The solubility modelling of non-polar gases in polar and non-polar solvent based on scaled particle theory by artificial neural network

摘要

In this work, the effective parameters of the scaled particle theory (SPT) are used as the input to the artificial neural network (ANN) to calculate as the output, the solubility (mole fraction of gas in liquid phase) of non-polar gases in polar and non-polar solvents at 298.15 K and 101.325 kPa. It has been found that ANN used in this work should has five neurons in the hidden layer to achieve the least error. The results of ANN have been compared with the experimental values. The results of this comparison are quite satisfactory. The average relative deviations of the simulations in training and testing stages have been calculated 0.92% and 0.89%, respectively. Finally, the results of ANN were compared with the results of SPT. According to this comparison, it is clear that SPT as a thermodynamic model predicts the solubility of the studied gases in the solvents with the same accuracy of ANN which is a purely mathematical model.