文献类型：期刊文献

英文题名：Cyclic scheduling for an ethylene cracking furnace system using diversity learning teaching-learning-based optimization

作者：Yu, Kunjie[1,2];While, Lyndon[2];Reynolds, Mark[2];Wang, Xin[3];Wang, Zhenlei[1]

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

年份：2017

卷号：99

起止页码：314

外文期刊名：COMPUTERS & CHEMICAL ENGINEERING

收录：;EI(收录号：20170703339231);WOS:【SCI-EXPANDED(收录号:WOS:000397351000026)】；

基金：This research was supported by National Natural Science Foundation of China (61422303, 61590922, 61533003, 61503138, 61673268), Shanghai Natural Science Foundation (14ZR1421800), and the State Key Laboratbry of Synthetical Automation for Process Industries. The authors would like to acknowledge the financial support of China Scholarship Council (CSC) and the Dr. Y. Jin for providing the cracking furnace surrogate model.

语种：英文

外文关键词：Ethylene cracking furnace; Cyclic scheduling; Teaching-learning-based optimization

摘要：The ethylene cracking furnace system is central to an olefin plant. Multiple cracking furnaces are employed for processing different hydrocarbon feeds to produce various smaller hydrocarbon molecules, such as ethylene, propylene, and butadiene. We develop a new cyclic scheduling model for a cracking furnace system, with consideration of different feeds, multiple cracking furnaces, differing product prices, decoking costs, and other more practical constraints. To obtain an efficient scheduling strategy and the optimal operational conditions for the best economic performance of the cracking furnace system, a diversity learning teaching-learning-based optimization (DLTLBO) algorithm is used to simultaneously determine the optimal assignment of multiple feeds to different furnaces, the batch processing time and sequence, and the optimal operational conditions for each batch. The performance of the proposed scheduling model and the DLTLBO algorithm is illustrated through a case study from a real-world ethylene plant: experiments show that the new algorithm out-performs both previous studies of this set-up, and the basic TLBO algorithm. (C) 2017 Elsevier Ltd. All rights reserved.