research outcomes are listed in Table 1.


**Table 1.** Summary of diffusion coefficient changing trend with the increase in pressure.

By reviewing the previous studies, it can be concluded that the effect of pressure on gas diffusion coefficient remains controversial till now. It is difficult to compare the research outputs horizontally because of the diversified calculation models and experimental methods, such as experimental apparatus, gas pressure and gas type. Moreover, most of the coal samples used in the studies was bituminous coal, because gas diffusibility and diffusion coefficient in coal are closely correlated with the types of coals. It is unknown whether the results stand for coal with different metamorphic grades.

In the present study, we aim at investigating the influence of sorption pressure on gas diffusion and examining which previous finding is more convincible. To guarantee the comparability of the results, all the experiments are carried out under similar gas pressure range. Both adsorption and desorption kinetics test are conducted. Three widely used diffusion models are adopted to analyze the results and eliminate the possible differences induced by diffusion models. Four coal samples with different ranks are collected from typical mining areas in China, the test results are cross-compared to understand does the coal rank have impacts on the relationship between gas pressure and diffusion coefficients.

#### **2. The Di**ff**usion Models**

Three widely used diffusion models, i.e., the unipore diffusion (UD) model, the bidisperse diffusion (BD) model and the dispersive diffusion (DD) model, will be used in this study. The expressions and the assumptions are introduced as follows.

#### *2.1. The Unipore Di*ff*usion Model*

The UD [13,16,17] model assumes that the coal particle has only one type of pore and the gas diffuses under the concentration gradient between exterior and interior of the coal particle. The UD model is illustrated in Figure 1. Both UD model and BD model follow the following assumptions: (a) the diffusion system is isothermal; (b) the geometric shape of the particle coal is the standard sphere; (c) coal and pore system are isotropic and homogeneous; (d) the pores are incompressible; (e) gas follows the linear adsorption rule; (f) gas diffusion in pores is in line with Fick's Second Law. It can be expressed as [18],

$$\frac{m\_t}{m\_{\infty}} = 1 - \frac{6}{\pi^2} \sum\_{n=1}^{\infty} \frac{1}{n^2} \exp\left(-\frac{Dn^2 \pi^2 t}{r^2}\right) \tag{1}$$

*m*<sup>t</sup> in the equation refers to the total gas adsorption/desorption quantity at time *t*, *m*<sup>∞</sup> is the total quantity after the gas adsorption/desorption is balanced, *r* represents the radius of spherical coal particle, *D* refers to the diffusion coefficient (m2/s) and the value of *<sup>D</sup> <sup>r</sup>*<sup>2</sup> is defined as the effective diffusion coefficient *De* (1/s).

**Figure 1.** Concepts of gas diffusion under unipore diffusion (UD) Model [19].

#### *2.2. The Bidisperse Di*ff*usion Model*

The BD model [5,8,9,14,20,21] assumes that the coal particle includes independent macropore and micropore systems, which are represented by *Da* and *Di* respectively. The gas diffusion under the two systems are driven by the concentration gradients between exterior and interior of the coal particle. The BD model is illustrated in Figure 2. The simplified BD model includes the fast macropore diffusion stage and the slow micropore diffusion stage [5,22].

**Figure 2.** Concepts of gas diffusion under bidisperse diffusion (BD) Model [19].

Concerning the fast macropore diffusion stage, the diffusion model is denoted as,

$$\frac{m\_a}{m\_{a\infty}} = 1 - \frac{6}{\pi^2} \sum\_{n=1}^{\infty} \frac{1}{n^2} \exp\left(-\frac{D\_a n^2 \pi^2 t}{r\_a^2}\right) \tag{2}$$

*ma* in the equation refers to the total gas adsorption/desorption quantity at time *t* in the macropore, *ra* and *Da* represent the radius of macropore spherical coal particle and macropore diffusion coefficient (m2/s), respectively. The value of *Da r*2 *a* is defined as the effective diffusion coefficient *Dae* (1/s).

Concerning the lower micropore diffusion stage, the diffusion model is denoted as,

$$\frac{m\_{\rm i}}{m\_{\rm ios}} = 1 - \frac{6}{\pi^2} \sum\_{n=1}^{\infty} \frac{1}{n^2} \exp\left(-\frac{D\_{\rm i} n^2 \pi^2 t}{r\_{\rm i}^2}\right) \tag{3}$$

*m*<sup>i</sup> in the equation refers to the total gas adsorption/desorption quantity in the micropore at time *t*, *ri* and *Di* represent the radius of micropore spherical coal particle and micropore diffusion coefficient (m2/s), respectively. The value of *Di r*2 *i* is defined as the effective diffusion coefficient *Die* (1/s).

The BD model is expressed as,

$$\frac{m\_{\text{t}}}{m\_{\text{co}}} = \frac{m\_{\text{a}} + m\_{\text{i}}}{m\_{\text{a}\text{co}} + m\_{\text{io}}} = \beta \frac{m\_{\text{a}}}{m\_{\text{a}\text{co}}} + (1 - \beta) \frac{m\_{\text{i}}}{m\_{\text{io}}} \tag{4}$$

β = *ma*<sup>∞</sup> *mi*∞+*ma*<sup>∞</sup> is the ratio of macropore adsorption/desorption to the total adsorption/desorption.

#### *2.3. The Dispersive Di*ff*usion Model*

In recent years, the dispersive diffusion model was developed and it assumes that a distribution of characteristic times for diffusion. The diffusion is dispersed and represents the wide distribution of diffusion feature time. Therefore, theoretically, the DD model can avoid the simplification of pore structure and reflect the real physical experimental process. The DD model is expressed as,

$$\frac{m\_{\rm t}}{m\_{\rm ox}} = 1 - \exp\left[-\left(k\_{\rm \phi}t\right)^{\alpha}\right] \tag{5}$$

*m*<sup>t</sup> in the equation refers to the total gas adsorption/desorption quantity, *m*<sup>∞</sup> is the total quantity after the gas adsorption/desorption is balanced, *k*<sup>Φ</sup> is the characteristic rate parameter, α is the stretching parameter (0 < α < 1). The research of Staib et al. [23] shows that α is an intrinsic property of coal and is greatly influenced by the coal pore characteristic.

#### **3. Di**ff**usion Experiments**

To carry out the diffusion experiments and analyze the impact of pressure on the methane diffusion, the iSorb HP (Quantachrome) instrument was used. The set maximum adsorption pressure is 6 MPa, the coal sample quality is 40 g and the experimental temperature is 315 K. Coal samples were collected from the HuiChun long frame coal at Jilin Province, Hedong coking coal at Shanxi Province, Xinmi lean coal at Henan Province, and Qinshui meager coal at Shanxi Province (Figure 3), with the four coal samples are denoted as HC, HD, XM and QS, respectively. The coal samples were ground into 0.2~0.25 mm particle samples and the prepared coal particles were dried under the 100 ◦C vacuum state for 24 h to remove moisture. The proximate analysis results are shown in Table 2.

