気候強靱材料のための計算サステナビリティ：セメント・産業システムにおけるAI駆動型脱炭素経路
Computational Sustainability for Climate-Resilient Materials: AI-Driven Decarbonization Pathways for Cement and Industrial Systems (原題)
Oluwafemi Ezekiel Ige, Musasa Kabeya
🤖 gxceed AI 要約
日本語
本レビューは、AI・機械学習・デジタルツイン・最適化・LCA・技術経済分析を統合した「計算サステナビリティ」が、セメント産業の脱炭素化を多層的に支援し得ることを示す。SCM混合材置換とエネルギー効率改善が最も成熟した短期策である一方、代替バインダー・電化・水素・CCUSは検証とコスト低減が課題。データ公開性・外部検証・不確実性評価・長期耐久性・実機実証に重要なギャップが残る。
English
This scoping review shows how computational sustainability—integrating AI, ML, digital twins, optimization, LCA, and techno-economic analysis—can support multi-scale decarbonization of cement production. SCM clinker substitution and energy efficiency are the most mature near-term options, while alternative binders, electrification, hydrogen, and cement-specific CCUS need further validation and cost reduction. Key gaps remain in open datasets, external validation, uncertainty quantification, durability evidence, and multi-plant field trials.
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
日本のセメント業界は既に高炉スラグ等の混合材利用で世界をリードするが、SSBJ・有報でのScope3開示やGXリーグでの排出量取引対応が進む中、本レビューが示すAI・LCA・デジタルツイン統合は、国内企業の脱炭素投資判断やサプライチェーン排出管理の高度化に直結する。
In the global GX context
Globally, cement is a hard-to-abate sector central to ISSB/CSRD disclosure and transition finance. This review maps how AI, LCA, and systems modeling can operationalize decarbonization pathways, offering a framework for credible transition plans and climate-resilient infrastructure investment.
👥 読者別の含意
🔬研究者:AI・LCA・技術経済分析を統合したセメント脱炭素研究の課題と方法論を整理するのに有用。
🏢実務担当者:SCM最適化やキルン制御へのAI適用可能性を評価し、脱炭素ロードマップ策定に活用できる。
🏛政策担当者:セメント部門のCCUS・水素・代替バインダー普及には標準整備とインフラ投資が不可欠である点を政策設計に反映すべき。
📄 Abstract（原文）
Cement production remains a major industrial source of anthropogenic CO₂ because emissions arise from both limestone calcination and high-temperature fuel combustion. This review examines how computational sustainability can support climate-resilient and low-carbon cement production by integrating artificial intelligence (AI), machine learning (ML), digital twins, optimization, life cycle assessment (LCA), techno-economic analysis, and industrial systems modeling. An interdisciplinary scoping review was conducted using Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) informed procedures to synthesize evidence across materials discovery, cement and concrete mix optimization, plant operations, LCA integration, and system-level decarbonization planning. The review identifies computational sustainability as a multi-scale decision-support framework that links materials design, plant operations, supply-chain coordination, and policy/system planning. The synthesis shows that supervised learning, ensemble models, physics-informed neural networks, explainable AI, multi-objective optimization, digital twins, and system dynamics can support low-carbon binder screening, SCM optimization, kiln control, predictive maintenance, environmental-impact assessment, and infrastructure planning. SCM-based clinker substitution and energy-efficiency improvements are the most mature near-term options, while alternative binders, electrification, hydrogen integration, and cement-specific carbon capture, utilization, and storage (CCUS) require further validation, cost reduction, infrastructure development, and alignment with standards. Critical gaps remain in open and representative datasets, external model validation, uncertainty quantification, long-term durability evidence, multi-plant field trials, and integrated prospective LCA–techno-economic–system modeling. The review concludes that computational sustainability can accelerate cement-sector decarbonization when data-driven prediction, physical constraints, environmental assessment, economic evaluation, and policy-aware systems planning are integrated into transparent and validated decision frameworks.
🔗 Provenance — このレコードを発見したソース
- crossref https://doi.org/10.54327/set2026/v6.i2.351first seen 2026-09-22 05:45:19
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