文献类型：期刊文献

英文题名：Refinery production planning optimization under crude oil quality uncertainty

作者：Li, Fupei[1,2];Qian, Feng[1];Du, Wenli[1];Yang, Minglei[1];Long, Jian[1];Mahalec, Vladimir[2]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]McMaster Univ, Dept Chem Engn, Hamilton, ON L8S 4L8, Canada

年份：2021

卷号：151

外文期刊名：COMPUTERS & CHEMICAL ENGINEERING

收录：;EI(收录号：20212110401336);WOS:【SCI-EXPANDED(收录号:WOS:000659842800003)】；

基金：This work has been supported by National Natural Science Foundation of China (Basic Science Center Program: (61988101) , International (Regional) Cooperation and Exchange Project (61720106008) and National Natural Science Fund for Distinguished Young Scholars (61925305) . and by McMaster University WBooth School of Engineering Practice and Technology. We thank Sayyed Faridoddin Afzali for proposing the random vector sampling method to minimize the number of scenarios used in the twostage stochastic programming problem.

语种：英文

外文关键词：Two-stage stochastic programming; Refinery production planning; Quality uncertainty; Random vector sampling; Product tri-section CDU model

摘要：Current practice in refinery planning is to assume that the qualities of the crude oil feedstocks are known, even though they often vary. The uncertainty of the quality properties can significantly impact the profit of the refinery and needs to be considered in purchasing decisions. This work employs the product trisection CDU model (Li et al. 2020) to build an accurate refinery model and determines the optimal crude selection by two-stage stochastic programming. The uncertainty of the crude oil quality properties is defined via the uncertainty of the TBP curves, which is described by the uncertain parameters of the beta functions approximating the TBP curves. The probabilistic scenarios are generated via random vector sampling method, leading to a relatively small number of scenarios required for the two-stage-stochastic programming model convergence. This enables us to determine the best crude oil choice, while requiring acceptable computational times, as illustrated by computational experiments. (c) 2021 Elsevier Ltd. All rights reserved.