Two‐stage multi‐objective stochastic optimization of a wind‐solar powered steam and green methanol co‐production system
風力・太陽光発電による蒸気とグリーンメタノールの同時生産システムの二段階多目的確率最適化 (AI 翻訳)
Jian Long, Ruiqi Song, Lei Wan, Wenze Guo
🤖 gxceed AI 要約
日本語
この論文は、再生可能エネルギーを統合した蒸気とメタノールの同時生産システム（RE-ISMP）を提案し、不確実性下での投資・運用決定を二段階確率多目的最適化フレームワークで解決する。製油所への適用で、コストとCO2排出の大幅削減を示し、頑健な運用が可能であることを実証した。
English
This paper proposes a Renewable Energy-Integrated Steam and Methanol Production (RE-ISMP) system coupling steam network with carbon capture-enabled methanol process. A two-stage stochastic multi-objective optimization framework addresses investment and operational decisions under wind-solar uncertainty. Applied to an industrial refinery, it shows significant cost and CO2 emission reductions, with operational robustness across scenarios.
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
日本の製油所・化学プラントでは再生可能エネルギー統合とCCUSが重要であり、本手法は確率最適化により不確実性下での運用堅牢性を担保する点で、日本のGX政策（特に産業部門の脱炭素化）に示唆を与える。
In the global GX context
This work provides a generalizable optimization paradigm for transforming energy-intensive industries into low-carbon hubs. Globally, it contributes to the literature on industrial decarbonization through renewable integration and carbon capture, relevant for achieving net-zero targets in the refining and chemical sectors.
👥 読者別の含意
🔬研究者:Provides a novel optimization framework combining stochastic programming with Benders decomposition for industrial decarbonization.
🏢実務担当者:Can be used by industrial firms to design cost-effective renewable energy integration and carbon capture systems.
🏛政策担当者:Offers insights for designing policies that incentivize integrated renewable-CCUS systems in industrial clusters.
📄 Abstract（原文）
The transition to low‐carbon refineries requires integrated systems that coordinate renewable energy with industrial production. This work bridges a critical gap by proposing a novel Renewable Energy‐Integrated Steam and Methanol Production (RE‐ISMP) system, which couples a steam network with a carbon capture‐enabled methanol process. A two‐stage stochastic multi‐objective optimization framework is developed to jointly address investment and operational decisions under wind‐solar uncertainty, solved efficiently via Benders decomposition. Application to an industrial refinery demonstrates that the RE‐ISMP system significantly reduces annual cost and CO 2 emissions versus conventional baselines. Crucially, the stochastic solution guarantees operational robustness across scenarios and identifies the optimal cost‐CO 2 emissions Pareto frontier. This work establishes a generalizable paradigm for transforming energy‐intensive industries into resilient, low‐carbon integrated hubs.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1002/aic.70475first seen 2026-05-24 04:49:51 · last seen 2026-05-27 05:03:42
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