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 cont rol of biological wastewater treatment plants during extreme events is pres ented. 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 (FC M) clustering is used to classify the operational state. Performing cluster ing on scores from PCA solves computational problems as well as increases r obustness due to noise attenuation. The class-membership information from F CM is used to derive adequate control set points for the local control loop s. The methodology is illustrated by a simulation study of a biological was tewater treatment plant, on which disturbances of various types are imposed . The results show that the methodology can be used to determine and co-ord inate control actions in order to shift the control objective and improve t he effluent quality.