Available online at www.sciencedirect.com
ScienceDirect
Computer-aided molecular design of solvents for
chemical separation processes
Shiyang Chai1, Zhen Song2, Teng Zhou3,4, Lei Zhang1 and Zhiwen
Qi2
Solvents are widely used in chemical industries, especially in
various separation processes. As traditional trial-and-error
solvent selection is time-consuming and expensive, modelbased methods for solvent selection/design become important
for efficient and sustainable chemical manufacturing. A lot of
contributions have been made in this area in the past few
decades. This article first reviews the prediction methods for
solvent properties, including single molecular properties and
mixture properties. Then, the solution strategies of solvent
design problems are summarized, including generate-and-test,
deterministic optimization, and stochastic optimization
methods. Next, latest progresses of computer-aided solventprocess design in separation processes including liquid–liquid
extraction, extractive distillation, gas absorption, and
crystallization are reviewed. Finally, several remaining
challenges and possible future directions for solvent design in
separation processes are pointed out.
Addresses
1
Institute of Chemical Process Systems Engineering, School of Chemical Engineering, Dalian University of Technology, 116024 Dalian, China
2
School of Chemical Engineering, East China University of Science and
Technology, 130 Meilong Road, 200237 Shanghai, China
Process Systems Engineering, Max Planck Institute for Dynamics of
Complex Technical Systems, Sandtorstr. 1, D-39106 Magdeburg,
Germany
4
Process Systems Engineering, Otto-von-Guericke University
Magdeburg, Universitätsplatz 2, D-39106 Magdeburg, Germany
3
Corresponding authors:
Zhang, Lei (keleiz@dlut.edu.cn), Qi, Zhiwen (zwqi@ecust.edu.cn)
Current Opinion in Chemical Engineering 2022, 35:100732
This review comes from a themed issue on Frontiers in chemical
engineering; chemical product design – II
Edited by Rafiqul Gani, Lei Zhang and Chrysanthos Gounaris
For complete overview of the section, please refer to the article collection, “Frontiers in Chemical Engineering; Chemical Product
Design – II”
Available online 13th September 2021
https://doi.org/10.1016/j.coche.2021.100732
2211-3398/ã 2021 Elsevier Ltd. All rights reserved.
Introduction
Solvents are widely used in many chemical and related
processes, such as reactions, separations, additives, and
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transportations. For example, as reaction media, solvents
have a great influence on the reaction rate and selectivity
for some specific reactions; as separation agents, solvents
are indispensable in various processes, such as gas absorption, liquid–liquid extraction, extractive distillation, and
crystallization [1,2]. Because of many factors that need to
be considered, such as solvent properties (melting point,
boiling point, flash point, viscosity, toxicity, safety and
environmental impact, etc.) and process properties
(vapor–liquid equilibrium, liquid–liquid equilibrium,
solid–liquid equilibrium, etc.) [3,4], it is usually a difficult task to select suitable solvents in chemical separation
processes.
Knowing the important role of solvents in chemical
processes, a careful solvent selection is often necessary
to reduce process costs and improve product quality.
However, it is obviously challenging to balance so many
different aspects, such as solvent chemical/physical properties, mixing rules, phase equilibrium, mass/energy
transfer, process design, economics, environmental
impact, safety and health issues, and so on. Meanwhile,
considering the large number of potential solvents, the
trial-and-error method for solvent selection could be
extremely time-consuming and expensive, and promising
solvents may be missed if the selection is based on a fixed
solvent database. To overcome this problem, modelbased solvent design/selection approaches are highly
desirable, which can narrow down the huge solvent
searching space and help focus the limited experimental
resources on several promising candidates [5].
In model-based solvent design methods, property estimation models play an irreplaceable role as they connect
the molecular structures with macroscopic properties.
The performance of designed solvents depends largely
on the accuracy of the used property estimation methods.
Different property estimation methods have been developed for solvents, including Group Contribution (GC) [6],
Quantum Mechanics (QM) [7], Machine Learning (ML)
[8,9], and Molecular Dynamics (MD) [10]. Struebing
et al. [11] used QM to calculate the reaction rate constant,
and then established a surrogate model for the rate
constant of the Menschutkin reaction. Liu et al. [12]
proposed a novel ML-based atom contribution method to
predict molecular surface charge density profiles (s-profiles). Wang et al. [13] developed ML models based on an
extended experimental dataset to predict the toxicity of
ionic liquids in leukemia rat cell line (IPC-81). To
Current Opinion in Chemical Engineering 2022, 35:100732
2 Frontiers in chemical engineering; chemical product design
achieve a higher accuracy, property prediction models are
constantly being improved and developed.
As a kind of model-based design method, Computer
Aided Molecular Design (CAMD) is a powerful technique for pre-screening existing solvents and designing
novel ones. In CAMD, a set of preselected building
blocks of molecules (usually functional groups) are assembled by mathematical algorithms to generate promising
molecules according to the objective functions and constraints (structural constraints, property constraints, process constraints, etc.) [14,15]. The mathematical formulation of CAMD is essentially a Mixed Integer Linear
Programming (MILP) or Mixed Integrated Non-Linear
Programming (MINLP) problem, and different solution
approaches have been developed to solve such MILP/
MINLP problems (generally classified as enumerationbased generate-and-test, deterministic optimization
methods, and stochastic optimization methods) [2].
Since CAMD methods were first proposed for the design
of extractants for liquid–liquid extraction processes [16],
it has been widely extended to extractive distillation [17–
19], liquid–liquid extraction [20–25], crystallization
[4,12,26,27], reaction [28–30], CO2 capture [31–36],
and many other fields [37–39]. The applications of
CAMD have greatly freed researchers from extremely
heavy experimental work.
In this review paper, solvent design methods for chemical
separation processes are specifically discussed, which are
based on multi-scale simulation and optimization methods and tools, as shown in Figure 1. The topics cover the
levels of solvent molecular structures, Quantitative
Structure Property Relationship (QSPR), mixture properties, and separation processes. In Section ‘Methods for
solvent property estimation’, methods of solvent property
estimation are reviewed, which is a crucial step in solvent
design. In Section ‘Computer-aided methods for separation solvent design’, the solution methods for the solvent
design models are summarized. In Section ‘Solvent-process design’, several selected cases of solvent-process
design problems for separation processes are discussed.
