文献类型：期刊文献

英文题名：A stability-oriented stochastic optimization strategy for refinery scheduling during unit shutdowns

作者：Liang, Ziting[1,2];Li, Zhi[1,2];Dai, Xin[1,2];Cao, Yue[1,2,3];Qian, Feng[1,2,3]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[3]East China Univ Sci & Technol, Engn Res Ctr Proc Syst Engn, Minist Educ, Shanghai 200237, Peoples R China

年份：2026

卷号：206

外文期刊名：COMPUTERS & CHEMICAL ENGINEERING

收录：;EI(收录号：20254919634353);WOS:【SCI-EXPANDED(收录号:WOS:001633454000001)】；

基金：This work was supported in part by National Major Science and Technology Project of China (2025ZD1607702) , National Natural Science Foundation of China (Key Program: 62136003) , National Natural Science Foundation of China (62203173, 62303186) , Shanghai Pujiang Program (2022PJD018) and the Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：Refinery scheduling; Unit shutdowns; Operational stability; Stochastic optimization

摘要：Planned unit shutdowns are critical to refinery operations. Careful scheduling is required to balance maintenance needs, safety inspections, and regulatory compliance. At the same time, it is essential to ensure production stability. Traditional refinery scheduling models primarily focus on economic objectives. The operational challenges introduced by shutdown conditions, such as variations in unit flow rates and inventory instability, are often overlooked in these models. Existing approaches predominantly utilize deterministic optimization frameworks. The frameworks fail to adequately address the uncertainties in process yields which arise from variations in feedstock properties and operational conditions. Also, Frequent unit transitions and inventory fluctuations is not considered in those frameworks. In order to overcome these limitations, a novel optimization strategy which explicitly incorporates stability considerations into shutdown scheduling is proposed in this paper. A new metric based on discrete switching event counts is introduced, which quantifies and limits the variability of unit flow rate. This metric helps reduce unnecessary adjustments and operational disruptions, as evidenced by few unit flow rate transitions observed in the optimized scheduling. Additionally, a two-stage stochastic optimization model is developed to handle unit yield uncertainties. The model improves the robustness of schedules by mitigating the impacts of uncertainty on key operational variables. The proposed method is validated using real industrial case studies. The scheduling results demonstrate that the proposed method has better performance on improving operational stability during refinery shutdowns.