Online performance grading assessment method based on multiset dynamic latent variables

被引：1
|
作者：
Cao C.-X. [1 ]
Wang X. [2 ]
Wang Z.-L. [1 ]
机构：
[1] Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Shanghai
[2] Electrical and Electronic Experimental Teaching Center, Shanghai Jiao Tong University, Shanghai
来源：
Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2020年 / 37卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Dynamic autocorrelation; Ethylene cracking; Multiset dynamic latent variables; Neural network; Online performance grading assessment;
D O I：
10.7641/CTA.2019.80833
中图分类号：
学科分类号：
摘要：
In the dynamic multivariate process, the dynamic relations among process variables are implicit and difficult to interpret. An online performance grading assessment method based on multiset dynamic latent variables (MSDLV) is proposed in this paper to solve the problem. First, a similar historical dataset is divided into different sets according to performance grades. Then, variations related to performance are reserved in the training data due to common basis vector obtained through MSDLV algorithm and decomposed into dynamic part and static part. The dynamic factors in auto-correlated process are extracted, the offline model of latent variables and performance grades is established. Current performance can be assessed online, the steady-state performance grades and the transition performance grades are recognized and distinguished. Finally, the method is applied to the online performance assessment of ethylene cracking process, which illustrates the accuracy of proposed performance assessment method. © 2020, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.
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页码：658 / 666
页数：8
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