Comparison of two soft computing approaches for predicting CO2 solubility in aqueous solution of piperazine

Removal of sour gases such as carbon dioxide (CO2) and hydrogen sulfide (H2S) is of great importance in various processes such as streams of sour natural gas, production of ammonia by treating synthesis gas, and hydrogen purification processes. Hence, developing accurate tools for prediction of solubility of CO2 in aqueous solutions of different substrates seems to be of great importance. This study aims to develop two reliable models namely CHPSO-ANFIS and CSA-LSSVM for prediction of CO2 solubility in aqueous solution of mixture of 1,4-diazacyclohexane (piperazine, PZ). The experimental data were gathered from several published works in the literature. The effectiveness and accuracy of the proposed models were evaluated by both statistical and graphical approaches. Results show that the developed CHPSO-ANFIS model presents more accurate and reliable predictions compared to the outcomes of CSA-LSSVM model. The overall R2 and AARD% values for CHPSO-ANFIS model were 0.9902 and 3.76 which indicate accuracy and robustness of the developed model.