Machine learning models for the density and heat capacity of ionic liquid-water binary mixtures

doi:10.1016/j.cjche.2024.04.019

中国化学工程学报 ›› 2024, Vol. 73 ›› Issue (9): 244-255.DOI: 10.1016/j.cjche.2024.04.019

Machine learning models for the density and heat capacity of ionic liquid-water binary mixtures

Yingxue Fu¹, Xinyan Liu¹, Jingzi Gao¹, Yang Lei¹, Yuqiu Chen², Xiangping Zhang³

1. School of Chemistry and Chemical Engineering, Hubei Key Laboratory of Coal Conversion and New Carbon Materials, Wuhan University of Science and Technology, Wuhan 430081, China;
2. Department of Chemical and Biomolecular Engineering, University of Delaware, 150 Academy Street, Newark, DE 19716, United States;
3. College of Chemical Engineering and Environment, China University of Petroleum, Beijing 102249, China

收稿日期:2023-08-18 修回日期:2024-04-07 接受日期:2024-04-08 出版日期:2024-11-21 发布日期:2024-05-25
通讯作者: Xinyan Liu,E-mail:liuxinyan@wust.edu.cn;Yuqiu Chen,E-mail:yuqch@udel.edu
基金资助:
This work is financially supported by the National Natural Science Foundation of China (22208253), and the Key Laboratory of Hubei Province for Coal Conversion and New Carbon Materials (Wuhan University of Science and Technology, WKDM202202).

Machine learning models for the density and heat capacity of ionic liquid-water binary mixtures

Yingxue Fu¹, Xinyan Liu¹, Jingzi Gao¹, Yang Lei¹, Yuqiu Chen², Xiangping Zhang³

1. School of Chemistry and Chemical Engineering, Hubei Key Laboratory of Coal Conversion and New Carbon Materials, Wuhan University of Science and Technology, Wuhan 430081, China;
2. Department of Chemical and Biomolecular Engineering, University of Delaware, 150 Academy Street, Newark, DE 19716, United States;
3. College of Chemical Engineering and Environment, China University of Petroleum, Beijing 102249, China

Received:2023-08-18 Revised:2024-04-07 Accepted:2024-04-08 Online:2024-11-21 Published:2024-05-25
Contact: Xinyan Liu,E-mail:liuxinyan@wust.edu.cn;Yuqiu Chen,E-mail:yuqch@udel.edu
Supported by:
This work is financially supported by the National Natural Science Foundation of China (22208253), and the Key Laboratory of Hubei Province for Coal Conversion and New Carbon Materials (Wuhan University of Science and Technology, WKDM202202).

摘要/Abstract

摘要： Ionic liquids (ILs), because of the advantages of low volatility, good thermal stability, high gas solubility and easy recovery, can be regarded as the green substitute for traditional solvent. However, the high viscosity and synthesis cost limits their application, the hybrid solvent which combining ILs together with others especially water can solve this problem. Compared with the pure IL systems, the study of the ILs-H₂O binary system is rare, and the experimental data of corresponding thermodynamic properties (such as density, heat capacity, etc.) are less. Moreover, it is also difficult to obtain all the data through experiments. Therefore, this work establishes a predicted model on ILs-water binary systems based on the group contribution (GC) method. Three different machine learning algorithms (ANN, XGBoost, LightBGM) are applied to fit the density and heat capacity of ILs-water binary systems. And then the three models are compared by two index of MAE and R². The results show that the ANN-GC model has the best prediction effect on the density and heat capacity of ionic liquid-water mixed system. Furthermore, the Shapley additive explanations (SHAP) method is harnessed to scrutinize the significance of each structure and parameter within the ANN-GC model in relation to prediction outcomes. The results reveal that system components (X_IL) within the ILs-H₂O binary system exert the most substantial influence on density, while for the heat capacity, the substituents on the cation exhibit the greatest impact. This study not only introduces a robust prediction model for the density and heat capacity properties of IL-H₂O binary mixtures but also provides insight into the influence of mixture features on its density and heat capacity.

关键词: Ionic liquids, Density, Heat capacity, Group contribution method, Machine learning

Abstract: Ionic liquids (ILs), because of the advantages of low volatility, good thermal stability, high gas solubility and easy recovery, can be regarded as the green substitute for traditional solvent. However, the high viscosity and synthesis cost limits their application, the hybrid solvent which combining ILs together with others especially water can solve this problem. Compared with the pure IL systems, the study of the ILs-H₂O binary system is rare, and the experimental data of corresponding thermodynamic properties (such as density, heat capacity, etc.) are less. Moreover, it is also difficult to obtain all the data through experiments. Therefore, this work establishes a predicted model on ILs-water binary systems based on the group contribution (GC) method. Three different machine learning algorithms (ANN, XGBoost, LightBGM) are applied to fit the density and heat capacity of ILs-water binary systems. And then the three models are compared by two index of MAE and R². The results show that the ANN-GC model has the best prediction effect on the density and heat capacity of ionic liquid-water mixed system. Furthermore, the Shapley additive explanations (SHAP) method is harnessed to scrutinize the significance of each structure and parameter within the ANN-GC model in relation to prediction outcomes. The results reveal that system components (X_IL) within the ILs-H₂O binary system exert the most substantial influence on density, while for the heat capacity, the substituents on the cation exhibit the greatest impact. This study not only introduces a robust prediction model for the density and heat capacity properties of IL-H₂O binary mixtures but also provides insight into the influence of mixture features on its density and heat capacity.

Key words: Ionic liquids, Density, Heat capacity, Group contribution method, Machine learning

Yingxue Fu, Xinyan Liu, Jingzi Gao, Yang Lei, Yuqiu Chen, Xiangping Zhang. Machine learning models for the density and heat capacity of ionic liquid-water binary mixtures[J]. 中国化学工程学报, 2024, 73(9): 244-255.

Yingxue Fu, Xinyan Liu, Jingzi Gao, Yang Lei, Yuqiu Chen, Xiangping Zhang. Machine learning models for the density and heat capacity of ionic liquid-water binary mixtures[J]. Chinese Journal of Chemical Engineering, 2024, 73(9): 244-255.

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Machine learning models for the density and heat capacity of ionic liquid-water binary mixtures

Machine learning models for the density and heat capacity of ionic liquid-water binary mixtures

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