A combined approach of partial least squares and fuzzy c-means clustering for the monitoring of an activated-sludge waste-water treatment plant

In this paper, a combined approach of partial least squares (PLS) and fuzzy c-means (FCM) clustering for the monitoring of an activated-sludge waste-water treatment plant is presented. Their properties are also investigated. Both methods were applied together in process monitoring. PLS was used for extracting the most useful information from the control and process variables in order to predict a response variable, namely the diluted sludge volume index. Score values were used in FCM, which utilizes the principle of an object belonging to several classes at the same time instead of just one class. The memberships of each of the classes are defined by the membership values. Corresponding membership plots were used to help in the interpretation of the score plots. Short-term changes were considered to be disturbances and long-term changes due to drifting.