Prediction of phase equilibria of HIx system using artificial neural network: Experimental verification

Thermochemical sulfur–iodine (SI) cycle is one of the promising technologies investigated for hydrogen production using solar and nuclear energy. The development and validation of a reliable thermodynamic model for the HIx mixture (HI–H2O–I2) encountered in the SI cycle have been identified as a central research issue to provide estimations on the HIx section energy demand. s contribution, we develop an artificial neural network (ANN) model to predict the real time phase equilibrium behavior. For the binary HI–H2O system, the ANN model is constructed for a pressure up to 84 bar, while for the ternary HI–H2O–I2 system, the model describes the equilibrium behavior for a pressure up to 53 bar. The proposed models show their potential with a maximum relative deviation (RD) of about 2.5% and a root mean square percentage error (RMSPE) of within 0.9% for binary, and a maximum RD of 3.6% along with an RMSPE of 0.64% for ternary systems.