Towards Sustainable Green Methane: A Review of Catalysis, Process Engineering, and Artificial Intelligence Applications
持続可能なグリーンメタンへの展望：触媒、プロセス工学、人工知能応用のレビュー (AI 翻訳)
Zekun Liu, Jiaze Ma, Yufei Wang
🤖 gxceed AI 要約
日本語
Power-to-Gas技術によるグリーンメタン合成の触媒、反応器工学、AI応用、経済・環境評価を総括。再生可能電力と非化石炭素源の利用が持続可能性の鍵であり、過渡運転下での触媒安定性向上とデジタルツインによる制御が商業化の課題。
English
This review synthesizes recent advances in catalysts, reactor engineering, AI, and techno-economic/environmental assessments for green methane production via Power-to-Gas. Sustainability hinges on renewable electricity and non-fossil carbon sources; commercial deployment requires improved catalyst stability under transient conditions and digital twin implementation.
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GX政策でカーボンリサイクルとe-fuel推進を掲げており、本レビューはグリーンメタンの研究開発と実装戦略に示唆を与える。特に再生可能エネルギー変動下でのプロセス制御や触媒開発は、日本の技術優位性と直結する。
In the global GX context
As global efforts toward energy de-fossilization accelerate, this review provides a comprehensive baseline for Power-to-Gas technology, highlighting key levers for sustainability. The emphasis on AI and digital twins aligns with trends in smart grid and industrial decarbonization, offering insights for ISSB-aligned climate transition planning.
👥 読者別の含意
🔬研究者:Identifies key research gaps in catalyst stability and digital twin integration for green methane systems.
🏢実務担当者:Provides techno-economic benchmarks and operational considerations for green methane pilot and commercial projects.
🏛政策担当者:Summarizes conditions under which green methane supports national decarbonization targets, informing subsidy and grid integration policies.
📄 Abstract（原文）
Global energy de-fossilization requires scalable solutions for extended energy storage and industrial emission reduction. Synthesizing green methane via Power-to-Gas technology offers a viable pathway to store renewable electricity while utilizing captured carbon dioxide. This review evaluates recent advancements in catalytic mechanisms, reactor engineering, artificial intelligence applications, and techno-economic and life cycle assessments of green methane production systems. Analysis shows that advanced reactor configurations effectively manage the exothermic heat of the Sabatier reaction. Furthermore, integrating machine learning algorithms accelerates catalyst discovery and enables dynamic process control under fluctuating renewable energy loads. Economic and environmental assessments indicate that the sustainability of green methane depends strictly on utilizing renewable electricity and sourcing non-fossil carbon. Commercial deployment must focus on improving catalyst stability during transient operations and implementing digital twins to establish green methane as a sustainable carbon backbone for chemical industries.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/pr14091477first seen 2026-05-15 20:31:43
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