Prediction of Chemical Oxygen Demand from The Chemical Composition of Wastewater by Artificial Neural Networks

Abstract

In our era, many technical applications are being used. Artificial Neural Networks (ANNs) as one of the artificial intelligence tools have emerged to learn and discover a model of dynamic nonlinear. In this study, six input parameters were taken to predict the value of the Chemical Oxygen Demand (COD) in the wastewater before and after the treatment at the North Gas Company/Kirkuk, by using the standard back propagation algorithm. The network was trained with the 150 data collected from the quality indices of the untreated and treated wastewater, such as total chloride ions Cl-, nitrate ions NO3 −, phosphate ions PO4 −3, sulfate ions SO4 −2, ammonia NH3, Biochemical Oxygen Demand (BOD5) to predict one element, that is the COD. After properly training of the neural network, it was tested by using the test data, and the best results were selected by the consideration of the mean square error and the regression coefficient, where the best result appeared before wastewater treatment is 0.98235 and the best result after wastewater treatment is 0.99999. The findings of this study suggest that artificial neural networks are accurate and effective tools for predicting the COD values of treated wastewater.