Modeling and predicting the Henryʹs law constants of methyl ketones in aqueous sodium sulfate solutions with artificial neural network

Henryʹs law constant is an important property for predicting the solubility and vapor–liquid equilibrium. Usually, Henryʹs law constants increase as temperature and salt concentration increase and polynomial correlations are commonly used to model these effects. s article, the artificial neural network (ANN) method is used for modeling the Henryʹs law constant dependence on temperature and salt concentration, with methyl ketones in aqueous sodium sulfate solutions chosen for the study. first part, one network is used for each system. The network topology is optimized and the 2-2-1 architecture is found to be the best. The network satisfactorily estimates the Henryʹs law constants of all systems in the study with an average relative deviation (ARD) of less than 1% for all systems, which is comparable to available correlations. ond part, which is based on the results of the first part, an ANN is designed for all systems. The new network has a 3-2-1 topology, giving an ARD of correlation of less than 1% and ARD of prediction, depending on systems and data availability, of less than 3.5%. The predictive ability is the most important advantage of the 3-2-1 ANN compared to 2-2-1 ANN and other correlations.