Supervisory Control of Wastewater Treatment Plants by Combining Principal Component Analysis and Fuzzy C-Means Clustering
(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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4810870
- author
- Rosén, Christian LU and Yuan, Z.
- organization
- publishing date
- 2001
- 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
- 2016-04-04 09:40:59
- date last changed
- 2022-01-29 19:04:58
@article{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 = {{IWA Publishing}},
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}},
}