文献类型：期刊文献

英文题名：Prediction of CO2 solubility in deep eutectic solvents using random forest model based on COSMO-RS-derived descriptors

作者：Wang, Jingwen[1,2];Song, Zhen[2];Chen, Lifang[2];Xu, Tao[1];Deng, Liyuan[3];Qi, Zhiwen[2]

机构：[1]Guangzhou Univ, Acad Bldg Energy Efficiency, Sch Civil Engn, Guangzhou 510006, Peoples R China;[2]East China Univ Sci & Technol, Sch Chem Engn, State Key Lab Chem Engn, 130 Meilong Rd, Shanghai 200237, Peoples R China;[3]Norwegian Univ Sci & Technol, Dept Chem Engn, Sem Saelandsvei 4, N-7491 Trondheim, Norway

年份：2021

卷号：2

期号：4

起止页码：431

外文期刊名：GREEN CHEMICAL ENGINEERING

收录：EI(收录号：20224313011011);WOS:【ESCI(收录号:WOS:001072910600001)】；

基金：The fi nancial support from National Natural Science Foundation of China (21861132019 and 21776074) is greatly acknowledged.

语种：英文

外文关键词：CO 2 solubility prediction; Deep eutectic solvents; Quantitative structure-property relationship; model; COSMO-RS-derived descriptors; Random forest

摘要：This work presents the development of molecular-based mathematical model for the prediction of CO2 solubility in deep eutectic solvents (DESs). First, a comprehensive database containing 1011 CO2 solubility data in various DESs at different temperatures and pressures is established, and the COSMO-RS-derived descriptors of involved hydrogen bond acceptors and hydrogen bond donors of DESs are calculated. Afterwards, the efficiency of the input variables, i.e., temperature, pressure, COSMO-RS-derived descriptors of HBA and HBD as well as their molar ratio, is explored by a qualitative analysis of CO2 solubility in DESs using a simple multiple linear regression model. A machine learning method namely random forest is then employed to develop more accurate nonlinear quantitative structure-property relationship (QSPR) model. Combining the QSPR validation and comparisons with literature-reported models (i.e., COSMO-RS model, traditional thermodynamic models and equations of state methods), the developed QSPR model with COSMO-RS-derived parameters as molecular descriptors is suggested to be able to give reliable predictions of CO2 solubility in DESs and could be used as a useful tool in selecting DESs for CO2 capture processes.