THE USE OF A NEURAL NETWORK TECHNIQUE FOR THE PREDICTION OF SLUDGE VOLUME INDEX IN MUNICIPAL WASTEWATER TREATMENT PLANT

Abstract

Modeling Sludge volume index of activated sludge process in municipal WWTP is a difficult task to accomplish due to the high nonlinearity of the plant and the non-uniformity and variability of influent quantity, quality parameters, and operation condition.

ANNs were developed for the prediction of the Sludge Volume Index using influent quality parameters and operating parameters of Batna Wastewater Treatment Plant from 2011 to 2014. The best model given by the neural network for the SVI prediction composed of one input layer with fifteen input variables, one hidden layer with thirteen nodes and one output layer with one output variable with R = 0.8784 and RMSE = 0.443. The results demonstrate the ability of the appropriate Neural Network models for the prediction of SVI. This provides a very useful tool that can be used by WWTP operators in their daily management to increase treatment process performances and WWTP reliability