文献类型：期刊文献

英文题名：An Integrated Multi-Unit and Multi-Objective Optimization Approach for Enhancing the Efficiency of Ethylene Distillation Process

作者：Wang, Zhe[1]; He, Renchu[2]; Chen, Jing[3]; Zhao, Yunmeng[1]

机构：[1] East China University of Science and Technology, Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, Shanghai, China; [2] China University of Petroleum, College of Information Science and Engineering, College of Artificial Intelligence, Beijing, China; [3] Petro-Cyber Works Information Technology Co., Ltd., Shanghai Branch, Shanghai, China

年份：2023

起止页码：41

外文期刊名：Proceedings - 2023 2nd International Conference on Advanced Sensing, Intelligent Manufacturing, ASIM 2023

收录：EI(收录号：20242116109257)

语种：英文

外文关键词：Distillation - Ethylene - Machine learning - Multilayer neural networks - Multiobjective optimization - Network layers - Reboilers

摘要：For complex multi-Unit ethylene distillation process, This work proposes a strategy referred to as knowledge and data-driven hybrid modeling and multi-objective cooperative optimization (KDHM-MOCO). First, the distillation tower group is divided into several units for individual modeling, and a cooperative optimization model is established based on the relationships between tower group variables. In this regard, a three-hidden-layer backpropagation neural network (3h-BPNN) is employed to train proxy models for the individual unit operations. The training datasets are derived from both real-world manufacturing processes and simulated environments. Ultimately, the multi-objective grey wolf optimization (MOGWO) algorithm is employed to achieve the most advantageous solution set, taking into account both product quality and energy cost, as well as the corresponding optimal operating parameters for reflux and reboiler steam rates. ? 2023 IEEE.