Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2020, Cilt: 38 Sayı: 4, 1713 - 1728, 05.10.2021

Öz

Kaynakça

  • [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.G., Zhang L., Liu H.X., Qiao J.F., “Multiobjective design of fuzz neural network controller for wastewater treatment process”, Applied Soft Computing 67, 467-478, 2018.
  • [14] Qiao J.F., Hou Y., Zhang L., Han H.G., “Adaptive fuzzy neural network control of wastewater treatment process with multiobjective operation”, Neurocomputing 275, 383-393, 2018.
  • [15] Hamed M.M., Khalafallah M.G., Hassanien E.A., “Prediction of wastewater treatment plant performance using artificial neural networks”, Environ. Modell. Softw. 19(10), 919-928, 2004.
  • [16] Mjalli F.S., Al-Asheh A., Alfadala H.E., “Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance”, J. Environ. Manage. 83(3), 329-338, 2007.
  • [17] Pendashteh A.R., Fakhru’l-Razi A., Chaibakhsh N., Abdullah L.C., Madaeni S.S., Abidin Z.Z., “Modeling of membrane bioreactor treating hypersaline oily wastewater by artificial neural network”, J. Hazard. Mater. 192(2), 568-575, 2011.
  • [18] Yılmaz E.C., Doğan E., “Artificial neural networks application for modelling of wastewater treatment plant performance” Electronic Letters on Science & Engineering 4(2), 1-9, 2008.
  • [19] Manav Demir N., “Use of artificial neural networks as a tool to predict carbon and nitrogen removal efficiencies in biological wastewater treatment plants”, Ömer Halisdemir University Journal of Engineering Sciences 6(2), 375-386, 2017.
  • [20] Güçlü D., Dursun Ş., “Artificial neural network modelling of a large-scale wastewater treamtne plant operation”, Bioproc. Biosyst. Eng. 33(9), 1051-1058, 2010.
  • [21] Nasr M.S., Moustafa M.A.E., Seif H.A.E., El Kobrosy G., “Application of artificial neural network (ANN) for the prediction of Al-Agamy wastewater treatment plant performance-Egypt”, Alexandria Engineering Journal 51(1), 37-43, 2012.
  • [22] Tümer A.E., Edebali S., “An artificial neural network model for wastewater treatment plant of Konya”, International Journal of Intelligent Systems and Applications in Engineering 3(4), 131-135, 2015.
  • [23] Türkmenler H., Pala M., “Performance assessment of advanced biological wastewater treatment plants using artificial neural networks”, International Journal of Engineering Technologies 3(3), 151-156, 2017.
  • [24] Rieger L., Koch G., Kühni M., Gujer W., Siegrist H., “The eawag bio-P module for activated sludge model no.3”, Water Res. 35(16), 3887-3903, 2001.
  • [25] Rössle W.H., Pretorius W.A., “A review of characterization requirements for in-line prefermenters. Paper 1: Wastewater characterization”, Water SA, 27(3), 405-412, 2001.
  • [26] Demir S., Manav Demir N., Karadeniz A., Civelek Yörüklü, H., “Implementation of an MS Excel tool for backpropagation neural network algorithm in environmental engineering education”, Sigma Journal of Engineering and Natural Sciences, 36(1), 251-260, 2018.
  • [27] Demir S., Karadeniz A., Manav Demir N., “Artificial neural network simulation of cyclone pressure drop: Selection of the best activation function”, Polish Journal of Environmental Studies, 25(5), 1891-1899, 2016.
  • [28] Demir S., Karadeniz A., Manav Demir N., “Using steepness coefficient to improve artificial neural network performance for environmental modeling”, Polish Journal of Environmental Studies, 25(5), 1891-1899, 2016.
  • [29] Sibi P., Jones A.A., Siddarth P., “Analysis of different activation functions using back propagation neural networks”, Journal of Theoretical and Applied Information Technology 47(3), 1264-1268, 2013.

ARTIFICIAL NEURAL NETWORK SIMULATION OF ADVANCED BIOLOGICAL WASTEWATER TREATMENT PLANT PERFORMANCE

Yıl 2020, Cilt: 38 Sayı: 4, 1713 - 1728, 05.10.2021

Öz

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.

