Green blasting policy: Simultaneous forecast of vertical and horizontal distribution of dust emissions using artificial causality-weighted neural network

The environmental impacts of the mining industry as well as reduction strategies are important issues that have been highlighted as a result of the environmentally-friendly policy of green mining. As part of that policy, this study developed an approach based on a multi-layer artificial neural network and fuzzy cognitive map to predict the vertical and horizontal distribution of blast-induced dust emissions, simultaneously. Hence, a fuzzy cognitive map based on the cause-and-effect analysis concept was first designed to extract inputs' weights. An optimal network with two hidden layers was then implemented to predict the vertical and horizontal dust distributions in a mine close to residential and agricultural areas. The performance evaluation of the approach indicated good results by the R-2 of 0.9933 and 0.9267 between the measured and predicted values for the horizontal and vertical distributions, respectively. Furthermore, the predicted and measured outputs over every blasting round were in accordance due to the slight mean absolute and root mean square errors of 0.009 and 0.018, respectively. A sensitivity analysis revealed that all inputs, excluding air humidity on the vertical distribution, had acceptable effects on the dust distribution outputs by the strength of higher than 0.7. Based on a reduction solution that we suggested, one round was blasted by taking water capsules together with stemming material in 40% of the whole blast holes. The results indicated that the maximum horizontal distribution of blast-induced dust was decreased about six times. The approach can be straightforwardly updated and used for other mining cases. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

This study developed an approach based on a multi-layer artificial neural network and fuzzy cognitive map to predict the vertical and horizontal distribution of blast-induced dust emissions. The performance evaluation indicated good results with high accuracy and the approach successfully reduced the distribution of dust in the surrounding areas.