Artificial Neural Network Modelling of a Large-Scale Wastewater Treatment Plant Operation
Yükleniyor...
Dosyalar
Tarih
2010
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
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.
Açıklama
Anahtar Kelimeler
Activated sludge process, Modelling, Artificial neural network, Wastewater treatment plant
Kaynak
Bioprocess and Biosystems Engineering
WoS Q Değeri
Q1
Scopus Q Değeri
Q2
Cilt
33
Sayı
Künye
Güçlü, D., Dursun, Ş., (2010). Artificial Neural Network Modelling of a Large-Scale Wastewater Treatment Plant Operation. Bioprocess and Biosystems Engineering, (33), 1051-1058. Doi: 10.1007/s00449-010-0430-x
