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Prediction of Dust Dispersion by Drilling Operation Using Artificial Neural Networks

K. V. Nagesha, K. Ram Chandar, V. R. Sastry


During various stages of mining, a large quantity of dust generates and disperses into the atmosphere. Apart from all activities, the dust generated by drilling activity usually in fugitive form and it will have more harmful particulate matters. It is necessary to identify emission rate and concentration of dust from drilling operation. The modeling of dust dispersion in the ambient air is a tool used for prediction and simulation of dust concentration level in the ambient air. In this paper, Artificial Neural Network (ANN) approach is used for development of airborne dust model for drilling operation in opencast coal mines. Field investigations were carried out in three large opencast coal mines in India, and the data used for developing dust prediction models via ANN. The correlation coefficients for emission and concentration models are 0.96 and 0.78 respectively, which shows better predictability by ANN method.

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