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

作     者：徐欧官 傅永峰 苏宏业 李丽娟 

作者机构：Zhijiang College Zhejiang University of Technology State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University College of Automation and Electrical Engineering Nanjing University of Technology 

出 版 物：《Chinese Journal of Chemical Engineering》 (中国化学工程学报（英文版）)

年 卷 期：2014年第22卷第7期

页      面：799-804页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 071102[理学-系统分析与集成] 

基　　金：Supported by the National Natural Science Foundation of China(61203133,61203072) the Open Project Program of the State Key Laboratory of Industrial Control Technology(ICT1214) 

主　　题：SMW-PLS Hydro-isomerizafion process Selective updating strategy Soft sensor 

摘      要：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 PLS methods, a selective updating strategy was developed. The model adaptation is activated once the prediction error is larger than a preset threshold, or the model is kept *** a result, the frequency of model updating is reduced greatly, while the change of prediction accuracy is *** performance of the proposed model 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.