**Exergy Analysis and Evaluation of the Di**ff**erent Flowsheeting Configurations for CO2 Capture Plant Using 2-Amino-2-Methyl-1-Propanol (AMP)**

**Ebuwa Osagie 1,\*, Aliyu M. Aliyu <sup>2</sup> , Somtochukwu Godfrey Nnabuife 1, Osaze Omoregbe <sup>3</sup> and Victor Etim <sup>1</sup>**


Received: 27 May 2019; Accepted: 19 June 2019; Published: 24 June 2019

**Abstract:** This paper presents steady-state simulation and exergy analysis of the 2-amino-2-methyl-1-propanol (AMP)-based post-combustion capture (PCC) plant. Exergy analysis provides the identification of the location, sources of thermodynamic inefficiencies, and magnitude in a thermal system. Furthermore, thermodynamic analysis of different configurations of the process helps to identify opportunities for reducing the steam requirements for each of the configurations. Exergy analysis performed for the AMP-based plant and the different configurations revealed that the rich split with intercooling configuration gave the highest exergy efficiency of 73.6%, while that of the intercooling and the reference AMP-based plant were 57.3% and 55.8% respectively. Thus, exergy analysis of flowsheeting configurations can lead to significant improvements in plant performance and lead to cost reduction for amine-based CO2 capture technologies.

**Keywords:** 2-Amino-2-Methyl-1-Propanol; modelling and Simulation; post-combustion capture; exergy analysis; flowsheeting configurations

#### **1. Introduction**

Natural gas power plants play a vital role in meeting energy demands. Power generation from gas-fired power plants produces lots of emissions, which increases the concentration of greenhouse gases in the atmosphere. Thus, CO2 emissions reduction is a high priority demand, and one of the solutions to this problem is carbon capture and storage (CCS). The main restriction for deploying large-scale CO2 capture systems is that these processes reduce the plant net power output for fixed energy due to the addition of carbon capture plant, thereby increasing the net cost of capture [1]. However, the cost associated with commercial capture plants is about 80% of CCS cost [2], which poses a major setback. The reduction in the power output is as a result of the parasitic load of the capture plant, the load demand comes from the reboiler steam requirements drivers such as pumps, compressors, cooling duty needed for the amine process, etc. leading to an energy penalty. This energy penalty can be reduced in a number of ways, many of which are specific to the capture technology employed. For absorption processes, the total reboiler energy can be lowered by an improved process design of the solvent plant [3,4]. Examples of these improved process design include absorber intercooling, rich split, lean amine flash, vapor recompression, configurations and stripping with flash steam, etc. The total energy consumption can be reduced up to 20% in pilot-scale plant studies for the different configurations compared to the conventional amine plants [5].

Several configurations for minimizing energy consumption have been suggested and studied. Leites et al. [6] modelled the intercooler and varied temperatures between 40–80 ◦C, the whole liquid was removed from the column at each cooling stage and pumped to 1.1 bar to overcome pressure drop. It was concluded that cooling to 40 ◦C was found to have the maximum effect on reboiler duty and also minimization of additional equipment. Karimi et al. [7] investigated the intercooling effect in CO2 capture energy consumption, the optimal location for intercooling was about 1/4th to 1/5th of the height of the column from the bottom which brought about 2.84% savings in reboiler energy and it was concluded that intercooling is an option for reducing energy consumption. Aroonwilas and Veawab [8] modelled an intercooler configuration which has been integrated with an amine process to evaluate the energy savings effect as a result of enhanced working capacity. The methodology involved the withdrawal of all the liquid at 1/5th of the column height from the bottom and cooled at 45 ◦C by varying the lean loading between 0.12–0.25 mol CO2/mol monoethanolamine (MEA). With the intercooling, a reduction of 10% in the solvent required led to energy savings in the stripper reboiler and also, it was concluded that lean loading above 0.18 mol CO2/mol MEA had a minimal effect on reboiler duty. Reddy et al. [9] modelled lean amine flash configuration which generates additional steam by flashing hot lean amine leaving the stripper. Results showed 11% reduction in reboiler steam, 16% reduction in cooling water and 6% in stripper diameter. It was observed that hot lean amine temperature was lowered from 120 ◦C to 103 ◦C by the flash; this low temperature increases the energy consumption in the stripper bringing about the additional steam generation and improved working capacity. Eisenberg and Johnson [10] modelled a rich split configuration and this resulted in 7.1% savings in reboiler duty over the reference case. It was concluded that for loadings greater than 0.15 mol CO2/mol MEA, a clear benefit was obtained. But it was later observed that increasing the packing height for a lean loading of 0.2 mol CO2/mol MEA, for 30% of the cold solvent split, a reboiler duty of 97.8 kW was required, which is about 10.3% higher than the reference case.

Cousins et al. [11] reviewed fifteen amine process configurations (multi-component columns, inter-stage temperature control, heat-integrated stripping column, split flow process, vapor recompression, matrix stripping, heat integration, etc.). The configurations which involved both experimental and simulation-based methods were evaluated with different solvents, and different operating conditions (temperature, pressure and feed composition). It was, therefore, difficult to compare the energy savings on a fair basis. Thus, it was concluded that the configurations considered reduced the energy consumption, but increased the plant complexity. Also, configurations with less additional equipment (e.g., vapor recompression, etc.) gave higher efficiencies than those with more equipment. Ahn et al. [12] evaluated ten different configurations capture plants, this included the multiple alterations (absorber intercooling combined with condensate evaporation and lean amine flash) which were novel in the study using 30 wt% MEA to capture 90% CO2, reboiler duty savings was maximized by simultaneous application of previous strategies. The comparison was based on total energy consumption (thermal and electrical energy used), the multiple strategies achieved a greater reduction in the energy requirement reducing steam consumption by up to 37% when compared to the simple absorber/stripper configurations. Sharma et al. [13] reviewed and assessed the advantages of fourteen different flow sheeting configurations. The comparison was based on cooling duty and equivalent work. Results showed pump-around was more beneficial than intercooling, while intercooling with rich split was found to be the most beneficial based on additional equipment, and the equivalent energy consumption was 12.9% reduction over the base case. Lars et al. [14] compared different configurations; vapor recompression with split stream gave the best reduction of 11% compared to the conventional. Liang et al. [15] studied five different flow sheeting configurations, the new innovation was the combination of split flow with overhead exchanger and improved split flow with vapor recompression. These innovations decreased equivalent work by 17.21% and 17.52% respectively. Jung et al. [16] suggested a new combination; rich vapor recompression and cold solvent split. Results showed that reboiler heat was reduced from 3.44 MJ/kg CO2 to 2.75 MJ/kg CO2. All of

these configurations presented above (summarised in Table 1) have achieved the aim of reducing the energy consumption for the MEA capture plant compared to the conventional flowsheet.



Also, rate-based modelling of CO2 absorption in a packed column using AMP solutions for the capture plant has been carried out in the literature. Alatiqi et al. [21] used a rate-based model in simulating CO2 absorption in AMP, MEA, and diethanolamine (DEA) solution. AMP was used to compare the absorption of CO2 in MEA and DEA solutions. Aboudheir et al. [22] used a rate-based model in simulating the absorption of CO2 using AMP solutions. Results were validated with experimental plant data. Gabrielsen et al. [23,24] carried out an experimental study using AMP solution and this was used as validation for the simulation of a rate-based model for CO2 capture in a structured packed column. Afkhamipour and Mofarahi [25] compared rate-based and equilibrium-based models simulation results of a packed column using AMP solution. The rate-based models gave a better prediction of the concentration and temperature profiles than the equilibrium based. Dash et al. [26] explored the benefits of using blended solvents AMP with Piperazine (PZ). These studies presented above have all worked on models for the AMP capture process. There are limited studies on the different configurations using the AMP solvent. Kvamsdal et al. [27] presented a simulated model for the Cesar 1 (AMP + PZ) involving modifications such as intercooling and vapor recompression. The comparison was made with the MEA process using the same modifications. Results showed that the MEA process had lower energy requirements as compared to Cesar 1. Energy consumption accounts for about 25% of total cost thus, the AMP solvent, which has more favourable operating parameters as compared to the MEA solvent as shown in studies [23,28,29] will further minimize energy requirements which will reduce cost. It is therefore important to develop these configurations and utilize the energy savings provided.

Furthermore, exergy analysis which identifies where exergy is destroyed is carried out. The destruction of exergy in a process is proportional to the entropy generation in it; which accounts for the inefficiencies due to irreversibility [30]. Exergy analysis of capture plants using MEA and ammonia solvents have been carried out by few authors [17–20] as shown above in Table 1, to investigate the effects of the associated losses. However, studies to analyse where the losses occur in the AMP-based CO2 capture plant is lacking. This study includes (i) steady-state rate-based simulation and conventional exergy analysis of the AMP-based PCC process (ii) evaluation of exergy destruction and efficiency in the AMP capture system, (iii) exergy analysis of the different flowsheeting configurations with AMP solvent.

#### **2. Modelling Framework**

#### *2.1. Model Description of the Capture Plant*

The AMP-based process model was developed using the operating parameters in Aspen Plus® software Version (V) 8.4 (Aspen Technology, Inc., Bedford, MA, USA, released in 2013), and consists of an absorber and a stripper column, with a cross heat exchanger and a pump, all connected in a closed loop cycle as described in studies [26,31]. The validation of the capture plant model with experimental data is presented in the literature [26,28].

#### *2.2. Exergy Analysis*

Exergy is defined as the maximum theoretical work that is obtained from a system when its state is brought to the reference state [30]. Exergy analysis is a method employed in the evaluation of the use of energy [32]. Exergy gives the identification of the location, the magnitude and the causes of thermodynamic inefficiencies in a thermal system. In this section, the conventional exergy approach is used to evaluate the exergy destruction and potential for improvement of the CO2 capture plant. The values of the exergy reference temperature and pressure, which are default parameters in Aspen Plus® V8.4 simulation tool are 298.15 K and 1.013 bar respectively, and these are used in the simulations. A theoretical process in which the thermodynamic reversibility requires that all the process driving forces such as pressure, temperature and chemical potential differences be zero at all points and times [6] leads to producing a maximum amount of high energy consumption [19]. These losses can be reduced by several methods that are based on the second law of thermodynamics such as the counteraction, quasi-static method and the driving force method [6]. In this study, the driving force method is used to reduce the exergy destruction, leading to a reduction in energy consumption in the AMP-based PCC process and using the same absorbent throughout.

The Aspen Plus® V8.4 exergy estimation property set (EXERGYMS) is used in calculating the methods for physical and chemical exergies of the material and heat flows for each component, using the individual streamflow in the AMP-based capture plant. Furthermore, in other to determine the exergy of the reactions containing electrolytes, the thermodynamic properties of the ionic species of AMP were retrieved from the Aspen Plus® databank, V8.4 (Aspen Technology, Inc., Bedford, MA, USA, released in 2013). The standard Gibbs free energy of formation of AMP in the water at infinite dilution (DGAQFM) values used in this study are based on an estimate given in studies [33,34]. Gibbs free energy data which is called from Aspen database is used in the estimation of Gibbs free energy using the empirical relation in Aspen Plus® V8.4 software. The DGAQFM values of <sup>−</sup>1.628054 <sup>×</sup> <sup>10</sup><sup>8</sup> <sup>J</sup>/kmol and 4.574 <sup>×</sup> <sup>10</sup><sup>8</sup> <sup>J</sup>/kmol were obtained for AMPH<sup>+</sup> (AMP protonation) and AMPCOO- (AMP carbamate formation) respectively. Table 2 below shows the exergy destruction and efficiencies for the equipment in the AMP-based PCC process.


**Table 2.** Conventional exergy analysis of the AMP-based CO2 capture plant.

The equations below are used in evaluating the individual component and the total exergy destruction rate within a component. Thus, the exergy balance [35] for the whole system are given in Equations (1)–(3), while the exergetic performance of the AMP-based capture plant is given in Table 2. Equation 1 which is the fuel exergy of each component is as follows:

$$E\_{fuel(total)} = E\_{product(total)} + E\_{Destraction(total)} + E\_{Loss(total)}\tag{1}$$

While for the exergy efficiency of each component (n) which accounts for the thermodynamic losses is given as shown in Equation (2):

$$E\_{(n)} = E\_{product(n)} / E\_{fuel(n)}\tag{2}$$

And the exergy destruction ratio of the nth component is presented in Equation (3):

$$X\_{d(n)} = \; E\_{\text{Destrución}(n)} \; / \; E\_{\text{fucl}(total)} \tag{3}$$

Table 2, shows the exergy destruction and efficiency for the sub components in the AMP-based capture plant. As observed, the absorber and stripper components had exergy efficiency of 59.8 and 88.5%, respectively, while the heat exchanger gave the lowest exergy efficiency of 25%, these values are close in range with the literature [19].

#### **3. Flowsheeting Configurations**

The amine flow sheeting configurations are set up for the capture plant. The reference plant is the standard AMP-based capture plant configuration, as described in Section 2.

#### *3.1. Intercooling Configuration*

Absorption of CO2 from the gas streams is mostly done between temperature 40–60 ◦C, this is because rates of CO2 absorption for a 30 wt% amine solvent are highest in this temperature range (11). In other to control the temperature in the absorber so as to reach a higher rich CO2 loading (high absorption capacity), inter stage cooling as shown in Figure 1 is required.

In this configuration, the exothermic nature of CO2 absorption in amine present in the absorber leads to a temperature bulge and this impacts absorption negatively [36]. At the location of the T-bulge, the solvent is being extracted, cooled to 40 ◦C and returned to the absorber which leads to the enhancement of absorption driving force [11]. A lower temperature leads to a reduction in the absorption rates such as chemical kinetics, diffusivities, etc., while an increase in temperature favours the absorption capacity. These two operations compete with each other in the absorber. The temperature in the absorber can be controlled by adjusting the flue gas, the lean solvent temperature and flowrate coming into the absorber. Thus, this balances the temperature at either end of the absorber. With this modification, a reduction in solvent circulation rate which leads to higher absorption capacity is achieved. Hence, this process configuration enables the control of temperature within the absorber and is capable of enhancing CO2 recovery [11], which is very effective in reducing the energy requirement of a CO2 capture plant [7]. Results obtained are given in Table 3.

