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

Authors : Metin DAĞTEKİN, Bekir YELMEN

Pages : 579-584

Doi:10.31015/jaefs.2022.4.10

View : 14 | Download : 7

Publication Date : 2022-12-30

Article Type : Research Paper

Abstract :In this study, performance estimation of biological wastewater treatment plants insert ignore into journalissuearticles values(WWTP); was made by applying Artificial Neural Network insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(COD);, daily water flow insert ignore into journalissuearticles values(Qw); and daily suspended solids insert ignore into journalissuearticles values(SS); parameters at the entrance of the WWTP were used as input parameters. The daily biological oxygen demand insert ignore into journalissuearticles values(BOD); parameter was determined as the output parameter. In the study, feed forward back propagation ANN model insert ignore into journalissuearticles values(FFBPANN); was used to estimate the daily BOD amounts at the entrance of the WWTP. In the statistical analysis, the correlation insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(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