Hydrogen Purification Performance of Pressure Swing Adsorption Based on Cu-BTC/zeolite 5A Layered Bed

A pressure swing adsorption (PSA) hydrogen purification model for the four-component gas (H_(2)/CO_(2)/CH_(4)/CO=73/16/8/3 mol%) in a layered bed packed with Cu-BTC and zeolite 5A was established to achieve better hydrogen purification performance.By comparing its simulation results with the experimental data,the adsorption isotherm model was validated and could be used to accurately describe the adsorption process of the gas mixture on the two adsorbents.The breakthrough curves of the mixed gas on the layered bed were studied to verify the correctness of the established simulation models.Based on the validated model,the performance of the PSA system based on the layered bed was carried out,including the hydrogen purity and recovery.The simulation results show that the hydrogen purification system based on the layered bed model can achieve hydrogen purity of 95.469% and hydrogen recovery of 83.219%.Moreover,a parametric study was carried out and its results show that reductions in feed flow rate and adsorption time result in an increase in hydrogen purity and a decrease in hydrogen recovery.A longer equalization time between the two adsorption beds can simultaneously increase the hydrogen purity and recovery.

Artificial neural network based optimization of a six-step two-bed pressure swing adsorption system for hydrogen purification

The pressure swing adsorption(PSA)system is widely applied to separate and purify hydrogen from gaseous mixtures.The extended Langmuir equation fitted from the extended Langmuir-Freundlich isotherm has been used to predict the adsorption isothermal of hydrogen and methane on the zeolite 5A adsorbent bed.A six-step two-bed PSA model for hydrogen purification is developed and validated by comparing its simulation results with other works.The effects of the adsorption pressure,the P/F ratio,the adsorption step time and the pressure equalization time on the performance of the hydrogen purification system are studied.A four-step two-bed PSA model is taken into consideration,and the six-step PSA system shows higher about 13%hydrogen recovery than the four-step PSA system.The performance of the vacuum pressure swing adsorption(VPSA)system is compared with that of the PSA system,the VPSA system shows higher hydrogen purity than the PSA system.Based on the validated PSA model,a dataset has been produced to train the artificial neural network(ANN)model.The effects of the number of neurons in the hidden layer and the number of samples used for training ANN model on the predicted performance of ANN model are investigated.Then,the well-trained ANN model with 6 neurons in the hidden layer is applied to predict the performance of the PSA system for hydrogen purification.Multi-objective optimization of hydrogen purification system is performed based on the trained ANN model.The artificial neural network can be considered as a very effective method for predicting and optimizing the performance of the PSA system for hydrogen purification.