Modeling Study on Heat Capacity, Viscosity, and Density of Ionic Liquid–Organic Solvent–Organic Solvent Ternary Mixtures via Machine Learning
| Author(s) | Shu, You | |
| Author(s) | Du, Lei | |
| Author(s) | Lei, Yang | |
| Author(s) | Hu, Shaobin | |
| Author(s) | Kuang, Yongchao | |
| Author(s) | Fang, Hongming | |
| Author(s) | Liu, Xinyan | |
| Author(s) | Chen, Yuqiu | |
| Date Accessioned | 2024-07-15T17:17:06Z | |
| Date Available | 2024-07-15T17:17:06Z | |
| Publication Date | 2024-07-07 | |
| Description | This article was originally published in Processes. The version of record is available at: https://doi.org/10.3390/pr12071420 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/ 4.0/). | |
| Abstract | 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. | |
| Sponsor | The authors gratefully acknowledge the financial support from t Science and Technology Major Project of Wuhan (2023020302020572) and the National Natural Science Foundation of China (21706198). | |
| Citation | Shu, You, Lei Du, Yang Lei, Shaobin Hu, Yongchao Kuang, Hongming Fang, Xinyan Liu, and Yuqiu Chen. 2024. "Modeling Study on Heat Capacity, Viscosity, and Density of Ionic Liquid–Organic Solvent–Organic Solvent Ternary Mixtures via Machine Learning" Processes 12, no. 7: 1420. https://doi.org/10.3390/pr12071420 | |
| ISSN | 2227-9717 | |
| URL | https://udspace.udel.edu/handle/19716/34576 | |
| Language | en_US | |
| Publisher | Processes | |
| dc.rights | Attribution 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| Keywords | IL-organic solvent–organic solvent ternary systems | |
| Keywords | heat capacity | |
| Keywords | viscosity | |
| Keywords | density | |
| Keywords | group contribution method | |
| Keywords | machine learning | |
| Title | Modeling Study on Heat Capacity, Viscosity, and Density of Ionic Liquid–Organic Solvent–Organic Solvent Ternary Mixtures via Machine Learning | |
| Type | Article |
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