*2.3. Stage 3: Multi-Objective Optimisation Problem Formulation* 2.3.1. Formulation of Pricing Model

After the potential solvent candidates were identified in the previous stage, a detailed economic analysis was conducted to determine the selling price of the new solvent-oil blend based on the current market demand, availability and existing competitors identified via a comprehensive background study. The pricing model proposed by Bagajewicz [36] was employed to relate the product quality to demand and price of the product [37]. In the past, the pricing model has been incorporated in various product design such as wine [38], carpet deodorizers/disinfectants [39], skin moisturising lotion [40], die-attach adhesive [37] and dry-cleaning solvent [41]. The mathematical expression for the pricing has been shown in Equations (9) and (10).

$$A^P T^P = A^\mathbb{C} \left( T^P \right)^\delta \left( \frac{a}{\beta} \right)^\delta \left( \frac{\mathcal{Y} - A^P T^P}{A^\mathbb{C}} \right)^{1-\delta} \tag{9}$$

$$Y \ge A^P T^P + A^c T^c \tag{10}$$

here, *AP* and *TP* refer to the price and demand of the new solvent-oil blend while *A<sup>C</sup>* and *TC* refer to the price and demand of the competitor's product. In this study, *A<sup>P</sup>* can be obtained by summing up the cost of bio-oil production, cost of solvent and the profit obtained by selling the solvent-oil blend (Equation (11)).

$$A^p = \text{Cost}\_{Bio-oil} + \text{Cost}\_{Solvent} + \text{Profit} \tag{11}$$

*Y* is the total market size for the solvent-oil blend and *δ* is the elasticity of substitution, which is an adjustable parameter that measures the change in the ratio of products demand in response to a change in the ratio of their prices. On the other hand, *α* is expressed as the consumer's awareness on the new product, which can be raised by allocating higher budget in the marketing of new product. The value of parameter *α* ranges between 0 and 1, where *α* with value 0 indicates that the consumers have no knowledge about the new product, and vice versa. Lastly, *β* is the consumer preference coefficient that relates the consumer's interest in the new product over the competing product, which can be determined using Equation (12), where the *λ<sup>C</sup>* and *λ<sup>P</sup>* are the consumer's preference function of competitor's product and new product, respectively. In this study, the consumer's preference was related to the HHV of the solvent-oil blend, which possess a significant influence on the functionality of the solvent-oil blend.

$$
\beta = \frac{\lambda^{\mathbb{C}}}{\lambda^{P}} \tag{12}
$$

From Equation (12), the *λ<sup>C</sup>* and *λ<sup>P</sup>* refer to the consumer's preference function of competitor's product and new product, respectively. The new solvent-oil blend is said to be preferred by consumers if *β* is smaller than 1. However, the competitor's product is preferred by consumers when the value of *β* greater than 1.

Based on the market analysis conducted, the total market size, *Y* of solvent-oil blend was identified to be USD 500 million annually [42]. The price elasticity was defined between the range of 0.11 to 0.33, based on the previous studies for diesel fuel. The demand of bio-oil blend was said to be price inelastic when the parameter *δ* lies between 0.1 to 1. Thus, in this work, the parameter *δ* was assumed to be 0.1 [43]. On the other hand, the

parameter *α* was estimated to be at the value 0.85. These values should be revised and updated based on the response received once the solvent-oil blend was introduced into the market. The benchmark for this work is the reported solvent-oil blend consisting of 50 wt.% pyrolysis bio-oil and 50 wt.% of iso-propanol, with a HHV of 27.55 MJ/kg [44]. The cost of iso-propanol and bio-oil was assumed to be USD 1336.57 per tonne of iso-propanol and USD 354 per tonne of pyrolysis bio-oil [45,46]. The cost of the competitor's solvent-oil blend can be calculated using Equation (13), where *xi* and *Ci* are the ratio and costs of the solvent and bio-oil, respectively.

$$\mathbb{C}\_{blend} = \Sigma\_i \mathfrak{x}\_i \mathbb{C}\_i \tag{13}$$

Next, the selling price of competitor's blend *A<sup>C</sup>* can be calculated by summing up the cost of solvent-oil blend and the profit obtained, which was assumed to be USD 50 per tonne of solvent-oil blend, in this case. Table 2 summarises the parameters and its respective values obtained from this market analysis.


