文献类型：期刊文献

英文题名：Data-driven two-stage distributionally robust optimization for refinery planning under uncertainty

作者：He, Wangli[1];Zhao, Jinmin[1];Zhao, Liang[1];Li, Zhi[1];Yang, Minglei[1,2];Liu, Tianbo[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, Engn Res Ctr Proc Syst Engn, Minist Educ, Shanghai 200237, Peoples R China;[3]Sinopec Jinan Refining & Chem Co, Jinan 250101, Shandong, Peoples R China

年份：2023

卷号：269

外文期刊名：CHEMICAL ENGINEERING SCIENCE

收录：;EI(收录号：20231113707618);WOS:【SCI-EXPANDED(收录号:WOS:000925613100001)】；

基金：The authors acknowledge the supports from the National Key Research and Development Program of China under Grant 2018AAA0101602, Shanghai International Science & Technology Cooperation Program (21550712400) , National Natural Science Foundation of China (22178103) , Shanghai Pilot Program for Basic Research, Innovative development project of the industrial internet in 2020 (TC200802D) , Fundamental Research Funds for the Central Universities and Shanghai AI Lab.

语种：英文

外文关键词：Data-driven; Refinery planning; Distributionally robust optimization; Wasserstein metric; Uncertainty

摘要：This work investigates the refinery planning problem under uncertainty in product prices. A novel data-driven Wasserstein distributionally robust optimization framework is proposed for handling uncertain-ties in the refinery-wide planning operations. A data-driven ambiguity set is constructed based on the Wasserstein metric to model the distributional uncertainty. The robust kernel density estimation tech-nique is adopted to establish the support set to reduce the effect of the potential outliers. Based on the derived support set and ambiguity set, a data-driven two-stage distributionally robust optimization model for refinery planning is developed. Then, the robust counterpart of the proposed model is formu-lated to make the problem computationally tractable. Finally, a real-world case study on a petroleum refinery is presented to illustrate the effectiveness and applicability of the proposed framework.(c) 2023 Elsevier Ltd. All rights reserved.