文献类型：期刊文献

英文题名：Distributed Optimization Subject to Inseparable Coupled Constraints: A Case Study on Plant-Wide Ethylene Process

作者：Liu, Weihan[1];Wang, Ting[1];Li, Zhongmei[1];Ye, Zhencheng[1];Peng, Xin[1];Du, Wenli[1]

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

年份：2023

卷号：19

期号：4

起止页码：5412

外文期刊名：IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

收录：;EI(收录号：20220811686803);WOS:【SCI-EXPANDED(收录号:WOS:001006958800006)】；

基金：Manuscript received 19 December 2021; accepted 8 February 2022. Date of publication 16 February 2022; date of current version 22 March 2023. This work was supported in part by the National Natural Science Foundation of China Basic Science Center Program under Grant 61988101, in part by the National Natural Science Fund for Distinguished Young Scholars under Grant 61725301, and in part by the National Natural Science Foundation of China under Grant 61873093, and Grant 62003140. Paper no. TII-21-5609. (Corresponding authors: Wenli Du; Zhongmei Li.)

语种：英文

外文关键词：Optimization; Energy consumption; Production; Raw materials; Informatics; Feeds; Data models; Constraint node; distributed optimization; energy saving; parameter projection; plant-wide optimization

摘要：Plant-wide optimization plays a vital role in improving the overall performance of large-scale industrial processes. Considering the modeling complexity and convergence difficulty of centralized plant-wide optimization, in this article, we propose a distributed framework by decomposing the global optimization problem into a set of subproblems, where multiple local units interact with each other between nodes. According to the proposed framework, plant-wide optimization problem can be effectively solved by distributed optimization. To eliminate the limitations of existing distributed algorithms, we introduce constraint node to describe the inseparable coupled constraints between nodes. By combining Lagrange duality and parameter projection, the proposed algorithm can solve optimization problems with multiple constraints. Taking ethylene production process as an example, the global energy consumption optimization is guaranteed without the whole-process mechanism model. Numerical simulation and industrial experimental results demonstrate that the proposed algorithm can reduce the energy consumption of the entire ethylene process with fewer computation time.