Environment and process-specific multi-objective optimization analysis of an industrial carbon capture plant towards the decarbonization goal
環境とプロセス固有の多目的最適化分析：産業用炭素回収プラントの脱炭素化目標に向けて (AI 翻訳)
Swaprabha P. Patel, A. Gujarathi
🤖 gxceed AI 要約
日本語
本研究では、Promax V6を用いた産業用炭素回収プロセスのシミュレーションと、非優越ソート遺伝的アルゴリズム（NSGA-II）による多目的最適化を実施。環境目的（地球温暖化ポテンシャル）とプロセス目的（炭化水素回収）のトレードオフを分析し、決定変数としてMDEA/ピペラジン濃度や温度・圧力を考慮。グレイ関係分析とSWARA重み付けでパレート解を評価し、決定木による目的予測の有効性を示した。
English
This study simulates an industrial carbon capture process using Promax V6 and optimizes it with NSGA-II, considering conflicting objectives of global warming potential and hydrocarbon recovery. Eight decision variables include amine concentrations, temperatures, and pressures. Pareto front analysis using GRA and SWARA weighting, plus decision tree prediction, demonstrates the ability to model the highly nonlinear capture process.
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
本論文はアミン系炭素回収プロセスの多目的最適化手法を提供する。日本のCCS戦略（天然ガス処理、火力発電など）に応用可能だが、具体的な日本データを用いていないため、直接的な政策連動性は低い。
In the global GX context
This paper contributes optimization techniques for amine-based carbon capture, a key technology for industrial decarbonization globally. The multi-objective framework with decision tree prediction can improve capture efficiency and reduce GWP, relevant to global CCUS deployment.
👥 読者別の含意
🔬研究者:Offers a multi-objective optimization approach combining NSGA-II, GRA, SWARA, and decision trees for carbon capture process design.
🏢実務担当者:Provides a framework to optimize operating conditions (e.g., lean amine temperature) for balancing environmental and process performance in amine-based capture.
📄 Abstract（原文）
Considering the importance of natural gas as a significant energy source and its utility as a fuel in both domestic and industrial applications, the industrial carbon capture process is simulated using Promax V6 software. The industrial carbon capture process is further optimized by considering two conflicting objectives, one environmental and one process-based, and utilizing the non-dominated sorting genetic algorithm-II. Eight sets of decision variables, including the molar flow rate of feed natural gas, pressures of two streams, temperatures of three streams, and concentrations of methyldiethanolamine (MDEA) and piperazine amines in the absorber column, are utilized in this multi-objective optimization study. The optimization study resulted in widespread values of global warming potential (GWP) ranging from 11427 to 17336 mPEW94. The results also showed that increasing lean amine temperature favors hydrocarbon recovery. The lean amine temperature mostly converged at 70 °C, which reveals less affinity of the solvent towards absorption of hydrocarbons, and thus more hydrocarbons can be obtained in the sweet gas. The Grey Relational Analysis (GRA) algorithm is used for Pareto ranking, whereas the stepwise weight assessment ratio analysis (SWARA) weight algorithm is used to generate weights of objectives. The decision tree algorithm is used to predict the values of the conflicting objectives. This study shows the decision trees’ ability to predict the objectives for a highly nonlinear and complex carbon capture process.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1088/1742-6596/3191/1/012051first seen 2026-05-05 23:30:30
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