In Section ‘Discussion and perspectives’, challenges and
perspectives of separation solvent design are elaborated.
Methods for solvent property estimation
Property estimation models are the prerequisite for solvent design, which determine the range of application
and the accuracy of the solvent design results. In this
section, property estimation methods for single molecular
and mixture properties are discussed.
Single molecular properties
The properties of a molecule are determined by its
molecular structure. Conversely, with the molecular
structure as the input, the properties could be estimated.
The QSPR model addresses the relationship between
molecular structure and molecular properties. The Group
Contribution (GC) method is one of the most commonly
used and effective methods to predict the properties of
single molecules [14]. The GC methods are also known as
the addition methods, which consider the properties of a
molecule to be the summation of the property contributions of all the groups constituting the molecule [40].
Despite the popularity and effectiveness of first-order GC
models in many cases, their accuracy is sometimes limited
Figure 1
Quantitative Structure Property Relationship
Property Prediction
Multi-scale simulation and optimization
GC method
QM method
Solvent group
ML method
Single molecular
properties
Mixture properties
Phase equilibrium
Process model
Process simulation
MD method
Optimized solvent structure
&
process parameters
CAMD
Applications
Liquid-liquid
extraction
Extractive
distillation
Gas
absorption
Crystallization
Separation
processes
Current Opinion in Chemical Engineering
Multi-scale simulation and optimization-based solvent design methodology.
Current Opinion in Chemical Engineering 2022, 35:100732
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Computer-aided molecular design of solvents for chemical separation processes Chai et al.
due to the neglect of the interaction between different
groups. To overcome this problem, more sophisticated
GC models have been developed, such as high-order GC
and Artificial Neural Network-Group Contribution
(ANN-GC) methods [41]. Higher-order groups can be
used to distinguish some structural isomers, for example,
2-methyl pentane and 3-methyl pentane, and therefore,
the GC-based methods are able to distinguish between
isomers like this. Nevertheless, for many stereoisomers
(such as cis and trans isomers), their properties cannot be
differentiated by GC-based methods, which need to be
essentially determined by experimental measurement
[40].
QM-based method overcomes the shortcomings of GC
methods, that is, the missing molecular properties are
obtained by ab initio calculation and do not depend on
experimental data. Some QM-based property prediction
methods have already been used in CAMD problems,
such as the Conductor-like Screening Model (COSMO)
based ones (e.g. COSMO-RS and COSMO-SAC) [42].
Recently, QM-based CAMD methods have been applied
to the design of homogeneous catalysts, separation, and
reaction solvents [43]. However, the application of QM
in CAMD is limited by the calculation speed. One of the
future directions of QM for CAMD can be integrating
ML methods into semi-empirical QM methods to speed
up the calculation of QM. Similar to QM, MD can also
provide property estimation through its molecular scale
simulation results, which can be used for product design
or to establish QSPR models [44]. MD methods have
been applied in fields such as gas adsorption and polymer
design. Kupgan et al. [45] used MD to design polymers for
CO2 capture. Liang et al. [37] proposed a new optimization-based Computer-Aided Polymer Design (CAPD)
framework by combining MD and CAMD. However,
similar to the QM methods, the computational speed
limits its application in CAMD. The establishment of
QSPR models based on MD simulation results is one of
the future directions for chemical product design model.
With the help of ML-based methods, it is possible to
establish QSPR models to correlate molecular descriptors
and target physical, chemical, and biological properties of
molecules with higher computation speed and accuracy
[8]. The steps of establishing QSPR models based on
ML methods include data collection, data pre-processing
and feature engineering, and model establishment [46],
as shown in Figure 2.
It is essential to establish a database before establishing
ML models. The data can be obtained from experiments,
databases and/or model simulation such as QM, MD,
CFD, and so on. However, these data are not always
valid when directly applied in the establishment of ML
models. Experimental system errors, inconsistent order of
magnitude and redundant data often lead to poor training
results of ML models. Therefore, it is necessary to
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3
employ data pre-processing and feature engineering
methods to process the original data. After data collection,
pre-processing and feature engineering, the features are
generated and prepared for the establishment of ML
model, which includes model selection, model training
and validation process. The model selection depends on
the application scenarios, which are generally categorized
into supervised learning, unsupervised learning, and
Reinforcement Learning (RL) [47]. Supervised learning
[48] is employed to solve two problems of regression and
classification, while unsupervised learning [49] is used to
solve the problems such as clustering and association
analysis. RL [50] is the process of training the model
through sequences of state-action pairs, observing the
rewards that result, and adapting the model predictions
to those rewards using policy iteration or value iteration
until it accurately predicts the best results. Su et al. [9]
proposed a deep learning approach for QSPR modeling of
critical properties, while Datta et al. [51] used decision
trees and genetic programming to develop predictive
models for reaction rate constants.
Mixture properties
Mixture properties include functional and equilibriumbased properties. As for functional properties, the prediction is based on the properties of pure components and a
mixing rule for a given set of molecules and their compositions with a predefined phase identity. Liu et al. [52]
summarized 26 mixture properties prediction models
based on mixing rules, including molecular weight, density, and enthalpy. The equilibrium-based properties
usually include vapor–liquid equilibrium (VLE), liquid–liquid equilibrium (LLE), solid–liquid equilibrium
(SLE), and so on. The estimation of these properties
requires the integration of pure component properties
and thermodynamic property models. Mixture thermodynamic models used in CAMD include the UNIQUAC
functional group activity coefficients (UNIFAC) methods
[53], statistical associating fluid theory (SAFT) theory
equation of state [54], COSMO-based activity coefficient
methods [42], and so on. The UNIFAC methods, as a
kind of GC methods, are widely used for estimating
activity coefficient in phase equilibria calculation. However, due to the deficiency of GC methods mentioned in
Section ‘Single molecular properties’, it is difficult to
distinguish all isomers using UNIFAC methods. In addition, the UNIFAC methods require experimental data to
obtain the binary interaction parameters between groups
[55]. COSMO is a continuum solvation models based on
first principles. Klamt et al. [56] proposed the first
COSMO-based activity coefficient model, named as
COSMO-RS (Conductor-like Screening Model for Real
Solvents), which is suitable for the prediction of thermodynamic properties of fluid systems. Based on the principle similar to COSMO-RS, Lin and Sandler [57] proposed
the COSMO-SAC model (Conductor-like Screening
Model for Segment Activity Coefficient). With
Current Opinion in Chemical Engineering 2022, 35:100732
4 Frontiers in chemical engineering; chemical product design
Figure 2
Step 1: Data collection
Experiments
Step 2: Data pre-processing
and feature engineering
Databases
Experimental system errors
Model simulation
Inconsistent order of magnitude
(QM, MD, CFD, etc)
Redundant data
Step 3: Model establishment
Supervised learning
Unsupervised learning
Reinforcemant learning
Current Opinion in Chemical Engineering
Flow chart of establishing QSPR models based on ML methods.
