Estimation of particle concentration emitted from the stacks of Kerman Cement Plant using artificial neural networks

In this study a new approach based on artificial neural networks (ANNs) has been designed to estimate the concentration of PM10 from the Kerman Cement Plant. Some measured data have been used to create an artificial neural network for predicting suspended particle concentration. The data include particle concentration, distance from source, mixing height, lateral and vertical dispersion parameters, and 10 meter wind speed. The present work applies a three-layer back-propagation neural network with 10 neurons in the hidden layer. The results from the network are in good agreement with the measured data, with an average absolute percent deviation of 5.91%. The results of ANNs have also been compared with the results of the Gaussian plume model and multivariable regression model.