文献类型：期刊文献

英文题名：A bilevel data-driven framework for robust optimization under uncertainty - applied to fluid catalytic cracking unit

作者：Li, Tianyue[1];Long, Jian[1];Zhao, Liang[1];Du, Wenli[1];Qian, Feng[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2022

卷号：166

外文期刊名：COMPUTERS & CHEMICAL ENGINEERING

收录：;EI(收录号：20223812756317);WOS:【SCI-EXPANDED(收录号:WOS:000860550300004)】；

基金：The work is supported by the National Natural Science Foundation of China (Basic Science Center Program: 61988101) , National Natural Science Fund for Distinguished Young Scholars (61725301) , National Natural Science Foundation of China (61973124, 62173147) and Shanghai AI Lab.

语种：英文

外文关键词：Data driven; Robust optimization; Bilevel optimization framework; Correlated uncertainty; Fluid catalytic cracking; Process operation optimization; Kinetic modeling

摘要：The operational optimization of refining process is facing the complex coupled state and frequently changed conditions. Especially the feed of fluid catalytic cracking (FCC) has property fluctuations which may lead to uncertainties in profit and lead to suboptimal optimization schemes from the deterministic optimization model. This study designed a bilevel data-driven robust optimization framework that optimizes the feed selection and reaction temperature of an industrial FCC unit under feed property uncertainty. Two uncertainty sets based on the feed properties data were derived from 2-year historical industrial data and simulation data. As most of the chemical reaction models are differential equations, a bilevel programming framework designed in Julia was the key point to solve the nested numerical and mathematic problems. A real-world case study is conducted to demonstrate the effectiveness of the proposed approach in protecting against uncertainties to ensure profits for FCC units.