Akademik Veri Yönetim Sistemi
International Journal of Agriculture, Environment and Food Sciences, cilt.6, sa.4, ss.579-584, 2022 (Hakemli Dergi)
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.