Construction of COD simulation model for activated sludge process by fuzzy neural network

Abstract

Fuzzy neural network (FNN) was applied to construct a simulation model for estimating the effluent chemical oxygen demand (COD) value of an activated sludge process in a 'U' plant, in which most of process variables were measured once an hour. The constructed FNN model could simulate periodic changes in COD with high accuracy. Comparing the simulation result obtained using the FNN model with that obtained using the multiple regression analysis (MRA) model, it was found that the FNN model had 3.7 times higher accuracy than the MRA model. The FNN models corresponding to each of the four seasons were also constructed. Analyzing the fuzzy rules acquired from the FNN models after learning, the operational characteristic of this plant could be elucidated. Construction of the simulation model for another plant 'A', in which process variables were measured once a day, was also carried out. This FNN model also had a relatively high accuracy.