Epistemic Uncertainty Analysis and Robust Optimization of a Second‐Generation Solvent‐Based Post‐Combustion Carbon Capture Process
第2世代溶剤ベースの燃焼後二酸化炭素回収プロセスにおける認識論的不確実性解析とロバスト最適化 (AI 翻訳)
Ilayda Akkor, Shachit S. Iyer, John Dowdle, Le Wang, C. Gounaris
🤖 gxceed AI 要約
日本語
本論文は、第2世代溶剤ベースの炭素回収プロセス（PZ/AFS）を対象に、モデルの不確実性を解析し、ロバスト最適化を実施した。感度分析とPedigree分析により重要な不確実性を特定し、12次元の不確実性セットを構築。ロバスト最適化により、CO2平衡圧や吸収熱などの不確実性がコストに大きく影響することを示し、ポンプ設計の重要性を指摘した。
English
This paper conducts epistemic uncertainty analysis and robust optimization for a second-generation solvent-based post-combustion carbon capture process (PZ/AFS). Sensitivity and Pedigree analyses identify key uncertainties, leading to a 12-dimensional uncertainty set. Robust optimization reveals that CO2 equilibrium pressure and heat of absorption have the greatest cost impact, and pump design changes are critical for robustness.
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
日本はGX推進の一環としてCCUS技術の実用化を目指しており、本論文のロバスト最適化手法は国内の炭素回収プロジェクトの設計信頼性向上に貢献できる。特に不確実性を考慮した設計は、投資リスク低減に有用である。
In the global GX context
This paper advances the reliability of carbon capture process design under uncertainty, which is critical for global CCUS deployment. The robust optimization framework can inform engineering decisions in large-scale projects, aligning with the need for cost-effective and reliable carbon removal technologies worldwide.
👥 読者別の含意
🔬研究者:Provides a methodology for uncertainty quantification and robust optimization in carbon capture processes, applicable to other chemical engineering systems.
🏢実務担当者:Offers insights on which parameters to prioritize for data collection and design changes (e.g., pumps) to achieve robust carbon capture plant designs.
🏛政策担当者:Highlights the importance of supporting R&D to reduce epistemic uncertainties in CCUS, thereby lowering financial risks for deployment.
📄 Abstract（原文）
Amine‐based carbon capture is widely regarded as a promising avenue for reducing industrial emissions, with the piperazine advanced flash stripper (PZ/AFS) process being a prominent example under active development. While process optimization can be employed to improve its economics, the many inherently uncertain parameters in its process model compromise the reliability of the resulting designs. In this work, the previously developed Pyomo‐based PZ/AFS process model is subjected to a comprehensive uncertainty analysis. The parameters that have the greatest impact on capture performance and costs are determined through sensitivity analysis, followed by a Pedigree analysis to assess knowledge strength for epistemic uncertainties. The chosen uncertainties are characterized through parameter estimation and expressed as a 12‐dimensional uncertainty set. Based on this, a robust optimization problem is solved to mitigate the risk posed by parametric uncertainties in the PZ/AFS flowsheet designs. Uncertainties in CO2 equilibrium pressure and heat of absorption, along with the reaction rate, have the greatest impact on optimal costs, suggesting that one can benefit from additional efforts to estimate these parameters with better confidence. Compared to the deterministically optimal solution, the most substantial design changes to achieve robustness were noted for the pumps, highlighting the need for their careful design.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1002/eng2.70728first seen 2026-05-05 23:32:02
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