**Table 2.** Parameters and values from market analysis.

#### 2.3.2. Formulation of Fuzzy Multi-Objective Optimisation via Max-Min Aggregation

The HHV of pyrolysis bio-oil can be increased with the addition of solvent. The higher the mass fraction of solvent in the solvent-oil blend, the higher is the energy content. However, a higher amount of solvent was often associated with higher cost, and thus, lower profitability obtained from the solvent-oil blend. In this study, a MOO problem was developed to investigate the trade-off between high HHV and high profitability. Most of the current CAMD techniques focus on optimising a single objective or property of the chemical product [29], but having a multi-objective problem necessitates the use of more complex optimisation methods.

Thus, fuzzy mathematical programming was applied to solve the MOO design problem. The satisfaction degree of fuzzy, *λ* is introduced to both property functions that were to be optimised. The degree *λ* is a continues variable that lies between the value 0 and 1, where 0 indicates unsatisfactory and 1 is completely satisfactory (Equation (14)).

$$0 \le \lambda \le 1\tag{14}$$

The objective function of the fuzzy optimisation model was to maximise the overall satisfaction degree of fuzzy constraint *λ* as shown in Equation (15). The max-min aggregation was applied to the fuzzy programming, where every fuzzy constraint should be satisfied partially at least to the degree *λ*.

$$f\_{obj} = \max \lambda \tag{15}$$

Fuzzy goals for the HHV of solvent-oil blend and the profitability were expressed using a linear membership function, as shown in Equations (16) and (17).

$$\lambda\_{p(\text{max})} = \begin{cases} 0, \left| \begin{array}{c} V\_p \le v\_p^L \\ \frac{V\_p - v\_p^L}{v\_p^L - v\_p^L} \end{array} \right| & v\_p^L \le V\_p \le v\_p^{lI} \\\ 1, \left| \begin{array}{c} V\_p \ge v\_p^{lI} \end{array} \right. \end{cases} \quad \forall p \in P \tag{16}$$

$$
\lambda\_p \ge \lambda \tag{17}
$$

where *Vp* is the target property values bounded by the lower and upper bounds, *v<sup>L</sup> <sup>p</sup>* and *vU <sup>p</sup>* , respectively. The values for the lower and upper bound can be obtained by performing single objective optimisation for both objective functions.

#### *2.4. Stage 4: Phase Stability Analysis*

With the identification of optimal solvent-oil blend ratio from the previous step, phase stability analysis was carried out to ensure the miscibility of the designed solvent-oil blend at the targeted mixing ratio. In this work, the phase stability analysis was conducted via computation of the tangent plane distance. With fixed temperature and pressure, Gibbs tangent plane distance function was employed for the phase stability analysis of *N*-component mixture (Equation (18)) [47]:

$$d(\mathbf{x}) = \sum\_{i=1}^{n} \mathbf{x}\_i [\ln \mathbf{x}\_i \gamma\_i(\mathbf{x}) - \ln z\_i \gamma\_i(z)] \tag{18}$$

From Equation (18), *z* refers to the compositions of component *i* in mole fractions of the tested phase, *x* is the composition component *i* of a trial phase and *γ* is the activity coefficient of component *i* in respective phases. For a solvent-oil blend that is stable and demonstrates homogenous single-phase, Equation (19) can be followed [47]:

$$d(x) \ge 0 \tag{19}$$

Additional information on the computation of tangent plane distance can be found in the Appendix A: Equations (A1)–(A11). The solvent-oil blend can be concluded as stable if the tangent plane distance is non-negative. If otherwise, the previous steps are repeated by revising the property attributes and constraints.

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

A case study on solvent design for bio-oil applications was conducted to illustrate the application of this proposed methodology. The fast pyrolysis process considered in this work is related to an application in Malaysia. All pricing in this study was converted to U.S. Dollar at the exchange rate of RM 1 = USD 0.24 and adjusted to 2021 values using appropriate indices.

