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

Research output: Contribution to journalArticleResearchpeer-review

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.

Original languageEnglish
Pages (from-to)837-854
Number of pages18
JournalInternational Journal of Hydrogen Energy
Volume69
DOIs
Publication statusPublished - 5 Jun 2024

Keywords

  • Artificial neural network
  • Coke oven gas
  • Exergy analysis
  • Hydrogen production
  • Multi-objective optimization
  • Vacuum pressure swing adsorption

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