Vacuum pressure swing adsorption intensification by machine learning: Hydrogen production from coke oven gas
Jian Wang, Xu Chen, Liying Liu, Tao Du, Paul A. Webley, Gang Kevin Li
Abstract
Hydrogen is a vital resource in the fight against climate change, and it has the potential to revolutionize the energy sector. Our research focused on optimizing the production of high-purity hydrogen using coke oven gas (COG), a valuable hydrogen source in the steel industry. By leveraging advanced artificial neural networks (ANNs), we can predict the performance and exergy efficiency of a 6-bed 12-step vacuum pressure swing adsorption (VPSA) process accurately and efficiently. The Pareto fronts were addressed by combining the evolutionary algorithm with ANNs, and the effects of operating parameters were discussed in detail. Importantly, we found that our VPSA process can achieve a hydrogen purity of 99.99% with 45.2% exergy efficiency. We also demonstrated that using ANNs can significantly enhance VPSA process optimization, making it a valuable tool for extracting high-purity hydrogen from COG.