#### *3.1. Identification of Feasible Solvent Candidates*

In the initial part of CAMD optimisation, 32 feasible solvent candidates, which are mostly petroleum and natural gas-based solvents that are commonly used as lubricants, lubricant additives, and food additives, were identified according to the pre-defined target properties constraints. The candidates list included mostly higher alkanes and alkenes, with few esters and aromatic compounds. The list was also comprised of several alcohols and nitriles, which are known to be miscible in water. However, there are only a few that can be identified as common chemicals. Most of the molecules are complex and may be challenging to even validate in a lab-scale process, and may not be available at chemical suppliers. Among the favourable candidate was benzyl acetate which is a readily available ester often employed in the food industry as a flavouring agent. 1-Pentanol is also a well-known alcohol employed in the food industry and used as a solvent for lubricants.

Among the candidates for alkenes were 1-octadecene, 1-tetradecene, 1-hexadecene, and 1-dodecene. Most of these alkenes were produced through the oligomerisation of ethylene using triethyl-aluminium catalyst, followed by fractional distillation of the resulting alpha-olefin mixture. In other words, these chemicals were produced from downstream processing of petroleum- or natural gas-based raw materials. Therefore, their availability can be guaranteed, and the price variation could be related to that of the hydrocarbons. Comparatively, more alkanes were chosen as they are commonly used as industrial solvents

and additives. Among those were octadecane, *N*-tridecane, dodecane and undecane. Only one nitrile molecule was included, which is decanenitrile. Among the chemicals used in the additives and food flavouring industry were three ketones: 1-octanone, 2-undecanone and 2-nonanone. Two aldehydes were included too: 1-nonanal and octanal. Hexyl acetate and nonyl acetate were some of the esters included in the list as well. Esters are particularly important for any further research into the reaction system in the blend as they may react with some of the components of pyrolysis bio-oil. Table 3 summarises the 32 solvent candidates identified along with their respective target properties estimated from the GC prediction models. Based on the potential solvent candidates identified in the previous stage, a thorough search was conducted on the cost of solvent as listed in Appendix A: Table A4. The solvents and chemicals were of analytical grade and the costs were obtained from chemical vendors.

**No. Compound Name Chemical Formula Melting Point (K) Boiling Point (K) Flash Point (K) Density** *ρ* **(kg/m3) Viscosity** *v* **(mm2/s) HHV (MJ/kg)** S1 1-Pentanol C5H12O 221.44 408.63 320.35 805.74 3.83 37.97 S2 1-Hexanol C6H14O 229.47 432.62 334.83 812.14 4.70 39.31 S3 1-Octanone C8H16O 234.67 449.51 325.07 817.42 1.05 40.24 S4 Octanal C8H16O 243.73 453.46 322.20 818.04 1.45 40.90 S5 Hexyl acetate C8H16O2 208.70 449.66 329.02 872.42 1.14 34.75 S6 Pentyl propionate C8H16O2 205.48 449.57 329.02 870.32 0.96 34.75 S7 Phenylacetaldehyde C8H8O 265.52 468.63 336.74 1025.68 1.74 35.36 S8 Benzyl Acetate C9H10O 243.14 483.92 358.03 1056.67 1.75 31.21 S9 2-Nonanone C9H18O 242.03 469.65 339.54 820.95 1.29 40.98 S10 1-Nonanal C9H18O 250.66 473.27 336.68 821.52 1.79 41.58 S11 Hexyl propionate C9H18O2 214.41 469.71 343.49 868.91 1.19 35.91 S12 Benzyl acetone C10H12O 270.12 501.17 368.55 987.48 1.99 37.14 S13 Decanenitrile C10H19N 238.23 521.90 387.44 822.90 2.45 43.60 S14 Octyl acetate C10H20O2 225.69 488.25 357.97 869.50 1.76 36.87 S15 Hexyl butyrate C10H20O2 222.82 488.18 357.97 867.74 1.48 36.87 S16 4-tert-Butyltoluene C11H16 246.79 462.25 335.65 858.44 0.26 43.19 S17 2-Undecanone C11H22O 255.73 505.18 368.49 826.34 1.96 42.10 S18 Undecanal C11H22O 263.62 508.26 365.63 826.82 2.72 42.60 S19 Nonyl acetate C11H22O2 233.50 505.29 372.44 868.37 2.18 37.69 S20 Undecane C11H24 191.06 466.22 324.06 737.02 1.18 48.49 S21 1-Dodecene C12H24 209.17 484.87 334.48 754.08 1.19 48.46 S22 Dodecanal C12H24O 269.70 523.89 380.11 828.88 3.35 42.99 S23 Dodecane C12H26 200.87 484.96 338.53 745.48 1.44 48.43 S24 *N-*Tridecane C13H28 210.08 502.25 353.01 752.80 1.76 48.38 S25 1-Tetradecene C14H28 226.11 518.22 363.43 766.99 1.78 48.36 S26 Tetradecane C14H30 218.74 518.30 367.48 759.21 2.16 48.34 S27 *N-*Pentadecane C15H32 226.92 533.26 381.96 764.85 2.65 48.30 S28 1-Hexadecene C16H32 241.30 547.21 392.38 776.96 2.69 48.28 S29 *N-*Hexadecane C16H34 234.67 547.28 396.44 769.87 3.26 48.27 S30 *N-*Heptadecane C17H36 242.03 560.47 410.91 774.35 4.01 48.24 S31 1-Octadecene C18H36 255.07 572.86 421.34 784.90 4.08 48.23 S32 Octadecane C18H38 249.04 572.92 425.39 778.39 4.93 48.21

