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

Using a fuzzy neural network (FNN), we constructed a simulation model which estimates the effluent chemical oxygen demand (COD) value from daily routi ne measurements. Since the water quality of wastewater is changing day by d ay, an FNN model with a recursively renewing method of learning data (R-FNN ) is proposed. With this R-FNN, Learning data used to construct an FNN mode l are renewed with elapsed time so as to estimate the effluent COD value wi th good accuracy. The estimation results for 9 weeks data using R-FNN were compared with those using a conventional FNN. The average error using the R -FNN model was 0.36 mg/l, while that using the conventional FNN was 1.50 mg /l. Moreover, estimation of the effluent COD throughout one year was carrie d out, and the average error was only 0.40 mg/l. This result can show the u sefulness of the R-FNN for the simulation model of the activated sludge pro cess.