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- W2788477188 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." @default.
- W2788477188 created "2018-03-06" @default.
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- W2788477188 date "2000-01-01" @default.
- W2788477188 modified "2023-09-26" @default.
- W2788477188 title "Supervisory Control of Wastewater Treatment Plants by Combining Principal Component Analysis and Fuzzy C-Means Clustering" @default.
- W2788477188 hasPublicationYear "2000" @default.
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