**Table 3.** Feasible solvent candidates generated from Stage 1 Optimisation.

## *3.2. Multi-Objective Optimisation Model*

Here, a multi-objective optimisation model was developed via fuzzy max-min aggregation approach to optimise the higher heating value (HHV) and the profitability of the solvent-oil blend, simultaneously. Two case studies were presented to investigate the effect of different constraints on the outcome while optimising both objective functions.

#### 3.2.1. Estimation of Pyrolysis Bio-Oil Production Cost

In this study, a pyrolysis plant was proposed to aid the estimation of pyrolysis bio-oil production cost. The pyrolysis plant was designed to produce 120 tonne of pyrolysis bio-oil from 200 dry tonne of PKS biomass daily via fast pyrolysis. The overall pyrolysis bio-oil yield was assumed to be 60%. It is expected for the pyrolysis plant to operate on a continuous operation daily for 24 h and 300 days, with a plant lifetime of 30 years. The production costs of the pyrolysis plant include the biomass, capital, labour, electrical and other operational costs. Assumption was made that the PKS biomass used in the pyrolysis plant were supplied by a palm oil mill at no cost.

On the other hand, the capital cost of the pyrolysis plant was estimated based on the sizing curve developed in Rogers et al. [48] which relates both the total plant cost and the plant capacity. In this case, the total plant cost of the designed pyrolysis plant was estimated to be USD 16 million. In addition, the capital cost for the biomass pre-processing plant was included, with an estimated cost of USD 2.98 million. As for the labour cost estimation, the following rough scenario was assumed where the designed pyrolysis plant operates on a shift-work basis, with 5 operators and 1 supervisor per shift. Three 8-h shifts pattern was implemented with 4 teams to provide 24/7 coverage. An average annual salary of USD 13 K was allocated for each employee, which cover the employers' insurance cost, pension contribution, anti-social hours payments, training and administration charges [49].

A total electrical consumption of 240 kWh per dry tonne of biomass was estimated for both the biomass pre-processing plant and the pyrolysis plant [50]. The electric tariff of E1 for general industry with medium voltage as defined by Malaysia's energy provider (Tenaga Nasional Berhad) was considered in this study. The price of tariff E1 is USD 0.08/kWh [51] was used to calculate the total cost of electricity. Lastly, an allowance of 4% of the total plant cost (USD 771.48 K per year) has been made to cover other miscellaneous cost such as repair, maintenance, insurance and business costs [50]. Thus, the total cost to produce 1 tonne of pyrolysis bio-oil was calculated to be USD 80.37, as shown in Table 4.


