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Adaptive multiscale principal components analysis for online monitoring of wastewater treatment

Lennox, J and Rosén, Christian LU (2002) In Water Science and Technology 45(4-5). p.227-235
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)... (More)
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. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
multivariate statistical process monitoring, multiscale PCA, fault detection and isolation, adaptive PCA, confidence limits
in
Water Science and Technology
volume
45
issue
4-5
pages
227 - 235
publisher
IWA Publishing
external identifiers
  • wos:000174872000029
  • pmid:11936638
  • scopus:0036209691
ISSN
0273-1223
language
English
LU publication?
yes
id
6da79c80-3792-4d46-88e2-b8feb16cb55e (old id 340478)
date added to LUP
2007-11-14 13:19:19
date last changed
2017-01-01 07:23:25
@article{6da79c80-3792-4d46-88e2-b8feb16cb55e,
  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.},
  author       = {Lennox, J and Rosén, Christian},
  issn         = {0273-1223},
  keyword      = {multivariate statistical process monitoring,multiscale PCA,fault detection and isolation,adaptive PCA,confidence limits},
  language     = {eng},
  number       = {4-5},
  pages        = {227--235},
  publisher    = {IWA Publishing},
  series       = {Water Science and Technology},
  title        = {Adaptive multiscale principal components analysis for online monitoring of wastewater treatment},
  volume       = {45},
  year         = {2002},
}