**Figure 3.** The diagram of coal samples collection places.

**Table 2.** Proximate analysis results.


Manometric method is used in the experiments and the methane isothermal adsorption and diffusion kinetics are tested [24]. The gas state equation that implies the void volume of reference tank and coal samples tank is,

$$V = \frac{ZRTN\_{He}}{P\_{He}}\tag{6}$$

Four series data were recorded by pressure sensor in the experiments; (a) gas pressure in the reference tank before the gas is injected into the coal samples tank, *Pm*1; (b) gas pressure in the reference tank after the gas is injected into the coal samples tank, *Pm*2; (c) gas pressure in the coal samples tank before the reference tank gas is injected, *Pc*1; (d) gas pressure in the coal samples tank after the reference tank gas is injected, *Pc*2. *Pm*1, *Pm*<sup>2</sup> and *Pc*<sup>1</sup> are constant while *Pc*<sup>2</sup> is flexible.

The adsorption gas quantity at the *i* pressure step and time *t* in the adsorption diffusion process is,

$$N\_{\rm ti} = (N\_{m1} - N\_{m2}) - (N\_{c2} - N\_{c1}) = \left(\frac{P\_{m1}V\_m}{Z\_{m1}RT} - \frac{P\_{m2}V\_m}{Z\_{m2}RT}\right) - \left(\frac{P\_{c2}V\_c}{Z\_{c2}RT} - \frac{P\_{c1}V\_c}{Z\_{c1}RT}\right) \tag{7}$$

Therefore, the adsorption diffusion ratio of each pressure step is,

$$\frac{m\_{\rm f}}{m\_{\infty}} = \frac{N\_{\rm ti}M\_{CH\_4}}{N\_{\rm oxi}M\_{CH\_4}} = \frac{N\_{\rm ti}}{N\_{\rm coi}}\tag{8}$$

∞ in the equation represents the required time when the *i* pressure step is balanced.

While the total adsorption quantity at the pressure balance point is,

$$Q\_{ad} = \frac{22.4 \times 1000 \times \sum\_{i=1}^{n} N\_{\text{osi}}}{m\_{\text{CH}\_4}} \tag{9}$$

Therefore, *mt <sup>m</sup>*<sup>∞</sup> is equal to the gas adsorption quantity at the *<sup>i</sup>* pressure step and time *<sup>t</sup>* divided by the gas adsorption quantity when the *i* pressure step is balanced. Formula (9) is used to calculate the adsorption gas quantity at each balanced gas point and the adsorption/desorption isothermal lines [19] of coal samples are shown in Figure 4.

**Figure 4.** Adsorption-desorption isotherm of coal samples.

Langmuir model (Equation (10)) is used to fit the adsorption and desorption data of CH4 and the correlation R<sup>2</sup> is listed in Table 3. It can be seen that the adsorption and desorption characteristics of CH4 are represented well by the Langmuir model. The adsorption characteristic parameters are calculated in Table 3.

$$X = \frac{aP}{b+P} \tag{10}$$

*a*, *b* in the equation are the adsorption characteristic parameters. *a* represents the Langmuir adsorption quantity and *b* refers to the Langmuir adsorption pressure. *X* is the adsorbed gas quantity and *P* refers to the gas pressure.


**Table 3.** Adsorption characteristic parameters of coal samples.

Overall, similar to the previous findings [25,26], no apparent hysteresis phenomenon is found in the absorption/desorption process, in other words, the absorption/desorption process of CH4 can be reversed.

#### **4. Analysis and Discussion**

#### *4.1. Model Applicability Analysis*

Based on the Equation (8), the diffusion ratio can be calculated at any moment. The approximate method was used to fit the experimental results when applying the UD and BD models. In the process of fitting the UD and BD models, findings show that the calculation results are adequately convergent when the infinite series *n* is expanded to the fifth term. This can ensure the accuracy of model fitting results and further reduces the calculation difficulties. Therefore, all data was processed by setting *n* expand to five as the standard for calculation.

Taking the gas balance pressure increases from 0.7 MPa to 1 MPa as an example, the experimental results and the fitting diffusion lines of coal samples are shown in Figure 5.

It is shown in Figure 5 that the fitting line by the UD model is below the experimental line before some critical moment regardless of the coal samples, indicating that the fitting value is smaller than the actual value. After a certain time, the fitting line keeps above the experimental line and implies that fitting value is larger than the actual value. Therefore, the experimental results cannot be restored by the fitting line regardless of moderating the UD coefficient. The fitting effect of the BD model is superior to the UD model and the fitting line of the DD model is closer to the actual line. It suggests that the whole gas adsorption/desorption process cannot be accurately described by the UD model due to the complicated pore structures. The fitting line through the BD model includes the double structure of macropore and micropore and thus shows a higher coincidence degree with the experimental results. The DD model shows the best coincidence degree with the real experimental results. Therefore, the BD model and DD model are selected to calculate the gas diffusion characteristic parameter.

**Figure 5.** Experimental results and fitting diffusion lines of coal samples.

#### *4.2. Analysis of Pressure's E*ff*ect on the Gas Di*ff*usion*

#### (1) The BD Model

Equation (4) implies that the BD model includes three unknown parameters, including fast effective diffusion coefficient *Dae*, slow effective diffusion coefficient *Die* and the ratio of macropore adsorption/desorption to the total adsorption/desorption β. Using Equation (4) to calculate the BD characteristic parameters and analyze the impact of pressure on *Dae* (Figure 6) and quadratic polynomial is to fit the results, the fitting goodness and calculated coefficient are shown in Table 4. As can be seen from Figure 6, the macropore diffusion coefficient *Dae* decreases with the increase in pressure in three out of four sample coals (i.e, HC, XM and QS). Concerning the HD, *Dae* shows a V-shape trend, which first decreases and then increases as the increases in pressure. Figure 6 also shows that the impact law of pressure on *Dae* is better illustrated by the quadratic polynomial. When comparing the values of *Dae*, in both the adsorption and desorption processes, *Dae*(HC) > *Dae*(XM) > *Dae*(QS) > *Dae*(HD). The difference of *Dae* in the absorption versus the desorption process becomes larger from HC to QS. No significant increasing trend of HC, XM and QS is found when the pressure increases, It is suspected that the set maximum pressure is not in the threshold level.

**Figure 6.** The diagram of the variation of macro-diffusion coefficients with pressure.

The impact of gas pressure on *Die* is analyzed and is shown in Figure 7. Linear regression is used to fit the results and, results of the fitting goodness and calculated coefficient are given in Table 5. It can be clearly seen that the slow efficient diffusion coefficient *Die* increases with the increase in pressure for all four samples. The impact law of pressure on *Die* is better explained by the linear regression. When comparing the values of *Die*, the order is *Die*(HC) > *Die*(XM) > *Die*(QS) > *Die*(HD) in the adsorption process and *Die*(HC) > *Die*(QS) > *Die*(XM) > *Die*(HD) in the desorption process.

