An Identification Model of Sludge Bulking Based on Self-Organized Recurrent Fuzzy Neural Network

Abstract

Sludge bulking in the municipal wastewater treatment process will cause low sludge settling performance and deterioration of effluent quality. Accurate identification and prediction of sludge bulking is an effective solution. Based upon the measured data, a fuzzy neural network-based identification model with self-organizing recurrent structure is established in this article, which can realize the high-precision identification of sludge bulking. First, a self-organized method of FNN with recurrent structure is designed. The recurrent parameters can realize weight allocation of different time series. Second, the network structure adjustment index and dynamic structure adjustment threshold are defined. A dynamic threshold structure increment and subtraction method for the self-organized FNN is designed. The neuron rules and the number of neurons are modified according to the conditions of increasing and decreasing, which can determine the most appropriate neurons number and neuronal rules. Then, the improved stochastic gradient method and the improved recursive least squares method are employed to adjust network parameters to obtain accurate network output. Finally, the output accuracy and prediction accuracy of the investigated model are verified by simulation and comparison experiments.