**Figure 1.** Intercooling Configuration [11].



As shown in Table 3, lower reboiler duty and higher absorption capacity are achieved for AMP when compared with the reference; this is one of the main benefits of intercooling. This occurs due to the higher rich loading obtained from the intercooled model which leads to a lower circulation rate. Also, literature studies [37] has proven AMP to have a higher loading capacity and lower heat of reaction due to the formation of bicarbonates. The reference plant has a higher temperature bulge (63 ◦C) in the absorber compared to AMP-cooled (47 ◦C), thus having a higher heat of reaction which leads to an increment in the reboiler duty. With this intercooling, a gain of 24.5% savings in reboiler duty for AMP-cooled over the reference is achieved.

#### *3.2. Lean Amine Flash Configuration*

As shown in Figure 2, the stripper design comprises additional equipment such as a pump, compressor, and the flash drum. Additional steam is generated by flashing hot lean amine exiting the stripper close to ambient pressure, followed by the compressing of the gas stream up to the stripper pressure and re-introducing it into the stripper column [38,39]. In the flash drum present, more CO2 is desorbed by reducing the pressure in the flash drum and lean loading in the stripper out is further reduced. Thus, additional steam is generated for the AMP-based process since the gas stream is compressed at a higher pressure. Results for the lean amine flash configuration is given in Table 4.

**Figure 2.** Lean amine flash modification [12].

**Table 4.** Results for lean amine flash configuration.


Table 4 shows that the loading out of the stripper is further reduced and the solvent working capacity is increased for the AMP-based process with amine flash. For the AMP lean amine flash, compressing at a higher pressure, flashed vapor is heated at a higher temperature of over 140 ◦C, cross heat exchanger duty is further reduced and a higher stripping efficiency is achieved leading to a savings of 20.92% in reboiler duty of AMP compared to the reference. As a result of the flash, which helps to obtain saturated steam before feeding it into the stripper, hot rich amine temperature is reduced from 140 ◦C to 113 ◦C, thus leading to an increase in consumption of energy, for the reference plant as compared to the AMP lean-amine flash.

#### *3.3. Rich Split Configuration*

The process in Figure 3 involves the splitting of the rich stream where the split entering the top of the stripping column stays unheated. It has the capability of pre-stripping the cold rich solvent entering at the top of the stripping column; this can be attained due to the vapor released from the rich solvent steam which moves up the stripping column. This helps to thermally regenerate less solvent, thereby reducing regeneration energies [11]. Hence, this configuration process is beneficial for lean loadings above 0.15 mol/mol and this reduces the energy required for regeneration [11].

In the configuration obtained in a study by Cousins et al. [11], 30% of the cold rich solvent was split to the top of the column with a condensate packing height of 1.12 m and a minimum reboiler duty of 97.8 kW was obtained which was about 10.3% higher savings than the reference case. The reason for this is that the reboiler duty achieves a combination of four functions: (i) providing sensible heat to the rich solvent to increase its temperature to the specified reboiler temperature, in which some heat is attained in the lean/rich heat exchanger [11]. (ii) evaporating water in the reboiler, which acts as the stripping agent, aiding the CO2 removal from the solvent. Thus, steam released will replace

steam generated in the reboiler, this is because the generation of steam within the column reduces the operating CO2 partial pressure below that of the partial pressure in the column, enabling stripping to occur. (iii) Providing heat to reverse the absorption reaction in the absorber, which is in theory, equal to that released due to the exothermic reactions and (iv) providing heat to liberate dissolved CO2 out of the solvent. Depending on the function which is most dominant under a given set of conditions, the reboiler duty will adjust accordingly to maintain the required stripping rate. The study also revealed that the possibility of obtaining a higher CO2 flashing will allow the further release of CO2 in the upper stages of the stripping column and give additional benefits [11].

**Figure 3.** Rich split modification [11].

It should be noted however that the efficiency of the lean/rich heat exchanger will have a significant effect on the results of this process modification. The objective of the lean/rich exchanger is energy conservation. The energy available from the lean amine stream is transferred to the rich amine prior to introducing the rich amine to the stripper. This energy transfer results in a decreased energy requirement for the stripper as observed in the results presented in Table 5.



During the operation considered here, the solvent split fraction was found to have a significant effect on the temperature approach achieved through the lean/rich heat exchanger. As more of the cold rich solvent is split to the top of the column, the lower flowrate through the heat exchanger means that the hot rich solvent can be raised to higher temperatures. The vapor fraction in the hot rich solvent will increase, providing more steam for pre-stripping, which reduces the reboiler duty, as shown in Table 4. Thus, the reboiler duty slightly reduces from 4.77 kW to 4.73 kW for rich split configuration as compared to the reference plant.

#### *3.4. Vapor Recompression*

This process modification as shown in Figure 4 involves the extraction of steam from a part of the stripping column, which is recompressed and re-introduced into the regenerator. This operation turns mechanical energy into thermal energy to provide more stripping steam [11]. Hence, this configuration works by providing an additional source of stripping steam for the column, as this lowers the thermal input required by the stripper. The vapor is compressed to five times the operating pressure of the stripping column, before being separated with the condensate recycled back to the stripper [11]. Although this process configuration reduces the reboiler duty, it leads to a corresponding increase in the power requirement due to the addition of the compression stages [11]. Thus, in this study, one expansion stage is used to make comparison easier.

**Figure 4.** Vapor recompression modification [11].

As shown in Table 6, 59.5% savings in reboiler duty is obtained for the AMP-based plant compared to the reference case. For the vapor recompression configuration, additional stripping steam is generated, this is as a result of the higher pressure which leads to a higher temperature and enables the lean solvent flash pressure drop to increase. Also observed is the reduced heat exchanger duty obtained for the vapor recompression flowsheet, this is because lean solvent temperature increases which lead to a higher stripping efficiency.



#### *3.5. Rich Split with Intercooling*

As mentioned earlier, splitting the rich stream by 20–30% as recommended in the literature [11], can increase the absorption capacity which brings about the energy savings for the stripper design. In addition, the rich split modification requires minimal additional equipment. Furthermore, cooling at different stages in the absorber column has a significant effect in reducing the reboiler duty. Thus, with multiple alterations, reboiler duty can be further reduced [40]. Also, since the highest and lowest

savings in the reboiler duty for intercooling and rich split configurations respectively, were obtained in this study, a combination of rich split and intercooling configurations will be necessary, to observe if any benefit can be obtained, and to enable further reduction in reboiler duty as shown in Figure 5.

**Figure 5.** Rich split with intercooling configuration.

Simulation results are reported in Table 7 below, the multiple measures taken (rich split with intercooling) for the AMP-based process led to the energy savings of about 88.5% higher than the reference case. Thus, comparing the five different flowsheeting configurations, the rich split with intercooling configuration gave the best performance. This is in accordance with the literature [13].


**Table 7.** Results for rich split and intercooling

#### **4. Exergy Analysis for the Flowsheeting Configurations**

Table 8 below shows the total exergy analysis and performance evaluation of the different flowsheeting configurations. Results show that the rich split with intercooling and vapor recompressions configurations gave the best and worst exergetic performances respectively as compared to the other configurations. Also, as compared to the reference plant which is presented in Table 2, two configurations (rich split with intercooling and intercooling alone and gave higher exergy efficiency than the reference case. While lean amine flash, rich split alone and vapor recompressions gave lower exergy efficiencies as compared to the reference plant. A reason for the low efficiencies could be that, the more efficient the configurations (based on the reboiler duty), the less outlet for exergy losses. Furthermore, Figure 6, shows the efficiency pecentage and the amount of exergy for the different configurations. As clearly seen, the rich split with interccoling configuration, gave the highest exergy efficiency amd the lowest exergy destruction.

**Table 8.** Summary of Exergy Analysis for the Different Configurations.


**Figure 6.** Summary Results of Exergy Analysis for the Different Configurations. For the horizontal "Configuration" axis, 1. Intercooling 2. Rich Split 3. Rich Split with Intercooling 4. Lean Amine Flash 5. Vapor Recompression.

#### **5. Conclusions**

In this study, the exergy analysis of the AMP PCC process and its flowsheeting configurations have been evaluated. The operating parameters for the rate-based AMP model present in Aspen Plus® software were used to describe the PCC process. The conventional exergy analysis performed provides an evaluation of energy consumption the CO2 capture plant from the thermodynamic point of view, and also evaluates the reduction of the exergy destruction. The pump and stripper subsystems of the AMP-based capture plant had the highest exergy destruction, and the cross-heat exchanger subsystem gave the lowest exergy destruction performance.

Several configurations were proposed in the literature to reduce energy requirements in the amine-based CO2 capture plant, these configurations at atmospheric pressure have been simulated in Aspen Plus® software. Flowsheeting configurations considered in this study include intercooling, lean-amine flash, rich split, vapor recompression and the rich split with intercooling configurations. Results show that the combination configuration (rich split with intercooling) had the highest savings (88.5%) in reboiler duty as compared to the reference AMP-based plant, and the other flowsheeting configurations. Furthermore, exergy analysis performed showed that the rich split with intercooling configuration had the highest exergy efficiency of 74%, followed by the intercooling configuration with 57% exergy efficiency, and that of the reference AMP plant was obtained to be 56%. The other configurations considered in the study had exergy efficiencies lower than that of the reference plant. This study has shown that some of the flowsheeting configurations can reduce the heat required for regeneration, and others can both reduce reboiler duty and at the same time increase the exergy efficiency. Thus, the flowsheeting configurations have significant improvements in the plant performance and may lead to cost reduction for the amine-based CO2 capture technology. Although the additional equipment for each configuration may incur extra cost, economic analysis is therefore required to ascertain if any cost benefits can be obtained with flowsheeting configurations for the AMP-based PCC process.

**Author Contributions:** E.O.—methodoogy and writing original draft; A.M.A.—supervision, review and editing; S.G.N.—writing, review and editing; O.O.—review and editing; V.E.—review and editing.

**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* **Reaction Kinetics of Carbon Dioxide in Aqueous Blends of N-Methyldiethanolamine and L-Arginine Using the Stopped-Flow Technique**

**Nafis Mahmud <sup>1</sup> , Abdelbaki Benamor 1,\*, Mustafa Nasser 1, Muftah H. El-Naas <sup>1</sup> and Paitoon Tontiwachwuthikul <sup>2</sup>**


Received: 7 January 2019; Accepted: 29 January 2019; Published: 6 February 2019

**Abstract:** Reduction of carbon dioxide emission from natural and industrial flue gases is paramount to help mitigate its effect on global warming. Efforts are continuously deployed worldwide to develop efficient technologies for CO2 capture. The use of environment friendly amino acids as rate promoters in the present amine systems has attracted the attention of many researchers recently. In this work, the reaction kinetics of carbon dioxide with blends of N-methyldiethanolamine and L-Arginine was investigated using stopped flow technique. The experiments were performed over a temperature range of 293 to 313 K and solution concentration up to one molar of different amino acid/amine ratios. The overall reaction rate constant (kov) was found to increase with increasing temperature and amine concentration as well as with increased proportion of L-Arginine concentration in the mixture. The experimental data were fitted to the zwitterion and termolecular mechanisms using a nonlinear regression technique with an average absolute deviation (AAD) of 7.6% and 8.0%, respectively. A comparative study of the promoting effect of L-Arginine with that of the effect of Glycine and DEA in MDEA blends showed that MDEA-Arginine blend exhibits faster reaction rate with CO2 with respect to MDEA-DEA blend, while the case was converse when compared to the MDEA-Glycine blend.

**Keywords:** Reaction; kinetics; carbon dioxide; N-methyldiethanolamine; L-Arginine; stopped flow technique

#### **1. Introduction**

The rapid growth of world economies associated with increased fossil fuel consumption for energy needs resulted in the generation of large amounts of greenhouse gases accumulated in the atmosphere. Carbon dioxide (CO2) is a major contributor to greenhouse gases accountable to the observed climate change and associated environmental problems. Reducing CO2 concentration in the atmosphere to an acceptable level is necessary for future generation's well-being. Different options are available to capture CO2; however, amine based reactive solvents is one of the most mature and successful technology used in the industry, especially from large point sources, such as natural gas treatment units and power generation plants [1–6]. Large variants of amine based solvents are available in the market, many of which contain proprietary additives to enhance their absorption performances. Amine based solvents are known by their high absorption capacities and their ability to selectively absorb CO2/H2S from natural and flue gases. Conventional amine solvents, such as primary monoethanolamine (MEA), 2-amino-2-methyl-1-propanol (AMP), secondary diethanolamine (DEA), tertiary amine N-methyldiethanolamine (MDEA) and polyamines (such

as piperazine (PZ), 2-(2-aminoethylamino) ethanol (AEEA) are efficient for capturing CO2 from various industrial processes and are still the choice in the industry because of the well-known absorption-regeneration process. However, several drawbacks like low absorption rate, periodic solvent make up to compensate for solvent losses, high regeneration energy requirement and severe equipment corrosion are still associated with their use [7–11].

Liquid tertiary amines, such as MDEA have higher theoretical sorption capacity with a ratio of 1:1 mol [12] but the reaction rate is much slower. To overcome this drawback, blended amines have been suggested [7]. To take advantage of their high loading capacity, low degradation rate and low energy for regeneration, tertiary amines are mixed with faster reacting primary/secondary amines or piperazine to develop new solvents with better CO2 capture performance such as high absorption and cyclic capacity, fast reaction kinetics, low corrosion, degradation and less heat duty requirement [13,14].

Amino acids, usually called alkaline salts of amines, have recently drawn attention to CO2 capture due to their exceptional properties [15]. The structure of amino acids consists of two important functional groups, namely amine (-NH2) and carboxylic acid (-COOH) or a sulfonic acid group [16]. Their salt nature makes their volatilities negligible which results in low solvent losses [17]. Their low environmental impact and high biodegradability [18] make them more environmentally friendly [19]. In addition, amino acids have high resistance to oxidative degradation making them a right choice for CO2 capture from flue gases containing large amounts of oxygen [20]. However, at high concentration or at high CO2 loading, they tend to precipitate resulting in lower mass transfer [21], which is a major drawback.