**Table 4.** Goodness of fit and diffusion coefficient.

**Figure 7.** The diagram of variation of micro-diffusion coefficient with pressure.

**Table 5.** Goodness of fit and diffusion coefficient.


The calculation results show that β is 0.74~0.76 for HC, 0.58~0.6 for HD, 0.67~0.69 for XM and 0.69~0.7 for QS, respectively, implying that the diffusion characteristic parameter β keeps constant in the adsorption/desorption process. This further indicates that β which represents the intrinsic property would not show a significant fluctuation with the change in pressure.

⎯

 ⎯

#### (2) The DD Model

The DD model includes two unknown characteristic parameters, the characteristic rate parameter *k*<sup>Φ</sup> and the stretching parameter α. The influencing law of pressure on the *k*<sup>Φ</sup> and α are re-analyzed, and calculated based on the gas diffusion experimental results and Equation (5). The results are shown in Figures 8 and 9, respectively.

**Figure 8.** The diagram of variation of characteristic rate parameter *k*Φ with pressure.

**Figure 9.** The diagram of variation of stretching parameter α with pressure.

⎯

Gregory Staib et al. [23,27] found that *k*<sup>Φ</sup> decreases with the increase in pressure which ranges from 0~3 MPa in their studies. In terms of the vitrinite-rich coal samples, α increases with gas pressure while for the inertinite-rich coal samples, no significant changing trend is found for α. Figure 8 shows that in our study, *k*<sup>Φ</sup> keeps unchanged in the pressure fluctuation process. Concerning XM and QS, *k*<sup>Φ</sup> slightly decreases with the increase in pressure when the pressure is less than P0, but it keeps constant while the pressure is larger than P0.

As shown in Figure 9, α increases with pressure. The mean values of α were calculated in Table 6. The mean value of α is ordered as α(HD) > α(QS) > α(XM) > α(HC) in the absorption process, while the order is α(QS) > α(HD) > α(HC) > α(HM) in the desorption process.


**Table 6.** Stretching parameter α.

(3) Analysis of the correlation of diffusion characteristics parameters

The five diffusion characteristics parameters of the BD and DD models are treated by the homogenization procedure and the results are shown in Table 7. It can be seen that *Dae*, *Die* and *k*<sup>Φ</sup> are the largest for HC, in the middle for XM and QS, and the smallest for HD. The linear regression results of *k*<sup>Φ</sup> on *Dae* and *k*<sup>Φ</sup> on *Die* are shown in Figures 10 and 11, respectively. CC C C C

**Table 7.** Average gas diffusion parameters of experimental coal samples.


**Figure 10.** The linear regression of *k*Φ on *Dae*.

CC

 C C C

**Figure 11.** The linear regression of *k*<sup>Φ</sup> on *Die*.

The results show a good linear correlation of *Dae*, *Die* and *k*<sup>Φ</sup> in our experimental results, and the goodness of fit is the best for *Dae* and *k*Φ. It suggests that both the diffusion coefficients *Dae* and *Die* and characteristic rate parameter *k*<sup>Φ</sup> are suitable for describing the coal gas diffusion rate. The analysis above suggests that the fast diffusion coefficient *Dae* decreases with the increase in pressure while the slow diffusion coefficient *Die* increases with the increase in pressure. *k*<sup>Φ</sup> keeps fixed and thus may be considered as a combined effect of *Dae* and *Die*.

#### *4.3. Analysis of the Relationship between Pore Structure Characteristics and Gas Di*ff*usion*

By analyzing and summarizing the impact, law of CH4 diffusion under different pressures, we found the diversity of gas diffusion coefficients in both absorption and desorption process for different coal samples. Because the coal pore structures might directly affect the diffusion process of gas [28], experiments on the low temperature nitrogen absorption and mercury penetration were conducted to test the characteristics of coal pore structure.

The Quadrasorb instrument is used for the low temperature nitrogen absorption experiment and the PoreMaster60 mercury porosimeter instrument is applied for the mercury penetration. coal samples particles with 1~3 mm in size are prepared and dried. The low temperature nitrogen absorption method is suitable for testing the distribution of coal micropore ranging from 0~25 nm, which determines the coal specific surface area [28]. Because the mercury penetration method is not accurate in testing the micropore, it is only suitable for analyzing the pores which are bigger than 25 nm. Therefore, in this study, the computation of pore volume is calculated by the low temperature nitrogen absorption method when the pore size ranges from 0~25 nm and by the mercury penetration method if the pore size is bigger than 25 nm. The specific surface area and pore volume are given in Tables 8 and 9, respectively.

To further understand the impact of coal pore structure characteristics on the gas diffusion, we run the linear regressions of *Dae*, *Die* and *k*<sup>Φ</sup> on pore volume. As shown in Figure 12, the correlation between the pore volume and the diffusion coefficients is, largest for *k*<sup>Φ</sup> (R<sup>2</sup> = 0.912), middle for *Dae* (R2 = 0.793) and smallest for *Die* (R<sup>2</sup> = 0.722).

**Pore volume**˄**ml/g**˅

a) The impact of pore volume on⎯*Dae*

**Figure 12.** The impact of pore volume on *Dae*, *Die* and *k*Φ.


**Table 8.** Specific surface area of coal samples.



Table 8 shows that the specific surface area is larger in HC relatively to other coal samples, indicating that the porosity in HC is well developed than other coal samples. It is shown in Table 7 that *Dae*, *Die* and *k*<sup>Φ</sup> of CH4 is the largest in HC, suggesting the porosity development level is correlated with the diffusion rate. However, Figure 13a shows that *Dae* of HD, QS and XM significantly increases when *Dae* is smaller than 1.6 <sup>×</sup> <sup>10</sup>−<sup>11</sup> while the specific surface area keeps unchanged. Figure 13b,c show that the impact of specific surface area on *Die* and *k*<sup>Φ</sup> is small in all coal samples excluding HC. It is worth to mention that our experimental results can only be considered as reference due to the small number of coal samples. The impact of coal structure on the diffusion parameters requires further study. In conclusion, the fluctuation of diffusion coefficients with respect to the gas pressure is correlated to the variation of pore characteristics, but the reason is still mysterious due to lack of evidence.

a) The impact of specific surface area onC*Dae*

**Figure 13.** *Cont*.

b) The impact of specific surface area onC*Die*

#### *4.4. Discussion on the Influence of Inconstant Di*ff*usion Coe*ffi*cients on CBM Recovery*

Previous studies have demonstrated that the BD diffusion cannot be overlooked and replaced by UD diffusion if diffusion is a constraint of gas production, especially for the coal seam with relatively large cleat spacing [24]. In this study, we found the BD and DD models are more accurate in describing the diffusion process, while pressure has apparent influence on the diffusion coefficients. From this point of view, the inconstant diffusion coefficients will have impacts on the CBM recovery rate. In terms of BD coefficients, most samples show an increase of fast diffusion coefficient *Dae* but a decrease of slow diffusion coefficient *Die* during the drop of coal seam pressure. The increase or decrease of diffusion coefficient will certainly accelerate or hinder gas flow, but these two effects might be compromised for the BD model and the ultimate effect depends on the net value of these two effects. For the DD model, *k*Φ keeps at a stable level, this phenomenon proves the above speculation as *k*Φ can be seen as a

combination of *Dae* and *Die*. However, the stretching parameter α decreases during pressure dropping, which indicates the CBM recovery rate will be reduced due to the change of diffusion coefficient.