COSMO-RS/SAC models, the activity coefficients of
systems involving new molecules can be estimated
directly and quickly without the need for molecular
and group-specific parameters. Thus, COSMO-RS/SAC
models are also widely used in many fields to predict
thermodynamic properties [58,59]. In addition, MLbased methods are also used to predict the mixture
properties. Zhang et al. [60] established the StructureOdor Relationship (SOR) model of aroma mixtures using
the molecular surface charge density distribution as
descriptors.
process constraints including equipment design equations, mass balance equations, energy balance equations,
and phase equilibrium equations, which can also be linear
or nonlinear equations [61].
For different MINLP/MILP problems, the solution strategies for the solvent design problems are different, which
can be classified as ‘generate-and-test’, deterministic
optimization and stochastic optimization methods [2].
Generate and test method
Computer-aided methods for separation
solvent design
The separation solvent design problem can be expressed
as an MILP/MINLP model, as shown in Eqs. (1)–(4)
[52,61].
Min=Max f obj ðX; N Þ
ð1Þ
Structural constraints : g 1 ðN Þ 0
ð2Þ
Property constraints : g 2 ðN Þ 0
ð3Þ
Process model and other constraints : g 3 ðX; N Þ 0
ð4Þ
Here, X is a vector of continuous variables related to the
process variables, N is a vector of integer variables related
to the molecular structure of the molecule. Eq. (1) is the
objective function, which could be economic benefits,
product quality, social demand, sustainability, and so on
[27]. Eq. (2) is the structural constraints, which includes
the octet rule and structural complexity constraints [45].
Eq. (3) represents the property constraints of single
molecules/mixtures, which can be linear or nonlinear,
depending on the used property models. Eq. (4) is the
Current Opinion in Chemical Engineering 2022, 35:100732
Gani and Brignole [16] proposed the ‘generation-andtest’ method for the design of liquid extraction solvents.
The method is to generate all possible candidate molecules from a set of given groups, and then use constraints
to test each candidate molecule. Finally, molecules that
cannot meet any of the constraints are discarded, and the
remaining molecules are sorted according to their performance. Because all candidate molecules generated by
group combination need to be tested, the generate-andtest method becomes very time-consuming when the
group number of the molecule increases. To overcome
this problem, Gani et al. [62] improved the method: first,
structural feasibility constraints are used to ensure that
only chemically feasible molecules are generated; then,
these molecules are screened through multi-level property constraints, and molecules that cannot meet the
constraints will be eliminated. This method greatly
reduces the molecular design space, which is very beneficial to the calculation efficiency of the generate-and-test
method. However, once the number of given groups is too
large, the method still faces the problem of combinatorial
explosion. In addition, the traditional generate-and-test
method cannot distinguish isomers. To solve this problem, Harper and Gani [63] proposed a multi-level CAMD
framework: first, all combinations of first-order groups are
determined by the generate-and-test method; then, by
using higher-order groups, molecular modeling techniques or experimental data, all possible isomers of the
candidate molecules obtained in the first step are further
evaluated. The multi-level CAMD method has been
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Computer-aided molecular design of solvents for chemical separation processes Chai et al.
implemented in the software ProCAMD (https://www.
pseforspeed.com/icas/procamd/).
Deterministic optimization methods
The deterministic optimization methods are based on
gradient-based algorithms for nonlinear programming
(NLP) problems, and branch-and-bound type algorithms
for integer variables to obtain the optimal molecules from
the solutions of MILP or MINLP problems. For the
CAMD problem formulated as an MILP, deterministic
optimization methods can always guarantee global optimal solution. However, for nonconvex MINLP problem,
the global optimal solution cannot be always guaranteed.
Karunanithi et al. [4] proposed the decomposition algorithm to solve MINLP problems for product design. In
this algorithm, the complex MINLP problem is decomposed into a set of MILP and NLP subproblems. First,
the candidate molecules satisfying the structural constraints and linear property constraints are obtained by
solving the MILP problem. Then, the nonlinear constraints are used to verify each candidate molecule.
Finally, the molecules satisfying all constraints are
retained and sorted according to the objective function
value. The prerequisite for using the decomposition
algorithm is that the constraints of the MINLP problem
are independent of each other. Since the constraints of
solvent properties (linear/nonlinear) in CAMD problem
are usually independent, the decomposition algorithm is
suitable for solving most solvent design problems. The
gap between the solution obtained from decompositionbased algorithms and the truly optimal solution depends
on the number of feasible solutions generated in the first
step. If all feasible solutions are generated in the first step,
the global optimality of the solution can be ensured [61].
Stochastic optimization methods
The stochastic optimization methods are to find the
optimal solution by using an adaptive search strategy.
With this kind of methods, global or nearly global optimal
solution can be obtained for convex and nonconvex
problems. The commonly used stochastic optimization
methods are genetic algorithm [64], tabu search algorithm
[65], and so on. Genetic algorithm is a kind of heuristic
algorithm based on natural selection. In CAMD problems, genetic algorithm usually searches in the space
of feasible molecular structure. Each molecule in a generation is evaluated, and the best molecular characteristics are passed on to the next generation, and the process
is continued until the algorithm converges. Venkatasubramanian et al. [66] first introduced genetic algorithms to
the CAMD problem by encoding the molecular structure
into a series of substituents and performing genetic
manipulation on the individual. Van Dyk and Nieuwoudt
[17] proposed a coding method based on the UNIFAC
groups. Scheffczyk et al. [67] applied genetic algorithm to
the design of liquid–liquid extractant based on the
COSMO-RS model. Zhou et al. [68] combines genetic
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5
algorithm (GA) with gradient-based deterministic algorithm to solve the continuous nonlinear optimization
problem with fixed molecular variables. Tabu search is
also a stochastic optimization method. In tabu search
algorithm, a set of initial solutions (molecules) needs to
be given first, and these solutions are appropriately changed through some operations. The operation is repeated
as long as the changed molecules do not appear in the
tabu list (that is, the list of molecules forbidden to be
considered based on various factors). The forbidden
factors in tabu list include frequency of occurrence (to
ensure that the same molecules are not always visited),
infeasible molecules, poor target properties, and other
factors. Mcleese et al. [69] applied the tabu search algorithm to the design of ionic liquids in absorption
refrigeration.
