Supervisory Control of Wastewater Treatment Plants by Combining Principal Component Anlaysis and Fuzzy C-Means Clustering
(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:
https://lup.lub.lu.se/record/4810884
- author
- Rosén, Christian LU and Yuan, Z.
- organization
- publishing date
- 2000
- type
- Contribution to conference
- publication status
- published
- subject
- conference name
- 5th International Symposium on System Analysis and Computing in Water Quality Management
- conference location
- Gent, Belgium
- conference dates
- 2000-09-18 - 2000-09-20
- language
- English
- LU publication?
- yes
- id
- 446d51c4-b8f2-4c10-ae3c-80c01a9c52e2 (old id 4810884)
- date added to LUP
- 2016-04-04 14:08:29
- date last changed
- 2018-11-21 21:18:30
@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}},
}