PARTIAL LEAST-SQUARES MODELING OF AN ACTIVATED-SLUDGE PLANT - A CASE-STUDY

Many variables are normally measured in an activated sludge waste wate r 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 visual ized by loading weights and object groupings by scores. The aim of thi s paper was to utilize PLS and auto-correlation function in modeling t he multivariate process. Loadings, loading weights, scores, MLR-type r egression coefficients and auto-correlation functions were used to stu dy the model, PLS results were visualized and it was shown how these r esults can be used to get a more profound look into the process. Somet imes 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 strongl y from the normal operating conditions. (C) 1997 Elsevier Science B.V.