The selection of solution strategy depends on the characteristics of specific problems. A brief list of references
and main applications classified by solution strategy is
summarized in Table 1. At present, the requirement for
solution strategy is to minimize the computational cost
while ensuring the global optimality of the solution.
Solvent-process design
The solvent design is closely related to its related process.
Therefore, it is necessary to consider process models in
the solvent design to obtain the overall optimal solvents.
However, the existence of process models could make the
solvent design problem very complex and highly nonlinear, which may bring about great challenges to the solution strategy. For different solvent design problems, the
modeling and solution methods are also different.
In this section, several selected applications of solventprocess design in separation processes are briefly discussed, which include liquid–liquid extraction, extractive
distillation, gas absorption, and crystallization. These four
areas are chosen because they have been extensively
studied and can basically cover different types of solvent-process design problems in separation processes.
Solvent-process design for liquid–liquid extraction
The selection of extractant is an important issue in
liquid–liquid extraction, which affects the quality and
composition of extraction products and the performance
of extraction operation [87]. A lot of work on the optimal
selection and design of solvents for liquid–liquid extraction has been reported, as shown in Table 2. It can be seen
from Table 2 that for liquid–liquid extraction processes,
several important solvent properties such as Tm, Tb, toxicity and LLE need to be considered. Here, some
selected studies and recent progress are reviewed. Gani
and Brignole [16] first proposed a CAMD method and
applied it to extractant design. Among them, g and g 1 are
predicted by the UNIFAC method, and the extractants
with good performance are determined using generate
Current Opinion in Chemical Engineering 2022, 35:100732
6 Frontiers in chemical engineering; chemical product design
Table 1
A brief list of references and main applications classified by
solution strategy
Solution
strategy
Applications
Generate and Liquid–liquid
test
extraction
Extractive
distillation
Gas
absorption
Crystallization
Deterministic Liquid–liquid
optimization extraction
Extractive
distillation
Gas
absorption
Crystallization
Reaction
solvent
Stochastic
Liquid–liquid
optimization extraction
Gas
absorption
References
Gani and Brignole [16], Shankar et al.
[21], Song et al. [22], Gani et al. [62],
Harper and Gani [63], Song et al. [70],
Yang and Song [71], Chao et al. [72]
Karunanithi et al. [4], Liu et al. [12],
Karunanithi et al. [26], Chai et al. [27],
Chong et al. [32], Liu et al. [52], Cheng
and Wang [73], Xu et al. [74], Scilipoti
et al. [75], Harini et al. [76], Lekutaiwan et al. [77], Cignitti et al. [78],
Chen et al. [79], Ahmad et al. [80],
Scilipoti et al. [81], Zhang et al. [82],
Watsona et al. [83]
Wang et al. [34],
Venkatasubramanian et al. [66],
Scheffczyk et al. [67], Zhou et al. [68],
Mcleese et al. [69], Zhou et al. [84],
Zhang et al. [85], Gebreslassie and
Diwekar [86]
and test method. Cheng and Wang [73] presented computer-aided process/solvent design to find a feasible biocompatible solvent for the extractive fermentation and
separation process. In this model, the goals including the
maximum production rate, extraction efficiency, and the
limitation of solvent utilization were considered simultaneously. Among them, Tb, Tm, –log(LC50), and 4G were
predicted by the GC methods, and g was predicted by the
UNIFAC method. The process constraints were steadystate material balance and LLE. Finally, the established
MINLP model was solved using Mixed-Integer Hybrid
Differential Evolution (MIHDE), which belongs to the
deterministic optimization algorithm. Shankar et al. [21]
employed CAMD to design organic solvents for the
liquid–liquid extraction of ephedrine from its aqueous
solution. Three main solvent performance indicators
(high ephedrine solubility, low solvent loss and high
partition coefficient) were used to identify the solvents.
Here, Tb, Tm, 4Hfus, h and –log(LC50) were predicted by
the Joback-Reid method, which is a simple GC method; d
was predicted by Albahari’s GC-based correlation, and g
was predicted by the UNIFAC method. The SLE equations were considered as the process constraints. Finally,
the optimal solvents were determined by the Exhaustive
Direct Search (EDS), which belongs to the generationand-test method.
The above two cases are examples for the design of single
molecular extractant. Xu et al. [74] proposed a new
blended extractant design and screening method to solve
the problem of co-extraction of phenols, polycyclic
Current Opinion in Chemical Engineering 2022, 35:100732
aromatic hydrocarbons (PAHs) and nitrogen heterocyclic
compounds (NHCs) in coal chemical wastewater. First,
two single-component extractant candidate sets were
generated by database search based on property constraints (Tm, Tb, Psat, r, s, h and Mw). Then, the composition of the extractant mixture was determined by solving
a series of NLP problems. The solution method was grid
parallel computing method, which belongs to deterministic optimization algorithm. Among them, g of component i in the solution was calculated by the Dortmund
UNIFAC model, and the process constraints include
LLE and mass balance equations.
In addition to traditional organic solvents, the design of
ionic liquids (ILs) has also been reported considering the
attractive physico-chemical properties of ILs for liquid–
liquid extraction process, such as non-volatility, selective
solubility for different compounds, and widely tunable
character. For instance, Song et al. [22] proposed a systematic method combining phase equilibrium calculation, physical property prediction and process simulation
to select suitable ILs as extraction solvents. The
COSMO-RS model was employed to predict r and
LLE data and the GC methods were used to estimate
the key physical properties (Tm, Tb and h) of the prescreened ionic liquids. For the top IL candidates, the
performance was further analyzed using Aspen Plus to
determine the optimal solvents based on the process.
