文献类型：期刊文献

英文题名：Multiobjective optimization of ethylene cracking furnace system using self-adaptive multiobjective teaching-learning-based optimization

作者：Yu, Kunjie[1,2,3];While, Lyndon[2];Reynolds, Mark[2];Wang, Xin[4];Liang, J. J.[3];Zhao, Liang[1];Wang, Zhenlei[1]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Univ Western Australia, Sch Comp Sci & Software Engn, Nedlands, WA 6009, Australia;[3]Zhengzhou Univ, Sch Elect Engn, Zhengzhou 450001, Henan, Peoples R China;[4]Shanghai Jiao Tong Univ, Ctr Elect & Elect Technol, Shanghai 200240, Peoples R China

年份：2018

卷号：148

起止页码：469

外文期刊名：ENERGY

收录：;EI(收录号：20180704791824);WOS:【SCI-EXPANDED(收录号:WOS:000429764000034)】；

基金：This research was supported by the National Natural Science Foundation of China (61533003, 61590922, 61673268, 61473266), International (Regional) Cooperation and Exchange Project (61720106008), Programme of Introducing Talents of Discipline to Universities (the 111 Project) (B17017), China Postdoctoral Science Foundation (2017M622373), Shanghai Natural Science Foundation (14ZR1421800), and the State Key Laboratory of Synthetical Automation for Process Industries. The first author also thanks the financial support of China Scholarship Council (CSC).

语种：英文

外文关键词：Ethylene cracking furnace; Product yield; Fuel consumption; Multiobjective optimization; Itaching-learning-based optimization

摘要：The ethylene cracking furnace system is crucial for an olefin plant. Multiple cracking furnaces are used to convert various hydrocarbon feedstocks to smaller hydrocarbon molecules, and the operational conditions of these furnaces significantly influence product yields and fuel consumption. This paper develops a multiobjective operational model for an industrial cracking furnace system that describes the operation of each furnace based on current feedstock allocations, and uses this model to optimize two important and conflicting objectives: maximization of key products yield, and minimization of the fuel consumed per unit ethylene. The model incorporates constraints related to material balance and the outlet temperature of transfer line exchanger. The self-adaptive multiobjective teaching-learning-based optimization algorithm is improved and used to solve the designed multiobjective optimization problem, obtaining a Pareto front with a diverse range of solutions. A real industrial case is investigated to illustrate the performance of the proposed model: the set of solutions returned offers a diverse range of options for possible implementation, including several solutions with both significant improvement in product yields and lower fuel consumption, compared with typical operational conditions. (C) 2018 Elsevier Ltd. All rights reserved.