Modeling Study on Heat Capacity, Viscosity, and Density of Ionic Liquid-Organic Solvent-Organic Solvent Ternary Mixtures via Machine Learning

被引:1
作者
Shu, You [1 ]
Du, Lei [1 ]
Lei, Yang [1 ]
Hu, Shaobin [2 ]
Kuang, Yongchao [2 ]
Fang, Hongming [1 ]
Liu, Xinyan [1 ]
Chen, Yuqiu [3 ]
机构
[1] Wuhan Univ Sci & Technol, Sch Chem & Chem Engn, Hubei Key Lab Coal Convers & New Carbon Mat, Wuhan 430081, Peoples R China
[2] Hubei Energy Grp Jiangling Power Generat Co Ltd, Jiangling 434100, Peoples R China
[3] Univ Delaware, Dept Chem & Biomol Engn, 150 Acad St, Newark, DE 19716 USA
基金
中国国家自然科学基金;
关键词
IL-organic solvent-organic solvent ternary systems; heat capacity; viscosity; density; group contribution method; machine learning; ARTIFICIAL NEURAL-NETWORK; BPIE SCHIFF-BASE; THERMOPHYSICAL PROPERTIES; THERMODYNAMIC PROPERTIES; ETHYL-ACETATE; VOLUMETRIC PROPERTIES; EXCESS PROPERTIES; SURFACE-TENSION; BINARY-MIXTURES; 298.15; K;
D O I
10.3390/pr12071420
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
Physicochemical properties of ionic liquids (ILs) are essential in solvent screening and process design. However, due to their vast diversity, acquiring IL properties through experimentation alone is both time-consuming and costly. For this reason, the creation of prediction models that can accurately forecast the characteristics of IL and its mixtures is crucial to their application. This study proposes a model for predicting the three important parameters of the IL-organic solvent-organic solvent ternary system: density, viscosity, and heat capacity. The model incorporates group contribution (GC) and machine learning (ML) methods. A link between variables such as temperature, pressure, and molecular structure is established by the model. We gathered 2775 viscosity, 6515 density, and 1057 heat capacity data points to compare the prediction accuracy of three machine learning methods, namely, artificial neural networks (ANNs), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). As can be observed from the findings, the ANN model produced the best results out of the three GC-based ML methods, even though all three produced dependable predictions. For heat capacity, the mean absolute error (MAE) of the ANN model is 1.7320 and the squared correlation coefficient (R2) is 0.9929. Regarding viscosity, the MAE of the ANN model is 0.0225 and the R2 is 0.9973. For density, the MAE of the ANN model is 7.3760 and the R2 is 0.9943. The Shapley additive explanatory (SHAP) approach was applied to the study to comprehend the significance of each feature in the prediction findings. The analysis results indicated that the R-CH3 group of the ILs, followed by the imidazolium (Im) group, had the highest impact on the heat capacity property of the ternary system. On the other hand, the Im group and the R-H group of ILs had the most effects on viscosity. In terms of density, the Im group of the ILs had the greatest effect on the ternary system, followed by the molar fraction of the organic solvent.
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页数:26
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