Comparison of LSSVM model results with artificial neural network model for determination of the solubility of SO2 in ionic liquids

Abstract

Abstract In this study, the least square support vector machine (LSSVM) as a robust approach along with genetic algorithm (GA) was utilized for prediction of SO2 solubility in ionic liquids (ILs). The proposed model used the pressure, temperature, critical temperature, boiling temperature, critical pressure, critical compressibility factor, and acentric factor as input parameters. To develop the highly accurate model, the 232 data points were randomly split into training and testing sets. This model provides an average absolute relative deviation (AARD) of 4.6%, illustrating good accuracy and validity. In addition, an artificial neural network (ANN) was developed for prediction of SO2 solubility in ionic liquids. A thorough comparison between the proposed LSSVM and ANN models was conducted through statistical and graphical tools demonstrating the superiority of the former over the latter to predict the solubility of SO2 in ILs. Afterward, a sensitivity analysis was performed on the LSSVM model, elucidating that the pressure and temperature have the greatest effects on the SO2 solubility. Furthermore, the Leverage approach identified that only 2.8% of data points are outside the applicability domain of the proposed LSSVM model, which highlights the model is statistically acceptable and can be used as a predictive approach.