Adaptive multiscale principal components analysis for online monitoring of wastewater treatment

Lennox, J. and Rosen, C. (2002) Adaptive multiscale principal components analysis for online monitoring of wastewater treatment. Water Science And Technology, 45 4-5: 227-235.


Author Lennox, J.
Rosen, C.
Title Adaptive multiscale principal components analysis for online monitoring of wastewater treatment
Journal name Water Science And Technology   Check publisher's open access policy
ISSN 0273-1223
Publication date 2002
Sub-type Article (original research)
Volume 45
Issue 4-5
Start page 227
End page 235
Total pages 9
Place of publication London
Publisher IWA Publishing
Collection year 2002
Language eng
Subject C1
Abstract Fault detection and isolation (FDI) are important steps in the monitoring and supervision of industrial processes. Biological wastewater treatment (WWT) plants are difficult to model, and hence to monitor, because of the complexity of the biological reactions and because plant influent and disturbances are highly variable and/or unmeasured. Multivariate statistical models have been developed for a wide variety of situations over the past few decades, proving successful in many applications. In this paper we develop a new monitoring algorithm based on Principal Components Analysis (PCA). It can be seen equivalently as making Multiscale PCA (MSPCA) adaptive, or as a multiscale decomposition of adaptive PCA. Adaptive Multiscale PCA (AdMSPCA) exploits the changing multivariate relationships between variables at different time-scales. Adaptation of scale PCA models over time permits them to follow the evolution of the process, inputs or disturbances. Performance of AdMSPCA and adaptive PCA on a real WWT data set is compared and contrasted. The most significant difference observed was the ability of AdMSPCA to adapt to a much wider range of changes. This was mainly due to the flexibility afforded by allowing each scale model to adapt whenever it did not signal an abnormal event at that scale. Relative detection speeds were examined only summarily, but seemed to depend on the characteristics of the faults/disturbances. The results of the algorithms were similar for sudden changes, but AdMSPCA appeared more sensitive to slower changes.
Keyword Adaptive Pca
Confidence Limits
Fault Detection And Isolation
Multiscale Pca
Multivariate Statistival Process Monitoring
Engineering, Environmental
Environmental Sciences
Water Resources
Multivariate Statistical Process Monitoring
Diagnosis
Pca
Q-Index Code C1

Document type: Journal Article
Sub-type: Article (original research)
Collections: Excellence in Research Australia (ERA) - Collection
Advanced Water Management Centre Publications
 
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