Development of a software sensor for phosphorus in municipal wastewater
Different multivariate techniques were applied to data from a wastewater treatment plant in order to develop a software sensor for phosphorus. The predictive power, measured as root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP), of partial least squares (PLS) models was found to be superior to that of multiple linear regression (MLR) and principal component regression (PCR) models. The result from using a static as well as a finite impulse response (FIR) model structure was also investigated. Good estimates of phosphorus were obtained for both kinds of model structure, but the FIR models were slightly better than the static ones. Out of the two different fractions of phosphorus that were modelled, i.e. total phosphorus and phosphate in solution, the best predictions were obtained for soluble phosphorus. Copyright (C) 2002 John Wiley Sons, Ltd.
software sensor pls wastewater treatment fir model phosphate