gxceed
← 論文一覧に戻る

AI-driven carbon capture, utilization, and storage (CCUS) for decarbonizing energy systems

エネルギーシステムの脱炭素化のためのAI駆動型炭素回収・利用・貯留（CCUS） (AI 翻訳)

Seyedeh Azadeh Alavi-Borazjani, Dr Muhammad Noman Shafique, Shehar Yar Khan

Applied Intelligence📚 査読済 / ジャーナル2026-05-30#CCUSOrigin: Global
DOI: 10.1007/s10489-026-07298-8
原典: https://doi.org/10.1007/s10489-026-07298-8
📄 PDF

🤖 gxceed AI 要約

日本語

本論文は、CCUSの各段階（回収、利用、貯留）におけるAI技術（機械学習、深層学習、強化学習）の応用を包括的にレビューする。AIによりプロセス最適化、材料発見、リアルタイム監視が可能となる一方、データ不足やモデル解釈性などの課題も指摘する。将来はシステム統合と再生可能エネルギーとの連携が重要と結論づける。

English

This paper reviews the application of AI (machine learning, deep learning, reinforcement learning) to enhance CCUS processes including capture, utilization, and storage. It identifies benefits such as optimization, materials discovery, and real-time monitoring, while noting challenges like data scarcity and model interpretability. The authors call for system-level integration and coupling with renewables to achieve net-zero targets.

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

日本ではNEDOや政府主導でCCUS実証が進むが、AI適用は萌芽的段階にある。本レビューは、日本企業や研究機関がCCUSにAIを導入する際の俯瞰的な知見を提供する。

In the global GX context

Globally, CCUS is critical for hard-to-abate sectors, and AI offers new efficiency gains. This review provides a structured overview of AI applications across the CCUS value chain, relevant for researchers and industry planning large-scale deployment.

👥 読者別の含意

🔬研究者:Provides a comprehensive mapping of AI techniques applied to CCUS, highlighting research gaps and future directions.

🏢実務担当者:Offers insight into how AI can optimize CCUS operations, but lacks detailed case studies; useful for initial technology screening.

🏛政策担当者:Demonstrates the potential of AI-CCUS synergies, supporting arguments for funding cross-disciplinary R&D and demonstration projects.

📄 Abstract（原文）

Abstract The transition to net-zero emissions has become a global priority, with Carbon Capture, Utilization, and Storage (CCUS) emerging as a key technology for decarbonizing hard-to-abate sectors such as cement, steel, and energy production. Despite its potential, CCUS faces significant challenges related to efficiency, scalability, and system integration. Artificial Intelligence (AI) offers promising solutions to enhance CCUS performance across the entire value chain, including capture, utilization, and storage processes. Advanced AI techniques, such as machine learning, deep learning, and reinforcement learning, are increasingly being employed to optimize capture operations, accelerate materials discovery, and enable real-time monitoring and fault detection in CCUS systems. However, several barriers remain, including limited availability of high-quality datasets, challenges in model interpretability, and insufficient cross-disciplinary integration between AI and CCUS research communities. AI-driven optimization of multi-stage CCUS systems is essential for improving operational efficiency and enabling large-scale deployment. Future research should therefore prioritize system-level integration, AI-guided materials design, and the coupling of CCUS with renewable energy systems to enhance economic and environmental performance. Addressing these challenges will strengthen the role of AI-enabled CCUS as a critical pathway toward achieving global net-zero emissions targets.

🔗 Provenance — このレコードを発見したソース

🔔 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。