#### **5. Conclusions**


**Author Contributions:** Conceptualization, G.W. and X.Y.; methodology, G.W.; validation, J.Z.; formal analysis, X.Y. and G.W.; data curation, G.W.; writing—original draft preparation, X.Y. and G.W.; writing—review and editing, G.W., J.Z. and T.R.; visualization, X.Y. and G.W.; supervision, J.Z. and T.R.

**Funding:** This work was supported by National Natural Science Foundation of China (51604153), National Science and Technology Major Project (2016ZX05045-004-006), National key research and development Project (2018YFB0605601).

**Acknowledgments:** We sincerely thank assistant professor Chunling Xia from Queen Mary University of London for improving the language of this paper.

**Conflicts of Interest:** The authors declare no conflicts of interest.

#### **References**


© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Article* **Theoretical Methodology of a High-Flux Coal-Direct Chemical Looping Combustion System**

#### **Xiaojia Wang 1,\*, Xianli Liu 1,2, Zhaoyang Jin 1, Jiewen Zhu <sup>1</sup> and Baosheng Jin <sup>1</sup>**


Received: 26 October 2018; Accepted: 30 November 2018; Published: 4 December 2018

**Abstract:** This study, as an extension of our previous experimental tests, presented a mechanism analysis of air reactor (AR) coupling in a high-flux coal-direct chemical looping combustion (CDCLC) system and provided a theoretical methodology to the system optimal design with favorable operation stability and low gas leakages. Firstly, it exhibited the dipleg flow diagrams of the CDCLC system and concluded the feasible gas–solid flow states for solid circulation and gas leakage control. On this basis, the semi-theoretical formulas of gas leakages were proposed to predict the optimal regions of the pressure gradients of the AR. Meanwhile, an empirical formula of critical sealing was also developed to identify the advent of circulation collapse so as to ensure the operation stability of the whole system. Furthermore, the theoretical methodology was applied in the condition design of the cold system. The favorable gas–solid flow behaviors together with the good control of gas leakages demonstrated the feasibility of the theoretical methodology. Finally, the theoretical methodology was adopted to carry out a capability assessment of the high-flux CDCLC system under a hot state in terms of the restraint of gas leakages and the stability of solid circulation.

**Keywords:** coal-direct chemical looping combustion; coupling mechanism; theoretical methodology; high-flux; gas leakage; pressure gradient

#### **1. Introduction**

Coal-direct chemical looping combustion (CDCLC) has been demonstrated as an attractive combustion technology of coal with the inherent feature for CO2 capture [1,2]. The CDCLC concept is typically implemented in two interconnected reactors, the so-called fuel reactor (FR) and the air reactor (AR), with an oxygen carrier (OC) circulating in between to transfer oxygen and heat. Specifically, in the FR, the fuel is first devolatilized and gasified by the gasification agent steam, and then the gasification products (mainly CO, H2, and CH4) are further oxidized to CO2 and H2O by the OC. In the AR, the reduced OC from the FR is oxidized by the air for regeneration, and then will be recirculated back to the FR. By means of the OC particles that deliver oxygen from the AR to FR, the direct mixing of the fuel and air can be avoided, and further highly purified CO2, without the dilution of N2, can be acquired at the outlet of the FR via the condensation of steam [3–12].

Alternatively, a characterized CDCLC system consisting of a high-flux circulating fluidized bed (HFCFB) riser as the FR and a counter-flow moving bed (CFMB) as the AR was proposed in our previous studies [13–15], as shown in Figure 1. The main advantages of this design are that the HFCFB FR can provide high solids concentration over the whole reactor height for favorable gas–solid contact efficiency and reaction performance, and that the CFMB AR possesses steady solids flow and low-pressure drop. Besides, an inertial separator, connecting the two reactors, was specially designed as the carbon stripper to separate the coarse OC particles off the FR into the AR for regeneration, and also recirculate the fine particles of the unconverted coal char back to the FR for further conversion.

**Figure 1.** Schematic of the high-flux coal-direct chemical looping combustion (CDCLC) system (OC: oxygen carrier).

Up to now, we have successively developed cold [14] and pilot-scale hot [15] experimental systems of this high-flux CDCLC concept, preliminarily realizing the whole-system stable operation with acceptable gas–solid reaction performance under certain conditions. Similar feasibility studies have been experimentally conducted in different pilot-scale CDCLC units, e.g., the 10 kWth [3] and 100 kWth [9] units at Chalmers University of Technology (Sweden), the 10 kWth [4] and 50 kWth [5] units at Southeast University (China), the 1 MWth unit from Technische Universität Darmstadt (Germany) [10], the 25 kWth unit at Hamburg University of Technology (Germany) [11], the 25 kWth unit from Ohio State University (America) [12], the 50 kWth unit at Instituto de Carboquimica (ICB-CSIC) (Spain) [16], and the 5 kWth CDCLC reactor at Huazhong University of Science and Technology (China) [17]. However, despite promising experimental results obtained in pilot-scale units, the CDCLC technology for CO2 capture has to be further developed towards large-scale commercial applications. In this aspect, it is essential to develop theoretical methodologies, beside experimental studies, for a better understanding of hydrodynamic and reaction mechanisms in CDCLC processes, which can provide vital references to the design, operation, and process optimization of the future large-scale CDCLC power plants. By far, compared to the extensive experimental studies, few studies are available in the literature on the development of theoretical methodologies in terms of hydrodynamics and/or reaction mechanisms for CDCLC processes. Su et al. [18], based on the hydrodynamic equations for fluidized beds and the reaction kinetics, simulated the CDCLC process in a dual circulating fluidized bed (DCFB) system. Ohlemüller et al. [19] developed a process simulation model to predict the flow and reaction performances of a 1 MWth unit at Technische Universität Darmstadt.

In our previous experimental tests, we have found that the coupling of the CFMB AR into the downcomer of the HFCFB FR makes the hydrodynamic mechanism of the whole system much more complicated, and hence leads to crucial effects on the operation independence of the two reactors (i.e., FR and AR) in terms of solid circulation stability and gas leakages [20]. In this context, it is necessary to carry out an in-depth mechanism investigation of this high-flux CDCLC system coupled by a CFMB AR, which is significant to the design and operation processes of the future CDCLC applications. Therefore, the objective of this study is to develop a theoretical methodology to illustrate the fundamental hydrodynamics of our high-flux CDCLC system, extending from the previous experimental studies. The main contributions of this work are listed as follows: (1) the screening of the feasible gas–solid flow states for solid circulation and gas leakage control on the strength of the dipleg flow diagrams of the CDCLC system; (2) the development of the semi-theoretical formulas of gas leakages to predict the optimal regions of the pressure gradients of the AR; (3) the development of the empirical formula of critical sealing to identify the advent of circulation collapse so as to ensure the whole-system operation stability; (4) the feasibility validation of the theoretical methodology through its application in the cold-state condition design; (5) the successful application of the theoretical methodology into the capability assessment of the high-flux CDCLC system under a hot state, in terms of the restraint of gas leakages and the stability of solid circulation.

