文献类型：期刊文献

英文题名：Novel hybrid data-driven modeling integrating variational modal decomposition and dual-stage self-attention model: Applied to industrial petrochemical process

作者：Long, Jian[1];Huang, Cheng[1];Deng, Kai[1];Wan, Lei[1];Hu, Guihua[1,3];Zhang, Feng[2]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]SINOPEC Res Inst Petr Proc Co Ltd, Beijing 100083, Peoples R China;[3]East China Univ Sci & Technol, Engn Res Ctr Proc Syst Engn, Minist Educ, Shanghai 200237, Peoples R China

年份：2024

卷号：304

外文期刊名：ENERGY

收录：;EI(收录号：20242416261358);WOS:【SCI-EXPANDED(收录号:WOS:001256467900001)】；

基金：This work was supported by National Natural Science Foundation of China (Basic Science Center Program: 61988101) , National Natural Science Foundation of China (62394345, 62373155, 62273149) and Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：Data -driven; Industrial petrochemical process; Variational mode decomposition; Dual -stage self -attention model; Error compensation

摘要：Data-driven modeling methods are extensively employed in the vast landscape of petrochemical industries. Due to their accuracy and rapidity, these models play a guiding role in the simulation and control of industrial petrochemical processes, such as the fluid catalytic cracking (FCC) process. However, in industrial process, there is a high degree of nonlinearity between influencing variables and predictive variables, making it challenging for models to accurately capture the relationship. Additionally, there are temporal and spatial characteristics, which conventional models struggle to learn. To tackle this challenge, a novel hybrid data-driven modeling is proposed, which combines variational mode decomposition (VMD) and dual-stage attention long short-term memory (DALSTM), incorporating error compensation (EC). VMD decomposes the predictive variables into multiple components to alleviate the nonlinear between the influencing and predictive variables. Each component is predicted by the DA-LSTM model, a dual-stage self-attention model that incorporates feature and time attention mechanisms. The EC method predicts the error term, primarily originating from residuals in the VMD decomposition and inaccuracies in the predictions made by DA-LSTM. The superiority of the proposed model is verified by the prediction of industrial data in FCC, Tennessee Eastman and debutanizer column processes.