**Table 4.** Summarised pyrolysis bio-oil production cost.

#### 3.2.2. Fuzzy Optimisation

In case study 1, the constraint on solvent fraction added to the blend was relaxed to allow higher HHV value of the generated solvent-oil blend. The parameter *β* was set to be lesser than 0.75 in this case to achieve HHV of at least 35 MJ/kg. In case study 2, the constraint on consumer preference coefficient was relaxed, thus lowering the HHV requirement of solvent-oil blend to allow higher profitability. As mentioned above, the competitor's product consisted of 50 wt.% solvent. Hence, the solvent fraction was set to be less than 0.5. Table 5 summarises the constraints defined in case study 1 and 2, respectively.

**Table 5.** Comparison of constraints for case study 1 and 2.


Firstly, single objective optimisation was conducted to generate the upper and lower bounds for both the objective functions, as shown in Table 6. The values obtained was then substituted into Equation (16) to solve the multi-objective fuzzy optimisation problem. The max-min aggregation approach was employed to study the trade-off between high HHV and high profitability.


**Table 6.** Results from single objective optimisation of HHV and profitability of solvent-oil blend.

Among the 32 solvents identified in the previous stage, only 4 solvents, including the octadecane, 1-octadecene, 1-tetradecene and 2-octanone, demonstrated promising performance in terms of functionality and economics. Table 7 shows the results obtained from case study 1 and 2. As the constraints on the HHV of the solvent-oil blend were relaxed in case study 1, higher HHV can be observed, ranging from 37.11 to 44.65 MJ/kg. However, a large amount of solvent was required to be blended with pyrolysis bio-oil, thus leading to the increased cost and low profitability. From Table 7, higher profit was obtained from case 2, which is a 1.6-fold increase as compared to the profit in case 1. Nonetheless, this was compensated with the lower HHV of solvent-oil blend ranging from 31.53 to 32.93 MJ/kg. The lowest profit was that of 2-octanone at USD 122.77 per tonne of solventoil blend. 2-octanone is a flavouring ingredient naturally present in apple, apricot, banana, papaya, wheat bread and alcoholic beverages. The ketone solvent was available on an industrial scale and should be delivered at a slightly higher cost than 1-tetradecene or octadecane, thus the lower profitability observed.


**Table 7.** Results for solvent blend candidates.

Apart from improving the HHV of solvent-oil blend, the miscibility of the final blend can also be improved with the addition of solvent candidates. Instead of dispersing in aqueous and organic phase, the strong intermolecular forces between the molecules in the crude pyrolysis bio-oil will attract each other [52]. However, the dispersion of bio-oil can be improved with addition of solvent candidates due to its amphiphilic properties, and thus improving the phase separation of bio-oil. In this work, the phase stability analysis was carried out by computing the tangent plane distance against the identified solvent candidates. Except for 2-octanone, the remaining solvent candidates identified in both case 1 and 2 (octadecane, 1-octadecene and 1-tetradecene) were immiscible with pyrolysis bio-oil at their respective solvent ratio. This could be explained by the existence of non-polar hydrocarbon groups in the solvent molecule. Figure 2 illustrates the Gibbs energy and tangent plot for 2-octanone-oil blend. The X-axis of the graph represents the mass fraction of 2-octanone solvent in solvent-oil blend, while the Y-axis indicates the calculated Gibbs Energy. As shown in Figure 2, the blend

of 2-octanone and pyrolysis bio-oil is stable and demonstrated homogenous single-phase as the tangent line was completely plotted below the Gibbs energy curve. This may be due to the polar carbonyl (C=O) functional group found in the 2-octanone which helps in promoting the miscibility of the solvent-oil blend. As the final solvent-oil blend was expected to be homogenous while demonstrating promising properties, it can be concluded that solvent-oil blend with 85 wt.% of 2-octanone is the most promising blend with HHV of 37.11 MJ/kg and profit of USD 122.77/tonne of blend.

