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.
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Research Article|
April 01 2001
Supervisory control of wastewater treatment plants by combining principal component analysis and fuzzy c-means clustering
C. Rosen;
C. Rosen
1Dept of Industrial Electrical Engineering and Automation, Lund University, Box 118, 221 00 Lund, Sweden
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Z. Yuan
Z. Yuan
2Advanced Wastewater Management Centre, University of Queensland, St Lucia, QLD 4072, Australia
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Water Sci Technol (2001) 43 (7): 147–156.
Citation
C. Rosen, Z. Yuan; Supervisory control of wastewater treatment plants by combining principal component analysis and fuzzy c-means clustering. Water Sci Technol 1 April 2001; 43 (7): 147–156. doi: https://doi.org/10.2166/wst.2001.0411
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