Modelling of COD removal in a biological wastewater treatment plant using adaptive neuro-fuzzy inference system and artificial neural network.
This work evaluated artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) modelling methods to estimate organic carbon removal using the correlation among the past information of influent and effluent parameters in a full-scale aerobic biological wastewater treatment plant. Model development focused on providing an adaptive, useful, practical and alternative methodology for modelling of organic carbon removal. For both models, measured and predicted effluent COD concentrations were strongly correlated with determination coefficients over 0.96. The errors associated with the prediction of effluent COD by the ANFIS modelling appeared to be within the error range of analytical measurements. The results overall indicated that the ANFIS modelling approach may be suitable to describe the relationship between wastewater quality parameters and may have application potential for performance prediction and control of aerobic biological processes in wastewater treatment plants.
Civelekoglu
G
G
Department of Environmental Engineering, Suleyman Demirel University, Isparta TR-32260, Turkey. civelek@sdu.edu.tr
Yigit
N O
NO
Diamadopoulos
E
E
Kitis
M
M
D003657:Decision Making; D017143:Fuzzy Logic; D016014:Linear Models; D017744:Medical Waste Disposal / Q000379:methods*; D008956:Models, Chemical; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D009930:Organic Chemicals / Q000737:chemistry / Q000302:isolation & purification; D010100:Oxygen / Q000737:chemistry* / Q000302:isolation & purification*; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D014865:Waste Disposal, Fluid / Q000379:methods*
D017744:Medical Waste Disposal; D009930:Organic Chemicals; D010100:Oxygen