Modeling wastewater treatment plant (WWTP) performance using artificial neural networks: Case of Adana (Seyhan)
Abstract
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.
Keywords
Artificial neural network, Biological oxygen demand, Modeling, Waste water treatment plant
References
- 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