Kaynakça

  • [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.G., Zhang L., Liu H.X., Qiao J.F., “Multiobjective design of fuzz neural network controller for wastewater treatment process”, Applied Soft Computing 67, 467-478, 2018.
  • [14] Qiao J.F., Hou Y., Zhang L., Han H.G., “Adaptive fuzzy neural network control of wastewater treatment process with multiobjective operation”, Neurocomputing 275, 383-393, 2018.
  • [15] Hamed M.M., Khalafallah M.G., Hassanien E.A., “Prediction of wastewater treatment plant performance using artificial neural networks”, Environ. Modell. Softw. 19(10), 919-928, 2004.
  • [16] Mjalli F.S., Al-Asheh A., Alfadala H.E., “Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance”, J. Environ. Manage. 83(3), 329-338, 2007.
  • [17] Pendashteh A.R., Fakhru’l-Razi A., Chaibakhsh N., Abdullah L.C., Madaeni S.S., Abidin Z.Z., “Modeling of membrane bioreactor treating hypersaline oily wastewater by artificial neural network”, J. Hazard. Mater. 192(2), 568-575, 2011.
  • [18] Yılmaz E.C., Doğan E., “Artificial neural networks application for modelling of wastewater treatment plant performance” Electronic Letters on Science & Engineering 4(2), 1-9, 2008.
  • [19] Manav Demir N., “Use of artificial neural networks as a tool to predict carbon and nitrogen removal efficiencies in biological wastewater treatment plants”, Ömer Halisdemir University Journal of Engineering Sciences 6(2), 375-386, 2017.
  • [20] Güçlü D., Dursun Ş., “Artificial neural network modelling of a large-scale wastewater treamtne plant operation”, Bioproc. Biosyst. Eng. 33(9), 1051-1058, 2010.
  • [21] Nasr M.S., Moustafa M.A.E., Seif H.A.E., El Kobrosy G., “Application of artificial neural network (ANN) for the prediction of Al-Agamy wastewater treatment plant performance-Egypt”, Alexandria Engineering Journal 51(1), 37-43, 2012.
  • [22] Tümer A.E., Edebali S., “An artificial neural network model for wastewater treatment plant of Konya”, International Journal of Intelligent Systems and Applications in Engineering 3(4), 131-135, 2015.
  • [23] Türkmenler H., Pala M., “Performance assessment of advanced biological wastewater treatment plants using artificial neural networks”, International Journal of Engineering Technologies 3(3), 151-156, 2017.
  • [24] Rieger L., Koch G., Kühni M., Gujer W., Siegrist H., “The eawag bio-P module for activated sludge model no.3”, Water Res. 35(16), 3887-3903, 2001.
  • [25] Rössle W.H., Pretorius W.A., “A review of characterization requirements for in-line prefermenters. Paper 1: Wastewater characterization”, Water SA, 27(3), 405-412, 2001.
  • [26] Demir S., Manav Demir N., Karadeniz A., Civelek Yörüklü, H., “Implementation of an MS Excel tool for backpropagation neural network algorithm in environmental engineering education”, Sigma Journal of Engineering and Natural Sciences, 36(1), 251-260, 2018.
  • [27] Demir S., Karadeniz A., Manav Demir N., “Artificial neural network simulation of cyclone pressure drop: Selection of the best activation function”, Polish Journal of Environmental Studies, 25(5), 1891-1899, 2016.
  • [28] Demir S., Karadeniz A., Manav Demir N., “Using steepness coefficient to improve artificial neural network performance for environmental modeling”, Polish Journal of Environmental Studies, 25(5), 1891-1899, 2016.
  • [29] Sibi P., Jones A.A., Siddarth P., “Analysis of different activation functions using back propagation neural networks”, Journal of Theoretical and Applied Information Technology 47(3), 1264-1268, 2013.
Toplam 29 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Research Articles
Yazarlar

Selami Demir Bu kişi benim 0000-0002-8672-9817

Yayımlanma Tarihi 5 Ekim 2021
Gönderilme Tarihi 29 Mart 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 38 Sayı: 4

Kaynak Göster

Vancouver Demir S. ARTIFICIAL NEURAL NETWORK SIMULATION OF ADVANCED BIOLOGICAL WASTEWATER TREATMENT PLANT PERFORMANCE. SIGMA. 2021;38(4):1713-28.

IMPORTANT NOTE: JOURNAL SUBMISSION LINK https://eds.yildiz.edu.tr/sigma/