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● A QSPR model is developed for predicting CO2 solubility in DESs.
● Efficiency of the input variables is explored by a multiple linear regression model.
● Importance of the input variables in the QSPR model is ranked by random forest model.
● CO2 solubility predictions by QSPR model are compared to the COSMO-RS model.
● QSPR model is suggested to be a reliable tool for selecting DESs for CO2 capture.
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.
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