AI-Driven Optimization and Predictive Control for Hydrogen Production, Storage, and Utilization Systems

Abstract

Hydrogen is increasingly recognized as a clean energy vector capable of decarbonizing multiple sectors, including transportation, industry, and power generation. However, the efficiency, safety, and cost-effectiveness of hydrogen production, storage, and utilization remain critical challenges. Recent advancements in Artificial Intelligence (AI) present transformative opportunities to address these issues. This paper explores the integration of AI techniques—such as machine learning, predictive analytics, and optimization algorithms—into hydrogen value chain operations. Applications include real-time monitoring of electrolysis processes, predictive maintenance of hydrogen storage systems, optimization of fuel cell performance, and demand forecasting for hydrogen distribution networks. Case studies and simulations demonstrate significant improvements in system efficiency, reduced operational costs, and enhanced safety outcomes. The research also discusses barriers to AI adoption in hydrogen technologies, including data availability, cybersecurity concerns, and the need for standardized protocols. The findings suggest that AI-enabled hydrogen systems can accelerate the transition to a sustainable and intelligent energy future.

Cite as:

S. Mulani, & Anant Awasare. (2025). AI-Driven Optimization and Predictive Control for Hydrogen Production, Storage, and Utilization Systems. Research and Reviews on Experimental and Applied Mechanics, 8(3), 35–43. 

https://doi.org/10.5281/zenodo.17853496