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Adaptive Multivariate Statistical Process Control for Monitoring Time-Varying Processes

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Centre for Process Analytics and Control Technology, School of Chemical Engineering and Advanced Materials, University of Newcastle upon Tyne, Newcastle NE1 7RU, U.K., and Department of Chemical Engineering, Pohang University of Science and Technology, San 31 Hyoja Dong, Pohang 790-784, Korea
Cite this: Ind. Eng. Chem. Res. 2006, 45, 9, 3108–3118
Publication Date (Web):March 24, 2006
https://doi.org/10.1021/ie050391w
Copyright © 2006 American Chemical Society

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    Abstract

    An adaptive multivariate statistical process monitoring (MSPC) approach is described for the monitoring of a process with incurs operating condition changes. Samplewise and blockwise recursive formulas for updating a weighted mean and covariance matrix are derived. By utilizing these updated mean and covariance structures and the current model, a new model is derived recursively. On the basis of the updated principal component analysis (PCA) representation, two monitoring metrics, Hotelling's T2 and the Q-statistic, are calculated and their control limits are updated. For more efficient model updating, forgetting factors, which change with time, for the updating of the mean and covariance are considered. Furthermore, the updating scheme proposed is robust in that it not only reduces the false alarm rate in the monitoring charts but also makes the model insensitive to outliers. The adaptive MSPC approach developed is applied to a multivariate static system and a continuous stirred tank reactor process, and the results are compared to static MSPC. The revised approach is shown to be effective for the monitoring of processes where changes are either fast or slow.

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     To whom all correspondence should be addressed. Tel.:  +44 191 222 6231. Fax:  +44 191 222 5748. E-mail:  [email protected].

     Pohang University of Science and Technology.

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