Gas permeation through H2-selective mixed matrix membranes: Experimental and neural network modeling

Gas permeability through synthesized polydimethylsiloxane (PDMS)/zeolite 4A mixed matrix membranes (MMMs) were investigated with the aid of artificial neural network (ANN) approach. Kinetic diameter and critical temperature of permeating components (e.g. H2, CH4, CO2 and C3H8), zeolite content and upstream pressure as input variables and gas permeability as output were inspected. Collected data of the experimental operation was used to ANN training and optimum numbers of hidden layers and neurons were obtained by trial-error method. The selected ANN architecture (4:10:1) was used to predict gas permeability for different inputs in the domain of training data. Based on the results, the predicted values demonstrate an excellent agreement with the experimental data, with high correlation (R2 = 0.9944) and less error (RMSE = 1.33E−4). Furthermore, using sensitivity analysis, kinetic diameter and critical temperature were found as the most significant effective variables on gas permeability. As a result, ANN can be recommended for the modeling of gas transport through MMMs.