Modeling wastewater treatment plant (WWTP) performance using artificial neural networks: Case of Adana (Seyhan)

Yıl 2022, Cilt: 6 Sayı: 4, 579 - 584, 30.12.2022

Metin Dağtekin Bekir Yelmen

https://doi.org/10.31015/jaefs.2022.4.10

Öz

In this study, performance estimation of biological wastewater treatment plants (WWTP) was made by applying Artificial Neural Network (ANN) techniques. As material, 355-day data from Adana Metropolitan Municipality Seyhan wastewater treatment plant for 2021 were used. Of the data used, 240 were evaluated as training data and 115 as test data. In the establishment of the ANN model, the daily chemical oxygen demand (COD), daily water flow (Qw) and daily suspended solids (SS) parameters at the entrance of the WWTP were used as input parameters. The daily biological oxygen demand (BOD) parameter was determined as the output parameter. In the study, feed forward back propagation ANN model (FFBPANN) was used to estimate the daily BOD amounts at the entrance of the WWTP. In the statistical analysis, the correlation (R2) values of the input parameters with BOD were found to be 0.906 for COD, 0.294 for Qw and 0.605 for SS. The R2 value was determined as 0.891, the MAE value was 10.32% and the RMSE value was 722.21 in the network structures where the best results were obtained for the test and training data (in the 4-4-1 ANN model). As a result of the study, it was concluded that the ANN model was successful in estimating the BODs of the WWTPs in obtaining reliable and realistic results, and that effective analyzes with the simulation of their nonlinear behavior could be used as a good performance evaluation tool in terms of reducing operating costs.

Anahtar Kelimeler

Artificial neural network, Biological oxygen demand, Modeling, Waste water treatment plant