Nevertheless, several studies has reported on CO2 capture using amino acids [22–25]. Siemens developed an amino acid based process and claim it has a reduced energy consumption of about 73% compared to the conventional MEA process [26]. Aqueous solutions of sodium glycinate were proposed for CO2 absorption [27,28]. Shen et al. [29,30] used potassium salts of lysine and Histidine for CO2 absorption and concluded that histidine reactivity towards CO2 was comparable to that of MEA. Portugal et al. [31] used potassium glycinate and potassium threonate for CO2 absorption purposes [32]. Huang et al. [33] and Wei et al. [34] determined the reaction rate constant of taurate carbamate formation during the absorption of CO2 into CO2-free and CO2-loaded taurate solutions using a wetted-wall column at a temperature range of 293–353 K. The properties necessary for mass transfer evaluation, such us density, viscosity, CO2 diffusivity, N2O solubility were reported for several amino acids under different conditions [35–40].

In a previous study on reaction kinetics of amino acids with CO2, it was observed, that L-Arginine, an amino acid, showed faster reaction rate compared to that of Glycine and Sarcosine when used as a single solvent [41]. Another study on the promoting effect of Glycine in MDEA blends showed that the reaction rate of MDEA with CO2 could be significantly increased by the presence of an amino acid promoter [42]. However, the promoting effect of L-Arginine in MDEA blends remain unknown. In this work, the reaction kinetics of CO2 with aqueous mixtures of MDEA and L-Arginine were determined using the stopped flow technique. The temperature was varied from 298 to 313 K and the amine total concentration was varied from 0.25 to 1 mol of different proportions of Arg/MDEA. Our findings provide a new insight to the use of Arg as rate promoter for CO2 capture blended tertiary amines. The molecular structure of MDEA and L-Arginine are shown in the Figure 1.

**Figure 1.** Molecular Structure of MDEA (N-methyldiethanolamine) and L-Arginine.

#### **2. Reaction Models**

#### *2.1. Reaction of CO2 with MDEA*

It is widely accepted that the reactions of CO2 with primary amines results in formation of a carbamate and a bicarbonate products. However, in case of tertiary amines, only bicarbonates are formed during the reaction with CO2. Therefore, MDEA being a tertiary amine will also not form any carbamates and their reaction of CO2 in aqueous solution is as follows [21]:

$$\mathrm{^{1}CO\_{2}-H\_{3}CN(C\_{2}H\_{4}OH)\_{2} + H\_{2}O \xrightarrow{k\_{\mathrm{MDEA}}} H\_{3}CNH^{+}(C\_{2}H\_{4}OH)\_{2} + HCO\_{3}^{-}} \tag{1}$$

Its pseudo-first-order reaction rate is:

$$\mathbf{r}\_{\text{CO}2} - \text{MDEA} = -\mathbf{k}\_{\text{MDEA}}[\text{CO}\_2][\text{MDEA}] \tag{2}$$

In addition to this reaction, the formation of bicarbonates in aqueous systems may be considered:

$$\text{CO}\_2 + \text{OH}^- \xrightarrow{\text{kOH}} \text{HCO}\_3^- \tag{3}$$

Its rate of reaction was given as [43]:

$$\text{tr}\_{\text{CO}\_2-\text{OH}} = -\text{k}\_{\text{OH}}[\text{CO}\_2][\text{OH}^-] \tag{4}$$

#### *2.2. Reaction of CO2 with Amino Acid*

#### 2.2.1. Zwitterion Mechanism

This mechanism was first coined by Caplow to comprehend the reactions of primary or secondary amines with CO2 [44]. Amino acid [NHR1R2COO−] has a molecular structure similar to that of primary or secondary amines. Its reaction pathway with CO2 is considered to be similar to that of CO2-amines and usually yields the formation of carbamate ion through two successive steps: (i) the formation of an intermediate zwitterion according to Reaction 5, (ii) proton removal by any base present in the solution according to Reaction 6 [22].

$$\mathrm{^{1}CO\_{2} + NHR\_{1}R\_{2}COO^{-}} \overset{\mathrm{k\_{l}}}{\underset{\mathrm{k\_{-l}}}{\rightleftharpoons}} \mathrm{^{-}OOCN^{+}R\_{1}R\_{1}COO^{-}} \tag{5}$$

$$\mathrm{^{-}COCNH^{+}R\_{1}R\_{2}COO^{-}} + \mathrm{B\_{i}} \xrightarrow{\mathrm{k\_{\hat{b}j}}} \mathrm{^{-}COCNR\_{1}R\_{2}COO^{-}} + \mathrm{BH^{+}} \tag{6}$$

The corresponding reaction rate is given as [20]:

$$\sigma\_{\rm CO2-Ag} = -\,\mathrm{k}\_2[\mathrm{Arg}][\mathrm{CO}\_2] \left/ \left( 1 + \left( \mathrm{k}\_{-1} / \left( \sum\_{\mathrm{i}} \mathrm{k}\_{\mathrm{bi}}[\mathrm{B}\_{\mathrm{i}}] \right) \right) \right) \right. \tag{7}$$

where the term 'kbi' represents the deprotonation rate constant of the zwitterion by any base. The reaction rate constant can be written as:

$$\mathbf{k}\_{\rm Arg} = -\mathbf{k}\_2[\mathbf{A}\mathbf{r}\mathbf{g}] \Bigg/ \left( \mathbf{1} + \left( \mathbf{k}\_{-1} / \left( \sum\_{i} \mathbf{k}\_{\mathbf{b}\_i}[\mathbf{B}\_i] \right) \right) \right) \tag{8}$$

The analysis of this model reveals two asymptotic cases, namely, 1 >> <sup>k</sup>−<sup>1</sup> <sup>∑</sup><sup>i</sup> kbi [Bi] and 1 << <sup>k</sup>−<sup>1</sup> <sup>∑</sup><sup>i</sup> kbi [Bi] . When the formation of the zwitterion carbamate following Reaction 5 is the rate-limiting step. The first case prevails, thus, Equation (7) reduces to:

$$\mathbf{k}\_{\rm Arg} = -\mathbf{k}\_2[\mathbf{A}\mathbf{rg}] \tag{9}$$

In the opposite case, when 1 << <sup>k</sup>−<sup>1</sup> <sup>∑</sup>*<sup>i</sup>* <sup>k</sup>*bi* [*Bi*] , the proton removal from the zwitterion intermediate according to Reaction 6 is the rate limiting step; Equation (7) then becomes:

$$\mathbf{k}\_{\rm Arg} = -\mathbf{k}\_2[\mathbf{Arg}] \left( \sum\_{\mathbf{i}} \mathbf{k}\_{\mathbf{b}\_{\mathbf{i}}}[\mathbf{B}\_{\mathbf{i}}] \right) \bigg/ \mathbf{k}\_{-1} \tag{10}$$

In the latter case, the reaction order dependency on the amino acid concentration varies from one to two. This phenomenon is commonly observed in CO2 reaction with primary and secondary amines [26,27] and was proved to prevail in other salts of amino acids [28].

#### 2.2.2. Termolecular Mechanism

Crooks and Donnellan [45] proposed an alternative single-step termolecular mechanism, which involves only one-step in the reaction process as shown in the Figure 2 below.

$$\bigcup\_{R:\smile R}^{\mathsf{R}\mathsf{C}\mathsf{CO}} \bigcap\_{\bullet}^{\mathsf{O}} \overbrace{\bigcup\_{\mathsf{C}\mathsf{O}}^{\mathsf{k}\_{\mathsf{k}\_{\mathsf{k}}}}}^{\mathsf{O}}^{\mathsf{O}} \overbrace{\longleftrightarrows}^{\mathsf{k}\_{\mathsf{k}}} \cdot \mathsf{OOC}\mathsf{R}\mathsf{R}\mathsf{C}\mathsf{OOC}^{\mathsf{k}} + \mathsf{B}\mathsf{H}^{+}}$$

**Figure 2.** Termolecular Mechanism.

This mechanism was further investigated by Silva and Svendson [46], based on which they suggested that the reaction progresses through the bonding of the CO2 molecule with the amine, which is stabilized by solvent molecules with hydrogen bonds. This in turn results in the formation of loosely bounded complex. They also added that the carbamate will be formed only when the amine molecule is in the vicinity of zwitterion. An analysis of the rate expression of the termolecular mechanism shows that the reaction of CO2 with amine is second order with respect to amine. Therefore, in this case, Equation (7) becomes:

$$\mathbf{r}\_{\rm CO\_2} = \mathbf{k}\_{\rm av}[\rm CO\_2] = [\rm CO\_2] \left[ \rm Arg \right] \left\{ \sum \mathbf{k}\_{\rm b\_l} [\rm B\_l] \right\} \tag{11}$$

Regardless of the mechanism employed, a carbamate and a protonated base are the generally accepted products of the CO2 reaction with amine.

#### *2.3. Reaction of CO2 with Mixtures of MDEA and L-Arginine*

For blends of MDEA and L-Arginine, the overall reaction rate with CO2 is considered as the sum of reaction rates of CO2-MDEA and CO2-L-Arginine, hence:

$$\mathbf{r\_{CO\_2}} = \mathbf{r\_{CO\_2-Arg}} + \mathbf{r\_{CO\_2-MDEA}} + \mathbf{r\_{CO\_2-OH}} \tag{12}$$

which can be written as:

$$\mathbf{r}\_{\rm CO\_2} = -\left(\mathbf{k}\_2[\mathbf{A}\mathbf{r}\mathbf{g}]\right)\left(\mathbf{l} + \left(\mathbf{k}\_{-1}/\sum\_{\mathbf{i}}\mathbf{k}\_{\rm b\_i}[\mathbf{B}\_{\mathbf{i}}]\right)\right) + \mathbf{k}\_{\rm MDEA}[\mathbf{MDEA}]\right)[\mathbf{CO\_2}]\tag{13}$$

or

$$\mathbf{r}\_{\rm CO\_2} = \left(\mathbf{k}\_{\rm Arg} + \mathbf{k}\_{\rm MDEA}[\rm MDEA]\right)[\rm CO\_2] \tag{14} \\ = \mathbf{k}\_{\rm ov}[\rm CO\_2] \tag{14}$$

*Processes* **2019**, *7*, 81

In case of aqueous solution of L-Arginine, water molecules, 'H2O'; hydroxyl ions, 'OH-' and deprotonated amino acid, 'L-Arginine', act as bases. Therefore, if the reaction is expected to proceed via the zwitterion mechanism, then the based on Equation (8), the term 'kArg' can be defined as follows:

$$\mathbf{k}\_{\rm Arg} = -\frac{\mathbf{k}\_2[\rm Arg]}{1 + \left(\mathbf{k}\_{-1} / \left(\mathbf{k}\_{\rm Arg}'[\rm Arg] + \mathbf{k}\_{\rm OH^-}'[\rm OH^-] + \mathbf{k}\_{\rm MDEA}'[\rm MDEA] + \mathbf{k}\_{\rm H\_2O}'[\rm H\_2O]\right)\right)} \tag{15}$$

which can be written as:

$$\mathbf{k}\_{\text{Alg}} = -\frac{[\text{Arg}]}{\frac{1}{\text{k}\_2} + \left(1/\left(\frac{\text{k}\_2\text{k}\_{\text{Alg}}^\prime}{\text{k}\_{-1}}[\text{Arg}] + \frac{\text{k}\_2\text{k}\_{\text{OH}^-}^\prime}{\text{k}\_{-1}}[\text{OH}^-] + \frac{\text{k}\_2\text{k}\_{\text{OH}^-}^\prime}{\text{k}\_{-1}}[\text{MDEA}] + \frac{\text{k}\_2\text{k}\_{\text{H}\_2\text{O}}^\prime}{\text{k}\_{-1}}[\text{H}\_2\text{O}]\right)\right)}\tag{16}$$

By defining new constants, ka, khyd, kb and kw as <sup>k</sup>*<sup>a</sup>* <sup>=</sup> k2 <sup>k</sup> Arg <sup>k</sup>−<sup>1</sup> , khyd <sup>=</sup> k2 <sup>k</sup> OH <sup>k</sup>−<sup>1</sup> , <sup>k</sup>*<sup>b</sup>* <sup>=</sup> k2 <sup>k</sup> MDEA k−<sup>1</sup> and k*<sup>w</sup>* <sup>=</sup> k2k*H*2*<sup>O</sup>* <sup>k</sup>−<sup>1</sup> . Equation (13) becomes:

$$\mathbf{k}\_{\rm Arg} = \frac{[\rm Arg]}{(1/k\_2) + \left(1/\left(\mathbf{k}\_a[\rm Arg] + \mathbf{k}\_{\rm hyd}[\rm OH^-] + \mathbf{k}\_b[\rm MDEA] + \mathbf{k}\_w[\rm H\_2O]\right)\right)}\tag{17}$$

However, if the reaction is expected to proceed via the termolecular mechanism, the term, 'kArg' can be redefined as follows:

$$\mathbf{k}\_{\rm Arg} = \left[ \mathbf{Arg} \right] \left\{ \mathbf{k}\_{\rm a} [\mathbf{Arg}] + \mathbf{k}\_{\rm W} [\mathbf{H}\_2 \mathbf{O}] + \mathbf{k}\_{\rm hyd} [\mathbf{OH}^-] \right\} \tag{18}$$

#### **3. Materials and Methods**

#### *3.1. Materials*

Reagents used in this work were analytical grade N-methyldiethanolamine (MDEA) with a mass purity of 99% obtained from Sigma-Aldrich and L-Arginine with purity of 99% purchased from Fluka (St. Louis, MS, USA). All chemicals were used as received without further purification. CO2 solutions were prepared by bubbling analytical grade CO2 (99.99%) for at least half an hour in deionised water. Deionised water was used as solvent throughout the experiments.

#### *3.2. Methods*

Pseudo first-order kinetics of CO2 reaction with different aqueous mixtures of L-Arginine in MDEA were measured using stopped-flow apparatus (Hi-Tech Scientific Ltd., and Model SF-61DX2, Bradford-on-Avon, Wiltshire, UK) with a dead time of 1 ms. The apparatus essentially consists of working syringes immersed in water bath, where the reacting solutions are loaded. It also contains a pneumatically controlled drive plate to load the reacting solutions into a mixer and the conductivity of the mixed solution is measured within the conductivity cell. Finally, the mixed solution is collected in a stopping syringe. A schematic diagram of the Stopped-Flow Apparatus has been presented in Figure 3 below.

