Supervisory control of wastewater treatment plants by combining principal component analysis and fuzzy c-means clustering.
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 o-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.
Rosen
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Dept of Industrial Electrical Engineering and Automation, Lund University, Box 118, 221 00 Lund, Sweden.
Yuan
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D000465:Algorithms; D003198:Computer Simulation; D004784:Environmental Monitoring; D017143:Fuzzy Logic; D008962:Models, Theoretical; D014865:Waste Disposal, Fluid / Q000379:methods / Q000592:standards*