AI-ML techniques for green hydrogen: A comprehensive review

Mamta Motiramani , Priyanshi Solanki , Vidhi Patel , Tamanna Talreja , Nainsiben Patel , Divya Chauhan , Alok Kumar Singh
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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.

Abstract Image

绿色氢的AI-ML技术综述
绿色氢是替代化石燃料的一种更清洁的能源,在全球转向能源生产以应对气候变化方面至关重要。这篇关于在绿色氢价值链中嵌入人工智能(AI)和机器学习(ML)的综述概述了全面转型的巨大潜力。这包括优化可再生能源的利用、改进电解工艺、在条件更好的盐洞中储存氢气、更智能的配送系统以及廉价的物流。在这方面,它消除了泄漏风险,并通过人工智能检测保障了安全操作。因此,本文强调AI-ML方法在绿色氢技术的效率和可持续性方面取得了重大进展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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