Being familiar with characteristics of industrial town effluents from various wastewater treatment units, which have high qualitative and quantitative variations and more uncertainties compared to urban wastewaters, plays very effective role in governing them. With regard to environmental issues, proper operation of wastewater treatment plants is of particular importance that in the case of inappropriate utilization, they will cause serious problems. In order to achieve a better and efficient control over the operation of an industrial wastewater treatment plant (WWTP), powerful mathematical tool can be used that is based on recorded data from some basic parameters of wastewater during a period of treatment plant operation. For the first time in Iran, the multilayer perceptron feed forward neural network with a hidden layer and stop training method was used to predict quality parameters of the industrial effluent. Principal Component Analysis (PCA) technique was applied to improve performance of generated models of neural networks. Also, factor analysis method were used to determine the effective parameters that improve the models accuracy and efficiency.

Auteur

Hamed Hasanlou received his MSc in Environmental Engineering (Water & Wastewater) from Tehran University - Iran. His research interest is wastewater treatment plant modeling. He is considered as a pioneer in the field of wastewater treatment plant modeling.Hamidreza Hasanlou obtained his MSc of Environmental Sciences from Zanjan University, Iran.