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新興経済国向け最適化小型CCUSシステム：メタヒューリスティックに基づくモデリングアプローチと経済・環境評価

Optimized Small-Scale CCUS Systems for Emerging Economies: A Metaheuristic-Based Modeling Approach with Economic and Environmental Assessment (原題)

Safwan Nadweh, Nabil Mohammed, Saad Mekhilef

2026 6th International Conference on Emerging Smart Technologies and Applications (eSmarTA)学会2026-08-01#CCUS経営インパクト: コスト削減対象セクター: manufacturing
DOI: 10.1109/esmarta70636.2026.11652106
原典: https://doi.org/10.1109/esmarta70636.2026.11652106

🤖 gxceed AI 要約

日本語

新興経済国の小規模産業向けに、GA、PSO、ANNなどのメタヒューリスティック最適化と質量・エネルギー収支を統合したCCUSシステムのハイブリッドモデルを提案。設計パラメータを最適化し、CO2回収効率90-95%を達成しつつ、コストを約50%削減、年間約9万トンのGHG排出削減を実証。技術経済性とLCAで検証し、資源制約下での持続可能な解決策を示す。

English

This paper proposes a hybrid modeling framework integrating metaheuristic optimization (GA, PSO, ANN) with mass and energy balance equations for small-scale CCUS systems in emerging economies. The optimized design achieves 90-95% CO2 capture efficiency, reduces system costs by approximately 50%, and cuts GHG emissions by 90,000-95,000 tons annually, validated through techno-economic and life-cycle assessments.

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CCUS技術の国内展開に加え、アジア新興国への技術輸出がGX戦略の柱。本論文の小規模・低コストCCUSモデルは、日本の中小企業や国際協力事業に応用可能で、SSBJやカーボンプライシング政策との親和性が高い。

In the global GX context

Globally, CCUS is critical for hard-to-abate sectors, but small-scale applications are underexplored. This study provides a cost-effective optimization framework that could inform ISSB-aligned transition plans and climate finance for emerging markets, supporting the global push for scalable decarbonization solutions.

👥 読者別の含意

🔬研究者:Provides a novel hybrid optimization approach for small-scale CCUS, offering a methodological template for cost-efficiency analysis.

🏢実務担当者:Offers a viable CCUS design for small industrial facilities, potentially reducing compliance costs and enhancing sustainability reporting.

🏛政策担当者:Highlights the feasibility of small-scale CCUS in emerging economies, informing subsidy or incentive design for industrial decarbonization.

📄 Abstract（原文）

The issue of global warming caused by carbon dioxide emissions is regarded as a major environmental problem that should be mitigated. Carbon Capture, Utilization, and Storage (CCUS) technology is considered one of the most effective methods for reducing industrial emissions. However, small-scale industries in developing economies are not given sufficient attention, although an increasing contribution to emissions is observed. The existing literature is characterized by high implementation costs, integration complexity, and the lack of economically viable solutions for low-emission systems. To address these issues, an optimized small-scale CCUS system tailored for emerging industries is proposed in this paper. A hybrid modeling framework is adopted, in which mass and energy balance equations are integrated with metaheuristic optimization algorithms, such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Artificial Neural Networks (ANN). System performance is evaluated iteratively, where design parameters are optimized to maximize CO2 capture efficiency while cost and energy consumption are minimized. The proposed model is validated through techno-economic and life-cycle assessments (LCA). The results indicate that a capture efficiency of 90–95% is achieved, while system costs are reduced by approximately 50%. In addition, greenhouse gas emissions are reduced by about 90,000–95,000 tons annually. These results demonstrate that a viable and sustainable solution for small-scale industrial applications under resource-constrained conditions is provided.

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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。