Designing and optimizing a parallel neural network model for predicting the solubility of diosgenin in n-alkanols

Abstract

Abstract Accurate estimation of the solubility of a chemical compound is an important issue for many industrial processes. To overcome the defects of some thermodynamic models and simple correlations, a parallel neural network (PNN) model was conceived and optimized to predict the solubility of diosgenin in seven n-alkanols (C1–C7). The linear regression analysis of the parity plots indicates that the PNN model can give more accurate descriptions of the solubility of diosgenin than the ordinary neural network (ONN) model. The comparison of the average root mean square deviation (RMSD) shows that the suggested model has a slight advantage over the thermodynamic NRTL model in terms of the calculating precision. Moreover, the PNN model can reflect the effects of the temperature and the chain length of the alcohol solvent on the solution behavior of diosgenin correctly and can estimate its solubility in the n-alkanols with more carbon atoms.