Computational Methods, Artificial Intelligence, Modeling, and Simulation Applications in Green Hydrogen Production Through Water Electrolysis: A Review
Ahmed Y. Shash, Noha M. Abdeltawab, Doaa Hassan, Mohamed H. Darweesh, Y. G. Hegazy
Abstract
Green hydrogen production is emerging as a crucial component in global decarbonization efforts. This review focuses on the role of computational approaches and artificial intelligence (AI) in optimizing green hydrogen technologies. Key approaches to improving electrolyzer efficiency and scalability include computational fluid dynamics (CFD), thermodynamic modeling, and machine learning (ML). As an instance, CFD has achieved over 95% accuracy in estimating flow distribution and polarization curves, but AI-driven optimization can lower operational expenses by up to 24%. Proton exchange membrane electrolyzers achieve efficiencies of 65–82% at 70–90 °C, but solid oxide electrolyzers reach up to 90% efficiency at temperatures ranging from 650 to 1000 °C. According to studies, combining renewable energy with hydrogen production reduces emissions and improves grid reliability, with curtailment rates of less than 1% for biomass-driven systems. This integration of computational approaches and renewable energy ensures a long-term transition to green hydrogen while also addressing energy security and environmental concerns.