A Selective Moving Window Partial Least Squares Method and Its Application in Process Modeling

Ouguan Xu; Yongfeng Fu; Hongye Su; Lijuan Li

doi:10.1016/j.cjche.2014.05.012

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A Selective Moving Window Partial Least Squares Method and Its Application in Process Modeling

PROCESS MODEL | Updated：2026-01-06

- A Selective Moving Window Partial Least Squares Method and Its Application in Process Modeling
- A Selective Moving Window Partial Least Squares Method and Its Application in Process Modeling
- 中国化学工程学报（英文版） 2014年22卷第7期页码：799-804
- Affiliations：
  
  1. Zhijiang College, Zhejiang University of Technology,Hangzhou,China,310024
  2. State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University,Hangzhou,China,310027
  3. College of Automation and Electrical Engineering, Nanjing University of Technology,Nanjing,China,210009
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2014.05.012
  中图分类号：
- 纸质出版：2014
- Accepted：
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Ouguan Xu, Yongfeng Fu, Hongye Su, 等. A Selective Moving Window Partial Least Squares Method and Its Application in Process Modeling[J]. 中国化学工程学报（英文版）, 2014,22(7):799-804.

Ouguan Xu, Yongfeng Fu, Hongye Su, Lijuan Li . A Selective Moving Window Partial Least Squares Method and Its Application in Process Modeling[J]. , 2014, 22(7): 799-804.
Ouguan Xu, Yongfeng Fu, Hongye Su, 等. A Selective Moving Window Partial Least Squares Method and Its Application in Process Modeling[J]. 中国化学工程学报（英文版）, 2014,22(7):799-804. DOI： 10.1016/j.cjche.2014.05.012.

Ouguan Xu, Yongfeng Fu, Hongye Su, Lijuan Li . A Selective Moving Window Partial Least Squares Method and Its Application in Process Modeling[J]. , 2014, 22(7): 799-804. DOI： 10.1016/j.cjche.2014.05.012.

摘要

Abstract

A selective moving window partial least squares (SMW-PLS) soft sensor was proposed in this paper and applied to a hydro-isomerization process for on-line estimation of para-xylene (PX) content. Aiming at the high frequency of model updating in previous recursive PLSmethods

a selective updating strategywas developed. Themodel adaptation is activated once the prediction error is larger than a preset threshold

or themodel is kept unchanged. As a result

the frequency of model updating is reduced greatly

while the change of prediction accuracy is minor. The performance of the proposedmodel is better as compared with that of other PLS-based model. The compromise between prediction accuracy and real-time performance can be obtained by regulating the threshold. The guidelines to determine the model parameters are illustrated. In summary

the proposed SMW-PLS method can deal with the slow time-varying processes effectively.

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