Wastewater Pollutants Modeling Using Artificial Neural Networks

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.