The QSPR models to predict the solubility of CO2 in ionic liquids based on least-squares support vector machines and genetic algorithm-multi linear regression

Abstract

Global warming is an issue of high concern which is mostly caused by growing concentrations of carbon dioxide in the atmosphere. Many novel technologies offer different solutions to decline carbon dioxide emissions to the environment. Ionic liquids (ILs) are counted to be highly promising media for CO2 capture in the near future. Due to costly nature of ionic liquids and time consuming laboratory research procedures, modeling and prediction of solubility of CO2 based on the structure of ILs are highly required. Some studies on this field demonstrate the relationship between the structure and the CO2 absorption capacity of ILs. One of the modeling approaches for stating this relationship is quantitative structure-property relationship (QSPR). In this work, an efficient approach based on the combination of genetic algorithm-multi linear regression (GA-MLR) and least-squares support vector machines (LS-SVM) was utilized to build a nonlinear QSPR model. The nonlinear model can give very satisfactory prediction results: the square of correlation coefficient (R2) and the root mean square error (RMSE) were 0.962 and 0.015, respectively for the whole dataset. In addition, another QSPR model, multi-linear regression (MLR), was also implemented and R2and RMSE were 0.876 and 0.027, respectively. The results demonstrate that the LS-SVM model drastically enhances the ability of prediction in QSPR studies and is superior to MLR one.