Integrated soft sensor of COD for WWTP based on ASP model and RBF neural network

Abstract

For wastewater treatment process (WWTP), mechanism model for activated sludge process (ASP) is unsuitable for estimating the effluent COD (Chemical Oxygen Demand) as the parameters of ASM (Activated Sludge Model) series models are varying with operating conditions. This paper presents an integrated model to predict the effluent COD. The model consists of two sub-models which are simplified mechanism model of ASP and RBFNN (RBF Neural Network) with variable structure (VSRBFNN). ASP model can express the dynamic biochemical reactions occurred in WWTP, and VSRBFNN is used to reduce the prediction error of the ASP model as an error compensation model. To reduce the complexity of the mechanism model of ASP, the parameters of mechanism model are fixed. The layout and the parameters of VSRBFNN can be adjusted according to the training data, and the stable learning algorithm can restrict the modeling error of VSRBFNN within a bounded domain. The output value of the integrated model is weighted sum of those of two sub-models, where the weights denote the contributions of the two “sub-models” to the prediction error of integrated model and are rectified according to the relative prediction error online. The structure of the integrated soft sensor is concise and real-time capability is improved. Simulations show that the presented soft sensor has satisfactory prediction accuracy under various operating characteristics.