文献类型：期刊文献

英文题名：A data-driven strategy for industrial cracking furnace system scheduling under uncertainty

作者：Zhang, Chenhan[1];Wang, Zhenlei[1]

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

年份：2023

卷号：277

外文期刊名：CHEMICAL ENGINEERING SCIENCE

收录：;EI(收录号：20232114118233);WOS:【SCI-EXPANDED(收录号:WOS:001009379500001)】；

基金：This work is supported by National Natural Science Foundation of China (Basic Science Center Program: 61988101), the Shanghai Committee of Science and Technology, China (22DZ1101500), National Natural Science Foundation of China (22178103, 62073142) and the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017.

语种：英文

外文关键词：Uncertainty; Adaptive robust optimization; Machine learning; Cycle scheduling; Cracking furnace system

摘要：Cyclic scheduling of ethylene cracking furnace system (CSECFS) has a significant impact on raising economic performance of ethylene plants. However, in actual plants, optimal results of deterministic models may be suboptimal or ineffective because of various uncertainties. This paper proposes a novel data-driven adaptive robust optimization (DDARO) strategy that effectively bridges robust optimization and machine learning methods. A mixed-integer nonlinear programming (MINLP) model for CSECFS is developed initially. Second, data-driven uncertainty sets are generated using the historical data of processing flow rates: maximally correlated principal component analysis (MCPCA) is employed to partition the data into two subspaces, which are then delineated by support vector clustering (SVC) and generalized norm approaches, respectively. Third, a multi-stage DDARO model is established using the derived uncertainty sets and afterward transformed as a tractable single-stage model. Finally, a real-world case is performed to exemplify the effectiveness of the proposed framework.