文献类型：期刊文献

英文题名：Multi-strategy modeling integrating kinetics mechanism of cracking and pyrolysis and unsupervised dual-stage attention long and short-term memory network

作者：Wang, Bin[2];Luo, Kai[2];Chen, Xiangming[2];Deng, Kai[2];Long, Jian[1,2,3];Guo, Wenze[1,2,3]

机构：[1]East China Univ Sci & Technol, State Key Lab Ind Control Technol, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[3]Huzhou Inst Ind Control Technol, Huzhou 313099, Peoples R China

年份：2025

卷号：279

外文期刊名：FUEL PROCESSING TECHNOLOGY

收录：;EI(收录号：20254319363276);WOS:【SCI-EXPANDED(收录号:WOS:001604511900001)】；

基金：This work was supported by National Key Research and Development Program of China (2023YFB3307801) , National Natural Science Foundation of China (22408099, 62373155) , Major Program of Qingyuan Innovation Laboratory (Grant No. 00122002) , and the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017 and the Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：MIP-LTAG reaction-regeneration unit; Lumped kinetics model; VMD-UDA-LSTM model; Multiple-strategy modeling

摘要：The fluid catalytic cracking process utilizing the dual-riser reactors (MIP-LTAG) holds significant importance in the development of petrochemical enterprises. It aims to reduce fuel consumption while increasing output. Consequently, modeling for the production process is an essential task. However, traditional methods struggle to accurately describe the complex reaction mechanisms involved in the cracking/pyrolysis dual reaction pathways. Additionally, due to the coupling of variables and insufficiency of dynamic characteristics, capturing multivariable spatio-temporal dependencies remains challenging. This paper focuses on key indicators such as product yield and carbon emissions within the core reaction-regeneration unit of the target technological process. A lumped kinetic mechanism model is constructed to balance the reaction pathway. Variational mode decomposition (VMD) is employed to perform decomposition of the coupled variables. The unsupervised dualstage attentional long short term memory model (UDA-LSTM) is utilized to capture multi-scale characteristics. To leverage these advantages, this paper designs three hybrid model for collaborative optimization of multiobjective predictions. Finally, the effectiveness of the proposed hybrid modeling framework is validated through an actual industrial production case. The predicted mean squared error (MSE) of the main product yield does not exceed 0.2, and the constructed process model supports real-time monitoring of the production process by refineries.