**Figure 2.** Gibbs energy and tangent plot for 2—Octanone in case study 1.

#### *3.3. Economic Study on the Bio-Oil-Diesel Blend*

Based on the optimal solvent-oil blend identified in the previous section, an economic analysis was carried out to investigate the relationship between the ratio of bio-oil-diesel blend, the price and HHV of the bio-oil-diesel. The blend of bio-oil and petroleum diesel was commonly referred to as BX, where X refers to the volume percent of bio-oil in the blend. For example, B5, B10 and B100 consist of 5%, 10% and 100% bio-oil, respectively. Generally, bio-oil-diesel blend has a lower energy content and higher fuel consumption as compared to that of the conventional diesel. Although the bio-oil-diesel blend provides sufficient environmental advantages, the price of this blend is costlier than the conventional diesel fuel. As of September 2021, the average price of diesel around the world is USD 1.07 per litre [53]. Meanwhile, the energy content of the conventional diesel fuel generally ranged between 44 to 48 MJ/kg [54]. From the results obtained, the price of 2-octanone-oil blend was observed to cost USD 6249.38 per tonne of solvent-oil blend, with a HHV of 37.11 MJ/kg. Table 8 summarises the price and HHV of the solvent-oil blend and diesel fuel used in this study.

**Table 8.** Price and HHV for both solvent-oil blend and diesel fuel.


Figure 3 illustrates the effects of solvent-oil blend ratio on the price and HHV of the bio-oil-diesel blend. It was observed that as the ratio of solvent-oil blend increases, the price of bio-oil-diesel blend increases proportionately. However, the HHV of the bio-oil-diesel blend decreases as the amount of solvent-oil blend increases. In this study, biodiesel with HHV of 40 MJ/kg was used as benchmark to determine the desired ratio of solvent-oildiesel blend. As shown in Figure 3, blending with at least 40 wt.% of diesel fuels, or 60 wt.% of solvent-oil blend was required to generate bio-oil-diesel with HHV of at least 40 MJ/kg. However, blending with 60 wt.% solvent-oil blend will cost approximately USD 4.2 K per tonne bio-oil-diesel, which is equivalent to a 3.4-fold increase as compared to pure diesel fuel. To be competitive with conventional diesel fuel, substantial subsidies and tax incentives from government are crucial. In addition, the demand for bio-oil-diesel could be stimulated with the introduction of legislation mandating the blending of biofuel in conventional diesel fuel, thus making the bio-oil-diesel demand independent of the diesel fuel price [55].

**Figure 3.** The relationship between the price, HHV and ratio of the biodiesel blend.

#### **4. Conclusions**

A CAMD framework was developed to design solvent molecules that can upgrade the properties of bio-oil upon blending, while achieving a low mixing ratio with pyrolysis biooil and maintaining profitability. At the initial stage, the requirements of the solvent and solvent-oil blend were identified and translated into target properties. Suitable property prediction models were used to build the structures of 32 promising solvent molecules. In the second stage, a MOO model was developed to investigate the trade-off between high HHV and high profitability of the solvent-oil blend. The HHV and profitability of the solvent-oil blend were optimised simultaneously via the fuzzy max-min aggregation approach. Meanwhile, a pricing model was introduced to evaluate the profitability of the solvent-oil blend. In addition, a pyrolysis plant was proposed to aid the estimation of pyrolysis bio-oil production cost. Solvent-oil blend with octadecane, 1-octadecene, 1 tetradecene and 2-octanone demonstrated positive performance in terms of functionality and economical. Among the four solvent-oil blends, the blend with 85 wt.% of 2-octanone was selected as the most promising solvent-oil blend with a HHV of 37.11 MJ/kg and profit of USD 122.77/tonne of blend, while displaying other desirable attributes. As a conclusion, the developed framework in this work can be applied in the design of bio-oil solvents with different bio-oil types and compositions. However, financial and legislative supports from government are also critical in the commercialisation of bio-oil-diesel blend. In addition, further upgrading of bio-oil via other approaches than solvent addition needs to be considered to add value on this developed framework. It is recommended that the stability of said blends be experimentally verified and the results to be validated. Further investigation on the life cycle sustainability assessment is recommended to compare the sustainability to conventional diesel fuel.

