{"title":"AI-ML techniques for green hydrogen: A comprehensive review","authors":"Mamta Motiramani , Priyanshi Solanki , Vidhi Patel , Tamanna Talreja , Nainsiben Patel , Divya Chauhan , Alok Kumar Singh","doi":"10.1016/j.nxener.2025.100252","DOIUrl":null,"url":null,"abstract":"<div><div>Green hydrogen is a cleaner source to replace fossil-based fuels and is critical in the global shift toward energy production to combat climate change. This review of embedding artificial intelligence (AI) and machine learning (ML) in the value chain of green hydrogen outlines the significant potential for full transformation. These include optimizing the utilization of renewable sources of energy, improving electrolysis process, hydrogen storage in the salt cavern that has better condition, and smarter systems in distribution side with inexpensive logistics. In this, it nullifies leak risks and safeguards the safety operations with detection using AI. Consequently, it positions the paper emphasizing AI-ML approaches demonstrating significant advancements in efficiency and sustainability in green hydrogen technology.</div></div>","PeriodicalId":100957,"journal":{"name":"Next Energy","volume":"8 ","pages":"Article 100252"},"PeriodicalIF":0.0000,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Next Energy","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2949821X25000158","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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Abstract
Green hydrogen is a cleaner source to replace fossil-based fuels and is critical in the global shift toward energy production to combat climate change. This review of embedding artificial intelligence (AI) and machine learning (ML) in the value chain of green hydrogen outlines the significant potential for full transformation. These include optimizing the utilization of renewable sources of energy, improving electrolysis process, hydrogen storage in the salt cavern that has better condition, and smarter systems in distribution side with inexpensive logistics. In this, it nullifies leak risks and safeguards the safety operations with detection using AI. Consequently, it positions the paper emphasizing AI-ML approaches demonstrating significant advancements in efficiency and sustainability in green hydrogen technology.
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