Kaynakça

• Aguilera, P.A., Frenich, J.A., Torres Castro, H., Vidal, J.L.M., Canton, M., (2001). Application of the Kohonen Neural Network in Coastal Water Management: Methodological Development for the Assessment and Prediction of Water Quality. Water Research 35 (17), 4053-4062.
• Bechtler, H., Browne, M.W., Bansal P.K., Kecman, V., (2001). New approach to dynamic modeling of vapour compression liquid chillers: artificial neural networks, Applied Thermal Engineering, 21, 941-953.
• Ergezer, H., Dikmen, M., Özdemir, E., (2003). Artificial Neural Networks and Recognition Systems. Pivolka, 2(6), 14-17. http://iibf.erciyes.edu.tr/kutuphane/petas/petas.php?skip=0&keyword=HAL%C4%B0T+ERGEZER++MEHMET+D%C4%B0KMEN++ERKAN+%C3%96ZDEM%C4%B0R&type=5
• Elmas, C., (2007). Artificial Intelligence applications, Seçkin Publications, 425 p. https://dergipark.org.tr/tr/pub/okufbed/issue/71216/990995
• Elmas, C., (2003). Artificial Neural Networks: Theory, Architecture, Education, Practice. Seçkin Yayıncılık, Ankara, 2003. https://www.nadirkitap.com/yapay-sinir-aglari-kuram-mimari-egitim-uygulama-prof-dr-cetin-elmas-kitap8306289.html
• Hamed, M., Khalafallah, M.G., Hassanein, E.A., (2004). Prediction of wastewater treatment plant performance using artificial neural network. Environmental Modeling and Software 19;919-928. https://www.scirp.org/%28S%28351jmbntvnsjt1aadkposzje%29%29/reference/referencespapers.aspx?referenceid=2333401
• Hanbay, D., Türkoğlu, İ., Demir, Y., (2006). Modeling of Varikap Diode with Adaptive Network Based Fuzzy Inference System, ASYU-Intelligent Systems, Innovations and Application Symposium, İstanbul, 19-21. http://ibrahimturkoglu.com/?page_id=15
• Haykin, S., (1994). Neural Networks, A Comprehensive Foundation, Macmi, 1994. https://dl.acm.org/doi/10.5555/975792.975796
• Khataee, A.R., (2009). Photocatalyticremoval of C.I. Basic Red 46 on immobilized TiO2 nanoparticles: Artificialneural network modeling. Environ. Technol. 30; 1155-1168. https://doi.org/10.1080/09593330903133911
• Kologirou, S., (1999). Applications of artificial neural networks in energy systems: a review, Energy Conversion and Managemenet, 40 (3), 1073-1087. https://ktisis.cut.ac.cy/handle/10488/209
• Landeras, G., Ortiz-Barredo, A., Lopez, J.J., (2008). Comparison of Artificial Neural Network Models and Empirical and Semi-Empirical Equations For Daily Reference Evapotranspiration Estimation in the Basque Country (Northem Spain), Agricultural Water Management, 95:553-565.
• Sharma, V., Negi, S. C., Rudra, R. P., Yang, S., (2003). Neural networks for predicting nitrate-nitrogen in drainage water, Agricultural Water Management, 63(3), 169-183. https://doi.org/10.1016/S0378-3774(03)00159-8
• Yurtoğlu, H., (2005). Predictive Modeling with Artificial Neural Networks Methodology: The Case of Turkey for Some Macroeconomic Variables. General Directorate of Economic Models and Strategic Studies, Specialization Thesis, 104 p., Ankara.
• Keskin, M. E., Taylan, E. D., (2007). Stochastic Modeling of Flows in the Central Black Sea Basin. IMO Technical Journal, 4271-4291, 2007.
• Öztemel E, (2012). Artificial Neural Networks, Papatya Publications 3rd Edition. http://www.papatya.gen.tr/PDF/yapay_sinir_aglari.pdf
• Özçalık, H. R., Küçüktüfekçi, A., (2003). Flat and Inverse Modeling of Dynamic Systems with Artificial Neural Networks. KSU Journal of Science and Engineering, 6(1), 26-35. https://silo.tips/download/dinamik-sistemlerin-yapay-sinir-alar-ile-dz-ve-ters-modellenmesi
• URL1, (2019). Artificial Neural Network Model, https://data-flair.training/blogs/wp-content/uploads/sites/2/2019/07/Introduction-to-Artificial-Neural-Networks.jpg
• Kabalcı, E., (2014). Artificial neural networks. Lecture Notes, https://ekblc.files.wordpress.com/2013/09/ysa.pdf
• URL2, (2019). Wastewater Treatment Process Step by Step (https://www.gustawater.com/blog/wastewater-treatment-process.html)
• Traore, S., Wang, Y.M., Kerh, T., (2010). Artificial Neural Network for Modeling Reference Evapotranspiration Complex Process in Sudano-Sahelian Zone, Agricultural Water Management, AGWAT-2943; Page 8.
• Trejo-Perea, M., Herrera-Ruiz, G., Rıos- Moreno, J., Miranda, R. C., Rivas-Arazia, E., (2009). Greenhouse Energy Consumption Prediction using Neural Networks Models. Int. J. Agric. Biol., Mexico, 11(1).
• Weatherford, L.R., Gentry, T. W., Wilamowski, B., (2003). Neural network forecasting for airlines: A comparative analysis. Journal of Revenue and Pricing Management. 1(4):319-331. Doi:10.1057/palgrave.rpm.5170036.
• Yilmaz, E.C., Dogan, E., (2008). Modelling of Wastewater Treatment Plant Performance Using Adaptive Neuro Fuzzy Inference System, Elect Left Sci Eng, 4(1),1-9.
• Yıldız, Ö., (2006). Use of Artificial Neural Networks in Exchange Rate Estimation, Osmangazi University Institute of Social Sciences Master's Thesis, Eskişehir. https://silo.tips/download/mr-yildiz-eskiehir-osmangazi-niversitesi-sosyal-bilimler-enstits-letme-anabilim