Prediction of carbon dioxide solubility in aqueous mixture of methyldiethanolamine and N-methylpyrrolidone using intelligent models

Abstract Clear knowledge about the solubility of acid gases such as CO 2 in different solvents at different states is very important, especially for carbon capture from flue gases. This study highlights the application of artificial intelligence in prediction of carbon dioxide solubility in a mix solvent of methyldiethanolamine and N -methylpyrrolidone at wide range of temperature and pressure. The input data of the models were temperature, pressure, and saturation pressure and the output parameter was the solubility of CO 2 . Different intelligent approaches such as MLP-ANN, GA-RBF, CSA-LSSVM, Hybrid-ANFIS, PSO-ANFIS, and CMIS were developed and the reliability of models was investigated through different graphical and statistical methods. Result showed that the developed models are accurate and predictive for estimation of experimental solubility data. However, the CMIS approach exhibited better results compared to other intelligent approaches. Results of this study showed that intelligent based algorithms are powerful alternatives for time-consuming and difficult experimental processes of solubility measurement.