Finally, the proposed method was applied to the extraction desulfurization process, and two most promising ILs
were determined. As the design of ILs is often limited by
the lack of GC thermodynamic methods, Song et al. [70]
proposed an extended UNIFAC-IL model for the optimal design of ILs for the extractive desulfurization of fuel
oils. The extended UNIFAC-IL model was based on
3653 experimental data of infinite dilution activity coefficient, covering seven conventional main groups and
20 IL main groups, and its high reliability was verified
through a large experimental liquid–liquid equilibria
database. Subsequently, the computer-aided design of
ILs was performed by using the extended UNIFAC
model for estimating g in the objective function (extraction performance indicator). The main solvent properties
(Tm, h and Mw) were predicted by the GC methods.
Finally, Aspen Plus was used to analyze the performance
of the top candidate ILs. It can be seen from the above
discussions that the design of novel extractants such as
ILs has received more and more attention. For novel
extractant design, one of the challenges is the lack of
reliable predictive models for some physical properties.
Therefore, the establishment of QSPR models for such
properties is crucial for novel solvent design. Wang et al.
[13] developed ML models based on an extended experimental dataset to predict the toxicity of ionic liquids,
which provides a good reference for the use of data-driven
machine learning methods to establish property prediction models for novel extractants.
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Computer-aided molecular design of solvents for chemical separation processes Chai et al.
7
Table 2
A brief list of solvent-process design for liquid–liquid extraction
Reference
Properties
Property prediction
method
Process model
Solution strategy
Gani and Brignole [16]
Cheng and Wang [73]
g, g 1
UNIFAC
GC
LLE
Steady-state material
balance
LLE
Generate and test
SLE
Exhaustive Direct Search (EDS)b)
Tm, Tb, –log(LC50),
Shankar et al. [21]
Xu et al. [74]
Song et al. [22]
4G, g
UNIFAC
Joback-Reid method
Tm, Tb, 4Hfus, –log(LC50), h, Albahari’s GC-based
method
d, g
UNIFAC
Database-based
Tm, Tb, Psat, r, s, h, Mw, g method
Dortmund UNIFAC
GC
Tm, h, r, Mw, g
COSMO-RS
Song et al. [70]
UNIFAC-IL
Tm, h, Mw, g
COSMO-RS
Yang and Song [71]
Ss, Sd, Sp, Sl, Tm, Tb, g 1
Scilipoti et al. [75]
Zhang et al. [85]
Ss, Sd, Sp, Sl, g 1
Ss, Sd, Tm, h, g
1
Gebreslassie and Diwekar
Ss, Sd, Tb, g 1
[86]
Harini et al. [76]
Sd, Sp, Sl, Kow, Tm, Td, g 1
GC
UNIFAC
UNIFAC
GC-FFANN
COSMO-SAC
GC
UNIFAC
GC
UNIFAC
Mixed-Integer Hybrid Different
Evolutiona)
Mass conservation
Grid parallel computing methoda)
LLE
Mass conservation
Energy conservation
LLE
Mass conservation
Energy conservation
LLE
Generate and test
Generate and test
LLE
Generate and test
LLE
Deterministic optimization method
LLE
Stochastic optimization algorithm
LLE
Stochastic optimization algorithm
LLE
Deterministic optimization method
g: activity coefficients; g 1: infinite dilution activity coefficient; Tm: melting point; Tb: boiling point; –log(LC50): toxicity; 4G: Gibbs free energy;4Hfus:
latent heat of fusion; h: viscosity; d: solubility parameter; Psat: vapor pressure; r: density; s: surface tension; Mw: molecular weight; SS: solvent
selectivity ; Sd: solute distribution coefficient; Sp: solvent power; Sl: solvent loss; Kow: octanol water partition coefficient; Td: thermal decomposition
temperature.
The superscript a) indicates the deterministic optimization method.
The superscript b) indicates the generate and test method.
FFANN: Feed Forward Artificial Neural Network.
Solvent-process design for extractive distillation
Extractive distillation is commonly used to separate systems of close boiling compounds or azeotropes, in which a
solvent (also called entrainer) is introduced [88]. A lot of
work on the optimal selection and design of solvents for
extractive distillation has been reported, as shown in
Table 3. It can be seen that several important solvent
properties such as Tm, Tb and VLE need to be considered
in extractive distillation processes. Here, some selected
studies and recent progress are reviewed. Lek-utaiwan
et al. [77] proposed a framework for extractive distillation
that integrates solvent screening, experimental verification, VLE data analysis, and process design and optimization for separate close-boiling mixtures. First, CAMD
was used for solvent screening, where d, Tb, and Tm were
predicted by the GC methods, and g was predicted by
UNIFAC method. Second, the VLE experiments were
carried out for the top ranked solvents. In the situation
that the results of the existing performance prediction
models were inconsistent with the experimental data, the
interaction parameters in the performance prediction
model were refitted properly according to the
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experimental results before the design and optimization
of the distillation column. Finally, Aspen Plus was used
for the process design and optimization for the best
solvents from the previous step. The methodology was
successfully applied to an industrial case study of EB/
mixed-xylene separation. Cignitti et al. [78] used CAMD
to screen entrainers for extractive distillation processes
based on new thermodynamic criteria. In the proposed
CAMD problem, Tb and 4Hvb were predicted by GC
methods, and g was predicted by the UNIFAC method.
The new thermodynamic criteria take into account of the
thermodynamic properties of binary mixtures and the
isovolatility curves of ternary mixtures. Then, with the
goal of minimizing energy consumption, Aspen Plus was
used for process optimization. Finally, the feasibility of
the framework was verified by the entrainer design for
acetone-methanol separation. Zhou et al. [18] established
a multi-objective optimization-based CAMD method and
used it to search for promising entrainers for extractive
distillation processes. In this method, selectivity and
capacity as two important solvent properties determining
the efficiency of extractive distillation were optimized
Current Opinion in Chemical Engineering 2022, 35:100732
8 Frontiers in chemical engineering; chemical product design
Table 3
A brief list of solvent-process design for extractive distillation
Reference
Properties
Lek-utaiwan et al. [77]
Tm, Tb, d, g
Cignitti et al. [78]
4Hvb, g
Zhou et al. [18]
GC
Mass conservation
Energy conservation
VLE
Mass conservation
Energy conservation
VLE
Mass conservation
Energy conservation
VLE
UNIFAC
UNIFAC
GC
Tm, Tb, g, g 1
Chao et al. [72]
Process model
GC
Tb,
Chen et al. [79]
Property prediction method
1
1
a1
i;l , Si;j , Sp, Mw, Tb, g
1
Sp, S1
i;j , Tm, h, g
UNIFAC
GC
UNIFAC
GC
UNIFAC-IL
Solution strategy
Decomposition algorithm
Decomposition algorithm
Decomposition algorithm
VLE
Deterministic optimization method
VLE
Generate and test
1
4Hvb: vaporization enthalpy; a1
i;l :relative volatility at infinite dilution; Si;j : selectivity at infinite dilution.
