循環経済フレームワークにおける人工知能を活用した再生可能エネルギー由来水素製造
Harnessing renewable energy sources for hydrogen production using artificial intelligence within a circular economy framework (原題)
Sneha Nayak, Kapilan Natesan
🤖 gxceed AI 要約
日本語
太陽光・風力・地熱・バイオマスなど再生可能エネルギー由来のクリーン水素製造を概観し、効率・ライフサイクル評価・最新技術動向を整理したレビュー。AI・機械学習の水素製造への応用と課題を検討し、ScopusとVOSviewerによる文献計量分析でバイオマス由来水素や代替貯蔵などの研究ギャップを特定。循環経済原則とAIによる資源利用最大化・廃棄物最小化の意義を論じ、政策支援と官民連携が技術普及を導くと指摘する。
English
This review surveys renewable pathways (solar, wind, geothermal, biomass) for clean hydrogen production, covering efficiency, life-cycle assessment, and recent advances. It examines AI/ML applications in hydrogen production and uses Scopus/VOSviewer bibliometrics to map research gaps such as biomass-based hydrogen and alternative storage. It highlights circular-economy principles and AI's role in maximizing resource use while minimizing waste, noting policy support and public-private partnerships as key enablers.
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
水素は日本のGX実現の鍵となる成長分野であり、再エネ由来水素とAI最適化の動向は国内の水素戦略・脱炭素電源政策を検討する読者に参考となる。ただし日本固有の制度・事例への言及はなく、一般的な技術レビューとして位置づけられる。
In the global GX context
Hydrogen is central to global net-zero pathways and transition finance, and this review's mapping of AI-enabled optimization and circular-economy framing connects to international decarbonization and disclosure discussions. It offers a broad landscape view rather than new empirical evidence, useful as orientation for global hydrogen scholarship.
👥 読者別の含意
🔬研究者:再エネ由来水素とAI応用の研究ギャップを俯瞰する出発点として活用できる。
🏢実務担当者:水素調達・再エネ活用を検討する企業が技術選択肢とコスト・インフラ課題を把握する参考になる。
🏛政策担当者:水素普及に向けた政策支援と官民連携の論点整理に資する。
📄 Abstract（原文）
The alarming scale of devastation in our environment and the grim picture of the global fuel crisis has spurred the need for sustainable energy alternatives. The hydrogen production from renewable energy sources offers a promising solution to achieve a carbon–neutral energy future in line with UN sustainability development goals. This review explores various renewable energy options like solar, wind, geothermal, and biomass for clean hydrogen production, emphasizing their potential, efficiency, life cycle assessment, and recent technological advancements. It also investigates the applications of artificial intelligence and machine learning in hydrogen production, inspecting the associated challenges and the opportunities available for researchers to pursue further investigations. A bibliometric analysis using the Scopus database and VOS viewer identifies research gaps in this field, putting spotlight on areas such as biomass-based hydrogen production and alternative storage methods. The density maps highlight the less predominant renewable sources as biomass, natural gas (yellow cluster) and fuel cells, electric energy storage (purple cluster) indicating gaps which could be explored by future research enthusiasts. This study further highlights the significance of circular economy principles and role of AI in maximizing resource utilization while minimizing waste. While challenges such as high production costs and infrastructure limitations persist, policy support and technological maturity and market conditions are guiding the progress of this technology through public private partnerships. By mapping the current research landscape and identifying key gaps, this review aims to provide a valuable reference for researchers and policymakers to advance sustainable hydrogen production and ensure a smooth transition toward a clean energy economy.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1007/s42452-026-09064-4first seen 2026-10-18 04:41:54
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。