AI-Driven Intelligent CO2 Capture System for Decarbonization in Hard to Abate Industrial Sectors Towards Oman Vision 2040
オマーン・ビジョン2040に向けた、脱炭素が困難な産業部門向けAI駆動型インテリジェントCO2回収システム (AI 翻訳)
Ahmad Saeed, Mahmoud Al Mughairi, Ala'a Al Muhtaseb
🤖 gxceed AI 要約
日本語
本研究は、排出ガス組成の変動に対応可能なAIベースのCO2回収システムを提案。ランダムフォレスト回帰モデルを用いて、フルガス組成や運転変数から性能を予測し、実運用での変動を考慮した効率的な設計・運転を実現する。オマーンの産業脱炭素に貢献するスケーラブルなアプローチ。
English
This study proposes an AI-based CO2 capture system using chemical absorption that adapts to fluctuating flue gas compositions. A tuned Random Forest regression model maps inputs to performance metrics, enabling quick screening of feasible operating points. The approach ensures robust design and operation under realistic industrial conditions, supporting Oman's net-zero goals.
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
オマーン地域の事例だが、AIを活用したCO2回収システムの柔軟性向上は、日本国内の排出ガス変動が大きい産業（鉄鋼・化学等）への応用可能性を示唆する。ただし、日本の排出規制やCCSインフラとの整合性は別途検討が必要。
In the global GX context
The paper presents a novel AI-driven approach to make CO2 capture systems resilient to flue gas variability, a key challenge in hard-to-abate sectors globally. It aligns with global CCUS technology development and could inform similar applications in industries with fluctuating emissions.
👥 読者別の含意
🔬研究者:The use of Random Forest for feasibility mapping under variable conditions offers a methodological contribution to process optimization in carbon capture research.
🏢実務担当者:Corporations in hard-to-abate sectors can explore this AI approach to improve the economic viability of CO2 capture under real-world fluctuating conditions.
🏛政策担当者:Governments aiming for net-zero can consider supporting AI-integrated capture technologies to enhance flexibility and cost-effectiveness in industrial decarbonization.
📄 Abstract（原文）
Abstract Decarbonization of Oman's hard-to-abate industrial sectors requires an efficient CO2 capture system that operates under realistic, fluctuating flue gas conditions rather than a singular nominal condition. Thus, the current study proposes a new AI-based flexible carbon dioxide capture system that uses a chemical absorption process. The proposed system is not only feasible but also more economically efficient with varying levels of carbon dioxide in the inlet gases. In order to analyze the impact of varying flue gas composition on the entire carbon dioxide capture system and its feasibility, a detailed process model has been developed. The simulation results are combined with the use of the MATLAB programming language to obtain a strong dataset based on varying the flue gas composition and other significant factors. In the proposed framework, a tuned Random Forest regression model maps flue-gas composition and design/operating variables to predicted performance metrics. These predictions are used to quickly screen candidate operating points and retain only those that comply with all process constraints, forming the feasible operating set. The results show that a small change in the flue gas composition with respect to the base condition can render the system infeasible and increase the regeneration energy significantly. On the other hand, the proposed AI-based system can operate well within a short time while predicting the results over a wide range of flue gas compositions. This approach ensures that the fluctuations in flue gas composition are taken into consideration in the design as well as the operation of the CO2 capture system. This provides a scalable approach towards a smarter CO2 capture system that is in line with realistic industrial scenarios while keeping Oman's net-zero ambitions on track.
🔗 Provenance — このレコードを発見したソース
- crossref https://doi.org/10.2118/232482-msfirst seen 2026-05-18 05:29:05 · last seen 2026-05-20 05:50:43
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