文献类型：期刊文献

英文题名：A data-driven approach for crude oil scheduling optimization under product yield uncertainty

作者：Dai, Xin[1];Zhao, Liang[1,2];Li, Zhi[1];Du, Wenli[1,2];Zhong, Weimin[1,2];He, Renchu[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：2021

卷号：246

外文期刊名：CHEMICAL ENGINEERING SCIENCE

收录：;EI(收录号：20213210726583);WOS:【SCI-EXPANDED(收录号:WOS:000702894400011)】；

基金：The authors acknowledge the supports from National Natural Science Foundation of China (Basic Science Center Program: 61988101), International (Regional) Cooperation and Exchange Project (61720106008) and National Natural Science Foundation of China (61803158, 61925305, 62073142).

语种：英文

外文关键词：Crude oil scheduling; Support vector clustering; Product yield; Uncertainty; Data-driven robust optimization

摘要：Crude oil scheduling plays an important role in reducing production cost of refineries. However, fluctuations in operation conditions in crude distillation units (CDUs) and quality of crude oil will lead to uncertainties of product yields that may nullify the plan obtained from the deterministic model. A data-driven robust optimization (DDRO) method for crude oil scheduling is proposed in this study to address these uncertainties. Historical data of the product yield of crude oil are fully utilized by a generalized intersection kernel support vector clustering (GIKSVC) algorithm to construct uncertainty sets. A new DDRO model is then developed on the basis of derived uncertainty sets and further reformed as a solvable mixed integer nonlinear programming (MINLP) problem via dual transformation. Case studies from a refinery are performed to indicate the performance of this method and the influence of the regularization parameter nu on the optimization solution is explored to reach an acceptable balance between total production cost and robustness of crude oil scheduling. (C) 2021 Elsevier Ltd. All rights reserved.