文献类型：期刊文献

英文题名：A Distributed Proximal Consensus Algorithm for Energy Saving in Ethylene Production

作者：Nie, Rong[1];Du, Wenli[1,2];Wang, Ting[1];Li, Zhongmei[1];He, Shuping[3,4]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China;[2]Qingyuan Innovat Lab, Quanzhou 362801, Peoples R China;[3]Anhui Univ, Sch Elect Engn & Automat, Minist Educ, Key Lab Intelligent Comp & Signal Processing, Hefei 230601, Peoples R China;[4]Chengdu Univ, Sch Elect Informat & Elect Engn, Chengdu 610106, Peoples R China

年份：2024

卷号：35

期号：3

起止页码：3052

外文期刊名：IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS

收录：;EI(收录号：20234715088670);WOS:【SCI-EXPANDED(收录号:WOS:001103679900001)】；

基金：This work was supported in part by the National Natural Science Foundation of China through the Basic Science Center Program under Grant 61988101, in part by the National Natural Science Foundation of China under Grant 62293501 and Grant 62003140, in part by the Major Program of Qingyuan Innovation Laboratory under Grant 00122002, in part by the Shanghai Science and Technology Planning Program under Grant 23DZ2201700, and in part by the Shanghai AI Laboratory.

语种：英文

外文关键词：Production; Optimization; Energy consumption; Consensus algorithm; Upper bound; Petrochemicals; Linear programming; Distributed optimization; energy saving; ethylene plant-wide optimization; proximal consensus algorithm

摘要：This article presents a distributed optimization framework in order to solve the plant-wide energy-saving problem of an ethylene plant. First, the ethylene production process is abstracted into a distributed network, and then, a new distributed consensus algorithm is proposed, which is called adaptive step-size-based distributed proximal consensus algorithm (ASS-DPCA). This algorithm can dynamically adjust the step size and automatically abandon the irrational evolutionary route while eliminating the dependence of optimization algorithms on model gradient information. Moreover, the designed algorithm is able to converge to an optimal solution for any convex cost functions and approach to a convex constraint set of agents over an undirected connected graph. Finally, the results of numerical simulation and industrial experiments show that the algorithm can reduce the total energy consumption of an ethylene plant with less computing time and assured consensus.