simultaneously. Solvent properties (Tb and Tm) were
predicted by GC methods, and g and g 1 were predicted
by UNIFAC method. Then, the Pareto-optimal solvents
were further selected through rigorous thermodynamic
calculation and analysis. Finally, the extractive distillation process was optimized for each remaining solvent to
determine the best candidate solvent with the highest
process performance. It can be seen from the above
investigations that the modeling of the solvent-process
design of extractive distillation is different for different
problems. The selection of suitable entrainers is one of
the most important issues for extractive distillation.
There are also many other factors that need to be considered. Ma et al. [19] reviewed extractive distillation from
six aspects (thermodynamic analysis, QSPR, solvent
screening, process design, process intensification, and
dynamic control) and emphasized the importance of
QSPR in selecting suitable solvents in extractive distillation. Sun et al. [89] reviewed the key aspects (conceptual
design, solvent selection and separation strategies) of
extractive distillation, and elaborated the application of
CAMD in solvent selection of extractive distillation. The
challenge of solvent design for extractive distillation is
similar to that of liquid–liquid extraction, that is, sometimes lack of reliable property predictive models. In
addition, due to the high energy consumption and large
investment of extractive distillation, it is also necessary to
carry out technological innovation on the extractive distillation processes in addition to solvent design. Process
intensification and dynamic control are helpful to reduce
energy consumption and promote the development of
extractive distillation in the direction of intelligence and
security.
Solvent-process design for gas absorption
Because of the rapid economic growth all over the world,
reducing CO2 emissions has become a global challenge.
As one of the important applications of gas absorption,
carbon capture has attracted the attention of many
Current Opinion in Chemical Engineering 2022, 35:100732
researchers. Choosing/designing suitable solvents can
significantly reduce the energy consumption of the carbon capture process and relieve the problem of global
warming. A lot of work on the optimal selection and
design of solvents for carbon capture has been reported,
as shown in Table 4. It can be seen from Table 4 that for
carbon capture processes, several important solvent properties such as Tm and VLE need to be considered. Here,
some selected studies and recent progress are reviewed.
Papadopoulos et al. [31] proposed a CAMD framework
considering life cycle assessment (LCA), and safety,
hazard and environmental (EHS) properties. It calculates
a total of 11 sustainability-related indicators and integrates several impact categories. A design example of
phase-change solvents for chemisorption-based postcombustion CO2 capture was introduced. The proposed
framework identified verifiably useful phase-change solvents, which showed favorable performance compared
with a reference CO2 capture solvent. Ahmad et al. [80]
used CAMD method to design new alternative solvents
for the post-combustion carbon capture in power plants.
Solvent properties (d, r, m, Tb, Tm, s and Tf) were predicted by the GC methods, and g was predicted by the
UNIFAC method. The process performance was evaluated by calculating the heat required for the solvent
regeneration process. Finally, according to the specified
target properties, 25 candidate solvents have been successfully generated from amine and alcohol functional
groups. Compared with the traditional solvent (ethanolamine), the candidate solvent can significantly save up to
31.4% of energy requirement for the regeneration process, thus greatly reducing the cost of carbon capture.
Scilipoti et al. [81] successfully applied the CAMD
method based on the GC-EOS (GC Equation of State)
to the solvent selection for a pre-combustion CO2 capture
process. This work systematically studied the effects of
molecular functional groups on solvent properties and
successfully predicted the solvent properties (Sp, a and Sl)
under the changes of pressure, temperature, solvent
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Computer-aided molecular design of solvents for chemical separation processes Chai et al.
9
Table 4
A brief list of solvent-process design for carbon capture
Reference
Properties
Property prediction method
Process model
Solution strategy
Wang et al. [34]
m, Tm, g 1
GC
COSMO-SAC
GC
VLE
Stochastic optimization algorithm
Ahmad et al. [80]
d, r, m, s, Tm, Tb, Tf, g
UNIFAC
Scilipoti et al. [81]
Sp, a, Sl
Chong et al. [32]
Zhang et al. [82]
r,
4Hvap, Cp, h, g
Tm, Tb, Mw, Mv, h, Cp, l
Zhang et al. [93]
Tm, Tb, Mw, Mv, h, Cp, r
Tf : flash point;
GC-EOS
GC
UNIFAC
GC
ANN-GC
GC
ANN-GC
Mass conservation
Energy conservation
VLE
Mass conservation
Energy conservation
VLE
Decomposition algorithm
Deterministic optimization methods
VLE
Decomposition algorithm
VLE
Deterministic optimization methods
Mass conservation
Energy conservation
VLE
Deterministic optimization methods
4Hvap: heat of vaporization; Cp: heat capacity; Mv: molecular volume; l: thermal conductivity.
structure, and system composition. Finally, based on a hot
flash solvent recovery scheme, the most promising candidate solvents were optimized for the absorption cycle to
reduce the energy consumption. For the carbon capture
process, in addition to choosing traditional organic solvents, Chong et al. [32] proposed a design method of ILs
and IL mixtures to replace the traditional organic solvent
(ethanolamine) used in carbon capture. To overcome the
problem of missing property data of IL mixtures, this
paper presented a method which can directly use the
performance data of pure Ils and combine the existing
performance prediction models and experimental data to
predict the performance of IL mixtures. Here, r, 4Hvap,
Cp and h of pure Ils were predicted by GC methods and g
was predicted by the UNIFAC method. In addition to
GC-based methods, there are also QM and ML-based
methods for the prediction of the properties of solventprocess design for carbon capture. Wang et al. [34] proposed a systematic Computer-Aided Ionic Liquid Design
(CAILD) method for CO2 capture. Solvent properties (m
and Tm) were predicted by the GC methods, and g 1 was
predicted by COSMO-SAC model. Finally, the established MINLP model was solved using stochastic optimization algorithm. Tatar et al. [90] used artificial intelligence-based methods to successfully predict the
solubility of CO2 in 14 different ionic liquids. Sistla
and Sridhar [91] explored the interaction between CO2
and Ils in the process of CO2 adsorption at the molecular
level based on QM methods. Song et al. [92] used ML-GC
model to predict the solubility of CO2 in ionic liquids
(ILS) based on a database containing 10116 CO2 solubility data in Ils at different temperatures and pressures.
