Browse by author
Lookup NU author(s): Dr Jie ZhangORCiD
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
Because many of the current multivariate statistical process monitoring (MSPM) techniques are based on the assumption that the process has one nominal operating region, the application of these MSPM approaches to an industrial process with multiple operating modes would always trigger continuous warnings even when the process itself is operating under another normal steady-state operating conditions. Adopting principal angles to measure the similarities of any two models, this paper proposes a multiple principal component analysis model based process monitoring methodology. Some popular multivariate statistical measurements such as squared prediction error and its control limit can be incorporated straightforwardly to facilitate process monitoring. The efficiency of the proposed technique is demonstrated through application to the monitoring of the Tennessee-Eastman challenge process and an industrial fluidized catalytic cracking unit. The proposed scheme can significantly reduce the amount of false alarms while tracking the process adjustment.
Author(s): Zhao SJ, Zhang J, Xu YM
Publication type: Article
Publication status: Published
Journal: Industrial & Engineering Chemistry Research
Year: 2004
Volume: 43
Issue: 22
Pages: 7025-7035
ISSN (print): 0888-5885
ISSN (electronic): 1520-5045
Publisher: American Chemical Society
URL: http://dx.doi.org/10.1021/ie0497893
DOI: 10.1021/ie0497893
Altmetrics provided by Altmetric
