Online nonlinear process monitoring using kernel partial least squares

被引：0
|
作者：
Hu Y. [1 ]
Wang L. [2 ]
Ma H. [1 ]
Shi H. [1 ]
机构：
[1] Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology
[2] School of Electrical and Electronic Engineering, Shanghai Institute of Technology
来源：
Huagong Xuebao/CIESC Journal | 2011年 / 62卷 / 09期
关键词：
Kernel partial least squares; Nonlinear process; Process monitoring; Quality prediction;
D O I：
10.3969/j.issn.0438-1157.2011.09.025
中图分类号：
学科分类号：
摘要：
To handle the nonlinear problem for process monitoring, a new technique based on kernel partial least squares(KPLS) is developed. KPLS is an improved partial least squares(PLS)method, and its main idea is to first map the input space into a high-dimensional feature space via a nonlinear kernel function and then to use the standard PLS in that feature space. Compared to linear PLS, KPLS can make full use of the sample space information, and effectively capture the nonlinear relationship between input variables and output variables. Different from other nonlinear PLS, KPLS requires only linear algebra and does not involve any nonlinear optimization. For process data, firstly KPLS was used to derive regression model and got the score vectors, and then two statistics, T2 and SPE, and corresponding control limits were calculated. A case study of the Tennessee-Eastman(TE) process illustrated that the proposed approach showed superior process monitoring performance compared to linear PLS. © All Rights Reserved.
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页码：2555 / 2561
页数：6
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