#### **2. Materials and Methods**

#### *2.1. Visualization Experimental Device*

The visualization experimental device of the high-flux CDCLC system consists primarily of a FR, an inertial separator, a downcomer, an AR, and a J-valve. During the operation process, the FR, with an inner diameter of 60 mm and a height of 5.8 m, was operated in dense suspension upflow (DSU) regime with high solid circulation flux and solids holdup. An inertial separator was installed following the FR, and was used as the carbon stripper to separate the gas stream and elutriated particles off the FR. The particle outlet of the inertial separator was connected with the downcomer, and further the AR which was operated in moving bed regime with an inner diameter of 418 mm and a height of 0.7 m. After leaving the AR, the OC particles were transported back to the FR with the help of the J-valve. The drawing presented in Figure 2 schematically shows how the different sections of the visualization experimental setup are interconnected. The more detailed description can be found in our previous experimental studies with this system [14,20].

The tracer gas (99.99% CO) concentrations were continuously measured with a gas analyzer (MRU, Neckarsulm, Germany) at the outlets of the two reactors. The pressures were measured with pressure gages and a multi-channel differential pressure transducer. The gas flow rates were adjusted and measured by calibrated rotameters (Changzhou shuanghuan thermal instrument co., LTD, Changzhou, China) and then normalized to the standard state (labeled with a subscript *sta*). Specifically, *Q*1,*sta*, *Q*2,*sta*, *Q*3,*sta*, and *Q*4,*sta* represent the inlet air flow rate of the FR, the fluidizing air flow rate of the J-valve, the aeration air flow rate of the J-valve, and the inlet air flow rate of the AR, respectively. *Qa*,*sta* and *Qb*,*sta* represent the outlet air flow rates of the FR and the AR, respectively.

#### *2.2. Materials*

The OC material used in this study was a natural iron ore from Harbin, China with an average particle diameter of 0.43 mm and bulk density of 1577 kg/m3. The minimum fluidization gas velocity under the cold condition was 0.187 m/s [20].

*Processes* **2018**, *6*, 251

**Figure 2.** Schematic diagram of the cold-state experimental device of the high-flux CDCLC system. *P*: pressure; *Q*: gas flow; AR: air reactor; FR: fuel reactor.

#### *2.3. Performance Indicators*

The upper pressure gradient (Δ*P*1/*H*1) represents the pressure gradient between the AR and the carbon stripper, which was expressed as Equation (1). The lower pressure gradient (Δ*P*2/*H*2) represents the pressure gradient between the J-valve and the AR, which was expressed as Equation (2) [20].

$$
\Delta P\_1 / H\_1 = \left(\frac{p\_b + p\_c}{2} - p\_i\right) / H\_1 \tag{1}
$$

$$
\Delta P\_2/H\_2 = (p\_d - p\_{12})/H\_2 \tag{2}
$$

Solid circulation flux, *Gs*, represents the solid circulation ratio (kg/s) per unit area of the FR, which was estimated by [20,21]

$$\mathcal{G}\_{\rm s} = \frac{\rho\_b u\_s A\_{\rm ud}}{A\_f} = \frac{\rho\_b A\_{\rm ud}}{A\_f} (\Delta H/t) \tag{3}$$

The solids holdup in the FR, *εs*, could be estimated according to the local pressure drop [13,14,21–24].

$$
\Delta P\_{\mathbb{Z}} / \Delta \mathbb{Z} \approx [\rho\_s \varepsilon\_s + \rho\_\mathcal{g} (1 - \varepsilon\_s)] \mathcal{g} \tag{4}
$$

The FR leakage ratio, *f* 1, represents the gas leakage ratio from the FR to the AR. During the experimental process, the FR leakage ratio was measured by using tracer gas 1 [14,20].

$$f\_1 = -\frac{Q\_{b,sta} \chi\_{b,CO}}{Q\_{a,sta} \chi\_{a,CO} + Q\_{b,sta} \chi\_{b,CO}}\tag{5}$$

In this study, the upward direction is defined as the positive direction, and hence the FR leakage ratio should be negative.

The AR leakage ratio, *f* 2, represents the gas leakage ratio from the AR to the FR, which could be measured by using tracer gas 2 [14,20].

$$f\_2 = \frac{Q\_{a,sta} \mathbf{x}\_{a,CO}^{\prime}}{Q\_{a,sta} \mathbf{x}\_{a,CO}^{\prime} + Q\_{b,sta} \mathbf{x}\_{b,CO}^{\prime}}\tag{6}$$

The J-valve leakage ratio, *f* 3, represents the gas leakage ratio of the J-valve aeration air into the AR, which was measured by using tracer gas 3 [20].

$$f\_3 = \frac{Q\_{b,sta} \mathbf{x}\_{b,CO}^{''}}{Q\_{a,sta} \mathbf{x}\_{a,CO}^{''} + Q\_{b,sta} \mathbf{x}\_{b,CO}^{''}} \tag{7}$$

The detailed meanings of the symbols can be found in the nomenclature.

#### **3. Results and Discussion**

#### *3.1. Gas–Solid Flow Characteristics*

In typical circulating fluidized bed (CFB) reactors, the downcomer dipleg plays a critical role in solid circulation and gas seal. In order to drive particles, the whole dipleg is usually kept in a state of negative pressure gradient (i.e., a decrease of pressure with the increase of downcomer height) [25–31]. However, the coupling of the AR into the downcomer of our high-flux CDCLC system together with the special requirements of gas leakages makes the operation mechanism of the dipleg and even the whole system much more complicated. As shown in Figure 2, the existence of the AR divides the dipleg into the upper dipleg and the lower dipleg. During the CDCLC process, the lower dipleg stays at a negative pressure gradient state with the J-valve owning the maximum pressure so as to drive the solids for circulation. But the upper dipleg can situate at a pressure region across the positive and negative pressure gradient states, which has crucial effects on the circulation stability and gas leakage ratios. Therefore, a systematic study is necessary to improve the understanding of AR coupling effects on the hydrodynamic mechanism of this CDCLC system in terms of solid circulation stability and gas leakage controllability.

