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

Christian Rosén, Z. Yuan

Research output: Contribution to conferencePaper, not in proceedingpeer-review

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.
Original languageEnglish
Publication statusPublished - 2000
Event5th International Symposium on System Analysis and Computing in Water Quality Management - Gent, Belgium
Duration: 2000 Sep 182000 Sep 20

Conference

Conference5th International Symposium on System Analysis and Computing in Water Quality Management
Country/TerritoryBelgium
CityGent
Period2000/09/182000/09/20

Subject classification (UKÄ)

  • Other Electrical Engineering, Electronic Engineering, Information Engineering

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