An external water bath (Alpha RA8, Lauda, Delran, NJ, USA) was used to control the temperature of the sample flow circuits within ±0.10 K. Depending on the applied temperature, the run time of the experiment was varied from 0.05 to 0.2 s. Freshly saturated solutions of CO2 were prepared by bubbling CO2 in deionized water. Concentration of CO2 in the bubbled solution was measured with gas chromatography (GC-6890 from Agilent, Santa Clara, CA, USA) following Shell method®–SMS 2239-04. Fresh water used to dilute the solution in order to maintain the concentration ratio of the amine/amino acid solution 20 times higher than that of the CO2 solution. This was done to ensure that the reaction conditions with respect to [CO2] fall within the pseudo first order regime [47]. The

amine/amino acid solutions were also prepared using deionized water. For each run, the CO2 and amine/amino acid solutions were loaded in two separate syringes. Equal volumes of aqueous solutions of amine/amino acid and CO2 were mixed in the stopped-flow apparatus. The reaction was monitored by measuring the conductivity change as function of time. The change in the conductivity, Y, with respect to time as described by Knipe et al. [48] was fitted to an exponential equation resembling a first-order kinetics equation:

$$\mathbf{Y} = -\mathbf{A}. \exp(-\mathbf{k}\_{\rm op}.\mathbf{t}) + \mathbf{Y}\_{\rm os} \tag{19}$$

where, 'kov' is the pseudo first-order reaction rate constant. The averages of three experimental runs were considered to obtain a reproducible and consistent kov value for the whole range of the tested concentrations and temperatures. The reproducibility error of kov was found to be less than 3% in all experiments. The experimental results were obtained in the provided 'Kinectic Studio' Software (Bradford-on-Avon, Wiltshire, UK). A screenshot of typical conductivity profile is presented in Figure 4.

**Figure 3.** A Schematic Diagram of the Stopped-Flow Apparatus.

**Figure 4.** Typical Experimental run for MDEA-Arg at 303 K.

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

#### *4.1. Reaction of CO2 with MDEA and L-Arginine*

The obtained pseudo first order rate rate constants, 'kov', were plotted against temperature for one molar total concentration (see Figure 5). The overall rate constants (kov) increased with increasing solution temperature as well as with increased [Arg] proportion in the total mixture. Similarly, the plot of the overall rate constants against different Arg/MDEA ratios for a total concentration of 1 mol is shown in Figure 6.

**Figure 5.** Rate constant, 'kov', vs. temperature for total 1 M solution.

**Figure 6.** Overall rate constant, 'kov', vs. different Arg/MDEA ratios for a total 1 M solution.

Upon applying the power law kinetics to plot the overall rate constants against the concentrations of L-Arginine, an average exponent of 0.98 was obtained, which affirms that the pseudo first order regime prevails. Therefore, within the range of the experimental conditions, the reaction can be analysed via the zwitterion mechanism [49]. Additionally, the possibility of using the termolecular mechanism to interpret the obtained data was also verified by plotting kov/[ARG] against [ARG]. The plots show a satisfactory linear relationship indicating that the termolecular mechanism can also be applied to interpret the obtained experimental kinetics data [49,50]. A typical plot is shown in Figure 7. Hence, obtained experimental kinetics data were analysed using both the zwitterion and termolecular mechanisms.

**Figure 7.** Termolecular mechanism applicability Test for 0.025 M L-Arginine concentration.

#### *4.2. Zwitterion Mechanism*

The experimental data of the rate (kov) of the CO2 reaction with methyldiethanolamine (MDEA) and L-Arginine (Arg) were obtained by fitting the conductivity-time curves to Equation (19) Zwitterion mechanism was used to interpret the obtained experimental data and the obtained overall rates along with apparent and predicted kArg rates are presented in Table 1.


**Table 1.** Rate constants at different temperatures and (MDEA+Arg) concentrations.


**Table 1.** *Cont*.

The rate constant karg was calculated using Equation (17) by subtracting kMDEA and kOH values from kov values. The values of kMDEA, kOH, k2, ka and kw were estimated from the previous works [41–43,51]. It is to be noted that, there was a typographical error within the power of the previously reported rate expression for kw (1.23 <sup>×</sup> <sup>10</sup><sup>12</sup> <sup>e</sup><sup>−</sup> 4364.7 <sup>T</sup> was reported instead of 1.23 <sup>×</sup> <sup>10</sup><sup>9</sup> <sup>e</sup><sup>−</sup> 4364.7 <sup>T</sup> ) [41], this error has been revised in this work and the correct rate expression for kw along with the other expressions that were used in this work have been listed in Table 2.


**Table 2.** List of rate expressions used for zwitterion mechanism.

Experimental rate constants, 'kArg', data were fitted to Equation (17) to extract the individual blocks of rate constants described in this equation. The concentrations of water molecules [H2O] were calculated by mass balance while those of hydroxyl ions [OH−] were estimated from the relation given by Astarita et al. [52]. The use of this relationship is justified since the CO2 loading in the amine solution was very small as it was verified by Gas Chromatography throughout all experiments.

$$\left[\mathrm{OH}^{-}\right] = \sqrt{\frac{\mathrm{K}\_{\mathrm{W}}}{\mathrm{K}\_{\mathrm{Pi}}}[\mathrm{AM}]} \text{ for } \alpha \le 10^{-3} \tag{20}$$

Using Equation (20), the total [OH−] was taken to be the sum of [OH−] ions produced by MDEA and those produced by Arg. The water dissociation constant, 'Kw' and protonation constant, 'Kpi', for MDEA and L-Arginine were expressed as a function of temperature according to the following equation:

$$\mathbf{L}\mathbf{n}\mathbf{K}\_{\mathrm{i}} = \frac{\mathbf{a}\_{\mathrm{i}}}{\mathrm{T}} + \mathbf{b}\_{\mathrm{i}}\mathrm{ln}\mathrm{T} + \mathbf{c}\_{\mathrm{i}}\mathrm{T} + \,\mathrm{d}\_{\mathrm{i}} \tag{21}$$

Values of the constants ai–di are given in Table 3.

**Table 3.** Values of different equilibrium constant used to estimate OH− in Equation (20).


Applying a nonlinear regression technique using Excel solver, experimental karg values were fitted to Equation (17) taking into account the species concentrations, H2O, Arg, OH− and MDEA previously calculated. Since the rate expressions for the terms k2, ka and kw were already available from previous work [41]. The regression analysis was initially performed to generate the values of the term kb and kOH only. However, the obtained values for the kOH indicated no catalytic influence on the kArg values. This can be attributed to the fact that the concentration of the hydroxyl ions is very low compared to other bases in the system, which leads to the conclusion that there is no significant influence of catalytic hydroxyl ions on the kinetics. Furthermore, the reaction between hydroxyl and CO2 exhibits slower kinetics which has been previously demonstrated by Gou et al. [55]. Hence, the final regression analysis were performed excluding the kOH term. The generated rate constant values for the kb term are summarized in Table 4.

**Table 4.** Reaction rate constants at different temperatures using Zwitterion mechanism.


Using these generated rate constants, the overall reaction rate constant, 'kArg', values were predicted using Equation (17) and were plotted against real experimental data in a parietal plot as shown in Figure 8. It is very clear that the adopted rate model along with extracted individual rate constants represent very well the experimental results with an average absolute deviation, AAD, of 7.6%. Figure 8 validated the choice of the kinetics model used to interpret the data of the reaction of CO2 with mixtures of MDEA and Arg represented in Equation (12). Furthermore, these results confirm the contribution of Arg and MDEA species in the base-catalytic formation of carbamate.

The individual rate constants at different temperatures were plotted as a function of temperature according to Arrhenius equation as shown in Figure 9 and associated parameters are summarized in Table 5. The activation energy (Ea) of each reaction was derived from the Arrhenius plots along with the pre-exponential coefficient of each rate constant.

From Table 5, it can be observed that the activation energy of L-Arginine (39.15 kJ mol−1) is smaller than that of MDEA (49.24 kJ mol<sup>−</sup>1), which shows that L-Arginine reacts faster with CO2 than MDEA. In fact, L-Arginine having a molecular structure similar to that of primary amines, have a faster reaction rate compared to tertiary amine MDEA. The Ea for Arg, MDEA and H2O catalytic carbamate formation showed that the contribution of water to the overall formation of carbamate (36.29 kJ mol<sup>−</sup>1) is the most important followed by that of L-Arginine (38.28 kJ mol<sup>−</sup>1), while the contribution of MDEA to this reaction (42.27 kJ mol<sup>−</sup>1) were found to be the least.

**Figure 8.** Parity plot of experimental and predicted kArg values for Zwitterion mechanism.

**Figure 9.** Arrhenius plots of CO2-MDEA-Arg rate constants using zwitterion mechanism.


**Table 5.** Summarized kinetics rate constants for CO2-MDEA-Arg reaction using Zwitterion Mechanism.

\* Corrected expression.

#### *4.3. Termolecular Mechanism*

Since the termolecular applicability tests revealed the possibility of applying this mechanism to interpret the experimental data, the kinetics of CO2-MDEA-Arg were then further investigated via this mechanism. Similar to that of the zwitterion mechanism, the values of ka and kw were also estimated from previous work [41] and are listed in Table 6.

**Table 6.** List of rate expressions used for Termolecular mechanism.


Using the ka and kw values, previously obtained kArg values were then fitted in accordance to the Equation (18). Excel solver was then used to regress the experimental data to obtain the rate expressions. The apparent and predicted kArg values obtained using the termolecular mechanism are presented in Table 7.

Similar to the results obtained using the zwitterion mechanism, the obtained fitting results showed that hydroxyl ion (khyd) had a negligible effect for CO2-MDEA-Arginine reaction using termolecular mechanism. Only L-Arginine, MDEA and water concentrations effects were found to be significant. The natural logarithm of the individual rate constants was plotted against T−<sup>1</sup> according to Arrhenius equation as shown in Figure 10. The generated rate constant values for the kb term are summarized in Table 8.

The predicted rate constant values were compared to the experimental ones as via a parity plot shown in Figure 11, which displayed good agreement between both values with an AAD of 8.0%. Since the AAD% is very close to that obtained in case of zwitterion mechanism (7.60%), it can be suggested that termolecular mechanism can be also used to interpret the obtained experimental data. The obtained rate expressions using termolecular are summarized in Table 9.


**Table 7.** Rate constants at different temperatures and (MDEA+Arg) concentrations.

**Table 8.** Reaction rate constants at different temperatures using termolecular mechanism.


**Figure 10.** Arrhenius plots of CO2-MDEA-Arg rate constants using termolecular mechanism.

**Figure 11.** Parity plot of experimental and predicted 'kArg' values for termolecular mechanism.


**Table 9.** Summarized kinetics rate constants for CO2-MDEA-Arg reaction using Termolecular Mechanism.

Upon evaluating the obtained rate expression for the 'kb' term in both mechanisms, it is observed that activation energies in both models are comparable to each other (Ea <sup>Z</sup> = 42.27 kJ mol−<sup>1</sup> and Ea <sup>T</sup> = 40.65 kJ mol−1). Furthermore, it is noticed that regardless of the model used catalytic effect of L-Arginine (Ea <sup>Z</sup> = Ea <sup>T</sup> = 38.28 kJ mol−1) is higher than the catalytic effect of MDEA. Based on this, it can be concluded that the CO2-MDEA-Arg reactions can be effectively interpreted using both zwitterion and termolecular mechanisms.

#### **5. Comparison with Other Amine Systems**

#### *5.1. Comparison with Secondary, Tertiary and Sterically Hindered Amine*

The obtained rate constants data for 1M MDEA-Arg (0.9M MDEA + 0.1M Arg) in this work were compared with those of DEA [56], MDEA [42] and AMP [57] as shown in Figure 12. It was observed that the rate constants of MDEA-Arg were much lower than that of secondary amine (DEA) and lower than that of sterically hindered amine (AMP). However, the rate constant of MDEA-Arg mixtures were always higher than those of the tertiary amine (MDEA). This elucidates the effect of L-Arginine presence in the MDEA-Arg blend which can enhance the overall kinetics of the CO2-MDEA reaction and make it comparable to other secondary and hindered amines. Based on the above analysis, the overall rate constants of these amine systems with CO2 were found to be in the following order: DEA > AMP > MDEA-Arg > MDEA.

**Figure 12.** Comparison of the obtained data with MDEA-Arg with other amine systems.

#### *5.2. Comparison of the Promoting Effect of L-Arginine*

The promoting effect of L-Arginine investigated in this work was also compared with that of the available data of MDEA-Glycine [42] and MDEA-DEA [58] systems at different temperatures and at the same overall concentration (1 M) as shown in Figure 13. For all three compared systems, the same 0.1 M of the promoter was added. It is observed that the addition of 0.1 M L-Arginine in the MDEA-Arg has resulted in higher overall rate constant compared to the addition of the 0.1 M DEA. However, the addition of 0.1 M Glycine in the MDEA blend has resulted in higher overall rate constant compared to that of 0.1 M L-Arginine. Although the previous study [41] revealed that the kinetics of L-Arginine alone with CO2 is higher than that of the Glycine, Guo et al. [55] observed that the Glycine at higher pH exhibits faster kinetics. Since the presence of MDEA can increase pH of the solution, it triggers the base form of Glycine to react with the CO2 resulting in a higher overall rate constant in the MDEA-Glycine blend as observed in the work of Benamor et al. [42]. The presence of MDEA has more significant catalytic effect towards the formation of zwitterion intermediate in MDEA-Glycine (Ea = 24.67 kJ mol−<sup>1</sup> [42]) compared to that of MDEA-Arg (Ea = 42.27 kJ mol−1). Furthermore, activation energy for the reaction of zwitterion intermediate formation of Glycine in the MDEA-Gly is 22.95 kJ mol−<sup>1</sup> [42] is also lower than that of L-Arginine (Ea = 37.27 kJ mol−1) in the MDEA-Arg blend. Therefore, based on this analysis, the rate constants of the three blended amine systems with CO2 were found to be in the following order: MDEA-Gly > MDEA-Arg > MDEA-DEA.

