Using fuzzy inference system to improve neural network for predicting hospital wastewater treatment plant effluent
In this study, three types of adaptive neuro fuzzy inference system (ANFIS) and artificial neural network (ANN) were employed to predict suspended solids (SS(eff)) and chemical oxygen demand (COD(eff)) in the effluent from a hospital wastewater treatment plant. The results indicated that ANFIS statistically outperforms ANN in terms of effluent prediction. The minimum mean absolute percentage errors of 11.99% and 12.75% for SS(eff) and COD(eff) could be achieved using ANFIS. The maximum values of correlation coefficient for SSeff and COD(eff) were 0.75 and 0.92, respectively. The minimum mean square errors of 0.17 and 19.58, and the minimum root mean square errors of 0.41 and 4.42 for SS(eff) and COD(eff) could also be achieved. ANFIS's architecture consists of both ANN and fuzzy logic including linguistic expression of membership functions and if-then rules, so it can overcome the limitations of traditional neural network and increase the prediction performance. (C) 2009 Elsevier Ltd. All rights reserved.
hospital wastewater treatment plant adaptive neuro fuzzy inference system artificial neural network activated sludge process continuous sequence batch reactor