Application of Artificial Neural Networks in the Evaluation of Ekbatan Wastewater Treatment Plant

In this study artificial neural network (ANN) was used for modeling of wastewater treatment plants with using. For this purpose, the base of the quality parameters measured at the plant input, plant output value was predicted. Neural network input data, including temperature (T), biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), total solids (TS) and pH. Different structures of ANN with different number of neurons in middle layer, the structure of 6-12-6 with normal values of squared mean square error of 0.26 and the coefficient 0.82 as desired structure can be is proposed. This structure, predicting 72 to 97 percent of the effluent quality parameters, the changes in independent variables has been successful. With the removal of pollutants in the effluent treatment plant, was identified maximum removal efficiency in the plant, the pollutants TSS, equivalent to 97 percent and the lowest, compared to 32 percent, TS, respectively. Similarly, removal of these pollutants, the estimated values of the neural network, which is due to the 97 and 30 percent, with values close to observations, although the neural network performance is good . Overall, the comparison of results predicted in this study with other studies and the statistical indicators, the good performance of neural networks, in this study, to be sure. Also, treatment plants in the reduction of qualitative values in based of the values of the standard recommended by the environmental protection agency, the efficiency is high.