Prediction of effluent COD for Homs wastewater treatment plant using neural networks

Abstract

In this paper the artificial neural networks (ANN) is used for the  prediction of chemical  oxygen demand.

The data used in this research was collected over ten years through the daily records of the Homs wastewater treatment plant.

The model was built based on the approval of each of the values of (Q.BOD,COD ,SS ,SS out) as inputs to predict the value of the COD, and the performance of the model was evaluated by adopting an inverse validation error for selecting the best network structure in addition to other differential criteria.

The optimal structure of the neural network was determined after a number of attempts and errors, and the results showed a high efficiency of the proposed model algorithms  in predicting the value of effluent COD.

 As a  result of this research, a neural network structure was selected to predict the value of the COD indicator which is (5-20-1) using the Hyperbolic Tangent function in the hidden layer and the logistic function in the output layer, the Quick propagation was used as a training algorithm for training, The value of the performance function was 0.05, and the average error value of the three groups was 18.5, the value of the correlation coefficient was 0.71.