AI-DRIVEN OPTIMIZATION IN RENEWABLE HYDROGEN PRODUCTION: A REVIEW

Abstract

This paper presents a comprehensive systematic review of artificial intelligence (AI)-driven optimization in renewable hydrogen production, emphasizing its pivotal role over the past decade in enabling the transition toward a sustainable, low-carbon energy future. As green hydrogen gains prominence as a clean energy carrier—particularly in hard-to-decarbonize sectors such as transportation, heavy industry, and grid balancing—the demand for efficient, scalable, and economically viable production methods has intensified. AI has emerged as a transformative enabler, offering innovative solutions to technical and economic barriers across various production pathways, including electrolysis (proton exchange membrane, alkaline, and solid oxide), biomass gasification, solar-to-hydrogen, and wind-to-hydrogen systems. This study employs a structured methodology based on a systematic literature review (SLR), drawing from over 150 peer-reviewed journal articles, patents, industry reports, and conference proceedings published between 2014 and 2024. Data were sourced from academic databases, leading energy organizations, and international technology forums. The review categorizes AI techniques—machine learning, deep learning, reinforcement learning, and optimization algorithms—and examines their applications in process control, predictive maintenance, energy forecasting, material discovery, cost reduction, and hybrid renewable system integration. Emerging trends include AI-powered digital twins, AI-quantum hybrid frameworks, and intelligent supply chain management. However, the widespread deployment of AI in hydrogen systems faces challenges, such as limited access to high-quality real-time datasets, lack of standardization, regulatory hurdles, and high computational demands. The paper concludes by identifying key research gaps and outlining future directions, including the development of lightweight, explainable AI models, cross-sectoral collaborations, and supportive policy frameworks. Ultimately, this review underscores the transformative potential of AI in accelerating the commercialization, optimization, and global adoption of renewable hydrogen technologies, laying the groundwork for a robust, intelligent, and decarbonized energy infrastructure.