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

选择性移动窗部分最小二乘算法及其在过程建模中的应用(英文)

作　　者：徐欧官 傅永峰 苏宏业 李丽娟 

机构地区：[1]Zhijiang College, Zhejiang University of Technology [2]State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University [3]College of Automation and Electrical Engineering, Nanjing University of Technology

出　　处：《Chinese Journal of Chemical Engineering》2014年第7期799-804,共6页中国化学工程学报（英文版）

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)

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 unchanged.As a result, the frequency of model updating is reduced greatly, while the change of prediction accuracy is minor.The 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.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 frequen- cy 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 unchanged. As a result, the frequency of model updating is reduced greatly, while the change of prediction accuracy is minor. The 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.

关 键 词：SMW-PLS Hydro-isomerizafion process Selective updating strategy Soft sensor 

分 类 号：N945.12[自然科学总论—系统科学]