Accurate model based on artificial intelligence for prediction of carbon dioxide solubility in aqueous tetra-n-butylammonium bromide solutions

This study highlights the application of radial basis function (RBF) neural networks, adaptive neuro-fuzzy inference systems (ANFIS), and gene expression programming (GEP) in the estimation of solubility of CO2 in aqueous solutions of tetra-n-butylammonium bromide (TBAB). The experimental data were gathered from a published work in literature. The proposed RBF network was coupled with genetic algorithm (GA) to access a better prediction performance of model. The structure of ANFIS model was trained by using hybrid method. The input parameters of the model were temperature, pressure, mass fraction of TBAB in feed aqueous solution (wTBAB), and mole fraction of TBAB in aqueous phase (xTBAB). The solubility of CO2 (xCO2) was the output parameter. Statistical and graphical analyses of the results showed that the proposed GA-RBF, Hybrid-ANFIS, and GEP models are robust and precise in the estimation of literature solubility data.