Optimization of flyrock and rock fragmentation in the Tajareh limestone mine using metaheuristics method of firefly algorithm

A successful and favorable explosion not only causes a proper rock fragmentation, but also decreases the unfavorable and unwanted environmental issues caused by explosion such as ground vibration, flyrock, air-overpressure and back-break. Therefore, anticipation and optimization of these issues prod...

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Bibliographic Details
Published in:	Engineering with computers Vol. 34; no. 2; pp. 241 - 251
Main Authors:	Asl, Parvin Faraji, Monjezi, Masoud, Hamidi, Jafar Khademi, Armaghani, Danial Jahed
Format:	Journal Article
Language:	English
Published:	London Springer London 01-04-2018 Springer Nature B.V
Subjects:	Air overpressure Algorithms Artificial neural networks Blasting (explosive) CAE) and Design Calculus of Variations and Optimal Control; Optimization Classical Mechanics Computer Science Computer-Aided Engineering (CAD Control Delay Design factors Drilling Explosions Fragmentation Ground motion Heuristic methods Limestone Math. Applications in Chemistry Mathematical and Computational Engineering Mine explosions Neural networks Optimization Original Article Root-mean-square errors Sensitivity analysis Stone Systems Theory Artificial neural network Flyrock Firefly algorithm Rock fragmentation Optimization process
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Summary:	A successful and favorable explosion not only causes a proper rock fragmentation, but also decreases the unfavorable and unwanted environmental issues caused by explosion such as ground vibration, flyrock, air-overpressure and back-break. Therefore, anticipation and optimization of these issues produced by blasting operations is significant. In this study, an attempt has been made to design the effective factors on Tajareh limestone mine explosion [i.e., burden, blast-hole, spacing, hole length, sub-drilling, stemming, powder factor, charge in each delay and Geological Strength Index (GSI)] in order to reduce improper fragmentation and flyrock. Since the experimental methods are not suitable in terms of accuracy, using artificial neural network (ANN) and firefly algorithms, flyrock and rock fragmentation were predicted and optimized, respectively. After collecting data and selecting the most effective parameters on flyrock and rock fragmentation, an ANN model was developed, and then its results were called by firefly algorithm for optimizing process. ANN results according to coefficient of determination ( R 2 ) and root mean square error (RMSE) were obtained as 0.94 and 0.1, 0.93 and 0.09, respectively, for fragmentation and flyrock. The outcome results of modeling and optimization showed a decrease of 42.9 and 32.9% in results of flyrock and rock fragmentation, respectively. In addition, results of optimization process were obtained as: 2 m of burden, 2.9 m of spacing, 7.5 m of hole length, 0.7 m of sub-drilling, 1.9 m of obstruction length, 0.69 kg/m 3 of powder factor, 1443 kg of charge in each delay and 55.5 of GSI. Based on the obtained results of sensitivity analysis, it was found that GSI and burden receive the highest influence values on both flyrock and rock fragmentation.
ISSN:	0177-0667 1435-5663
DOI:	10.1007/s00366-017-0535-9