SavedAdd your journal

Journals / International Journal of Intelligent Systems and Applications in Engineering / 2015 / Cilt: 3 - Sayı: 4

An Artificial Neural Network Model for Wastewater Treatment Plant of Konya

ABDULLAH ERDAL TÜMER
SERPİL EDEBALİ
Pages
131–135
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)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, modelling of Konya wastewater treatment plant was studied by using artificial neural network with different architectures in Matlab software.
📊
Key Findings & Data:Performance of the model was compared via the parameters of Mean Squared Error (MSE), and correlation coefficient (R).
💡
Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

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.

🔖

Cite This Article

TÜMER, A. E., EDEBALİ, S. (2015). An Artificial Neural Network Model for Wastewater Treatment Plant of Konya. International Journal of Intelligent Systems and Applications in Engineering, Cilt: 3 - Sayı: 4, 131–135. https://ajindex.com/articles/198212

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (12)

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. S. Geissler, T. Wintgens, T. Melin, K. Vossenkaul, and C. Kullmann, “Modelling approaches f