文献类型：期刊文献

英文题名：Two-stage multi-objective stochastic optimization of a wind-solar powered steam and green methanol co-production system

作者：Long, Jian[1];Song, Ruiqi[1];Wan, Lei[1];Guo, Wenze[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai, Peoples R China

年份：2026

外文期刊名：AICHE JOURNAL

收录：;EI(收录号：20262120768657);WOS:【SCI-EXPANDED(收录号:WOS:001770153500001)】；

基金：This work was supported by National Natural Science Foundation of China (22408099, 62373155), the Program of Shanghai Leading Talents Overseas for Wenze Guo, and the Fundamental Research Funds for Central Universities.

语种：英文

外文关键词：carbon capture; multi-objective optimization; renewable integration; steam-methanol co-production; two-stage stochastic optimization

摘要：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 CO2 emissions versus conventional baselines. Crucially, the stochastic solution guarantees operational robustness across scenarios and identifies the optimal cost-CO2 emissions Pareto frontier. This work establishes a generalizable paradigm for transforming energy-intensive industries into resilient, low-carbon integrated hubs.