Supervisory control of wastewater treatment plants by combining principal component analysis and fuzzy c-means clustering
Rosen, C.; Yuan, Z.
Water Science and Technology a Journal of the International Association on Water Pollution Research 43(7): 147-156
2001
ISSN/ISBN: 0273-1223 PMID: 11385841 Document Number: 530575
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.