Figure 3 shows the possible gas–solid flow states in the upper dipleg during the high-flux CDCLC process, where the upward direction is defined as the positive direction [32]. As the abovementioned discussions, the upper dipleg flow can be categorized into seven flow states, according to the differential pressure between the two ends of the upper dipleg. In the first state, the top pressure of the upper dipleg is much larger than that at the bottom, and the gas flow moves downward with a much higher velocity than that of the solids. The big velocity difference between the gas–solid phases means the dramatic CO2 leakage from the carbon stripper into the AR, and hence the great reduction of CO2 capture efficiency. In the second state, the positive differential pressure between the top and bottom becomes much smaller, and the gas velocity is only slightly higher than the solids velocity, indicating a modest gas leakage from the FR to the AR. In the third state, when the differential pressure between the two ends of the upper dipleg becomes zero, the solids downward flow is controlled by gravity and the gas–solid relative velocity becomes zero. Then in the fourth state, the bottom pressure of the upper dipleg starts to outpace the top pressure, and the gas–solid flow becomes negative pressure gradient flow, leading to a further reduction of the downward velocity of gas phase. When the downward gas velocity further decreases to zero, it comes to the fifth state, so-called the ideal sealing state. At this point, the gas–solid relative velocity is equal to the absolute value of the solids descending velocity, indicating the ideal suppression of the gas leakages between the FR and AR. In the sixth state, with the further enhancement of the negative pressure gradient, the gas begins to flow upward with a low velocity, indicating a small amount of gas leakage from the AR to the FR. Finally, when a large amount of gas flow moves upward in terms of visible large bubbles, the upper dipleg will enter into the last state, so-called the critical sealing state, meaning that the whole-system particle circulation is about to be broken together with a dramatical leakage of N2 from the AR into FR. In general, States 1 and 2 belong to the positive pressure gradient flow, State 3 belongs to the zero pressure gradient flow, and States 4 to 7 belong to the negative pressure gradient flow.

**Figure 3.** Gas–solid flow diagrams in the upper dipleg under different pressure gradient conditions.

Our previous experimental studies found that with the increase of the upper pressure gradient Δ*P*1/*H*1, the FR leakage ratio *f* <sup>1</sup> had a linear drop until extinction while the AR leakage ratio *f* <sup>2</sup> firstly stayed at zero and then had a linear increase [20]. Thus, the variations of gas–solid flow state in the upper dipleg corresponding to the upper pressure gradient could be further deduced, as shown in Figure 4. It can be found that the gas flow direction in the upper dipleg changed from downward to upward. By referring to Figure 3, it can be concluded that the gas–solid flow in the upper dipleg had gone through States 2 to 6, demonstrating the feasibility of the selection of optimal operation region for the gas leakage control and solid circulation by means of the adjustment of the upper pressure gradient Δ*P*1/*H*1. Consistent with the experimental studies [20], we set −3% and 3% as the limit values of the two gas leakages *f* <sup>1</sup> and *f* 2, respectively, and as the selection criteria of the upper pressure gradient. Thus, we can get the optimal region of Δ*P*1/*H*<sup>1</sup> corresponding to States 2 to 6 under the involved operation conditions: State 2 (−2.1 kPa/m < Δ*P*1/*H*<sup>1</sup> < 0 kPa/m), State 3 (Δ*P*1/*H*<sup>1</sup> = 0 kPa/m), State 4 (0 kPa/m < Δ*P*1/*H*<sup>1</sup> < 1.6 kPa/m), State 5 (Δ*P*1/*H*<sup>1</sup> = 1.6 kPa/m), and State 6 (1.6 kPa/m < Δ*P*1/*H*<sup>1</sup> < 3.0 kPa/m).

**Figure 4.** Variations of gas–solid flow state in the upper dipleg with the upper pressure gradient.

On the other hand, as mentioned above, because the J-valve is the driving source for solid circulation, it has the maximum pressure of the whole system. Hence, the lower dipleg necessarily stays at a negative pressure gradient region, i.e., States 4 to 7 as shown in Figure 3. However, considering that the gas leakage from the AR to the J-valve will result in the mixing of N2 into the FR, and further the reduction of CO2 capture concentration, State 4 should also better be avoided during the CDCLC process. Moreover, an excess gas leakage (i.e., State 7 shown in Figure 3) will cause serious damage on the stability of the solids downward flow, and further the solid circulation. Therefore, only States 5 and 6 were regarded as the preferred gas–solid flow states in the lower dipleg. Consistent with our previous experimental studies [20], we set 20% as the upper limit value of the gas leakage *f* 3, and as the selection criterion of optimal region under the involved operation conditions.

#### *3.2. Theoretical Methodology for Gas Leakage Restraint*

#### 3.2.1. Semi-Theoretical Formulas of the Upper Pressure Gradient

From the analyses made above, we can find that the coupling of the CFMB AR makes the gas–solid flow in the upper dipleg very complicated, which covers a diversity of flow structures from positive pressure gradient to negative pressure gradient states. In a real CDCLC application, the optimal operation region should better locate among States 2 to 6 in order to acquire stable solid circulation and favorable restraint of gas leakages. Fortunately, we found that the optimal region exhibited a relatively symmetrical distribution rule with the ideal sealing state (i.e., State 5) as the core. Thus, it provides a possibility for us to propose an empirical equation applied to the high-flux CDCLC system based on the ideal sealing theory.

The modified Ergun equation was attempted to be applied in the stable moving bed flow of the upper dipleg under the ideal sealing state, which had the form of Equation (8) [33–35]. Meanwhile, according to the relationship between the solids velocity and solid circulation flux (see Equation (9)), we further got the correlation equation of the upper pressure gradient under the ideal sealing state with the solid circulation flux (i.e., Equation (10)).

$$\left(\left(\Delta P\_1/H\_1\right)\_i\right)\_i = 150 \frac{\left(1-\varepsilon\right)^2 \mu\_\text{g} \left|\mathcal{U}\_\text{s}\right|}{\left(\varepsilon \rho\_\text{s} d\_\text{s}\right)^2} + 1.75 \frac{\left(1-\varepsilon\right) \rho\_\text{g} \mathcal{U}\_\text{s}^2}{\varepsilon \rho\_\text{s} d\_\text{s}} \tag{8}$$

$$|\mathcal{U}\_{\mathfrak{s}}| = \left(\frac{D\_f}{D\_{\text{ud}}}\right)^2 \frac{G\_{\mathfrak{s}}}{\rho\_{\mathfrak{s}}(1-\mathfrak{s})} \tag{9}$$

*Processes* **2018**, *6*, 251

$$(\Delta P\_1/H\_1)\_{\bar{\mathbf{i}}} = \left[150 \frac{(1-\varepsilon)\mu\_{\bar{\mathbf{g}}}}{\rho\_\delta(\varepsilon\rho\_\delta d\_\delta)} \left(\frac{D\_f}{D\_{ud}}\right)^2\right] \mathbf{G}\_\mathbf{s} + \left[1.75 \frac{\rho\_\mathbf{g}}{\varepsilon\rho\_\delta d\_\delta(1-\varepsilon)\rho\_\mathbf{s}^2} \left(\frac{D\_f}{D\_{ud}}\right)^4\right] \mathbf{G}\_\mathbf{s}^2 \tag{10}$$

Figure 5 illustrates the comparison of predicted and experimental upper pressure gradients under the ideal sealing state (i.e., State 5). Table 1 lists the main parameters required for the calculation of the ideal upper pressure gradient. It can be seen that, with a solid circulation flux of 200 kg/m2·s, the value of the theoretical ideal pressure gradient was about 1.6 kPa/m which was almost the same with the experimental value. Then, when the solid circulation flux increased to 300 kg/m2·s, the theoretical and measured values of the ideal pressure gradient were increased to about 2.5 and 2.8 kPa/m, respectively. In general, the relative errors between the measured and predicted values of the ideal pressure gradient were kept to be lower than 15%, demonstrating the application feasibility of the modified Ergun equation in the prediction of the ideal pressure gradient of the high-flux CDCLC system.

