文献类型：期刊文献

英文题名：Performance assessment method of dynamic process based on SFA-GPR

作者：Wang, Haodong[1];Wang, Xin[2];Wang, Zhenlei[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Shanghai Jiao Tong Univ, Elect & Elect Expt Teaching Ctr, Shanghai 200240, Peoples R China

年份：2022

卷号：111

起止页码：27

外文期刊名：JOURNAL OF PROCESS CONTROL

收录：;EI(收录号：20220511543387);WOS:【SCI-EXPANDED(收录号:WOS:000779727100003)】；

基金：National Key Research and Development Program (2018YFB1701103) ; National Natural Science Foundation of China (Major Program: 61890930-3) , National Science Fund for Distinguished Young Scholars (61925305) and National Natural Science Foundation of China (Key Program: 62136003) .

语种：英文

外文关键词：Dynamic characteristics; Slow feature analysis; Gaussian process regression; Performance assessment

摘要：A dynamic process performance assessment method based on slow feature analysis and Gaussian process regression (SFA-GPR) is proposed to capture more complete feature information from industrial process data with dynamic characteristics, thus improving the accuracy of the assessment model. First, different binary number performance labels are used to categorize data setsin terms of performance grades, and the process feature subspace is extracted using the slow feature analysis algorithm, followed by the establishment of the Gaussian process regression offline model between the feature subspace and the performance labels. During the online assessment, in conjunction with the proposed performance assessment strategy, performance grades and the current process's operating status are determined based on the performance label of sample data obtained by the assessment model. Finally, the method is used to assess the online performance of the debutanizer and cracking furnace in the ethylene plant. The findings indicate that the method has a higher level of assessment accuracy.(c) 2022 Elsevier Ltd. All rights reserved.