application of statistical techniques in order to improve neural modeling of industrial waste water treatment plants

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 was used to determine the effective parameters that improve the models accuracy and efficiency. mean square error (mse), rout mean square error (rmse) and correlation coefficient (r) were used for performance evaluation of the models. correlation coefficients (r) was between 0.8 to 0.94 that showed good accuracy of the models in estimating qualitative profile of wastewater.