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Wastewater Pollutants Modeling Using Artificial Neural Networks

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this study, the execution and assessment of the ANN approach towards the declaration of the pollution was used. The ANN-based models for prediction of Chemical and Biological Oxygen demands, (COD & BOD5) and Total Suspended Solids (TSS) concentrations in the effluent were formed using a three-layered feed forward back propagation algorithm ANN towards assessing the performance of a wastewater treatment plant (WWTP). Two types of configurations were used, MISO and MIMO. The study showed the superiority of MIMO according to the results of R and MSE, which were used as evaluation functions for the predicted models. The results also showed that the model built to predict the values of BOD5 concentrations demonstrate the best performance among the rest of the models by achieving the value of correlation coefficient up to 0.99. Among the input combinations tested in the study, the models the inputs of which did not contain BOD5 had the best performance, which demonstrates that the BOD5 has the largest influence on the values of R in the COD prediction models as well as other predicted models than TSS and other parameters; consequently, the performance of the WWTP was greatly affected. This study demonstrated the value of using artificial networks to represent the complex and non-linear relationship between raw influent and treated effluent water quality measurements.
Słowa kluczowe
EN
Rocznik
Strony
35--45
Opis fizyczny
Bibliogr. 19 poz., rys., tab.
Twórcy
  • Civil Engineering Department, College of Engineering, University of Babylon, Iraq
Bibliografia
  • 1. Anupam, K., Dutta S., Bhattacharjee C. and Datta S. 2016. Artificial neural network modelling for removal of chromium (VI) from wastewater using physisorption onto powdered activated carbon. Desalination and Water Treatment, 57, 3632–3641, DO I:10.1080/19443994.2014.987172
  • 2. Boukef H., Benrejeb M., Borne P. 2018. Genetic Algorithm and Based Particle Swarm Optimization Comparison for Solving a Flow-Shop Multiobjective Scheduling Problem in Pharmaceutical Industries. International Journal on Engineering Applications, 6(6), 221–226, DOI: 10.15866/irea.v6i6.17000
  • 3. Del Pizzo A., Meo S., Brando G., Dannier A., Ciancetta F. 2014. An energy management strategy for fuel-cell hybrid electric vehicles via particle swarm optimization approach. International Review on Modelling and Simulations, 7(4), 543–553, DOI: 10.15866/iremos.v7i4.4227
  • 4. Delgrange V.N., Cabassud N., Cabassud M., Durand-Bourlier L., and Laine J.M. 1998. Neural networks for prediction of ultrafiltration transmembrane pressure: application to drinking water production. Journal of Membrane Science, 150(1), 111–123. DOI: 10.1016/S0376–7388(98)00217–8.
  • 5. Demuth H., Beale M., and Hagan M. 2007. Neural Network Toolbox 5: Users Guide. The Math Works Inc.
  • 6. Dogan E., Ates A., Yilmaz E.C., Eren B. 2008. Application of Artificial Neural Networks to Estimate Wastewater Treatment Plant Inlet Biochemical Oxygen Demand. Environmental Progress, 27(4), DOI: 10.1002/ep.10295.
  • 7. Güçlü, D., and Sükrü D. 2010. Artificial neural network modelling of a large-scale wastewater treatment plant operation. Bioprocess and Biosystems Engineering, 33(9), 1051–1058, DOI:10.1007/s00449–010–0430-x.
  • 8. Hanbay D., Turkoglu I., Demir Y. Prediction of wastewater treatment plant performance based on wavelet packet decomposition and neural networks. Expert Systems with Applications, 34(2), 1038–1043, 2008. DOI:10.1016/j.eswa.2006.10.030.
  • 9. Hassen E.B., and Asmare A.M. 2019. Predictive performance modeling of Habesha brewery wastewater treatment plant using artificial neural networks. Chemistry International, 5(1), 87–96, DOI: 10.31221/osf.io/k6bvj
  • 10. Hong S. H., Lee M. W., Lee D. S., Park J. M. Monitoring of sequencing batch reactor for nitrogen and phosphorus removal using neural networks. Biochemical Engineering Journal, Vol. 35, Issue 3, Pp. 365–370, 2007. DOI:10.1016/j.bej.2007.01.033.
  • 11. Hreshee S.S. 2013. Neural Network for Evolving and Optimizing Phased Array Antenna. Proceedings of International Conference on Control, Communication and Power Engineering, Bengaluru, India, 514–519,
  • 12. Israa M. and Hreshee S.S. 2018. Reduction of Side Lobe Level in a Time-Modulated Linear Array Using Invasive Weed Optimization and Particle Swarm Optimization. Journal of Engineering and Applied Sciences, 13, (14-SI), 11048–11054,
  • 13. Jaber A., Mohammed K., Shalash N. 2020. Optimization of Electrical Power Systems Using Hybrid PSO-GA Computational Algorithm: A Review. International Review of Electrical Engineering, 15(6), 502–511, DOI: 10.15866/iree.v15i6.18599
  • 14. Kundu P., Debsarkar A., and Mukherjee S. 2013. Artificial Neural Network Modeling for Biological Removal of Organic Carbon and Nitrogen from Slaughterhouse Wastewater in a Sequencing Batch Reactor, Advances in Artificial Neural Systems, 2013, 1–15, DOI: 10.1155/2013/268064
  • 15. Mallikarjuna H., and Mise S.R. 2019. ANN Model of Wastewater Treatment Process. International Journal of Advanced Research in Engineering and Technology, 10(3), 1–10.
  • 16. Rak A. 2013. Water Turbidity Modelling During Water Treatment Processes Using Artificial Neural Networks. International Journal of Water Sciences, 2(3), 1–10.
  • 17. Tumer A.E. and Serpil E. 2015.An Artificial Neural Network Model for Wastewater Treatment Plant of Konya. International Journal of Intelligent Systems and Applications in Engineering, 3, 131–135.
  • 18. Türkmenler H., Pala M. 2017. Performance Assessment of Advanced Biological Wastewater Treatment Plants Using Artificial Neural Networks. International Journal of Engineering Technologies, 3(3), 151–156. DOI: 10.19072/ijet.324091
  • 19. Younis R.A., Ibrahim D.K., Aboul-Zahab E. M., and El Gharably A. F. 2020.Techno-economic investigation using several metaheuristic algorithms for optimal sizing of stand-alone microgrid incorporating hybrid renewable energy sources and hybrid energy storage system. International Journal on Energy Conversion, 8(4), 141–152.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-de8585c9-dbd2-4e15-a10d-4b0bf307c884
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