Artificial neural network based optimization for hydrogen purification performance of pressure swing adsorption

Abstract

A pressure swing adsorption (PSA) cycle model is implemented on Aspen Adsorption platform and is applied for simulating the PSA procedures of ternary-component gas mixture with molar fraction of H2/CO2/CO = 0.68/0.27/0.05 on Cu-BTC adsorbent bed. The simulation results of breakthrough curves and PSA cycle performance fit well with the experimental data from literature. The effects of adsorption pressure, product flow rate and adsorption time on the PSA system performance are further studied. Increasing adsorption pressure increases hydrogen purity and decreases hydrogen recovery, while prolonging adsorption time and reducing product flow rate raise hydrogen recovery and lower hydrogen purity. Then an artificial neural network (ANN) model is built for predicting PSA system performance and further optimizing the operation parameters of the PSA cycle. The performance data obtained from the Aspen model is used to train ANN model. The trained ANN model has good capability to predict the hydrogen purification performance of PSA cycle with reasonable accuracy and considerable speed. Based on the ANN model, an optimization is realized for finding optimal parameters of PSA cycle. This research shows that it is feasible to find optimal operation parameters of PSA cycle by the optimization algorithm based on the ANN model which was trained on the data produced from Aspen model.