文献类型：期刊文献

英文题名：A deep learning-based robust optimization approach for refinery planning under uncertainty

作者：Wang, Cong[1];Peng, Xin[1];Shang, Chao[2];Fan, Chen[1];Zhao, Liang[1,3];Zhong, Weimin[1,3]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol, Dept Automat, Beijing 100084, Peoples R China;[3]Tongji Univ, Shanghai Inst Intelligent Sci & Technol, Shanghai 200292, Peoples R China

年份：2021

卷号：155

外文期刊名：COMPUTERS & CHEMICAL ENGINEERING

收录：;EI(收录号：20213710898836);WOS:【SCI-EXPANDED(收录号:WOS:000703984100004)】；

基金：The authors acknowledge the supports from National Science and Technology Innovation 2030 Major Project of the Ministry of Science and Technology of China under Grant 2018AAA0101602, National Natural Science Foundation of China (Major Program: 61890930-3) , International (Regional) Cooperation and Exchange Project (61720106008) and National Natural Science Fund for Dis-tinguished Young Scholars (61925305) .

语种：英文

外文关键词：Refinery planning; Robust optimization; Deep learning; Price uncertainty; Data-driven

摘要：Refinery planning under uncertainty has gained tremendous attention, and this paper bridges deep learn-ing and robust optimization to address this issue. First, we propose a large-scale mixed-integer linear programming model for refinery planning, where the fixed-yield models of the processing units are used. Prices of final products are considered uncertain parameters in the developed model to enhance the solu-tion's applicability. Second, historical data of different products are collected to construct the uncertainty set characterizing all possible realizations of uncertainty. Third, a deep learning method is employed to capture the uncertainties of product prices, which has been proven to be powerful for high-dimensional price data. Based on the constructed uncertainty set, a data-driven robust optimization model is further developed. Finally, an iterative constraint generation algorithm is applied to solve the data-driven robust optimization problem. Case studies from an actual refinery are presented to showcase the effectiveness of the proposed method, which owes particularly to the representation capability of deep learning. (c) 2021 Elsevier Ltd. All rights reserved.