Subsequently, Zhang et al. [82] determined the optimal
IL solvents based on the ML-GC model established by
Song et al. [92] with the maximum CO2 equilibrium
solubility as the objective function. More recently, Zhang
et al. [93] used this surrogate solubility model (ML-GC
model established by Song et al. [92]) to replace the
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traditional thermodynamic model and performed an integrated IL and process design work. It was found that to
use the ML-based solubility model can largely reduce the
computational difficulty, making it possible to find the
global optimum for the integrated design problem. Excitingly, the designed IL-based process can potentially save
14.8% cost compared to the industrial Selexol process.
These works provide accurate property prediction models
for the novel solvent design of carbon capture. In addition, the combination of adsorption and other separation
technologies (for example adsorption and membrane) has
attracted the attention of researchers. Hybrid technologies in the selection of carbon capture solvents may be
helpful to find more economical solutions, which deserves
further study [41].
Solvent-process design for crystallization
Solution crystallization is an important separation unit
operation in the pharmaceutical industry. Solvent is one
of the most important factors affecting product quality
(purity, yield, crystal form, crystal morphology, particle
size distribution, etc.). A lot of work on the optimal
selection and design of crystallization solvents has been
reported, as shown in Table 5. It can be seen from Table 5
that for the crystallization processes, several important
solvent properties such as Tm, Tb, Tf, toxicity and SLE
need to be considered. Here, some selected studies and
recent progress are reviewed. Karunanithi et al. [4,26]
first used the decomposition algorithm to design crystallization solvents for ibuprofen and carboxylic acids. The
objective function was the potential yield and the SLE
was considered as process constraints, wherein the main
physical properties (d, Tb, Tm, Tf, –log(LC50), dH and h)
were predicted by the GC methods and g was predicted
by the UNIFAC method. Among the considered physical
properties, dH and h are used to constrain the crystal
morphology. Because of the complexity of the crystallization process, the use of single solvent oftentimes cannot
Current Opinion in Chemical Engineering 2022, 35:100732
10 Frontiers in chemical engineering; chemical product design
Table 5
A brief list of solvent-process design for crystallization
Properties
Reference
Karunanithi et al. [4 ,26]
Watson et al. [83]
Liu et al. [52]
Chai et al. [27]
Tm, Tb, d, Tf, g, –log(LC50), dH , h
g, Mi
Tm, Tb, d, Tf, g, –log(LC50), dH , h
Mw, Tm, Tb, d, Tf, g, –log(LC50), h
Liu et al. [12 ]
Tm, Tb, d, Tf, g, –log(LC50), dH , h
Property prediction method
GC
UNIFAC
SAFT-g Mie equation
of state
GC
COSMO-RS
GC
COSMO-SAC
GC
MLAC
Process model
Solution strategy
SLE
Decomposition algorithm
SLE
Deterministic optimization
methods
SLE
Decomposition algorithm
Mass conservation
SLE
Decomposition algorithm
SLE
Decomposition algorithm
dH : hydrogen bond solubility parameter; Mi: miscibility.
meet the requirements. Therefore, Watson et al. [83]
proposed a general computer aided molecular/mixture
design (CAMbD) for the crystallization process of pharmaceutical products. The proposed method can simultaneously identify the optimal process temperature, solvent
and antisolvent molecules, and the composition of solvent
mixture, wherein g was calculated from the SAFT-g Mie
equation of state. The above crystallization solvent
design examples considered the perspective of product
performance but neglected the cost, pricing, and other
economic factors. Chai et al. [27] proposed a Grand
Product Design (GPD) model for the design of crystallization solvents, which contains objective function (single/
multi-objective) process submodel, property submodel,
quality submodel, cost submodel, pricing submodel, economic submodel, and environmental submodel as well as
other factors (such as company strategy, government
policies and regulations). Finally, the GPD-model was
successfully applied to the crystallization solvent design
for 2-Mercaptobenzothiazole (MBT), for which the main
solvent properties (Mw, Tb, Tm, Tf, –log(LC50), d, h) were
predicted by GC methods and g was predicted by
COSMO-SAC model. The process constraints include
mass conservation and SLE. Finally, the established
MINLP model was solved by the decomposition algorithm. Liu et al. [12] proposed an MLAC (Machine
Leaning-based Atom Contribution) method to predict
the charge density profiles and applied it to the crystallization solvent design of ibuprofen. This method balances
the high computational cost of QM and the limited
prediction accuracy of the GC method. Among them,
the objective function and constraints refer to the work of
Karunanithi et al. [4]. The difference is that g is predicted
by the MLAC method. At present, the crystallization
solvent-process design based on product purity and yield
is relatively mature while the solvent-process design
considering other product performances (such as crystal
form, crystal habit and particle size distribution) is still in
the exploratory stage. One possible research directions
include the establishment of the QSPR relationship
between solvent molecules and such product performances (crystal form, crystal habit, and particle size
Current Opinion in Chemical Engineering 2022, 35:100732
distribution) by combining the results of molecular/macro
simulation (through MD, CFD, population balance, etc.)
with ML.
Discussion and perspectives
Although CAMD methods have already been widely
applied in solvent design, it is still a research topic not
fully developed and most solvents are still developed
through experiment-based trial-and-error approaches.
The systematic model and/or data-based methods and
associated software tools should be able to make a major
contribution to solvent design, and thereby, significantly
reduce the design and development time and cost. The
following four aspects are discussed and prospected:
Different multidisciplinary methods and tools are used
to solve complex problems in solvent design. For
example, simplified models based on ML are used to
solve the time-consuming problems of MD, QM and so
on.
The product design software contains a wealth of
databases, property prediction models, solution strategies, and so on, which help to quickly screen/design all
solvent molecules that meet the requirements.
Data-driven based on machine learning is helpful to
establish property prediction models that are difficult
to obtain based on theoretical methods.
High-throughput solvent design tool can quickly realize the experimental verification of the designed/
screened solvents.
