文献类型：期刊文献

英文题名：Data-driven crude oil scheduling optimization with a distributionally robust joint chance constraint under multiple uncertainties

作者：Dai, Xin[1];Zhao, Liang[1,2];He, Renchu[1];Du, Wenli[1,2];Zhong, Weimin[1,2];Li, Zhi[1];Qian, Feng[1,2]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai, Peoples R China;[2]Tongji Univ, Shanghai Inst Intelligent Sci & Technol, Shanghai, Peoples R China

年份：2023

卷号：171

外文期刊名：COMPUTERS & CHEMICAL ENGINEERING

收录：;EI(收录号：20230513493690);WOS:【SCI-EXPANDED(收录号:WOS:000963671300001)】；

基金：This work was supported by the National Natural Science Foundation of China (Basic Science Center Program: 61988101) , National Natural Science Fund for Distinguished Young Scholars (61925305) , National Natural Science Foundation of China (62073142, 22178103) , Funda- mental Research Funds for the Central Universities (222202317006) and Shanghai AI Lab.

语种：英文

外文关键词：Distributionally robust joint chance; constrained optimization; Crude oil scheduling; Wasserstein distance; Data-driven optimization; Multiple uncertainties

摘要：Crude oil scheduling optimization is crucial for decreasing the production cost of refineries. However, the feasibility of the optimized schemes is challenged by uncertainties such as possible ship arrival delays and fluctuating crude demands. This study develops a novel data-driven continuous-time optimization model with a distributionally robust joint chance constraint to ensure the overall feasibility probability of the crude oil processing plan under multiple uncertainties. Industrial data are collected to build an ambiguity set using Wasserstein distance to include the potential joint probability distribution of uncertainties. The radius of the ambiguity set is chosen by cross-validation. The model is constructed considering the worst case in the ambiguity set. First, it is formulated as a conditional value-at-risk constrained optimization model. A big-M coefficient and additional binary variables are then utilized to convert the proposed model into a resolvable problem. The efficacy and reliability of the method are explored through case studies.