Gas sorption in H2-selective mixed matrix membranes: Experimental and neural network modeling

• Mixed matrix membranes (MMMs) for separating H2 from CO2, CH4 were synthesized.
• ANN simulation of gas sorption within H2-selective MMMs was developed.
• Gas sorption mechanisms through MMMs were investigated.

Robust artificial neural network (ANN) was developed to forecast sorption of gases in membranes comprised of porous nanoparticles dispersed homogenously within polymer matrix. The main purpose of this study was to predict sorption of light gases (H2, CH4, CO2) within mixed matrix membranes (MMMs) as function of critical temperature, nanoparticles loading and upstream pressure. Collected data were distributed into three portions of training (70%), validation (19%), and testing (11%). The optimum network structure was determined by trial-error method (4:6:2:1) and was applied for modeling the gas sorption. The prediction results were remarkably agreed with the experimental data with MSE of 0.00005 and correlation coefficient of 0.9994.