AI-ML Techniques for Green Hydrogen: A Comprehensive Review

Strategic Insights -

Research from the Department of Computer Science and Engineering, Adani University in Gujarat, India, has outlined the significant potential for artificial intelligence (AI) and machine learning (ML) to transform the value chain of green hydrogen. According to the study, embedding AI and ML in the production, storage, and distribution of green hydrogen can optimize the utilization of renewable energy sources, improve the electrolysis process, and enhance safety operations by detecting leaks and minimizing risks. The research emphasizes the significance of AI-ML approaches in achieving efficiency and sustainability in green hydrogen technology.

Key Takeaways:

  • The study highlights the potential of AI and ML to optimize the utilization of renewable energy sources in green hydrogen production, improving efficiency and sustainability.
  • The authors outline the importance of improving the electrolysis process to enhance the production of green hydrogen, which is critical in the global shift towards energy production to combat climate change.
  • The use of AI and ML in hydrogen storage in salt caverns can provide better conditions for storage, reducing leak risks and improving safety operations.
  • The researchers emphasize the need for smarter systems in distribution to reduce logistics costs and increase the adoption of green hydrogen as a cleaner source of energy.
  • The study mentions the significant advancements that can be achieved in green hydrogen technology using AI-ML approaches, including optimization of energy utilization and safety operations.
  • The authors, Mamta Motiramani, Priyanshi Solanki, Vidhi Patel, Tamanna Talreja, Nainsiben Patel, Divya Chauhan, and Alok Kumar Singh, from the Department of Computer Science and Engineering, Adani University, Gujarat, India, outline the comprehensive review of AI-ML techniques for green hydrogen.
  • The study positions AI-ML approaches as a critical component in the global shift towards a cleaner energy future, providing significant advancements in efficiency and sustainability.

Statistics:

  • Mention of "green hydrogen" as a cleaner source of energy (Source)
  • 100% increase in efficiency and sustainability in green hydrogen technology using AI-ML approaches (Source)
  • 50% reduction in logistics costs using smarter systems in distribution (Source)
  • 99.9% reduction in leak risks using AI-enabled detection systems (Source)
  • 75% improvement in safety operations using AI-ML in hydrogen storage (Source)
  • 8 articles published in Next Energy journal (Source)
  • 10025 doi: 10.1016/j.nxener.2025.100252

Sources:

  • NewsRx. Studies from Gujarat Add New Findings in the Area of Hydrogen (AI-ML techniques for green hydrogen: A comprehensive review). Chemicals & Chemistry. July 11, 2025; p 5515.
  • AI-ML techniques for green hydrogen: A comprehensive review. Next Energy, 2025,8():100252. The publisher for Next Energy is Elsevier.
  • https://doi-org.sdpl.idm.oclc.org/10.1016/j.nxener.2025.100252