文献类型：期刊文献

英文题名：Data-Driven Modeling and Cyclic Scheduling for Ethylene Cracking Furnace System with Inventory Constraints

作者：Lin, Xinwei[1];Zhao, Liang[1];Du, Wenli[1];He, Wangli[1];Qian, Feng[1]

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

年份：2021

卷号：60

期号：9

起止页码：3687

外文期刊名：INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

收录：;EI(收录号：20211410179370);WOS:【SCI-EXPANDED(收录号:WOS:000629059100019)】；

基金：This work was supported by the National Science and Technology Innovation 2030 Major Project of the Ministry of Science and Technology of China under Grant 2018AAA0101602, the International (Regional) Cooperation and Exchange Project (61720106008), the National Natural Science Fund for Distinguished Young Scholars (61725301), the and National Natural Science Foundation of China (61873092).

语种：英文

外文关键词：Fuels - Tanks (containers) - Computer software - Scheduling - Furnaces - Regression analysis

摘要：The optimization of cyclic scheduling for an ethylene cracking furnace system (ECFS) is beneficial for ethylene plants. The scheduling problem involves multiple feeds, tanks, furnaces, and different operating periods. Moreover, the operations of ECFS are subjected to several constraints, such as tank capacity, outlet temperature of transfer line exchangers (TLEOT), and non-simultaneous decoking. These problems bring challenges to the cyclic scheduling for ECFS. By considering tank capacity constraints, this paper proposes a data-driven modeling and cyclic scheduling framework to address this issue. Fuel consumption, generation of superhigh-pressure steam (SS), and dilution steam (DS) consumption are considered in the scheduling model. Case studies from the literature and an actual ethylene plant are conducted to determine the effectiveness of the proposed method. Cracking furnace simulation software is used to generate the data of key products such as ethylene, propylene, and benzene to develop data-driven yield models. Industrial data are employed to establish the regression models of fuel consumption, SS production, and TLEOT. The results of two case studies indicate that the proposed method can manage inventory constraints in cyclic scheduling and achieve feasible solutions.