An Artificial Neural Network Model for Wastewater Treatment Plant of Konya

Yıl 2015, Cilt: 3 Sayı: 4, 131 - 135, 15.12.2015

Abdullah Erdal Tumer Serpil Edebali

https://doi.org/10.18201/ijisae.65358

Cited By: 32

Öz

In this study, modelling of Konya wastewater treatment plant was studied by using artificial neural network with different architectures in Matlab software. All data were obtained from wastewater treatment plant of Konya during daily records over four month. Treatment efficiency of the plant was determined by taking into account of input values of pH, temperature, COD, TSS and BOD with output values TSS. Performance of the model was compared via the parameters of Mean Squared Error (MSE), and correlation coefficient (R). The suitable architecture of the neural network model is determined after several trial and error steps. According to the modelling study, the ANN can predict the plant performance with correlation coefficient (R) between the observed and predicted output variable reached up to 0.96.

Anahtar Kelimeler

Artificial Neural Network, Modelling, Wastewater Treatment Plant, Performance

Kaynakça

• D. Hanbay, I. Turkoglu, and Y. Demir, “Prediction of wastewater treatment plant performance based on wavelet packet decompositionand neural networks,” Expert Systems with Applications, vol. 34(2), pp.1038-1043, 2008.
• R.S. Govindaraju, “Artificial neural network in hydrology. II:hydrologic application, ASCE task committee application of artificial neural networks in hydrology,” Journal of Hydrologic Engineering vol 5, pp. 124–137, 2000.
• H.R. Maier, and G.C. Dandy, “Neural networks for prediction and forecasting of water resources variables: a review of modeling issues and applications,” Water Resources Research vol. 15, pp. 101–124, 2000.
• T.R. Neelakantan, T.R. Brion, and S. Lingireddy, “Neural network modeling of cryptoposporidium and giardia concentrations in Delware River, USA,” Water Science and Technology vol 43, pp. 125–132, 2001.
• C.S. Akratos, J.N. Papaspyros, and V.A. Tsihrintzis, “An artificial neural network model and design equations for BOD and COD removal prediction in horizontal subsurface flow constructed wetlands,” Chemical Engineering Journal, vol. 143(1), pp. 96-110, 2008.
• F.S. Mjalli, S. Al-Asheh, and H.E. Alfadala, “Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance. Journal of Environmental Management,” vol. 83(3), pp. 329-338, 2007.
• M. M. Hamed, M. G. Khalafallah, and E. A. Hassanien, “Prediction of wastewater treatment plant performance using artificial neural Networks,” Environmental Modelling and Software, vol. 19, pp. 919–928, 2004.
• L. Belanche, J. J. Valde´s, J. Comas, I. R. Roda, and M. Poch, “Prediction of the bulking phenomenon in wastewater treatment plants,” Artificial Intelligence in Engineering, vol. 14(4), pp. 307–317, 2000.
• D. J. Choi, and H. Park, “A hybrid artificial neural network as a software sensor for optimal control of a wastewater treatment process,” Water research, vol. 35(16), pp. 3959-3967, 2001.
• K. P. Oliveira-Esquerre, M. Mori, and R. E. Bruns, “Simulation of an industrial wastewater treatment plant using artificial neural networks and principal components analysis,” Brazilian Journal of Chemical Engineering, vol. 19(4), pp. 365-370, 2001.
• A. G. El-Din, and D. W. Smith, “A neural network model to predict the wastewater inflow incorporating rainfall events,” Water research, vol. 36(5), pp. 1115-1126, 2002.
• S. Geissler, T. Wintgens, T. Melin, K. Vossenkaul, and C. Kullmann, “Modelling approaches for filtration processes with novel submerged capillary modules in membrane bioreactors for wastewater treatment,” Desalination, vol. 178(1), pp. 125-134, 2005.
• A.G. El-Din, D.W. Smith, and M.G. El-Din, “Application of artificial neural networks in wastewater treatment,” Journal of Environmental Engineering and Science, vol. 3(S1), pp. S81-S95, 2004.
• S. Yordanova, R. Petrova, N. Noykova, and P. Tzvetkov, “Neuro-fuzzy modelling in anaerobic wastewater treatment for prediction and control,” International Journal of Computing, vol. 5(1), pp. 51-56, 2014.
• Y. M. Guo, Y. G. Liu, G. M. Zeng, X. J. Hu, W. H. Xu, Y.Q. Liu, and H. J. Huang, “An integrated treatment of domestic wastewater using sequencing batch biofilm reactor combined with vertical flow constructed wetland and its artificial neural network simulation study,” Ecological Engineering, vol. 64, pp. 18-26, 2014.
• M. Bagheri, S. A. Mirbagheri, M. Ehteshami, and Z. Bagheri, “Modeling of a sequencing batch reactor treating municipal wastewater using multi-layer perceptron and radial basis function artificial neural networks,” Process Safety and Environmental Protection, vol. 93, pp. 111-123, 2015.
• P. Kundu, A. Debsarkar, S. Mukherjee, and S. Kumar, “Artificial neural network modelling in biological removal of organic carbon and nitrogen for the treatment of slaughterhouse wastewater in a batch reactor,” Environmental technology, vol. 35(10), pp. 1296-1306, 2014.
• G. B. Gholikandi, S. Jamshidi, and H. Hazrati, H. “Optimization of anaerobic baffled reactor (ABR) using artificial neural network in municipal wastewater treatment,” Environmental Engineering and Management Journal, vol. 13(1), pp. 95-104, 2014
• V.N. Delgrange, N. Cabassud, M. Cabassud, L. Durand-Bourlier, and J.M. Laine, “Neural networks for prediction of ultrafiltration transmembrane pressure: application to drinking water production,” Journal of Membrane Science vol. 150, pp. 111–123, 1998.
• H. Demuth, M. Beale, and M. Hagan, “Neural Network Toolbox 5: Users Guide” Natick, MA, The MathWorks Inc., 2007.
• M.S. Nasr, M. A, Moustafa, H. A. Seif, and G. El Kobrosy, “Application of Artificial Neural Network (ANN) for the prediction of EL-AGAMY wastewater treatment plant performance- EGYPT,” Alexandria Engineering Journal, vol. 51(1), pp. 37-43, 2012.
• A.R. Khataee, G. Dehghan, A. Ebadi, M. Zarei, and M. Pourhassan, “Biological treatment of a dye solution by Macroalgae Chara sp.: effect of operational parameters, intermediates identification and artificial neural network modeling,” Bioresource Technol, vol. 101(7), pp. 2252-2258, 2010