**Figure 5.** Comparison of predicted and experimental upper pressure gradients under ideal sealing states with different solid circulation fluxes.

**Table 1.** Parameters for the calculation of the ideal upper pressure gradient (OC: oxygen carrier; FR: fuel reactor).


In addition, from Figure 4, we can get the optimal region of the upper pressure gradient Δ*P*1/*H*<sup>1</sup> under a high-flux condition of 200 kg/m2·s, which ranged between −2.1 kPa/m and 3.0 kPa/m. Thus, by associating the optimal region with the ideal pressure gradient (1.6 kPa/m), a semi-theoretical formula of gas leakages between the two reactors (i.e., Equation (11)) could be deduced, which includes two conterminal linear equations with the ideal pressure gradient chosen as the boundary point. This formula successfully established the important mapping relationships between the gas–solid flow states in the upper dipleg and the upper pressure gradient, which should be important coupling criteria of selecting design parameters and operating conditions.

$$f\_i = \begin{cases} f\_1 = a\_1[(\Delta P\_1/H\_1)\_t - (\Delta P\_1/H\_1)\_i]((\Delta P\_1/H\_1)\_t < (\Delta P\_1/H\_1)\_i, \mathfrak{a}\_1 \approx 0.8) \\\ f\_2 = a\_2[(\Delta P\_1/H\_1)\_t - (\Delta P\_1/H\_1)\_i]((\Delta P\_1/H\_1)\_t \ge (\Delta P\_1/H\_1)\_i, \mathfrak{a}\_2 \approx 2.2) \end{cases} \tag{11}$$

3.2.2. Semi-Theoretical Formulas of the Lower Pressure Gradient

From the analyses shown in Section 3.1, we can find that the optimal operation region of the lower dipleg should better locate between State 5 (i.e., the ideal sealing state) and State 6 in order to ensure stable solid circulation with acceptable gas leakage.

The correlation equation of the lower pressure gradient under the ideal sealing state was also derived from the modified Ergun equation [33–35], as shown in Equation (12). Here, it should be noted that the lower downcomer was designed to be cuboid shaped (0.1 m length × 0.1 m width) and the upper downcomer was cylinder shaped, which made the form of Equation (12) a bit different from that of Equation (10). Thus, the theoretical value of the ideal lower pressure gradient could be deduced to be about 1.3 kPa/m. Further, according to our previous experimental results [20], the optimal region of <sup>Δ</sup>*P*2/*H*<sup>2</sup> under a high-flux condition of 200 kg/m2·s should be limited within 6.0 kPa/m in order to guarantee the J-valve leakage ratio lower than 20%. Thus, by associating the optimal region of the lower pressure gradient with the ideal pressure gradient, a semi-theoretical formula of J-valve gas leakage (i.e., Equation (13)) could be deduced, in which the coefficient *β* was used as the slope. Similarly, with Equation (11), this formula established the mapping relationships between the J-valve gas leakage and the lower pressure gradient, enabling a coupling criterion of selecting design parameters and operating conditions during the CDCLC process.

$$(\Delta P\_2/H\_2)\_i = \left[150\frac{\pi}{4} \frac{(1-\varepsilon)\mu\_\mathcal{g}}{\rho\_s(\varepsilon\rho\_s d\_s)^2} \left(\frac{D\_f}{L\_{ld}}\right)^2\right] \mathcal{G}\_\mathbf{s} + \left[1.75 \left(\frac{\pi}{4}\right)^2 \frac{\rho\_\mathcal{g}}{\varepsilon\rho\_s d\_s (1-\varepsilon)\rho\_s^2} \left(\frac{D\_f}{L\_{ld}}\right)^4\right] \mathcal{G}\_\mathbf{s}^2 \tag{12}$$

$$f\mathfrak{z} = \beta[(\Delta\mathrm{P}\_2/H\mathrm{i}\_2)\_t - (\Delta\mathrm{P}\_2/H\mathrm{i}\_2)\_i]((\Delta\mathrm{P}\_2/H\mathrm{i}\_2)\_t \cong (\Delta\mathrm{P}\_2/H\mathrm{i}\_2)\_i, \mathfrak{f} \approx 4.3) \tag{13}$$

#### *3.3. Theoretical Methodology for Circulation Stability*

In a real CDCLC application, a critical sealing state (i.e., State 7 shown in Figure 3) can be used to identify the advent of circulation collapse. Therefore, it is also necessary to understand the critical sealing ability of the CDCLC system so as to prevent the emergency situation of operation instability. Here, an empirical formula of critical sealing proposed by Chang et al. [32] (see Equation (14)) was attempted to be applied in this high-flux CDCLC system, in which the coefficient *γ* was between 0.6–0.7.

$$
\left|\frac{\Delta P}{H}\right|\_{\varepsilon} = \gamma \lg[\rho\_s(1-\varepsilon) - 136] \tag{14}
$$

Figure 6 exhibits the comparison of predicted and experimental upper pressure gradients under the critical sealing state. It can be found that, with a solid circulation flux of 250 kg/m2·s, the experimental value of the critical sealing gradient was 10.7 kPa/m under an upper dipleg height of 1.07 m [20]. In order to ensure the accuracy of test measurement, another dipleg height (0.87 m) was adopted for the measure of the critical sealing gradient while the other operating conditions were kept constant. It can be seen that these two experimental results (10.9 kPa/m for 0.87 m height, and 10.7 kPa/m for 1.07 m height) were very close to each other, demonstrating the constancy of the critical sealing gradient. On the other hand, the calculation value of the critical sealing gradient based on Equation (14) was between 8.5 kPa/m and 9.9 kPa/m. Thus, the relative error between the measured and predicted values of the critical sealing gradient could be further calculated to be lower than 21% for *γ* of 0.6, and 8% for *γ* of 0.7, demonstrating the application feasibility of Chang et al. [32] equation in the prediction of the critical pressure gradient of the high-flux CDCLC system. Moreover, the value of 0.7 for the coefficient *γ* seems to be more suitable for this system, in view of the least

relative error with the experimental values. Therefore, the semi-theoretical formula for the circulation stability of this high-flux system can be finally expressed as Equation (15).

$$\left(\Delta P/H\right)\_{\varepsilon} = 0.7\mathcal{g}\_{\varepsilon}[\rho\_s(1-\varepsilon) - 136] \tag{15}$$

**Figure 6.** Comparison of predicted and experimental upper pressure gradients under the critical sealing state.

#### *3.4. Theoretical Methodology Application to Condition Designs of the Cold System*

Based on the theoretical methodology for the gas leakages and solid circulation, we proposed an optimal operation condition of the cold CDCLC system. Firstly, considering the feature and requirement of the high-flux operation, we selected a higher value of 300 kg/m2·s as the solid circulation flux *Gs* while the corresponding FR superficial gas velocity *Uf*,*sta* and the inlet air flow rate of the AR *Q*4,*sta* were set to be 10.7 m/s and 44 m3/h, respectively. Thus, according to Equations (10) and (12), the theoretical ideal pressure gradients of the upper dipleg and the lower dipleg should be about 2.5 kPa/m and 1.9 kPa/m, respectively. Then, on the basis of the above semi-theoretical formulas (i.e., Equations (11) and (13)), we could further deduce that the optimal regions for gas leakage restraint were about −1.3 to 3.9 kPa/m for the upper pressure gradient Δ*P*1/*H*1, and about 1.9 to 6.6 kPa/m for the lower pressure gradient Δ*P*2/*H*2. Under this premise, we selected 3.8 kPa/m and 5.2 kPa/m as the proposed values of Δ*P*1/*H*<sup>1</sup> and Δ*P*2/*H*2, respectively.

