title: Artificial neural network modelling of a large-scale wastewater treatment plant operation.
abstract: Artificial Neural Networks (ANNs), a method of artificial intelligence method, provide effective predictive models for complex processes. Three independent ANN models trained with back-propagation algorithm were developed to predict effluent chemical oxygen demand (COD), suspended solids (SS) and aeration tank mixed liquor suspended solids (MLSS) concentrations of the Ankara central wastewater treatment plant. The appropriate architecture of ANN models was determined through several steps of training and testing of the models. ANN models yielded satisfactory predictions. Results of the root mean square error, mean absolute error and mean absolute percentage error were 3.23, 2.41 mg/L and 5.03% for COD; 1.59, 1.21 mg/L and 17.10% for SS; 52.51, 44.91 mg/L and 3.77% for MLSS, respectively, indicating that the developed model could be efficiently used. The results overall also confirm that ANN modelling approach may have a great implementation potential for simulation, precise performance prediction and process control of wastewater treatment plants.
authors.affiliation: Department of Environmental Engineering, Engineering and Architectural Faculty, Selcuk University, Campus, Selcuklu, Konya, Turkey. bguclu@selcuk.edu.tr
authors.forename: Dünyamin
authors.initials: D
authors.lastname: Güçlü
authors.forename: Sükrü
authors.initials: S
authors.lastname: Dursun
mesh_terms: D000465:Algorithms; D002947:Cities; D003198:Computer Simulation; D003201:Computers; D004867:Equipment Design; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D013997:Time Factors; D014421:Turkey; D014865:Waste Disposal, Fluid / Q000379:methods*; D014874:Water Pollutants, Chemical / Q000032:analysis*; D018508:Water Purification / Q000379:methods*
chemical_list: D014874:Water Pollutants, Chemical