KaydedilenlerDerginizi ekleyin

Dergiler / Sigma Mühendislik ve Fen Bilimleri Dergisi / 2020 / Cilt: 38 - Sayı: 4

ARTIFICIAL NEURAL NETWORK SIMULATION OF ADVANCED BIOLOGICAL WASTEWATER TREATMENT PLANT PERFORMANCE

Selami DEMİR
Sayfa
1713–1728
Yayın Tarihi
2021-10-05
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Besides, wastewater temperature was found to be the major factor that determines the performance of a wastewater treatment system while RAS ratio, HRT, and influent wastewater characteristics are also effective on the performance.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Artificial neural network (ANN) simulation of chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) removal efficiencies of an advanced biological wastewater treatment process is presented in this study.
📊
Temel Bulgular & Veri:Mean square errors were 5.54*10-7, 2.06*10-4, and 2.26*10-3, respectively, for COD, TN, and TP removal efficiencies.
💡
Sonuç & Katkı:Besides, wastewater temperature was found to be the major factor that determines the performance of a wastewater treatment system while RAS ratio, HRT, and influent wastewater characteristics are also effective on the performance.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Wastewater treatment · biological nutrient removal · treatment performance

Abstract

Artificial neural network (ANN) simulation of chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) removal efficiencies of an advanced biological wastewater treatment process is presented in this study. Seven input parameters (predictors) were used: influent COD, TN, and TP concentrations, internal recycle (IR) and return activated sludge (RAS) ratios, wastewater temperature, and total hydraulic retention time (HRT) of process reactors. Results showed that open-source ANN tools can easily be employed for quick and reliable simulation results. ANN with the logistic, the sinc, and the Elliot functions can be confidently employed for predicting COD, TN, and TP removal efficiencies. Mean square errors were 5.54*10-7, 2.06*10-4, and 2.26*10-3, respectively, for COD, TN, and TP removal efficiencies. Besides, wastewater temperature was found to be the major factor that determines the performance of a wastewater treatment system while RAS ratio, HRT, and influent wastewater characteristics are also effective on the performance.

🔖

Bu Makaleye Atıf Yap

DEMİR, S. (2021). ARTIFICIAL NEURAL NETWORK SIMULATION OF ADVANCED BIOLOGICAL WASTEWATER TREATMENT PLANT PERFORMANCE. Sigma Mühendislik ve Fen Bilimleri Dergisi, Cilt: 38 - Sayı: 4, 1713–1728. https://ajindex.com/articles/417978

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (13)

  1. Demir S., Manav Demir N., “Implementation of activated sludge model no. 3 (ASM3) as an educational tool: bioXL3”, Computer Applications in Engineering Education, DOI: 10.1002/cae.22292, 2020
  2. Henze MGrady C.P.L., Gujer W., Marais G.v.R., Matsuo T., “Activated sludge model no.1” IAWPRC Scientific and Technical Report No. 1, London: IAWPRC, 1987
  3. Henze M, Gujer W., Mino T., Matsuo T., Wentzel M.C., Marais G.v.R., “Activated sludge model no.2”, IAWPRC Scientific and Technical Report No. 3, London: IAWPRC, 1995
  4. Henze M, Gujer W., Mino T., Matsuo T., Wentzel M.C., Marais G.v.R., van Loosdrecht M.C.M., “Activated sludge model no. 2d”, Water Science and Technology, 39(1), 165-182, 1999
  5. Gujer Q., Henze M., Mino T., van Loosdrecht M.C.M., “Activated sludge model no.3”, Water Sci. Technol. 39(1), 183-193, 1999
  6. Curteanu S., Piuleac C.G., Godini K., Azaryan G., “Modeling of electrolysis process in wastewater treatment using different types of neural networks”, Chemical Engineering Journal, 172(1), 267-276, 2011
  7. Şamlı R., Sivri N., Sevgen S., Kiremitci V.Z., “Applying artificial neural network for the estimation of chlorophyll-a concentrations along with the Istanbul coast”, Polish Journal of Environmental Studies, 23(4), 1281-1287, 2014
  8. Bayram A., Kankal M., “Artificial neural network modeling of dissolved oxygen concentrations in a Turkish watershed, Polish Journal of Environmental Studies, 24(4), 1507-1515, 2015
  9. Sakiewicz P., Piotrowski K., Ober J., Karwot J., “Innovative artificial neural network approach for integrated biogas-wastewater treatment system modelling: Effect of plant operating parameters on process intensification”, Renewable and Sustainable Energy Reviews, 124, 109784, 2020
  10. Mohammad A.Th., Al-Obaidi M.A., Hameed E.M., Basher B.N., Mujtaba I.M., “Modelling the chlorophenol removal from wastewater via reverse osmosis process using a multilayer artificial neural network with genetic algorithm”, Journal of Water Process Engineering 33, 100993, 2020
  11. Mojiri A., Ohashi A., Ozaki N., Aoi Y., Kindaichi T., “Integrated anammox-biochar in synthetic wastewater treatment: Performance and optimization by artificial neural network”, 243, 118638, 2020
  12. Ribeiro, T.d.S., Grossi C.D., Merma A.G., dos Santos B.F., Torem M.L., “Removal of boron from mining wastewaters by electrocoagulation method: Modelling and experimental data using artificial neural networks”, Minerals Engineering, 131, 8-13, 2019
  13. Han H.