Figure 7 exhibits the whole-system pressure profile and the apparent solids holdup along the FR height under the proposed operation condition. As shown in Figure 7a, the pressures of each part under the high-flux condition were smoothly connected with each other, demonstrating the operation stability and the favorable coupling between each component. Besides, the high-pressure drop in the FR and low-pressure drop in the AR, they successfully exhibited the operation features of HFCFB and CFMB. From Figure 7b, we further observed the high solids holdup along the whole FR height, indicating the positive effect of high solid circulation flux on the efficiencies of gas–solid contact and reaction [14]. In addition, Table 2 summarizes the pressure gradients and gas leakage ratios under the proposed operation condition. It can be found that, although the upper pressure gradient Δ*P*1/*H*<sup>1</sup> close to the upper limit of the optimal region, the FR leakage ratio *f* <sup>1</sup> (−0.1%) and the AR leakage ratio *f* <sup>2</sup> (2.5%) can still be limited within their limits (i.e., −3% for *f* <sup>1</sup> and 3% for *f* 2), demonstrating the feasibility of the semi-theoretical Equation (11) for the prediction of the optimal region for gas leakage control. On the other hand, the lower pressure gradient Δ*P*2/*H*<sup>2</sup> was located at a value of 5.2 kPa/m, in which the J-valve leakage ratio *f* <sup>3</sup> (15.2%) could be kept within a proposed region (<20%) in order to ensure a favorable solid circulation.

**Figure 7.** The pressure profile of the whole system and the apparent solids holdup along the FR under the proposed pressure gradient condition: (**a**) pressure profile, and (**b**) apparent solids holdup.


**Table 2.** Pressure gradients and gas leakage ratios under the proposed operation condition (AR: air reactor).

In general, the system operation stability, the high-flux feature, and particularly the gas leakage restraint were successfully achieved in this proposed operation condition, indicating the application feasibility of the semi-theoretical methodology to the system optimal design.

#### *3.5. Hot State Application Assessment of the Theoretical Methodology*

From the theoretical methodology of AR coupling principle with the high-flux CDCLC system, we could further carry out a capability assessment of the system in terms of the restraint of gas leakages and the stability of solid circulation under hot states. To facilitate the analysis and comparison, the structure parameters and OC material of the cold system were also adopted in the hot system. Besides, the solid circulation flux in the hot state was also selected as 300 kg/m2·s so as to keep consistent with the proposed cold-state operation condition mentioned above. The only difference was that under the hot state, the operating temperature was as high as 1243 K with the corresponding dynamic viscosity *<sup>μ</sup>g*,*hot* = 4.7 × <sup>10</sup>−<sup>5</sup> Pa·s. Table <sup>3</sup> details the parameters for the calculation of ideal pressure gradients under the hot state.

According to Equation (10), the theoretical ideal pressure gradient of the upper dipleg under the hot state should be about 6.4 kPa/m. Similarly, according to Equation (12), the theoretical ideal pressure gradient of the lower dipleg under the hot state was about 5.0 kPa/m. Figure 8 shows the comparison of the ideal pressure gradients between the hot and cold states. We can observe the ideal pressure gradients of the two diplegs under the hot state were about 2.6 times of those under the cold state. This implies a lower requirement of the sealing height in the hot state, which should be beneficial for the spatial arrangement of the hot-state system. In addition, on the basis of the semi-theoretical formulas for gas leakage restraint (i.e., Equations (11) and (13)), the optimal regions were further calculated to be about 2.7 to 7.8 kPa/m for Δ*P*1/*H*1, and about 5 to 9.7 kPa/m for Δ*P*2/*H*2. On the other hand, according to Equation (15), the critical pressure gradient for the circulation stability could be deduced to be about 9.9 kPa/m. It can be found that the upper limit of the optimal region of Δ*P*2/*H*<sup>2</sup> (9.7 kPa/m) for gas leakage restraint was very close to the critical pressure gradient for the circulation stability (9.9 kPa/m), demonstrating the rationality of the choice of 20% as the upper limit standard of the J-valve leakage. Certainly, it should be noted that the approach of the optimal pressure gradients for gas leakages to the critical pressure gradient for circulation stability also means the increase in the risk of circulation collapse during the hot-state operation process.


**Table 3.** Parameters for the calculation of the ideal pressure gradients under the hot state.

**Figure 8.** Comparison of the ideal pressure gradients between the hot and cold states.

For real high-flux CDCLC applications, we can first get the optimal sizes of the reactors and the downcomer based on the ideal pressure gradient equations and the solid circulation flux range in the design process. Then during the operating process, the relevant parameters (i.e., the solid-seal heights in the downcomer, the pressures of the FR and AR, the solid circulation flux, and the gas flow rates) can be adjusted flexibly and optimally to make sure the pressure gradients within the optimal regions for a favorable performance of operation and reaction.

#### **4. Conclusions**

Built upon the previous experimental studies of a high-flux CDCLC system, the objective of this study is to further investigate the fundamental coupling mechanism of the AR, and develop a comprehensive theoretical methodology to the system optimal design with favorable operation stability and low gas leakages. The following conclusions can be drawn from the present study:

(1) During the CDCLC process, the dipleg flow can situate at a pressure region across the positive and negative pressure gradients, which can be categorized into seven flow states. Considering the gas leakages and the circulation stability, the upper dipleg of the AR was recommended to be operated among State 2 to 6 while the lower dipleg of the AR should better run between States 5 and 6.


**Author Contributions:** conceptualization, X.W.; investigation, X.W. and X.L.; formal analysis, X.W. and X.L.; supervision, B.J.; writing—original draft preparation, X.W., X.L., and Z.J.; writing—review and editing, J.Z.

**Funding:** This research was funded by the National Natural Science Foundation of China (grant numbers 51806035, 51741603), the Natural Science Foundation of Jiangsu Province (grant number BK20170669), the Fundamental Research Funds for the Central Universities (grant number 2242018K40117), and the Guangdong Provincial Key Laboratory of New and Renewable Energy Research and Development (grant number Y707s41001).

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Nomenclature**



#### **References**


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