Role of Artificial Intelligence in Hydrogen-Based Green Energy Technologies
水素ベースのグリーンエネルギー技術における人工知能の役割 (AI 翻訳)
Ritik Raj, Survi Sinha, Atreyi Pramanik, Pradeep Yadav
🤖 gxceed AI 要約
日本語
本論文は、水素バリューチェーン全体でAIが効率性、持続可能性、システム統合を強化する方法をレビューする。AIモデリングと最適化により、廃棄物ポリマーやバイオマスガス化＋CCSなどの水素製造ルートの環境影響評価が改善される。また、再生可能エネルギー電解による水素製造のコスト削減や、予知保全・地下貯蔵・燃料電池への応用も示す。AIは低炭素水素エネルギーのデジタル基盤として位置づけられる。
English
This paper reviews how AI enhances efficiency, sustainability, and system integration across the hydrogen value chain. AI modeling and optimization improve environmental impact assessment of hydrogen production routes like waste polymer and biomass gasification with CCS. It also reduces cost in renewable-powered electrolysis via forecasting and control, and enables predictive maintenance, underground storage, and fuel cell applications. AI is positioned as a digital foundation for scalable low-carbon hydrogen energy.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本は水素基本戦略を掲げ、水素サプライチェーン構築を推進中。AIによる製造最適化や貯蔵管理は、コスト低減と安定供給に直結し、日本のグリーン水素普及に貢献する。
In the global GX context
Globally, hydrogen is a key pillar of decarbonization. AI-driven optimization can accelerate the commercial viability of green hydrogen, addressing intermittency and cost barriers. This aligns with the IEA's hydrogen roadmap and net-zero targets.
👥 読者別の含意
🔬研究者:Provides a comprehensive overview of AI applications across the hydrogen value chain, identifying optimization and forecasting opportunities for future research.
🏢実務担当者:Highlights AI tools for improving efficiency and cost-effectiveness in hydrogen production, storage, and distribution, useful for energy companies investing in green hydrogen.
🏛政策担当者:Demonstrates the enabling role of AI in hydrogen technology, supporting policies that integrate digital innovation into national hydrogen strategies.
📄 Abstract（原文）
Artificial Intelligence (AI) has turned out to be a major facilitator in the development of hydrogen-based green energy technologies by enhancing efficiency, level of sustainability and integration of the system at the hydrogen value chain level. Life cycle analysis reports indicate that other hydrogen routes, such as waste polymer and biomass-based gasification combined with carbon capture have reduced environmental effects as compared to standard steam methane reforming. The AI modeling and optimization complement them by increasing the efficiency of the processes, integration of renewable electricity, transport logistics, and evaluation of the environmental impact. With the help of AI-based forecasting and control, intermittency and market uncertainty can be reduced in renewable-powered electrolysis systems thus hydrogen can be produced at a cost-effective rate. The AI is used to provide intelligent energy management, grid stability, and techno-economic optimization at both centralized and decentralized levels. Moreover, AI increases the hydrogen capacity and application in predictive maintenance, safety, optimum underground storage, and fuel cell. All these developments put AI as a key digital foundation of scalable, resilient and low-carbon hydrogen energy services.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.55938/aeai.v2i1.432first seen 2026-06-21 05:52:31
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