Advanced

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

Rosén, Christian LU and Yuan, Z. (2000) 5th International Symposium on System Analysis and Computing in Water Quality Management
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)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to conference
publication status
published
subject
conference name
5th International Symposium on System Analysis and Computing in Water Quality Management
language
English
LU publication?
yes
id
446d51c4-b8f2-4c10-ae3c-80c01a9c52e2 (old id 4810884)
date added to LUP
2014-11-24 08:23:57
date last changed
2016-04-16 12:08:59
@misc{446d51c4-b8f2-4c10-ae3c-80c01a9c52e2,
  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.},
  language     = {eng},
  title        = {Supervisory Control of Wastewater Treatment Plants by Combining Principal Component Anlaysis and Fuzzy C-Means Clustering},
  year         = {2000},
}