Manage the complexity of the multiscale
multidisciplinary problems
The solvent design problems for separation processes are
multiscale and multidiscipline problems as molecular
QSPR, molecular interaction, fluid dynamics, and separation processes are involved. Thus, different methods
and tools from multidiscipline are needed. For example,
for designing a crystallization solvent, quantum mechanics models are needed for structural optimization and
obtaining missing force field parameters for MD simulation, MD models are used to predict the crystal
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Computer-aided molecular design of solvents for chemical separation processes Chai et al. 11
morphology and crystal growth rate, thermodynamic
models are used to predict the solid–liquid equilibrium,
population balance model and CFD simulation are
needed for flow distribution and crystal size distribution,
and finally process unit operation model is used for the
design and optimization of the crystallization process with
the optimal solvent. This problem is so complex that it is
almost impossible to solve it in a fast and efficient way.
One option of the solution strategy is using model reduction techniques. For example, use machine learning
methods to replace the time-consuming MD calculation
[94].
Apply solvent design tools to industrial cases
Product design software is based on computer-aided
molecular design method, which helps to quickly realize
solvent screening/design. Kalakul et al. [95] presented a
new version of the product design software tool ProCAPD. Compared with earlier versions, ProCAPD has
improved software architecture, new and extended databases, models in the model library and new solution
approaches. It can quickly and effectively solve wider
ranges of CAMbD problems, as well as other CAMD and
liquid formulation problems. Chai et al. [61] proposed a
versatile modeling framework consisting of a collection of
submodels (molecular structure, property, process, costing, pricing, economic analysis, quality, sustainability,
environmental impact, and performance). The developed
modeling framework has been incorporated into the
software tool ProCAPD, along with an extended database
and a library of product design templates. Although
product design tools [63,96], have been developed, these
tools focus on some specific types of products, mainly
small molecules and mixtures.
properties between compounds with very similar structures or even isomers, an appropriate molecular representation is critical. Recently, the adversarial autoencoder
technique has been applied for molecular design where
the ML models are trained on molecular descriptors, 3D
structures or molecular graphs [99]. The data collection
and processing steps are also important for ML-based
solvent design. The data can come from experiments and/
or simulations such as DFT, MD, CFD, and so on. The
consistent development of open-source databases and
various property models is vital to accelerate future
advances of this research field [44]. Moreover, raw
molecular data often feature a large degree of noise
and contain strongly correlated variables, which requires
a careful data pre-treatment before model training can be
performed. Finally, the selection of ML models is also
important for achieving a good fitting performance, which
is currently implemented using a heuristic-based or trialand-error strategy.
High-throughput solvent design technology
High throughput solvent design is the use of automated
equipment to rapidly test thousands to millions of solvent
samples in parallel. It utilizes robotics, liquid handlers,
data processing, software, and sensitive detection systems. For instance, Gu et al. [100] used a robot-based
high-throughput platform to quickly screen potential
anti-solvents for different combinations of solvent and
perovskite compositions. Because high-throughput technology typically aims to screen 100,000 or more samples
per day, relatively simple and automation-compatible
assay designs, robotic-assisted sample handling, and automated data processing are critical.
Conclusions
Data-driven solvent design based on machine learning
In solvent design, the property estimation models and
data need to be enlarged, which can be obtained from
theory-based methods, data-driven methods or their
hybrid. However, theoretical-methods are generally too
complex and almost impossible to be implemented in a
model-based solvent design. Therefore, with available
methods and tools from data science, data-driven MLbased solvent design can be considered. Alshehri et al.
[97] used machine learning and data science methods to
address the shortcomings of the current GC-based models
in fast and accurate estimation of 20 physicochemical
properties. Chen et al. [98] established a TransformerCNN model to quickly predict the surface charge density
profiles (s-profile) and cavity volumes (VCOSMO) of molecules by using the deep learning method. The model can
predict s-profile and VCOSMO of millions of molecules in
just a few minutes. Several similar studies have also been
carried out [12,13,60,82,90–93]. In data-driven ML
methods, the selection of molecular descriptors plays
an essential role for property prediction as it determines
the model performance. For the differentiation of
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This paper summarizes solvent-process design in chemical separation processes, including property prediction
methods, solution strategies, some representative studies,
challenges and future directions.
Although a large progress has been made in computeraided solvent design, there are still challenges for the
selection/design of the optimal solvents in different separation processes, as the requirements and properties
involved vary for different problems. Property prediction
models are the basis of solvent design, and these models
need to be extended to more complex problems, such as
reaction kinetics prediction, crystal morphology prediction, polymer properties prediction, design of cleaning
agents, and additives in different products. One of the
future directions is to consider data-driven ML-based
solvent design. For solvent design tools, although several
tools have been developed, the challenges are the needs
of mature tools for industrial solvent design, which are
easy to use and can accurately estimate all the solvent
properties needed in industrial applications. In developing such tools, database, experiments, heuristic rules and
Current Opinion in Chemical Engineering 2022, 35:100732
12 Frontiers in chemical engineering; chemical product design
models including DFT (Density Functional Theory),
MD, and so on. for the establishment of QSPR could
be integrated to estimate more types of properties in a
more accurate and efficient way. In high-throughput
solvent design technology, better industry relevant models are needed, as in this process, the scale-down processes are used that they do not always hold true at
commercial manufacturing scale or conditions. Another
challenge is the increasing use for robotics and automation, which need the efforts from different disciplines.
Conflict of interest statement
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This paper proposed a novel ML-based atom contribution method to
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Acknowledgements
14. Zhang L, Pang JQ, Zhuang Y, Liu LL, Du J, Yuan ZH: Integrated
solvent-process design methodology based on COSMO-SAC
and quantum mechanics for TMQ (2,2,4-trimethyl-1,2Hdihydroquinoline) production. Chem Eng Sci 2020,
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The financial support from National Nature Science Foundation of China
(22078041, 21808025, 21776074 and 21861132019) and ‘the Fundamental
Research Funds for the Central Universities (DUT20JC41)’ is
acknowledged.
15. Zhang L, Mao HT, Liu LL, Du J, Gani R: A machine learning based
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fragrance molecules. Comput Chem Eng 2018, 115:295-308.
Declaration of Competing Interest
16. Gani R, Brignole E: Molecular design of solvents for liquid
extraction based on UNIFAC. Fluid Phase Equilib 1983, 13:331340.
Nothing declared.
The authors report no declarations of interest.
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