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Supervisory Control of Wastewater Treatment Plants by Combining Principal Component Analysis and Fuzzy C-Means Clustering

Rosén, Christian LU and Yuan, Z. (2001) In Water Science and Technology 43(7). p.147-156
Abstract
In this paper a methodology for integrated multivariate monitoring and control of biological wastewater treatment plants during extreme events is presented. To monitor the process, on-line dynamic principal component analysis (PCA) is performed on the process data to extract the principal components that represent the underlying mechanisms of the process. Fuzzy c-means (FCM) clustering is used to classify the operational state. Performing clustering on scores from PCA solves computational problems as well as increases robustness due to noise attenuation. The class-membership information from FCM is used to derive adequate control set points for the local control loops. The methodology is illustrated by a simulation study of a biological... (More)
In this paper a methodology for integrated multivariate monitoring and control of biological wastewater treatment plants during extreme events is presented. To monitor the process, on-line dynamic principal component analysis (PCA) is performed on the process data to extract the principal components that represent the underlying mechanisms of the process. Fuzzy c-means (FCM) clustering is used to classify the operational state. Performing clustering on scores from PCA solves computational problems as well as increases robustness due to noise attenuation. The class-membership information from FCM is used to derive adequate control set points for the local control loops. The methodology is illustrated by a simulation study of a biological wastewater treatment plant, on which disturbances of various types are imposed. The results show that the methodology can be used to determine and co-ordinate control actions in order to shift the control objective and improve the effluent quality. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Water Science and Technology
volume
43
issue
7
pages
147 - 156
publisher
IWA Publishing
external identifiers
  • Scopus:0035013208
ISSN
0273-1223
language
English
LU publication?
yes
id
90e9bb54-27bc-44dd-a751-9a72b7cd36b4 (old id 4810870)
date added to LUP
2014-11-24 15:55:28
date last changed
2016-12-04 04:41:15
@misc{90e9bb54-27bc-44dd-a751-9a72b7cd36b4,
  abstract     = {In this paper a methodology for integrated multivariate monitoring and control of biological wastewater treatment plants during extreme events is presented. To monitor the process, on-line dynamic principal component analysis (PCA) is performed on the process data to extract the principal components that represent the underlying mechanisms of the process. Fuzzy c-means (FCM) clustering is used to classify the operational state. Performing clustering on scores from PCA solves computational problems as well as increases robustness due to noise attenuation. The class-membership information from FCM is used to derive adequate control set points for the local control loops. The methodology is illustrated by a simulation study of a biological wastewater treatment plant, on which disturbances of various types are imposed. The results show that the methodology can be used to determine and co-ordinate control actions in order to shift the control objective and improve the effluent quality.},
  author       = {Rosén, Christian and Yuan, Z.},
  issn         = {0273-1223},
  language     = {eng},
  number       = {7},
  pages        = {147--156},
  publisher    = {ARRAY(0x81b2f88)},
  series       = {Water Science and Technology},
  title        = {Supervisory Control of Wastewater Treatment Plants by Combining Principal Component Analysis and Fuzzy C-Means Clustering},
  volume       = {43},
  year         = {2001},
}