SavedAdd your journal

Journals / Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi / 2017 / Cilt: 6 - Sayı: 2

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

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

NESLİHAN MANAV DEMİR
Pages
375–386
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
📊
Key Findings & Data: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.
💡
Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:ASM1 · yapay sinir ağları · karbon giderimi

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.

Özet

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.

🔖

Cite This Article

DEMİR, N. M. (2017). USE OF ARTIFICIAL NEURAL NETWORKS AS A TOOL TO PREDICT CARBON AND NITROGEN REMOVAL EFFICIENCIES IN BIOLOGICAL WASTEWATER TREATMENT PLANTS. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 6 - Sayı: 2, 375–386. https://ajindex.com/articles/290359

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (15)

  1. https://esa.un.org/unpd/wpp/Download/Standard/Population/# (erişim tarihi 15.12.2016)
  2. MANAV DEMİR, N., “İleri Biyolojik Arıtma Proseslerinde Nütrient Giderimi ve Mikroorganizma Türlerinin İncelenmesi”, Doktora Tezi, Yıldız Teknik Üniversitesi Fen Bilimleri Enstitüsü, İstanbul, Türkiye, 2012
  3. HENZE, M., GRADY, C.P.L., GUJER, W., MARAIS, G.V.R., MATSUO, T., Activated Sludge Model No
  4. In M. HENZE, W. GUJER, T. MINO, M.V. LOOSDRECHT (Eds.), Activated Sludge Models ASM1, ASM2, ASM2d and ASM3 (pp. 1-37), IWA Publishing, London, UK, 2007
  5. HENZE, M., GUJER, W., MINO, T., MATSUO, T., WENTZEL, M.C., MARAIS, G.V.R., Activated Sludge Model No
  6. In M. HENZE, W. GUJER, T. MINO, M.V. LOOSDRECHT (Eds.), Activated Sludge Models ASM1, ASM2, ASM2d and ASM3 (pp. 39-73), IWA Publishing, London, UK, 2007
  7. HENZE, M., GUJER, W., MINO, T., MATSUO, T., WENTZEL, M.C., MARAIS, G.V.R., “Activated Sludge Model No. 2d”, Water Science and Technology, 39, 165-182, 1999
  8. GUJER, W., HENZE, M., MINO, T., VAN LOOSDRECHT, M.C.M., “Activated Sludge Model No. 3”, Water Science and Technology, 39, 183-193, 1999
  9. HUG, T., BENEDETTI, L., HALL, E.R., JOHNSON, B.R., MORGENROTH, E., NOPENS, I., RIEGER, L., SHAW, A., VANROLLEGHEM, P.A., “Wastewater Treatment Models in Teaching and Training: The Mismatch between Education and Requirements for Jobs”, Water Science and Technology, 60, 1721-1729, 2009
  10. COPP, J.B., MURPHY, K.L., “Estimation of the Active Nitrifying Biomass in Activated Sludge”, Water Research, 29, 1855-1862, 1995
  11. SMETS, I.Y., HAEGEBART, J.V., CARETTE, R., VAN IMPE, J.F., “Linearization of the Activated Sludge Model ASM1 for Fast and Reliable Predictions”, Water Research, 37, 1831-1851, 2003
  12. MULLER, A., WENTZEL, M.C., LOEWENTHAL, R.E., EKAMA, G.A., “Heterotroph Anoxic Yield in Anoxic Aerobic Activated Sludge Systems Treating Municipal Wastewater”, Water Research, 37, 2435-2441, 2003
  13. SONG, Y., XIE, Y., YUDIANTO, D., “Extended Activated Sludge Model No. 1 (ASM1) for Simulating Biodegradation Process Using Bacterial Technology”, Water Science and Engineering, 5, 278-290, 2012
  14. JANUS, T., ULANICKI, B., “ASM1-Based Activated Sludge Model with Polymer Kinetics for Integrated Simulation of Membrane Bioreactors for Wastewater Treatment”, Procedia Engineering, 119, 1318-1327, 2015
  15. CRUZ, J.A.S., MUSATTI, S.F., SCENNA, N.J., GERNAEY, K.V., MUSATTI, C.M., “Reaction Invariant-Based Reduction of the Activated Sludge Model ASM1 for Batch App