AI-Driven Optimization and Predictive Control for Hydrogen Production, Storage, and Utilization Systems
Year: 2025
Type: article
Abstract: Hydrogen is widely acknowledged as a clean energy vector capable of decarbonizing numerous sectors, including transportation, manufacturing, and power generation. However, the efficiency, safety, and cost-effectiveness of hydrogen generation, storage, and consumption remain major challenges. Recent breakthroughs in Artificial Intelligence (AI) present significant prospects to address these concerns. This article analyzes the integration of AI techniques—such as machine learning, predictive analytics, and optimization algorithms—into hydrogen value chain activities. 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.
Authors: S. Mulani, Anant Awasare
Language: English
Subfield: Energy Engineering and Power Technology
Field: Energy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7111349381
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