Interval Prediction of Effluent TP for Wastewater Treatment Plants

Abstract

In view of the difficulty of real-time measurement of the effluent total phosphorus (TP) for a wastewater treatment plant (WWTP), in this paper, a new TP soft sensor which is different from the traditional single value method is presented. It realizes the guaranteed estimation of the TP concentration by predicting the upper and lower bounds. Partial least squares is used to obtain the secondary variables of the effluent TP. Then, an input-output model with secondary variables as the inputs and the effluent TP as the output is built by the radial basis function neural network (RBFNN). Considering the bounded modeling error, the linear-in- parameter set membership identification algorithm is used to obtain a description of the uncertain set of the output weights of the RBFNN. During the operation of the WWTP, the established soft sensor can predict the upper and lower bounds of the effluent TP concentration. Besides, a bundle of soft sensors is constructed and the intersection of the results given by the soft sensors is used to reduce the conservativeness caused by using a single sensor. The experimental results show the effectiveness of the proposed method.