**Figure 13.** Comparison of MDEA-Arg with other MDEA blends.

#### **6. Conclusions**

The kinetics of the reaction of CO2 with MDEA + Arginine in aqueous solutions was studied using the stopped-flow technique for the first time. The measurements were performed for a concentration range from 0.25 M to 1 M and a temperature range from 298 to 313 K. The rate constants were well correlated by Arrhenius equation type. The activation energies for the rate constants were estimated. Both of the adopted zwitterion and termolecular models were very accurate in representing the experimental data over a range of five different temperatures from 293 to 313 K with an AAD of 7.6% and 8.0%, respectively. The contribution of L-Arginine, MDEA and H2O to the catalytic carbamate formation pathway was assessed using rate constants generated form the reaction of arginine alone with CO2. The results showed that the contribution of arginine to the overall formation is more significant followed by the contribution of water in both models, while the contribution of MDEA molecules was found to be the least. Based on the regression results, rate expression for the catalytic formation of zwitterion was to be kb <sup>=</sup> 6.07 <sup>×</sup> 1010e<sup>−</sup> 5083.8 <sup>T</sup> for the zwitterion mechanism and kb <sup>=</sup> 1.42 <sup>×</sup> <sup>10</sup>10e<sup>−</sup> 4888.6 <sup>T</sup> for the termolecular mechanism. A comparison of the obtained overall rate constant with other amine systems revealed the MDEA-Arginine-CO2 reaction was faster than that of MDEA-CO2 but slower than that of secondary and sterically hindered amine. A further comparison with MDEA-promotor blends showed that the reaction of MDEA-Arginine with CO2 is slower than MDEA-Glycine but faster than MDEA-DEA. This was attributed to the fact that the catalytic contribution of L-Arginine for the formation of zwitterion intermediate is lower compared to Glycine in MDEA blends. Furthermore, presence of MDEA can significantly catalyse the formation zwitterion intermediate in MDEA-Glycine blend (Ea = 24.67 kJ mol−<sup>1</sup> [43]) than that of MDEA-Arg blend (Ea = 42.27 kJ mol<sup>−</sup>1). Consequently, a faster reaction kinetics was observed in MDEA-Glycine-CO2 reactions than MDEA-Arg-CO2 reactions.

**Author Contributions:** Conceptualization, A.B., N.M., and P.T.; Methodology, A.B. and N.M.; Software, A.B.; Validation, A.B., N.M., M.N., M.H.E.-N. and P.T.; Formal Analysis, A.B., N.M, M.N. and M.H.E.-N.; Investigation, A.B., N.M. and M.H.E.-N.; Resources, A.B.; Data Curation, A.B., N.M. and M.N.; Writing-Original Draft Preparation, N.M., and A.B.; Writing-Review & Editing, N.M., A.B., M.N., M.H.E.-N. and P.T.; Visualization, A.B., M.N. and N.M.; Supervision, A.B. and P.T.; Project Administration, A.B. and M.H.E.-N.; Funding Acquisition, A.B.

**Funding:** This paper was made possible by an NPRP Grant # 7 - 1154 - 2 – 433 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.

**Acknowledgments:** The authors thank Ahmed Soliman and Dan Jerry Cortes for providing laboratory support.

**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* **Investigation of Pore-Formers to Modify Extrusion-Spheronized CaO-Based Pellets for CO2 Capture**

#### **Zonghao Zhang, Shuai Pi, Donglin He, Changlei Qin \* and Jingyu Ran \***

Key Laboratory of Low-grade Energy Utilization Technologies and Systems of Ministry of Education, School of Energy and Power Engineering, Chongqing University, Chongqing 400044, China; zzh.cqu@foxmail.com (Z.Z.); p.shuai@cqu.edu.cn (S.P.); cqu.hedl@foxmail.com (D.H.)

**\*** Correspondence: c.qin@cqu.edu.cn (C.Q.); ranjy@cqu.edu.cn (J.R.); Tel.: +86-6510-3101 (C.Q.); +86-6511-2813 (J.R.)

Received: 4 January 2019; Accepted: 22 January 2019; Published: 24 January 2019

**Abstract:** The application of circulating fluidized bed technology in calcium looping (CaL) requires that CaO-based sorbents should be manufactured in the form of spherical pellets. However, the pelletization of powdered sorbents is always hampered by the problem that the mechanical strength of sorbents is improved at the cost of loss in CO2 sorption performance. To promote both the CO2 sorption and anti-attrition performance, in this work, four kinds of pore-forming materials were screened and utilized to prepare sorbent pellets via the extrusion-spheronization process. In addition, impacts of the additional content of pore-forming material and their particle sizes were also investigated comprehensively. It was found that the addition of 5 wt.% polyethylene possesses the highest CO2 capture capacity (0.155 g-CO2/g-sorbent in the 25th cycle) and mechanical performance of 4.0 N after high-temperature calcination, which were about 14% higher and 25% improved, compared to pure calcium hydrate pellets. The smaller particle size of pore-forming material was observed to lead to a better performance in CO2 sorption, while for mechanical performance, there was an optimal size for the pore-former used.

**Keywords:** CO2 capture; calcium looping; chemical sorption; anti-attrition; pore-former particle size

#### **1. Introduction**

Greenhouse gases, such as CO2 which is mainly produced from fossil fuel combustion, are believed to be the major contributors to the rise of global temperatures [1]. It is predicted that the total emission amount of CO2 will increase to 40.2 Gt by 2030 [2]. Therefore, many researchers around the world focus their studies on various technologies to reduce the emission of CO2, and the capture, utilization, and storage (CCUS) of CO2 has been considered as the most effective way to solve the problem [3–6]. Capture is the key process in CCUS, and among different capture technologies, calcium looping (CaL) has been demonstrated as having good potential in achieving high-efficiency CO2 separation with affordable cost [7]. With the development of CaL, some pilot plant projects have already been put into large-scale testing [8–10].

Calcium looping is based on the reversible chemical reaction of CaO + CO2 ⇔ CaCO3. Generally, CO2 is captured in a carbonator at around 650 ◦C by CaO-based sorbents and released subsequently in a calciner above 900 ◦C. In this way, CO2 in the flue gas can be separated and collected in high purity. Though CaL has the significant advantages of low cost and high CO2 uptake capacity, there are still some obstacles in the commercialization of calcium looping. Firstly, all the natural sorbents will face a severe problem of loss-in-capacity due to sintering at a high temperature [11,12]. Another problem is attrition and fragmentation of sorbents due to its cyclic operation in fluidized bed systems [13–15]. As a result, partial sorbent will be lost and cannot be used repeatedly and economically. Although tremendous efforts have been made in improving the capacity and stability of sorbents [16–30], these materials still face the problem that powders are too small and must be produced in the form of pellets before they can be practically applied in CaL [14,31].

Pelletization is a good way to solve the problem by shaping powdered sorbent to the desired size with suitable anti-attrition property for industrial application. However, pelletization process usually causes the loss of CO2 uptake capacity of sorbent due to the destruction of the original porous structure, so pore-forming materials are needed in the process to obtain sorbent pellets with balanced mechanical and chemical performance. Sun et al. [27] found that carbide sorbent doped with microcrystalline via extrusion-spheronization could get a carbonation conversion of 52.64% in the 25th cycle on the premise of sacrificing mechanical strength. Firas et al. [32] reported that biomass-derived materials could increase the porosity of pellets and achieve a CO2 capture capacity of around 30% higher than biomass-free pellets. Therefore, it is important to find suitable pore-forming candidates to improve chemical sorption and minimize the negative impact on mechanical performance.

In order to obtain sorbent pellets with balanced mechanical and chemical performance, in this work, the pore-forming materials, widely used in industry, including polyethylene, polystyrene, graphite, and microcrystalline cellulose were screened and tested by the fabrication of CaO-based sorbent pellets using an extrusion-spheronization method. The effects of different pore-forming materials, their size, and doping ratio were comprehensively investigated and evaluated, and the roles of pore-forming materials in the preparation of sorbent pellets were well understood.

#### **2. Experimental**

#### *2.1. Materials*

Calcium hydroxide (CH) powder (>95%) with a particle diameter of 30–50 μm was used as the precursor of CaO. Four types of pore-forming materials including: polyethylene (denoted as PE, >99%), polystyrene (PS, >99%), graphite powder (C, >99%), and microcrystalline cellulose (MC, >99%) were screened and tested. For the specific test, PE powder with four different sizes of 6 μm, 12 μm, 30 μm, and 150 μm were utilized in the work.

#### *2.2. Sorbent Pellets Preparation*

Sorbent pellets were manufactured using an extrusion-spheronization method. For each sorbent, first, weighted calcium hydroxide and pore-forming material were vigorously dry-blended for 20 min and wet-mixed for 5 min in a stainless steel basin to get the homogeneous mixtures. Then, the wet mixtures were extruded into cylinders with a diameter of 1 mm by a mini-extruder (LEAP E-26, Chongqing, China). After that, the cylinders were cut off and rounded in a spheronizer (LEAP R-120, Chongqing, China) with a rotational speed of 3000 rpm for 25 min. Finally, pellets with a diameter of 0.75–1.25 mm were obtained through sieving and one-day air drying (called fresh pellets). A part of the pellets was calcined in a muffle furnace at 900 ◦C for 10 min (called pre-calcination). For simplicity, sorbent pellets were named as CH-PMX-Y, where PM refers to the pore-forming material, and X and Y are its initial mass content and particle size, respectively. For example, CH-PE10-12 means the pellet was doped with 10 wt. % PE whose particle size is 12 μm in preparation. The pellets consisting of pure calcium hydroxide is denoted as CH.

#### *2.3. Thermo-Gravimetric Analysis*

The CO2 capture capacity and CaO conversion of the samples were tested in a thermo-gravimetric analyzer (NETZSCH TG209 F3, Selb, Germany). Approximately 15 mg of the sample was placed on a corundum crucible in TGA and heated to 900 ◦C at a rate of 30 ◦C/min under a N2 flow of 85 mL/min, and the temperature was kept for 5 min to remove the moisture completely. Then, the sample was cooled down to 650 ◦C at a rate of −30 ◦C/min. Once the temperature was reached, a

CO2 flow of 15 mL/min was added immediately, and the CO2 sorption condition was kept for 20 min. The aforementioned process of carbonation and calcination was repeated totally 25 times to investigate the cyclic CO2 capture performance. Based on the mass data recorded, CaO carbonation conversion (Xn, %) and CO2 sorption capacity (Cn, g-CO2/g-sorbent) of the samples were calculated using the following formulas:

$$\text{Ch} = \frac{m - m\_0}{m\_0} \tag{1}$$

$$\text{Xn} = \frac{m - m\_0}{m\_0 \rho} \times \frac{M\_{\text{CaO}}}{M\_{\text{CO}\_2}} \times 100\% \tag{2}$$

where m is the maximum mass of CaO-based sorbents in sorption stage and m0 is minimum mass in calcination stage, ϕ is the mass content of CaO in the CaO-based sorbent, *MCaO* and *MCO*<sup>2</sup> are the molar mass of CaO and CO2, respectively. The CO2 sortion capacity uncertainty is around ±0.002 g-CO2/g-sorbent based on repeated tests.

#### *2.4. Pellets Impact Crushing Test*

Impact crushing of the pellet was carried out using an apparatus built by ourselves, as shown in Figure 1, according to the literature [33–35]. A high-pressure air bottle with a volumetric flow meter was used to control the velocity of air flow, and two valves were used to feed samples without the escaping of gas and particles. Particles were accelerated in an educator (1.2 m in length and 10 mm in I.D) by air at a speed of 18 m/s and impacted a stainless target (inclined by 60◦ with respect to the vertical direction) in the collection chamber. To filter the entrained fine particle (<12 μm), a sintered porous metal plate was installed on the top of the chamber.

**Figure 1.** Schematic diagram of experimental devices.

In each test, 0.5 g samples were loaded, and after impact crushing residual particles were collected and screened through sieving into 7 size ranges: 750~1250 μm, 500~750 μm, 375~500 μm, 187.5~375 μm, 75~187.5 μm, 12~75 μm, and <12 μm. In the work, particles with a diameter smaller than 187.5 μm are regarded as completely crushed particles.

#### *2.5. Compressive Strength Test*

The compressive strength of pellets was tested using a precision digital compression tester (SHIMPO FGP-10, Kyoto, Japan), and each sample was tested for 10 times. The maximum crushing force was obtained by slowly increasing the pressure until the particle was crushed. Compression strength was evaluated by the average crushing force and the error bar was calculated.

#### *2.6. Characterization of Sorbents*

Surface morphology of sorbents was captured using a field emission scanning electron microscopy (FESEM, SU8020, Hitachi, Tokyo, Japan). To determine the specific Brunauer–Emmett–Teller (BET) surface, Barrett–Joyner–Halenda (BJH) pore volume and pore size distribution of selected samples, N2 adsorption/desorption analysis was measured at approximately −196 ◦C using a Micromeritics TriStar II 3020 instrument after outgassing under vacuum for 18 h at 200 ◦C.

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

#### *3.1. Decomposition of Pore-Forming Materials*

To understand thermal properties of pore-forming materials, they were first tested in the TGA at a constant ramping rate of 30 ◦C/min to 900 ◦C followed by isothermal calcination for 5 min, and the results are shown in Figure 2. It was seen that under the atmosphere of N2, MC, PS, and PE started to pyrolyze at 300–400 ◦C with a quick weight loss until they were completely decomposed. In contrast, there was almost no decomposition of C in N2 flow. When the atmosphere was switched to a flow containing 15 vol. % O2 balanced with N2, C was observed to start burning at 600 ◦C until it was burnt out at around 900 ◦C. Based on the decomposition characteristics, it can be concluded that all pore-forming materials would be burnt into gases to create pores without any solid residues left in the prepared sorbent pellets.

**Figure 2.** Decomposition property of pore-forming materials at a constant ramping rate of 30 ◦C/min to 900 ◦C under the atmosphere of N2 or N2/O2.

