文献类型：期刊文献

英文题名：Industrial units modeling using self-attention network based on feature selection and pattern classification

作者：Wang, Luyao[1];Long, Jian[1,2];Li, Xiang Yang[3];Peng, Haifei[1];Ye, Zhen Cheng[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China;[2]Qingyuan Innovat Lab, Quanzhou 362801, Peoples R China;[3]Sinopec Jinan Refining & Chem Co, Jinan 250101, Shandong, Peoples R China

年份：2023

卷号：200

起止页码：176

外文期刊名：CHEMICAL ENGINEERING RESEARCH & DESIGN

收录：;EI(收录号：20234515009782);WOS:【SCI-EXPANDED(收录号:WOS:001106099500001)】；

基金：This work was supported by National Natural Science Foundation of China (Basic Science Center Program: 61988101), National Natural Science Foundation of China (62373155, 61973124), Major Program of Qingyuan Innovation Laboratory (Grant No. 00122002), and the Programme of Introducing Talents of Discipline to Unniversities (the 111 Project) under Grant B17017.

语种：英文

外文关键词：Data -driven; Feature selection; Pattern classification; Self-attention

摘要：Data-driven modeling method is an effective method for large-scale refinery industrial units. However, due to the long operational cycle, large number of measurement points, complex spatial and temporal correlations and dynamic switching of operation modes of refinery industrial units, utilizing a single data-driven model for process modeling may not be optimal. To tackle with the issues above, a self-attention model based on feature selection and pattern classification (FS-PC-SA) is develop. This approach uses Random Forest algorithm for feature selection and extract different operating modes from industrial data through K-means clustering algorithm. To capture complex spatial and temporal correlations in refinery units, sub-models based on self-attention for each mode are built for prediction. Finally, the method is used to predict the gasoline product yield in fluid catalytic cracking process and wax oil product yield in ebullated bed residue hydrocracking process, which demonstrates that the proposed method is better than other traditional methods.