Artificial intelligence for sustainable green hydrogen production: A systematic literature review

This paper presents a systematic literature review (SLR) on the application of artificial intelligence (AI) techniques to optimize the efficiency and sustainability of Green Hydrogen (Green H2) production. As the world seeks sustainable, scalable energy sources with high energy density to replace non-renewable energy systems, Green H2 has emerged as a promising alternative due to its zero carbon emissions during combustion and the potential to significantly reduce carbon dioxide release based on production methods. This systematic review highlights how AI technologies, including machine learning (ML), deep learning (DL), and optimization algorithms, have been successfully utilized to improve various aspects of Green H2 production. These include optimizing supply chain management, energy utilization, and electrolysis operations. The paper begins by discussing the importance of Green H2 as a clean, renewable energy source capable of addressing challenges related to energy transition and climate change. It then delves into the role of AI in enhancing the production processes and overall efficiency of Green H2. Additionally, the paper identifies key opportunities, challenges, and emerging trends in this domain while presenting analytical statistics and insights to support its findings. By offering a comprehensive understanding of AI applications in Green H2 production, this SLR provides valuable guidance for policymakers, researchers, and industry stakeholders striving to advance the hydrogen economy towards greater efficiency and sustainability.