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Wastewater Pollutants Modeling Using Artificial Neural Networks
 
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Civil Engineering Department, College of Engineering, University of Babylon, Iraq
AUTOR DO KORESPONDENCJI
Hadeel Ali Al Saleh   

University of Babylon, Iraq
Data publikacji: 06-07-2021
 
J. Ecol. Eng. 2021; 22(7):35–45
 
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DZIEDZINY
 
STRESZCZENIE
In this study, design; execution and assessment of ANN approach towards the declaration of the pollution was used. The ANN-based models for prediction of effluent Chemical and Biological Oxygen demands, (COD & BOD5) and Total Suspended Solids (TSS) concentrations were formed using a three-layered feed forward back propagation algorithm ANN towards assessing the performance of 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, who 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 whose inputs did not contain BOD5 had the best performance, which demonstrate that the BOD5 has the largest influence on the values of R in COD prediction models and other predicted models than TSS and other parameters; consequently, the performance of the WWTP was affected greatly. 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.