Partial least squares modeling of an activated sludge plant: A case study

Many variables are normally measured in an activated sludge waste water treatment plant. Some of them are strongly cross-correlated. Partial least squares (PLS) and principal component analysis (PCA) have been widely used with these kind of processes because they both can be used with redundant data sets. In PLS, variable interactions can be visualized by loading weights and object groupings by scores. The aim of this paper was to utilize PLS and auto-correlation function in modeling the multivariate process. Loadings, loading weights, scores, MLR-type regression coefficients and auto-correlation functions were used to study the model. PLS results were visualized and it was shown how these results can be used to get a more profound look into the process. Sometimes it is rather difficult to find out corresponding phenomena behind latent variables, but almost in every case one can easily isolate the disturbance and find out, i.e., variables which are deviating strongly from the normal operating conditions.