Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi

Yazarlar: Neslihan MANAV DEMİR

Konular:Çevre Mühendisliği

DOI:10.28948/ngumuh.341175

Anahtar Kelimeler:ASM1,Artificial Neural Networks,Carbon Removal,NitrogRemoval,Activation function

Özet:    Although Activated Sludge Model No. 1 (ASM1) was used for modelling biological nitrogen removal processes, estimation of input parameters required to run this model necessitates complicated laboratory analyses. In this study, the performance of Backpropagation Artificial Neural Networks (BPANN), which requires considerably less numbers of input parameters, in predicting chemical oxygen demand (COD), total Kjeldahl nitrogen (TKN), and total nitrogen (TN) removal efficiencies was tested. For this purpose, four activation functions were employed in BPANN. Results suggested that COD, TKN, and TN removal efficiencies in AO processes can be accurately estimated using BPANN, with the highest learning and prediction capacity when Sinc function is employed. The mean square errors (MSEs) with Sinc-BPANN were calculated as 2.50×10-4 for COD removal efficiency, 4.15×10-4 for TKN removal efficiency, and 2.65×10-4 for TN removal efficiency. Therefore, the Sinc-BPANN is concluded to be an efficient tool for estimating nonlinear nature of COD, TKN, and TN removal efficiencies in AO processes using considerably less numbers of input parameters.

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