**Author Contributions:** Conceptualization, J.W.C., N.G.C. and L.Y.N.; methodology, J.W.C., N.G.C. and S.T.-G.; software, O.A.A.; validation, S.T.-G. and K.M.; formal analysis, J.W.C.; investigation, J.W.C., S.T.-G. and K.M.; resources, N.G.C.; data curation, O.A.A. and S.T.-G.; writing—original draft preparation, J.W.C. and L.Y.N.; writing—review and editing, S.T.-G. and K.M.; visualization, L.Y.N.; supervision, N.G.C., S.T.-G. and K.M.; project administration, N.G.C., S.T.-G. and K.M.; funding acquisition, N.G.C. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Ministry of Higher Education, Malaysia, Grant number FRGS/1/2019/TK02/UNIM/02/1.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

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

#### **Appendix A**

**Table A1.** Property prediction models and mixing rules.


**Table A2.** Pyrolysis bio-oil properties.



**Table A3.** The functional groups considered for the solvent molecular design.

**Table A4.** Cost of solvent candidates.


#### *Phase Stability Analysis*

To estimate the activity coefficients in non-ideal liquid mixture, group contribution estimation approach developed by [60] was applied. In this work, the GC prediction model combines the solution-of-functional-groups concept with a model for activity coefficient based on UNIQUAC. In a multi-component mixture, the UNIQUAC equation for the activity coefficient of component *i* is given by:

$$\ln \gamma\_i = \ln \gamma\_i^C + \ln \gamma\_i^R \tag{A1}$$

In Equation (A1), *C* represent the combinatorial part while the residual part is denoted as *R*. Here, Equations (A2) and (A3) calculates the value ln *γ<sup>C</sup> <sup>i</sup>* and ln *<sup>γ</sup><sup>R</sup> i* :

$$\ln \gamma\_i^C = \ln \frac{\phi\_i}{\varkappa\_i} + 5\eta\_i \ln \frac{\theta\_i}{\phi\_i} + l\_i - \frac{\phi\_i}{\varkappa\_i} \sum\_j \varkappa\_j l\_j \tag{A2}$$

$$\ln \gamma\_i^R = \sum\_k \upsilon\_k^{(i)} (\ln \Gamma\_k - \ln \Gamma\_\mathbf{k}^{(i)}) \tag{A3}$$

Equations (A4)–(A11) represents the calculation for terms in Equations (A2) and (A3):

$$l\_{\dot{i}} = 5(r\_i - q\_{\dot{i}}) - (r\_{\dot{i}} - 1) \tag{A4}$$

$$\phi\_{\bar{i}} = \frac{r\_{\bar{i}} \mathbf{x}\_{\bar{i}}}{\sum\_{\bar{j}} r\_{\bar{j}} \mathbf{x}\_{\bar{j}}} \tag{A5}$$

$$\theta\_i = \frac{q\_i \mathbf{x}\_i}{\sum\_j q\_j \mathbf{x}\_j} \tag{A6}$$

$$r\_i = \sum\_k v\_k^{(i)} R\_k \tag{A7}$$

$$q\_i = \sum\_k v\_k^{(i)} Q\_k \tag{A8}$$

$$\ln \Gamma\_k = Q\_k \left[ 1 - \ln \sum\_m \theta\_m \psi\_{m,k} - \sum\_m \frac{\theta\_m \psi\_{m,k}}{\sum\_n \theta\_n \psi\_{n,m}} \right] \tag{A9}$$

$$\theta\_m = \frac{Q\_m X\_m}{\sum\_n Q\_n X\_n} \tag{A10}$$

$$\psi\_{m,n} = -\exp\left(\frac{a\_{mn}}{T}\right)l\_{\rangle} = \mathbf{5}(r\_{\bar{i}} - q\_{\bar{i}}) - (r\_{\bar{i}} - 1) \tag{A11}$$

where *γ<sup>i</sup>* = activity coefficient of component *i*,

*φ<sup>i</sup>* = segment fraction (volume fraction) of component *i*,

