文献类型：期刊文献

英文题名：Data-driven robust optimization for crude oil blending under uncertainty

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

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

年份：2020

卷号：136

外文期刊名：COMPUTERS & CHEMICAL ENGINEERING

收录：;EI(收录号：20201208326374);WOS:【SCI-EXPANDED(收录号:WOS:000525867600005)】；

基金：The authors acknowledge the supports from National Natural Science Foundation of China (Major Program: 61590923), International (Regional) Cooperation and Exchange Project (61720106008), National Natural Science Foundation of China (61873092, 61873093), National Science Fund for Distinguished Young Scholars (61725301) and the Fundamental Research Funds for the Central Universities

语种：英文

外文关键词：Crude oil blending; Blending effect; Uncertainty; Data-driven robust optimization

摘要：Optimization of crude oil blending helps improve the operating efficiency of refineries. However, widespread uncertainties, such as oil properties, bring difficulty in realizing this task. A data-driven robust optimization (DDRO) approach is proposed to optimize crude oil blending under uncertainty. First, the blending effect model is used to extract uncertainties of oil components from production data by recursive least squares method. Second, the uncertainty set is constructed by combining principle component analysis and robust kernel density estimation based on the historical data of blending effects. A novel data-driven robust model for recipe optimization of crude oil blending is developed by utilizing the obtained uncertainty set. The dual transformation is applied to derive the linear counterpart of the DDRO model. A case study is adopted to illustrate the effectiveness of the proposed method. (C) 2019 Elsevier Ltd. All rights reserved.