#### *3.2. Characterization of Sorbent Pellets*

To see the effect of pore-forming materials on the structure of sorbent pellets, cross-sectional images were captured of the calcined CH and CH-PE5 using the field emission scanning electron microscopy, and the results are depicted in Figure 3. It is very clear that grains and pores in CH-PE5 are much smaller and their distributions are more uniform than those in the sample of CH. These differences can be attributed to the positive pore-forming effect of PE on sorbents microstructure during its decomposition to gaseous phases. The roles of pore-formers could be further understood from the results of the N2 adsorption/desorption test. Table 1 summarizes the specific surface area, BJH average pore width, and pore volume of CH and CH-PE5. It is seen that CH-PE5 pellets have a BET surface area of 11.66 m2/g, BJH cumulative pore volume of 0.0436 cm3/g, and an average pore diameter of 15.2 nm, which are all superior to those of CH. Figure 4 indicates that the higher pore volume of CH-PE5 is mainly correlated to pores of size in the range of 2–40 nm, which also contributed to the higher specific surface area in comparison with CH. Based on these results, it is reasonable to predict that the better microstructure of CH-PE5 would lead to an easier diffusion of CO2 within the sorbent and the following carbonation reaction.

**Figure 3.** Cross-sectional images of calcined (**a**,**c**) CH (Ca(OH)2), and (**b**,**d**) CH-PE5 (Ca(OH)2 doped with 5 wt. % PE).

**Table 1.** Microstructures of sorbent pellets after calcination at 900 ◦C.


**Figure 4.** Pore size distribution of CH-PE5 and CH pellets.

#### *3.3. Effect of Various Pore-Forming Materials*

To understand the impact of different pore-forming materials, CO2 sorption/desorption performance, compressive strength, and anti-impact crushing capacity of prepared sorbent pellets were tested and evaluated in this section. Pore-forming materials with a particle size of 12 μm were used, and their contents were kept at the same of 5 wt. %.

#### 3.3.1. Sorption/Desorption Performance

Cyclic CO2 uptake capacity and CaO conversion of sorbent pellets prepared with different pore-formers were presented in Figure 5. It shows that CH possesses a CO2 capture around 0.556 g-CO2/g-sorbents initially, but the value quickly decreased to 0.136 g-CO2/g-sorbent after 25 cycles. For sorbent pellets with the addition of pore-forming materials in preparation, CH-PE5 shows the highest CO2 capture capacity of 0.574 g-CO2/g-sorbent in the first cycle and 0.155 g-CO2/g-sorbent after 25 cycles, which are both higher than those of CH. This is in accordance with the aforementioned characterization results, demonstrating that CH-PE5 modified inner structure of sorbents during the pyrolysis of PE, resulted in a shifting of pores to smaller sizes of 2–40 nm (see Figure 4), and an increase of surface area and pore volume (see Table 1). It is well known that a bigger pore volume means a smaller CO2 transfer resistance, and a higher specific surface area could supply more available sites in pellets to react with CO2 [32,34]. Thus, CO2 sorption performance of CH-PE5 was improved. The CH-C5 shows a similar CO2 uptake capacity, which is only slightly lower than CH-PE5 but higher than CH. For CH-PS5 and CH-MC5, no improvement in sorption performance was observed compared to CH. The CaO conversion, the other aspect of sorption property, shares the same trend with the CO2 uptake capacity.

**Figure 5.** (**a**) Carbonation conversion and (**b**) CO2 capture capacity of sorbent pellets prepared with different pore-forming materials. Testing conditions: calcination at 900 ◦C for 5 min in N2, and carbonation at 650 ◦C for 20 min in 15 vol. % CO2.

#### 3.3.2. Mechanical Performance

Figure 6 plots the compressive strength of sorbent pellets (with/without calcination) prepared from different pore-forming materials. Without the calcination, CH pellets show a compressive strength of 6.7 N, while CH-PE5 possesses a value of 7.6 N, the highest among all fresh pellets. After calcination, the mechanical strength of all pellets experiences a significant decline. Among them, CH shows the lowest compressive strength of 3.2 N, almost a half loss comparing to that without calcination. By contrast, the addition of PS leads to about 30% improvement of the compressive strength to 4.2 N, and calcined pellets of CH-PE5, CH-C5, and CH-MC5 demonstrated a compressive strength of 4.0 N, 4.0 N, and 3.2 N, respectively. Since the compressive strength of calcined pellets is more meaningful in CaL application, it is concluded that the addition of pore-formers PE, PS, and C could modify pore structure of sorbents in the way that benefits the mechanical strength of pellets.

**Figure 6.** Compressive strength of sorbent pellets (at a mean diameter of 1 mm) with different pore-forming materials.

The results of impact crushing test are shown in Figure 7. By comparing particle size distribution after impact crushing, attrition resistance of diverse sorbent pellets could be evaluated. Obviously, the anti-attrition ability of fresh pellets is much better than calcined pellets, which, however, is more meaningful in the practical CaL application. That is our focus in the following work. It is seen that when particles smaller than 187.5 μm are regarded as completely crushed materials, the mass loss fraction of CH pellets reach to nearly 24.4% from the cumulative percentage results. In contrast, the value for CH-PE5% and CH-C5 was only around 5.9% and 5.2%, respectively. Meanwhile, the mass fraction of residual particles within a diameter range of 750–1250 μm is 11.2% for CH-PE5 from the mass fraction results, which is the highest. These results indicate that the addition of a small amount pore-formers enhances the mechanical strength of sorbent pellets. It is possible that the pores formed during the pyrolysis of pore-formers at a relatively low temperature are able to act as channels for the release of CO2 in the decomposition of CaCO3, avoiding the formation of additional cracks, and is beneficial for keeping the mechanical strength.

**Figure 7.** Impact crushing resulted in particle size distribution of sorbent pellets with different pore-forming materials: (**a**) fresh, (**b**) after calcination at 900 ◦C.

#### *3.4. Effect of Addition Content of PE*

The above results indicate that the addition of PE resulted in the best improvement in both the CO2 sorption and the mechanical performance among all pore-formers, thus we optimized its addition content from 2.5% to 20% in this part, while keeping the particle size of PE at 12 μm.

#### 3.4.1. Sorption/Desorption Performance

Figure 8 shows that CO2 sorption capacity of all CH-PE samples were improved under the conditions studied. A tendency of first-increase-then-decrease was observed with the increasing addition of PE, and the peak appears when 5 wt. % PE was utilized. Theoretically, with the increasing addition of pore-forming materials, CO2 sorption performance should keep going up. However, it never happened in our experiment. The reason could be that the most suitable pore size range for long cyclic CO2 capture is 20–50 nm [35]. However, the addition of too much PE would result in pores that are out of the aforementioned optimum range and lead to a loss in CO2 capacity in a long cycle.

**Figure 8.** (**a**) Carbonation conversion and (**b**) CO2 capture capacity of sorbent pellets with different addition of PE. Testing conditions: calcination at 900 ◦C for 5 min in N2, and carbonation at 650 ◦C for 20 min in 15 vol. % CO2.

#### 3.4.2. Mechanical Performance

The compressive strength of pellets with different addition of PE is illustrated in Figure 9. Similar to the variation of chemical sorption, the compressive strength of calcined CH-PE goes up with the increasing addition of PE, especially for 2.5 wt. % and 5 wt. %, and then slightly declines. It is also clear that pellets without calcination have a much better mechanical strength. The results of impact crushing test for different CH-PE sorbent pellets are summarized in Figure 10. It shows that calcined CH-PE2.5 and CH-PE5 pellets have a much better attrition resistance, whose complete mass loss is 6.9% and 5.9%, respectively, from the cumulative percentage results. In contrast, the value for CH-PE10 and CH-PE20 pellets is 22.1% and 31.1%, which are similar to CH. In addition, CH-PE5% has the biggest mass fraction of residual particle whose size ranges in 750–1250 μm. So, it could be concluded that, within a small addition of PE, the mechanical strength could be significantly enhanced. However, when further increasing the amount of PE, the cavities and gases released in PE pyrolysis are too much to hold a favorable structure for the good mechanical strength of sorbent pellets.

**Figure 9.** Compressive strength of synthetic sorbent pellets (a mean diameter of 1 mm) with different addition of PE.

**Figure 10.** Impact crushing resulted in particle size distribution of sorbent pellets with different additions of PE: (**a**) fresh, (**b**) after calcination at 900 ◦C.

#### *3.5. Effect of Pore-Former Particle Size*

Particle size of the pore-forming material could possibly change the performance of sorbent pellets. However, few experiments were reported in this field deeply. Here, PE with varying particle sizes from 6 μm to 150 μm were selected and tested while its addition content was kept at 5%.

#### 3.5.1. Sorption/Desorption Performance

The CO2 sorption performance of sorbent pellets with different particle sizes of PE are presented in Figure 11. It is evident that sorbent pellets with a smaller particle size of PE have a better performance in CO2 capturing. The CH-PE5-6 pellet had the highest CO2 uptake capacity of 0.157 g-CO2/g-sorbent which was 6.8% higher than CH-PE5-150 pellets, followed by CH-PE5-12 and CH-PE5-50. It can be concluded that the chemical performance was inversely proportional to the particle size of pore-former utilized. It is very likely that with the same content of PE added, the smaller particle size could lead to the formation of a more uniform distribution of smaller pores, in other words, a bigger specific surface area, and bigger pore volume. Thus, there is more "activated space" for the CO2 to react with CaO.

**Figure 11.** (**a**) Carbonation conversion and (**b**) CO2 capture capacity of synthetic pellets with different particle sizes of PE. Testing conditions: calcination at 900 ◦C for 5 min in N2, and carbonation at 650 ◦C for 20 min in 15 vol. % CO2.

#### 3.5.2. Mechanical Performance

Compressive strength of synthetic sorbent pellets with different particle sizes of PE are shown in Figure 12. A tendency of first-increase-then-decrease compressive strength was observed with the increasing particle size of PE, for both fresh and calcined pellets. The CH-PE5-12 possessed the biggest compressive strength among all calcined pellets while CH-PE5-150 was the worst. The ratio of the mechanical strength value for pellets after calcination to fresh pellet was also calculated in order to understand the influence of different particle sizes. The value is 55.9%, 51.9%, 23.9%, and 18.9% for CH-PE5-6, CH-PE5-12, CH-PE5-50, and CH-PE5-150, respectively, showing that bigger particle size of the pore-former has a negative effect in maintaining the compressive strength of pellets during high-temperature calcination.

**Figure 12.** Compressive strength of synthetic sorbent pellets (a mean diameter of 1 mm) with different particle size of PE.

Figure 13 summarizes impacting test results for sorbent pellets with different particle sizes of PE. For the various fresh pellets, their particle distribution after impacting test is very similar, and the complete mass loss is around 1% from the cumulative percentage results. For calcined ones, due to the significant loss in mechanical strength of pellets added with bigger PE (50 μm, 150 μm), lots of them broke up during the pre-calcination process. Thus, we did not present results in the diagram. But conclusion can also be reached that with the smaller particle size of pore-former added, the better mechanical strength sorbent pellets have, which is also consistent with the conclusion obtained in the compression test.

**Figure 13.** Impact crushing resulted in particle size distribution of fresh sorbent pellets with different particle size of PE.

#### **4. Conclusions**

This work studied CO2 uptake and anti-attrition performance of CaO-based pellets synthesized via extrusion-spheronization with the addition of a small amount of pore-forming materials. Four kinds of pore-forming materials with varying additional content and particle size were investigated in this work. It was found that the addition of PE had a positive effect on enhancing the CO2 sorption capacity meanwhile maintaining a relatively high mechanical strength, compared to pure Ca(OH)2 pellets. The reason is micro-structures (i.e., pore distribution, surface area, and pore volume) of synthetic sorbents were modified in a way promoting the diffusion and subsequent CO2 reaction with sorbents. After 25 typical cycles, pellets with 5% PE captured 14% more CO2 and possessed 25% higher mechanical strength than pure Ca(OH)2 pellets. The lesser or greater addition of PE did not bring further performance enhancement. The particle size of pore-formers was also observed to affect the performance of prepared sorbent pellets, and the smaller ones led to a better chemical performance in CO2 sorption. In contrast, there was an optimal size of PE (12 μm) for the mechanical strength of sorbent pellets.

**Author Contributions:** Conceptualization, C.Q. and Z.Z.; investigation, Z.Z.; resources, C.Q.; data curation, S.P. and D.H.; writing—original draft preparation, Z.Z.; writing—review and editing, C.Q.; supervision, C.Q. and J.R..; project administration, C.Q and J.R.; funding acquisition, C.Q. and J.R.

**Funding:** This research was funded by the National Key R&D Program of China, grant number 2017YFB0603300; National Natural Science Foundation of China, grant number 51606018; Chongqing Basic Science and Advanced Technology Research Program, grant number cstc2017jcyjAX0324; the Innovation Program for Chongqing Overseas Returnees, grant number cx2017021; Fundamental Research Funds for the Central Universities, grant number 2018CDJDDL0005; Key Industrial Generic Technology Innovation Project of Chongqing, grant number cstc2016zdcy-ztzx20006.

**Acknowledgments:** The authors are grateful for financial support from the National Key R&D Program of China (No. 2017YFB0603300), National Natural Science Foundation of China (No. 51606018), Chongqing Basic Science and Advanced Technology Research Program (No. cstc2017jcyjAX0324), the Innovation Program for Chongqing Overseas Returnees (No. cx2017021), Fundamental Research Funds for the Central Universities (No. 2018CDJDDL0005), and Key Industrial Generic Technology Innovation Project of Chongqing (No. cstc2016zdcy-ztzx20006).

**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* **Optimal Design of a Carbon Dioxide Separation Process with Market Uncertainty and Waste Reduction**

**Juan Pablo Gutierrez 1,\*, Eleonora Erdmann <sup>1</sup> and Davide Manca <sup>2</sup>**


Received: 11 March 2019; Accepted: 27 May 2019; Published: 5 June 2019

**Abstract:** The aim of this work is to optimize the conceptual design of an amine-based carbon dioxide (CO2) separation process for Enhanced Oil Recovery (EOR). A systematic approach is applied to predict the economic profitability of the system while reducing the environmental impacts. Firstly, we model the process with UniSim and determine the governing degrees of freedom (DoF) through a sensitivity analysis. Then, we proceed with the formulation of the economic problem, where the employment of econometric models allows us to predict the highest dynamic economic potential (DEP). In the second part, we apply the Waste Reduction (WAR) algorithm to quantify the environmental risks of the studied process. This method is based on the minimization of the potential environmental indicator (PEI) by using the generalization of the Waste Reduction algorithm. Results show that the CO2 separation plant is promising in terms of economic revenues. However, the PEI value indicates that the higher the profitability, the larger the environmental risk. The optimal value of the DEP corresponds to 0.0274 kmol/h and 60 ◦C, with a plant capacity according to the mole flow rate of the produced acid gas. In addition, the highest environmental risk is observed at the upper bounds of the DoF.

