“…The c g of different groups are generally regressed from a large data set of diverse molecules with known p and then can be used to estimate the properties of new molecules outside the data set. For example, (1) Zhao et al 154 developed a GC 155 combined the two-parameter van't Hoff polynomial and the GC concept to estimate the infinite dilution activity coefficient of alcohols in ILs using MLR. The overall AARD is 7.49% and 9.91% in the cases of ethanol and propanol, respectively.…”
Section: Modeling Algorithmsmentioning
confidence: 99%
“…For example, (1) Zhao et al 154 developed a GC model for the viscosity of 45 imidazolium-based ILs with 1079 viscosity experimental data using MLR and obtained an AARD of 24.2% for the entire data set. (2) Thangarajoo et al 155 combined the two-parameter van't Hoff polynomial and the GC concept to estimate the infinite dilution activity coefficient of alcohols in ILs using MLR. The overall AARD is 7.49% and 9.91% in the cases of ethanol and propanol, respectively.…”
The unique physicochemical properties, flexible structural tunability, and giant chemical space of ionic liquids (ILs) provide them a great opportunity to match different target properties to work as advanced process media. The crux of the matter is how to efficiently and reliably tailor suitable ILs toward a specific application. In this regard, the computer-aided molecular design (CAMD) approach has been widely adapted to cover this family of high-profile chemicals, that is, to perform computer-aided IL design (CAILD). This review discusses the past developments that have contributed to the state-of-the-art of CAILD and provides a perspective about how future works could pursue the acceleration of the practical application of ILs. In a broad context of CAILD, key aspects related to the forward structure−property modeling and reverse molecular design of ILs are overviewed. For the former forward task, diverse IL molecular representations, modeling algorithms, as well as representative models on physical properties, thermodynamic properties, among others of ILs are introduced. For the latter reverse task, representative works formulating different molecular design scenarios are summarized. Beyond the substantial progress made, some future perspectives to move CAILD a step forward are finally provided.
“…The c g of different groups are generally regressed from a large data set of diverse molecules with known p and then can be used to estimate the properties of new molecules outside the data set. For example, (1) Zhao et al 154 developed a GC 155 combined the two-parameter van't Hoff polynomial and the GC concept to estimate the infinite dilution activity coefficient of alcohols in ILs using MLR. The overall AARD is 7.49% and 9.91% in the cases of ethanol and propanol, respectively.…”
Section: Modeling Algorithmsmentioning
confidence: 99%
“…For example, (1) Zhao et al 154 developed a GC model for the viscosity of 45 imidazolium-based ILs with 1079 viscosity experimental data using MLR and obtained an AARD of 24.2% for the entire data set. (2) Thangarajoo et al 155 combined the two-parameter van't Hoff polynomial and the GC concept to estimate the infinite dilution activity coefficient of alcohols in ILs using MLR. The overall AARD is 7.49% and 9.91% in the cases of ethanol and propanol, respectively.…”
The unique physicochemical properties, flexible structural tunability, and giant chemical space of ionic liquids (ILs) provide them a great opportunity to match different target properties to work as advanced process media. The crux of the matter is how to efficiently and reliably tailor suitable ILs toward a specific application. In this regard, the computer-aided molecular design (CAMD) approach has been widely adapted to cover this family of high-profile chemicals, that is, to perform computer-aided IL design (CAILD). This review discusses the past developments that have contributed to the state-of-the-art of CAILD and provides a perspective about how future works could pursue the acceleration of the practical application of ILs. In a broad context of CAILD, key aspects related to the forward structure−property modeling and reverse molecular design of ILs are overviewed. For the former forward task, diverse IL molecular representations, modeling algorithms, as well as representative models on physical properties, thermodynamic properties, among others of ILs are introduced. For the latter reverse task, representative works formulating different molecular design scenarios are summarized. Beyond the substantial progress made, some future perspectives to move CAILD a step forward are finally provided.
“…The value of γ ∞ can be measured by experiments, such as the gas chromatography method, − static differential technique, , and differential ebulliometry. , Moreover, various methods − are used to estimate γ ∞ . Among them, the method using the interaction of solute and solvent molecular structure groups to do prediction was first proposed by Pierotti et al With the introduction of the UNIFAC model, the group contribution method has become a hot research topic. − The idea behind this method is based on the simplifying assumption that the groups are independent and the molecules are classified as functional groups. And the molecule–molecule interaction is represented by an appropriately weighted sum of the group–group interaction.…”
Accurate prediction of infinite dilution activity coefficient (γ ∞ ) is essential for the calculation of phase equilibria, solubility, and related properties in molecular thermodynamics. Here, we propose a new model to accurately predict the value of γ ∞ . It is applicable to calculate γ ∞ for compounds in aqueous solution at different temperatures. The model is based on the relationship of (∂p/∂x) T,x→0 with γ ∞ and temperature at low pressure. First, we introduce the new idea of using the group contribution method to estimate (∂p/∂x) T,x→0 and then obtain the activity coefficient of a solute at infinite dilution in water based on the relationship between (∂p/∂x) T,x→0 and γ ∞ . The accuracy of this model is verified using experimental data from 46 systems and more than 450 data points. The result shows that the total average relative deviation of the predicted values from the experimental values for training data is 4.73%. Besides, we test the applicability of the model using solutes that are not part of the training data set. The result shows that the model is satisfactory for the prediction of testing data. Compared with other models, the results prove that the developed model outperforms the UNIFAC model, the modified UNIFAC model, and previous predictive models for aqueous systems. The final equation with only simple arithmetic is more easily applied in engineering practices.
“…Near-boiling or azeotropic systems are often separated by extractive distillation, and the choice of solvent determines the separation efficiency. − In extractive distillation, the selection of the best solvent can not only improve the purity of the product but also reduce the energy consumption of the system and save cost. At present, many methods can be used in solvent screening, the empirical method, experimental determination method, , group contribution method, , COSMO-RS model method, , etc. The results of the experimental measurement are the most reliable.…”
In
this work, the effects of N-methyl pyrrolidone, N,N-dimethylformamide, cyclohexanol, butyl
butyrate, and N-formylmorpholine on the separation
efficiency of a p-xylene and 1-pentanol azeotropic
system are investigated, and the best solvent is selected from these
solvents. The vapor–liquid equilibrium data of binary systems
of 1-pentanol + butyl butyrate, 1-pentanol + N-formylmorpholine,
and p-xylene + butyl butyrate are measured using
a modified Rose-type circulating still under 101.3 kPa. In order to
check the thermodynamic consistency of the experimental vapor–liquid
equilibrium data, the Herington and Van Ness methods are used. In
addition, the nonrandom two-liquid, universal quasichemical, and Wilson
models are used to correlate the experimental data of binary systems,
and the interaction parameters of binary systems are obtained.
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