Application of QSPR for prediction of percent conversion of esterification reactions in supercritical carbon dioxide using least squares support vector regression

A quantitative structure property relationship (QSPR) model was conducted for the first time prediction of the percent conversion of esterification reactions in supercritical carbon dioxide (scCO2). The data set consisting of 42 esterification reactions of different acids and alcohols in scCO2 using different temperatures and pressures. The percent conversion was related to the most feasible descriptors such as temperature, pressure, dipole moment and the highest occupied molecular orbital (HOMO) of the alcohols, dipole moment, the lowest unoccupied molecular orbital (LUMO) and heat of formation of the acids. These descriptors were related to the percent conversion of the esterification reactions using least squares support vector regression (LS-SVR) approach. The parameters of the LS-SVR consisting of the regularization and the kernel, were optimized by constructing models with all the possible combinations of these two parameters. The model with the minimum root mean squared error of cross-validation (RMSECV) for the calibration set was selected as the best model and the parameters of this model were selected as the optimized values. Both internal and external validations were performed to validate the performance of the model. The results revealed that the calculated conversion values are in good agreement with the experimental ones, and the performance of the LS-SVR model was superior to the multiple linear regression (MLR) ones.