Accelerating amine-based CO2 capture with machine learning: From molecular screening to process optimization
Ping Yang, Xiaoman Yu, Kyriakos C. Stylianou, Liang Huang, Qiang Wang
🤖 gxceed AI 要約
日本語
本論文は、アミン系CO2回収プロセスへの機械学習適用を包括的にレビュー。液相系ではアンサンブル学習により予測精度が向上し、固相系では仮想スクリーニングにより高効率吸着材候補を特定。産業応用では最大35%のコスト削減を実現。
English
This paper reviews machine learning applications for amine-based CO2 capture, from molecular screening to process optimization. Key breakthroughs include improved prediction accuracy in liquid systems and identification of novel adsorbents via virtual screening. Industrial applications demonstrate up to 35% cost reduction and 25% profit improvement.
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がGX実現の重要技術と位置づけられており、本論文の機械学習によるコスト削減や効率化の知見は、国内の実証・導入促進に直接貢献する。
In the global GX context
Globally, CCUS is critical for net-zero targets. This paper highlights how machine learning can overcome traditional barriers in solvent and sorbent design, offering a pathway to lower-cost carbon capture across industries.
👥 読者別の含意
🔬研究者:Provides a comprehensive overview of ML methods for amine-based capture, highlighting model accuracy improvements and descriptor importance.
🏢実務担当者:Demonstrates tangible cost reductions (35%) and profit improvements (25%) through ML-driven solvent selection and dynamic optimization.
🏛政策担当者:Shows that ML can accelerate CCUS deployment, supporting policy incentives for digitalization in carbon management.
📄 Abstract（原文）
Amine-based CO2 capture represents the most mature approach for large-scale carbon reduction, with systems implemented across multiple industrial demonstration projects globally. However, vast chemical spaces encompassing millions of potential formulations and complex multiscale coupling effects pose unprecedented challenges for traditional experimental methods. Machine learning applications have achieved revolutionary advances through differentiated strategies. In liquid amine systems, ensemble learning algorithms delivered breakthrough precision improvements from traditional 4–5% to below 0.93%, while interpretable models revealed that nitrogen atom charge distribution contributes 56% to reaction barriers, enabling rational biphasic solvent design (DETA/DEEA system) that achieved 34% regeneration energy reduction compared to benchmark MEA. For solid amine systems, differential descriptor methods overcame severe overfitting challenges, improving test set performance from R2 = 0.5102 to 0.79. Virtual screening of 1.6 million binding sites from the GDB-17 database identified 11% of candidates with stronger CO2 binding than the industrial benchmark BPEI (−0.04 eV). Among these high-performance candidates, 2642 molecules simultaneously satisfied synthesizability criteria (SAscore < 3.4, GDBscore > 0.64), demonstrating both favorable binding energetics and high experimental feasibility. Critically, mechanistic analysis revealed that support physical properties dominate adsorption performance over amine chemical characteristics, fundamentally transforming material design concepts. Industrial applications demonstrated 35.76% cost reductions through intelligent solvent selection and 15–25% profit improvements through dynamic capture-level optimization combined with market-responsive bidding strategies. Despite these breakthroughs, systematic limitations, including model generalization difficulties, cross-scale integration challenges, and data standardization, persist, requiring physics-constrained algorithms and unified modeling frameworks for laboratory-to-industrial translation. These developments establish machine learning as the core driving force transitioning amine-based CO2 capture from empirical development toward intelligent design paradigms.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1016/j.fmre.2025.12.022first seen 2026-05-05 23:37:22
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