Artificial Neural Network modeling of solubility of supercritical carbon dioxide in 24 commonly used ionic liquids

Application of supercritical CO₂ for separation of ionic liquids from their organic solvents or extraction of various solutes from ionic liquid solvents have found great interest during recent years. Knowledge of phase behaviors of the mixtures of supercritical CO₂+ionic liquids is therefore drastic in order to efficiently design such separation processes. In this communication, Artificial Neural Network procedure has been applied to represent the solubility of supercritical CO₂ in 24 mostly used ionic liquids. An optimized Three-Layer Feed Forward Neural Network using critical properties of ionic liquids and operational temperature and pressure has been developed. Application of this model for 1128 data points of 24 ionic liquids show squared correlation coefficients of 0.993 and average absolute deviation of 3.6% from experimental values for calculated/estimated solubilities. The aforementioned deviations show the prediction capability of the presented model.