Prediction of flyrock and backbreak in open pit blasting operation: a neuro-genetic approach View Full Text


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Article Info

DATE

2010-08-11

AUTHORS

M. Monjezi, H. Amini Khoshalan, A. Yazdian Varjani

ABSTRACT

An ideally performed blasting operation enormously influences the mining overall cost. This aim can be achieved by proper prediction and attenuation of flyrock and backbreak. Poor performance of the empirical models has urged the application of new approaches. In this paper, an attempt has been made to develop a new neuro-genetic model for predicting flyrock and backbreak in Sungun copper mine, Iran. Recognition of the optimum model with this method as compared with the classic neural networks is faster and convenient. Genetic algorithm was utilized to optimize neural network parameters. Parameters such as number of neurons in hidden layer, learning rate, and momentum were considered in the model construction. The performance of the model was examined by statistical method in which absolutely higher efficiency of neuro-genetic modeling was proved. Sensitivity analysis showed that the most influential parameters on flyrock are stemming and powder factor, whereas for backbreak, stemming and charge per delay are the most effective parameters. More... »

PAGES

441-448

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s12517-010-0185-3

DOI

http://dx.doi.org/10.1007/s12517-010-0185-3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1044029557


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