Machine learning–based optimization for hydrogen purification performance of layered bed pressure swing adsorption

Abstract

An adsorption, heat and mass transfer model for the five-component gas from coal gas (H2/CO2/CH4/CO/N2 = 38/50/1/1/10 vol%) in a layered bed packed with activated carbon and zeolite was established by Aspen Adsorption software. Compared with published experimental results, the hydrogen purification performance by pressure swing adsorption (PSA) in a layered bed was numerically studied. The results show that there is a contradiction between the hydrogen purity and recovery, so the multi-objective optimization algorithms are needed to optimize the PSA process. Machine learning methods can be used for data analysis and prediction; the polynomial regression (PNR) and artificial neural network (ANN) were used to predict the purification performance of two-bed six-step process. Finally, two ANN models combined with sequence quadratic program (SQP) algorithm were used to achieve multi-objective optimization of hydrogen purification performance. According to the analysis of the optimization results, the ANN models are more suitable for optimizing the purification performance of hydrogen than the PNR model.