Amelioration of carbon removal prediction for an activated sludge process using an artificial neural network (ANN)
Küçük Resim Yok
Tarih
2008
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
WILEY
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
A dynamic simulation model of the Ankara central wastewater treatment plant (ACWTP) was evaluated for the prediction of effluent COD concentrations. Firstly, a mechanistic model of the municipal wastewater treatment process was developed based on Activated Sludge Model No. 1 (ASM1) by using a GPS-X computer program. Then, the mechanistic model was combined with a feed-forward back-propagation neural network in parallel configuration. The appropriate architecture of the neural network models was determined through several iterative steps of training and testing of the models. Both models were run with the data obtained from the plant operation and laboratory analysis to predict the dynamic behavior of the process. Using these two models, effluent COD concentrations were predicted and the results were compared for the purpose of evaluation of treatment performance. It was observed that the ASM1 ANN model approach gave better results and better described the operational conditions of the plant than ASM1.
Açıklama
Anahtar Kelimeler
activated sludge model, artificial neural network, chemical oxygen demand, modeling, wastewater treatment plant
Kaynak
CLEAN-SOIL AIR WATER
WoS Q Değeri
Q2
Scopus Q Değeri
Q3
Cilt
36
Sayı
9