**Keywords:** optimal conceptual design; market prediction; economic uncertainty; environmental impact; carbon dioxide separation

#### **1. Introduction**

Several stages exist to recover the original pressure of mature oil and gas wells. Among those already applied, the Enhanced Oil Recovery (EOR) with carbon dioxide (CO2) proved to be a mid-term solution to increase the oil production to its original levels while capturing thousands of tonnes of CO2 [1,2].

Haszeldine [3] states that the first injections of carbon dioxide into the microscopic pores of sedimentary rocks date from the early 1970s. Successful cases of CO2-EOR have been reported in the United States, United Kingdom, Norway, and Canada by Wright et al. [4] and Mumford et al. [5]. The injection of CO2 was also evaluated in the reservoirs of Argentina, a region where EOR pilot experiences were barely intended. Although the results provided good revenues, the CO2-EOR in the region remains unmaterialized after more than twenty years since first being discussed [6].

The main problem related to this procedure is the large and continuous amount of CO2 necessary to start the EOR injection [7]. In this regard, Herzog [8] reports that the common sources for large amounts of CO2 correspond to the acid gas coming from natural gas processing.

Kwak et al. [9] compare different technologies for CO2 separation from natural gas. Based on simulation and economic studies, they conclude that chemical absorption with methyldiethanolamine (MDEA) is the least expensive and most feasible option to separate carbon dioxide. Moreover, Leung et al. [10] note the amine processes' high efficiency, large amounts of acid gas as a side product, and the possibility to regenerate the solvent. Other comparable processes include separation with polymeric membranes, cryogenic separation, physical solvents, and hybrid technologies.

Another task when evaluating CO2-EOR possibilities is the large dependence of oil and gas companies upon economic conditions and countries' institutional frameworks [11]. For instance, Ponzo et al. [12] state that changing market structures influence the long-term evolution of gas quotations and, consequently, the development of gas fields. Moreover, interdependency among variations of time with technical, operative, and economic conditions has been assigned to perform economic evaluation by Manolas et al. [13]. Classically, the interaction between the operating aspects and economic revenues during the definition of a process is first estimated according to the conventional conceptual design [14]. Conceptual process design (CD) consists of the selection of proper operation units, their sequences, and the recycling structure needed to obtain a specified product [15]. However, Sepiacci et al. [16] explain that this conventional method is no longer representative when considering market uncertainty, demand and offer fluctuations, and the price instability of commodities and utilities. Then, Manca and Grana [17] introduced the benefits of dynamic conceptual design (DCD). Based on CD and the economic potentials (EP) presented by [14], DCD takes into account the dynamic features of price/cost fluctuations within a given time horizon.

Indeed, the process design of chemical industries are considered complete when performing the environmental risk analysis of new process systems. Currently, there is a great deal of interest in the development of methods that can be used to minimize the generation of pollution, and there are numerous efforts underway in this area [18]. Specifically, this interest has increased with the world's awareness of CO2 emissions and made process engineering adopt practices to mitigate the effects of climate change [19].

For the above reasons, we apply the concept of DCD to obtain and condition CO2 for EOR purposes. As can be anticipated, we focus our study to establish the conceptual design of the process in the context of market instability and future uncertainties. CO2 for EOR is obtained from a natural gas sweetening design that uses MDEA as solvent; the specifications for the produced CO2 include a 95 mol% concentration of the acid gas, compressed at 6500 kPa [20].

An optimization problem is formulated with the aim of minimizing the Dynamic Economic Potential (DEP) of the design. In this sense, Mores et al. [21] state that two degrees of freedom (DoF) govern the optimization problem of the CO2 MDEA absorption—the recycled amine flow rate and its temperature. However, we extend the analysis to prove that the variable most affecting the energy demands of the plant is the water makeup of the amine solution, and thus more proper DoF.

Then, we analyze the historical prices of products and raw materials by using statistical tools. We present natural gas prices as references to estimate the evolution of the rest of the involved components by using numerical correlations. Linear Regression Models (such as AutoRegressive model with an eXternal input, ARX) are applied to interpret the behavior of past quotations. We switch the contribution of these economic models into econometrics to make them capable of predicting quotations and generating future market scenarios.

On the other hand, we perform an assessment to find the pair of DoF that reduce the environmental potential index. The method is adapted from the Waste-Reduction algorithm applied to chemical processes presented by Young et al. [22]. The Waste Reduction (WAR) algorithm has been developed to describe the flow and the generation of potential environmental impact through a chemical process.

#### **2. Process Description**

The purpose of a natural gas sweetening process is to remove the acid gases from a sour natural gas stream. Due to the high selectivity of the solvent, the by-product of this process is a high-purity CO2 material stream that, after conditioning, can be used as an EOR fluid.

The regular process of natural gas sweetening to obtain CO2 is divided into two parts [23]. In the first stage, which consists of an absorber column, the natural acid is put in countercurrent contact with a descending MDEA aqueous solution—a so-called lean amine [24]. Fouad and Berrouk [25] and Kazemi et al. [26] indicate that low temperatures and high pressures favor the exothermic reaction that occurs in the unit. After contact, the aqueous solution of amine is pressurized, heated, and sent to the regeneration stage [27]. This second stage consists of a distillation column where the acid gas is removed from the amine solution due to an external heat contribution. Different studies have been performed in order to optimize the energy requirements of the regeneration column [28–30]. The liquid from the regenerator column is cooled and pumped back to the absorption stage [31,32]. Water and MDEA are placed in the stream from the bottom of the column to the absorption tower to compensate for leaks within the operation. Meanwhile, the high-purity CO2 from the top of the regenerator is sent to a series of four centrifugal compressors to considerably increase the pressure. Original well pressures are required to dispose of the CO2 as an injection fluid; in this case the value remains over 6500 kPa. The 4-stage compression design includes intercooling units and intermediate separation stages [33].

#### **3. Methods**

#### *3.1. Simulation Base Case*

A process of CO2 absorption and compression is modeled by using UniSim [34]. Natural gas at a value of 2500 mm3/d is assumed as the plant's capacity. The conditions of the plant are those reported in the work of Gutierrez et al. [19]: sour natural gas at 35 ◦C and 6178 kPa with 93 and 4 mol% of CH4 and CO2, respectively. Also, the conditions of the lean amine are reproduced. We consider 21,000 kmol/h of an aqueous MDEA solution (38 wt%), at 42 ◦C and 9610 kPa. A 24-tray absorption column operates at the pressure of the inlet gas. Rich MDEA from the bottom of the absorber is flashed at 441 kPa, heated up to 90 ◦C, and then sent to regeneration. The regeneration column consists of 24 trays and operates at 90 ◦C and 443 kPa. To provide the column with an external heat, we assume a reboiler unit using natural gas as fuel. Recycled MDEA is pumped and cooled, first exchanging heat with the rich amine, and then with a cooler so that it reproduces the temperature of absorption.

A 4-stage compression system is employed to increase the pressure of the produced CO2 up to 6865 kPa [33]. Figure 1 shows the simulation of (**a**) the CO2 separation plant and (**b**) the compression sector to produce the high-purity CO2 stream.

Muhammad and GadelHak [35] explain that the main variables affecting CO2 absorption are solvent flow rate and the absorber temperature, this last through the cooling of the lean amine stream.

As we anticipated, two streams conform the solvent inlet flow stream, one corresponding to a pure MDEA stream and the other connected to the makeup water. Generally speaking, two independent variables are related to the same degree of freedom, so in this study we determine whether there is a strong dependency between the main energy requirements and the independent variables. Similar to Torres-Ortega et al. [36], we perform a sensitivity analysis to evaluate suitable ranges of variation for the decision variables along the optimization.

#### *3.2. Predictive Concept Design*

This section provides the dynamic approach to the economic assessment for the CO2 conditioning plant. Econometrics models (EM) are employed to simulate and evaluate future trajectories of prices and costs.

**Figure 1.** Simulation model in UniSim: (**a**) absorption sector and (**b**) compression sector.

#### 3.2.1. Development of Econometric Models

The first step while performing EM is the selection of a reference component (RC). Manca [37] employs RC historical quotations to estimate the economic dynamics of all commodities and utilities in the process he analyzed. Moreover, Manca [38] suggests that an RC must be representative of the sector where the plant operates, with the availability of frequent data and updated price evolution.

A good RC for the industry of Oil and Gas is crude oil (CO) [39]. CO, and also the evolution of natural gas (NG), quotations are traced daily for EIA [40]. However, the prices of natural gas produced in the basins of Argentina are also indicated monthly by the Ministry of Energy [41]. In this study, we perform the EM for both CO and NG as potential candidates for reference components.

A structural auto regression model is applied to separately autocorrelate both West Texas Intermediate (WTI) crude oil and US natural gas prices [42]. For both potential candidates, we analyze monthly quotations from July, 2007 to July, 2017 (the last available date). To correlate the historical values of the quotations, we use a similar methodology as the one used by Zhou et al. [43] regarding the coefficients of the Pearson equation (Equation (1)). Pearson coefficients (PCs) measure the strength and direction of the linear relationship between two random variables [44]. In this case, both variables represent the monthly quotations of the RC, but differ in one period:

$$r\_k = \frac{\sum\_{t=k+1} \left(\chi\_t - \overline{\chi}\right) \left(\chi\_{t-k} - \overline{\chi}\right)}{\sum\_{t=1}^n \left(\chi\_t - \overline{\chi}\right)^2} \tag{1}$$

where *rk* denotes the PC for a particular period. - *t*=*k*+1 *Yt* − *Y Yt*−*<sup>k</sup>* <sup>−</sup> *<sup>Y</sup>* is the covariance of the quotations (*Yt*) with respect to one-period of the previous quotations (*Yt*−*k*), and *n t*=1 *Yt* − *Y* 2 is the squared of the standard deviation. *rk* varies from –1 to 1 and, in general, the higher the correlation coefficient, the stronger the relationship is [45]. Dancey and Reidy [46] state that if *rk* ranges from 0.7 to 0.9, the strength of correlation is high, and quite enough to determine the size of the correlation. This characteristic can be visualized when plotting the coefficient versus the time lag between the quotations.

#### 3.2.2. Formulation of the Economic Optimization

Once the EM are identified, it is viable to run the grid-search optimization according to the regular process conceptual design (PCD). In the optimization problem, we determine the set of DoF that maximizes the cumulated value of the Dynamic Economic Potential of order four (DEP4), Equations (2)–(4).

$$(\text{Cumulated})\_i = \sum\_{j=1}^{N} DE4\_{j,i}; \ i = 1, \dots, I \tag{2}$$

$$\text{LEP4}\_l \left( \frac{lLSD}{\nu} \right) = \sum\_{j=1}^{N} \text{Revenues}\_{j,i} \cdot nHpY - \frac{\text{CAPEX}}{N/12} \tag{3}$$

$$\text{Revenues}\_{j,i} \left( \frac{lISD}{y} \right) = \sum\_{p=1}^{NP} \mathbb{C}\_{p,j,i} \cdot \mathbf{F}\_P - \sum\_{r=1}^{NR} \mathbb{C}\_{r,j,i} \cdot \mathbf{F}\_r - \text{OPE}X\_{j,i} \tag{4}$$

where *DEP*4 is the fourth-level economic potential calculated for the *i* − *th* economic scenario. *j*, *i* are the subscripts for a specific month and scenario, respectively; *nHpY* is the number of working hours per year. N stands for the number of months to perform the economic assessment. *NP*, *NR*, *FP*, and *Fr* represent the number of products and reactants, their flow rates, and *C* their costs. The *CAPEX* term is estimated according to the empirical equations reported by Douglas [14]. Six main units are considered for the calculation: absorber and distillation columns, MDEA heat exchanger, and two air coolers.

The *OPEX* term considers a price trajectory for each raw material, by-product, and utility, for the *i* − *th* scenario. The main contributors of the *OPEX* are two air coolers, a condenser, reboiler fuel, and the total power required for the acid gas compressors (Gutierrez et al. [19]). The material and energy balances required to calculate the *OPEX* are taken from the steady-state simulation of the process.

The goal of the optimization is to determine the combination of DoF that maximizes the value of (*Cumulated*)*<sup>i</sup>* , with respect to a set of generated scenarios, where the assessment becomes probabilistic. To obtain a high-purity CO2 material stream, Gutierrez et al. [19] use a limit value of 2 mol% in the gas coming from the top of the absorber, so we consider the molar fraction of CO2 as a restriction for the stated problem.

#### *3.3. Waste Reduction Algorithm*

We employ the Waste Reduction (WAR) algorithm to describe the flow and the generation potential environmental impact through the process under study [22]. The general methodology of the WAR algorithm defines Potential Environmental Impact (PEI) indexes to characterize the generation of the potential impact in a process, divided into eight categories.

The first four categories evaluate, globally, the environmental friendliness of a process: human toxicity potential by ingestion (HTPI), human toxicity potential by exposure (both dermal and inhalation) (HTPE), terrestrial toxicity potential (TTP), and aquatic toxicity potential (ATP).

On the other hand, the other four are related to the toxicological aspects of the involved chemicals within the process: global warming potential (GWP), ozone depletion potential (ODP), photochemical oxidation potential (PCOP), and acidification potential (AP).

The potential environmental impacts are calculated from stream mass flow rates, stream composition, and a relative potential environmental impact score for each chemical present in the separation process [18].

