Estimating solubility of supercritical H2S in ionic liquids through a hybrid LSSVM chemical structure model

Abstract

Abstract Development of a predictive tool for H2S solubility estimation can be very helpful in gas sweetening industry. Experimental databases on H2S solubility were rarely available, so as reliable predictive models. Thus, in this study the H2S solubility database was established, and then a Least-Squares Support Vector Machine (LSSVM) approach based on the established database is proposed. Group contribution method was also applied to eliminate the model's dependence on experimental data. Accordingly, our proposed LSSVM model can predict H2S solubility as a function of temperature, pressure, and 15 different chemical structures of Ionic liquids (ILs). Root Mean Square Error (RMSE) and coefficient of determination (R2) are 0.0122 and 0.9941, respectively. Moreover, comparison of our model with other existing models showed its reliability for H2S solubility in ILs. This can be very useful for engineers dealing with gas sweetening process in different applications of analysis, simulation, and designation.