USE OF ARTIFICIAL NEURAL NETWORKS AS A TOOL TO PREDICT CARBON AND NITROGEN REMOVAL EFFICIENCIES IN BIOLOGICAL WASTEWATER TREATMENT PLANTS

Year 2017, Volume: 6 Issue: 2, 375 - 386, 31.07.2017

Neslihan Manav Demir

https://doi.org/10.28948/ngumuh.341175

Abstract

   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.

Keywords

ASM1, Artificial Neural Networks, Carbon Removal, Nitrogen Removal, activation function

BİYOLOJİK ATIKSU ARITMA TESİSLERİNDE KARBON VE AZOT GİDERİM VERİMLERİNİN TAHMİNİ AMACIYLA YAPAY SİNİR AĞLARININ KULLANIMI

Abstract

   Biyolojik
azot giderimi gerçekleştirilen atıksu arıtma proseslerinin (AO prosesi)
modellenmesi amacıyla Aktif Çamur Modeli No. 1 (ASM1) kullanılagelmişse de bu
modelde ihtiyaç duyulan girdi parametrelerinin tahmin edilmesi çok zaman
almaktadır. Bu çalışma kapsamında, ASM1 kadar detaylı girdi verisi
gerektirmeyen geri beslemeli yapay sinir ağlarının (BPANN) AO proseslerindeki
kimyasal oksijen ihtiyacı (KOİ), toplam Kjeldahl azotu (TKN) ve toplam azot
(TN) giderim verimlerinin tahminindeki performansı test edilmiştir. Bu amaçla
BPANN’de dört farklı aktivasyon fonksiyonu kullanılmıştır. Elde edilen
sonuçlar, AO proseslerindeki KOİ, TKN ve TN giderim verimlerinin BPANN ile
yüksek doğrulukta tahmin edilebildiğini göstermiş; en iyi öğrenme ve tahmin
yeteneği ise Sinc fonksiyonu ile elde edilmiştir. Sinc-BPANN ile elde edilen
ortalama kare hatalar KOİ giderim verimi için 2,50×10^-4,
TKN giderim verimi için 4,15×10^-4,
TN giderim verimi için ise 2,65×10^-4
olarak hesaplanmıştır. Buna göre Sinc-BPANN AO proseslerindeki KOİ, TKN ve TN
giderim verimlerinin doğrusal olmayan doğasını ASM1’e nazaran çok daha az girdi
parametresiyle açıklayabilmektedir.

Keywords

ASM1, yapay sinir ağları, karbon giderimi, azot giderimi, aktivasyon fonksiyonu

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