According to the notation of Young and Cabezas [47], the output PEI to the chemical process can be rewritten as Equation (5):

$$\dot{\mathcal{M}}\_{out}^{(cp)} = \sum\_{w=1}^{N} \dot{\mathcal{M}}\_{w}^{(out)} \sum\_{i} x\_{i,w} \psi\_{i} \tag{5}$$

$$
\psi\_k = \sum\_l \alpha\_l \psi\_{i,l}^s \tag{6}
$$

where . *M*(*out*) *<sup>w</sup>* is the output mass flow rate of stream *w*, *xi*,*<sup>w</sup>* is the mass fraction of the chemical *i* in the stream is *w*, and ψ*<sup>i</sup>* is the overall PEI for the chemical *k*. ψ*<sup>i</sup>* can be calculated from Equation (6). In Equation (6), ψ*<sup>s</sup> <sup>i</sup>*,*<sup>l</sup>* is the normalized specific PEI of chemical *k* for the impact category *l*, and α*<sup>l</sup>* is the relative weighing factor of impact category *l* [39,47]. A unitary value is assigned to α, to illustrate the case where the eight categories have the same importance in our evaluation [48]. Normalized impact scores are obtained from the WAR algorithm add-in included in the latest release of the CAPE-OPEN to CAPE-OPEN (COCO) Simulation Environment, available from http://www.cocosimulator.org/ [49].

#### **4. Results**

#### *4.1. Simulation Output*

Figure 2 reports the evolution of the main energy requirements, according to the variation of the independent variables. For this case, we present the reboiler requirement versus (a) the flow rate of the make-up of MDEA, (b) the make-up of water, and (c) the temperature of the recycled amine.

Figure 3 reports the evolution of the condenser energy requirement, according to the variation of the chosen independent variables. Again, we present the condenser requirement versus (a) the flow rate of the make-up of MDEA, (b) the make-up of water, and (c) the temperature of the recycled amine.

Figure 4 shows the evolution of the total compressor power (kW), respect to the variation of the same independent variables. We present the compressor power demand versus (a) the flow rate of the make-up of MDEA, (b) the make-up of water, and (c) the temperature of the recycled amine.

**Figure 2.** Variation of reboiler energy requirements (kW) versus (**a**) the make-up of amine molar flow (kmol/h), (**b**) water make-up (kmol/h), and (**c**) the recycled methyldiethanolamine (MDEA) temperature ( ◦C).

**Figure 3.** Variation of condenser energy requirement (kW) versus (**a**) the make-up of amine molar flow (kmol/h), (**b**) water make-up (kmol/h), and (**c**) the recycled MDEA temperature (◦C).

**Figure 4.** Variation of compressor power demand (kW) versus (**a**) the make-up of amine molar flow (kmol/h), (**b**) water make-up (kmol/h), and (**c**) the recycled MDEA temperature (◦C).

Figure 5 shows the evolution of the cooling system requirements (kW), with respect to the variation of the available variables. We present the energy demand of the coolers (AC-100 and AC-101) versus (a) the flow rate of the make-up of MDEA, (b) the make-up of water, and (c) the temperature of the recycled amine.

Figures 2–5 expose a remarkable dependency between the main energy consumptions and the temperature of the recycled MDEA. Moreover, it was illustrated that the energy requirements strongly depend on the flow rate of the water make up. On the other hand, the variation of the MDEA flow rate proves to not alter the energy requirement of the reboiler, condenser, compressors, and the air-coolers. With this analysis, it is demonstrated that the proper DoF, representing the reduction of the recycle MDEA flow rate, corresponds to the water makeup of the process. Previous articles state that the decision variable is the recycled aqueous amine flowrate, but it is demonstrated here that the variable

of most impact is the water make-up to conform to that flowrate. Thus, for the objective functions in this work, the decision variables are the water mole flow and the temperature of the recycled amine.

**Figure 5.** Variation of air-coolers energy demand (kW) versus (**a**) the make-up of amine molar flow (kmol/h), (**b**) water make-up (kmol/h), and (**c**) the recycled MDEA temperature (◦C). Blue: cooling system of the recycled amine; orange: cooling system of the sweet natural gas.

#### *4.2. Economic Scenarios*

Figure 6 shows the autocorrelograms (PC versus lag time) of (a) CO and (b) NG.

**Figure 6.** Autocorrelograms of (**a**) crude oil (CO) and (**b**) natural gas (NG).

By analyzing the autocorrelograms shown in Figure 6, one can deduce that the CO quotation at the month *k* + 1 depends mostly on the two previous quotations.

#### 4.2.1. Correlation

In this subsection, we evaluate the relationship among all involved commodities with respect to the potential RC. In Figure 7, we expose the correlation between (a) CO2, (b) MDEA, and (c) Electric Energy (EE) with respect to crude oil quotations. It can be seen that correlation values change in the range of [−1, 1]. If the two sets are perfectly correlated (e.g., are the same set), the correlation index is 1. On the contrary, if they are anti-correlated (e.g., the two sets have opposite trends), it is −1.

Figure 8 exposes the correlation between the same components and natural gas quotations. CO2 quotations were estimated in accordance with the work presented by Cook [50]. It can be seen that values of correlation between the set of quotations present higher values compared to the ones obtained by correlating the crude oil. Then, NG is selected as a reference component and econometric model, as presented in Section 4.2.2.

**Figure 7.** Correlation between CO and (**a**) CO2, (**b**) MDEA, and (**c**) electricity quotations.

**Figure 8.** Correlation between NG and (**a**) CO2, (**b**) MDEA, and (**c**) electricity quotations.

#### 4.2.2. Econometric Models

From Figures 7 and 8, we observe better correlation indexes when comparing to the NG quotations. Then, the EM of NG as RC becomes the one expressed through Equation (5).

Where *PNG*,*k*+<sup>1</sup> is the monthly quotation of NG. σ and *P* are the standard deviation of the prices and the average of relative errors, respectively. *rand* is a stochastic function normally distributed, and *A*, *B*, and *C* are adaptive parameters calculated with linear regression, minimizing the square error between real quotations and those predicted by the model [51].

Manca [38] reports EM for toluene, benzene, propylene, and cumene prices based on a dedicated (auto)correlogram analysis. According to our correlation indexes, we elaborate the EM for the CO2 conditioning process. Table 1 presents Autoregressive Distributed Lag (ADL) models for estimating each quotation evolution, without the stochastic factor.

$$P\_{\rm NG,k+1} = \left(A + B \cdot P\_{\rm NG,k} + C \cdot P\_{\rm NG,k-1}\right) \cdot \left(1 + mnd \cdot \sigma\_{\rm NG} + \overline{P}\_{\rm NG}\right). \tag{7}$$

**Table 1.** ADL EM for NG, CO2, and MDEA prices, without the stochastic factor.


To simply the forecast EE quotations, we adopt previous monthly prices of the Ministry of Energy [41]. Similar to Manca [52], the EM for EE is based on (auto)correlograms and the economic dependency of the EE to NG. From these observations, it is feasible to apply the model represented by Equation (8):

$$P\_{EE,k+1} = A + B \cdot P\_{NG,k} + C \cdot P\_{EE,k} \tag{8}$$

where the price of EE (*PEE*,*k*<sup>+</sup>1) is estimated employing previous quotations of NG and EE. Table 2 reports the adaptive coefficients, including the models of NG, CO2, MDEA, and EE.


**Table 2.** Adaptive parameters of ADL EM of NG, CO2, and MDEA.

We use the EM of CO2, MDEA and EE to generate a set of random economic scenarios. Figure 9 shows eight predicted trajectories from the EM of NG, MDEA, CO2, and EE, during a time horizon of 120 months, in different random colors. It shows a probabilistic approach, based on a distribution of multiple viable economic scenarios.

**Figure 9.** Random price trajectories, for (**a**) NG expressed, (**b**) MDEA, (**c**) CO2, and (**d**) EE.

Particularly for the case of the Electric Energy (Figure 9d), we present a brief predictive model where A = 2.98, B = 1.316, and C = 0.81 (Sepiacci et al. [16]), in Equation (8). The predictive nature of this model is given by its dependency with the forecasted prices of NG. Other reported models associate the EE prices with the crude oil quotations, but those forecasts are also of random variability [53]. The Electric Energy has a great impact as a process utility because of the type of its cooling equipment and compression system. Although the prices of the utility vary periodically with the time domain, we assume this simplified behavior for the scope of this article.

Each colorful line corresponds to random trajectories generated from the econometric models of Equations (6)–(8). The prices of each item can follow one of the colorful trajectories within the time horizon.

#### *4.3. Optimal Economic and Environmental Friendly Design*

Results concerning the optimal design of the CO2 separation plant are shown in this section.

#### 4.3.1. DEP4 Cumulated

Figure 10 shows, on the y-axis, the value of the cumulated DEP4 (USD), and on the x-axis it shows the time series of market quotations. Each bar represents the value of the DEP4 calculated by considering the market quotations of the corresponding month based on the specific plant configuration that maximizes the DEP4 value.

**Figure 10.** Fluctuation of the DEP4 (USD) according to the number of the generated scenario.

The generated models are used to produce a set of economic scenarios that are distributed according to the modeled fluctuations of quotations and the stochastic contribution of the reference component. The cornerstone of this methodology is symbolized by the number of scenarios that are called for quantifying a set of different scenarios subject to the price/cost trajectories obtained by the econometric models through their stochastic contribution (Random). Therefore, it refers to a probabilistic concept of PCD that is grounded in the distribution of possible economic scenarios for this specific process. A necessary condition for economic sustainability is that the DEPs are positive.

It can be seen that DEP4 varies, even to negative values, during the time domain. Each bar of the graph represents the higher value of EP4, corresponding to the best combination of the DoF, at one particular month. In general, the economic potential fluctuation strongly depends on the price volatility of raw materials and final products. Where positive, the obtained DEP4 is of an eight-power magnitude, which demonstrates the economic potential of the plant in accordance with the predictive models.

#### 4.3.2. Economic Optimal

Figure 11 illustrates the trend of the cumulated DEP4 as a function of the DoF, the water flow make-up, and the temperature of the recycled amine. The presented surface represents the maximization of Equation (4), where a total capital expenditure of 1.44 <sup>×</sup> 10<sup>7</sup> USD is estimated from the calculation. As previously stated, the DEP4 is not represented by a single value but by a distribution of values, one for each scenario. In order to have a simple representation of the economic objective function, we present the average value of the cumulated DEP4. The results of Equation (4) show that the average of the cumulated DEP4 reaches eight order values.

The configuration yielding the maximum value of the cumulated DEP4 corresponds to a temperature equal to 60 ◦C for the MDEA to recycle and value the water amine flow rate equal to 0.0274 kmol/h.

Based on this experience, high temperatures of MDEA imply that the conversion of the absorption reaction is increased and, consequently, the produced CO2 is increased. Interestingly, an increment of the water flow rate proves that the MDEA concentration of 38 wt% can be modified to obtain a better performance in terms of the economical aspect of this process. At the same value of temperature, 60 ◦C, and 0.1074 kmol/h, the cumulated DEP4 is equal to 1.06 <sup>×</sup> 10<sup>8</sup> USD. The order of magnitude of this DEP4 is even higher than the one obtained by Sepiacci et al. [16], who obtained a six-order DEP4 while applying this methodology in a petrochemical process.

#### 4.3.3. Minimal Environmental Risks

Figure 12 shows the behavior of the PEI. In this case, the highest environmental risk is observed at the upper bounds of the DoF.

A probabilistic approach to future scenarios is concerned to find the combination of decisive DoF that maximizes the indicator of economic sustainability. Similarly, the potential environmental risk is also evaluated. Results show that this CO2 separation design is promising, although the PEI indicates that the higher the profitability, the larger the environmental risk is. The environmental risk appears at high values of water make-up flow and recycle amine temperatures. This situation may be explained by the toxicological aspects of the involved chemicals within the process—an increase in the power of the cooling stage and modification of the reboiler combustion parameters.

**(a)**

**(b)** 

**Figure 11.** (**a**,**b**). Average cumulated DEP4 (USD) function with respect to water amine molar flow rate (kmol/h) and recycle MDEA temperature (◦C), based on the PCD method.

**Figure 12.** (**a**,**b**). PEI function with respect to water amine molar flow rate (kmol/h) and recycle MDEA temperature (◦C), based on Waste Reduction.

#### **5. Conclusions and Future Developments**

This paper evaluates the process to obtain and condition CO2 to be used as an EOR fluid, in the Argentine Basin of Neuquén. We focus the study on the evaluation of economic aspects in a context of market variability and price uncertainties. PCD methodology is adopted to achieve the aim of the article. With this technique, a probabilistic approach to future scenarios is used to find the combination of decisive DoF that maximizes the indicator of economic sustainability. According to the results, the implementation of the plant at this stage of the study is feasible and suggests promising values for revenues and economic profitability.

The results of this preliminary study are promising. The economic potential of the four order is proven to be high, with a magnitude of eight order in USD/y. Further, the statistical indexes prove that the plant is profitable within 12 years of the process time's life. Finally, the conditions of the plant maximizing the EP are identified—a recycle amine flow of 0.0274 kmol/h at 60 ◦C proved to be an optimal combination of the decision variables. In respect to the 'green' risks, it is demonstrated that the higher the upper bounds of the DoF, the higher the environmental risk is.

The evaluation of DoF and their impact on the energy requirements of the plant have led to a notable conclusion—the decision variable affecting the consumer is the water makeup of the plant. Thus, a new perspective for authors working with a similar process is presented in this paper.

Future work can extend the limits of this methodology and include a higher number of DoFs, such as the ones related to the regeneration of the column, which is rarely discussed in the bibliography. In addition, the economic potential evaluation can be extended with heat integration coming from the pinch technology.

The last important aspect to be noted is that the CO2 was historically considered to be a by-product, and in the past, it was a common practice to flare it. However, the recuperation and condition of this gas, and the installation of a proper plant operating at proper conditions, might be the starting point for implementing the technology of EOR in the region, taking into account volatile market scenarios.

**Author Contributions:** Conceptualization, J.P.G., E.E. and D.M.; Methodology, J.P.G., E.E. and D.M.; Validation, J.P.G., E.E. and D.M.; Investigation, J.P.G., E.E. and D.M.; Resources, E.E. and D.M.; Writing-Original Draft Preparation, J.P.G. and E.E.; Writing-Review & Editing, J.P.G., E.E. and D.M.; Supervision, E.E. and D.M.; Funding Acquisition, J.P.G., E.E. and D.M.

**Funding:** This publication has been produced with the funding of the ERASMUS MUNDUS (Action 2 Strand 1) SUSTAIN-T Program, under the coordination of Politecnico di Milano, Italy. The authors also acknowledge the funding of CONICET (Grant 2222016000218900) and the Universidad Nacional de Salta (CIUNSa Projects 2253/0, 2465, and 2645), Argentina.

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

#### **References**


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