文献类型：期刊文献

英文题名：Data-Driven Robust Optimization for Steam Systems in Ethylene Plants under Uncertainty

作者：Zhao, Liang[1,2];Zhong, Weimin[1,2];Du, Wenli[1,2]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Tongji Univ, Shanghai Inst Intelligent Sci & Technol, Shanghai 200092, Peoples R China

年份：2019

卷号：7

期号：10

外文期刊名：PROCESSES

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000495436200106)】；

基金：This research was funded by the National Natural Science Foundation of China (Major Program: 61590923; 61873092), the International (Regional) Cooperation and Exchange Project (61720106008), the National Natural Science Fund for Distinguished Young Scholars (61725301), and the Fundamental Research Funds for the Central Universities (222201917006).

语种：英文

外文关键词：ethylene plant; steam system; data-driven robust optimization; uncertainty

摘要：In an ethylene plant, steam system provides shaft power to compressors and pumps and heats the process streams. Modeling and optimization of a steam system is a powerful tool to bring benefits and save energy for ethylene plants. However, the uncertainty of device efficiencies and the fluctuation of the process demands cause great difficulties to traditional mathematical programming methods, which could result in suboptimal or infeasible solution. The growing data-driven optimization approaches offer new techniques to eliminate uncertainty in the process system engineering community. A data-driven robust optimization (DDRO) methodology is proposed to deal with uncertainty in the optimization of steam system in an ethylene plant. A hybrid model of extraction-exhausting steam turbine is developed, and its coefficients are considered as uncertain parameters. A deterministic mixed integer linear programming model of the steam system is formulated based on the model of the components to minimize the operating cost of the ethylene plant. The uncertain parameter set of the proposed model is derived from the historical data, and the Dirichlet process mixture model is employed to capture the features for the construction of the uncertainty set. In combination with the derived uncertainty set, a data-driven conic quadratic mixed-integer programming model is reformulated for the optimization of the steam system under uncertainty. An actual case study is utilized to validate the performance of the proposed DDRO method.