Active filters :
Discard Filter
Library

Prediction of Rock Fragmentation Using a Gamma-based Blast Fragmentation Distribution Model

$22.00

Want a discount? Become a member!

Author F Faramarzi, M A Ebrahimi Farsangi and H Mansouri
ID P201507076

Description

A blast fragmentation model is developed based on the gamma function to describe the run-of-mine fragmentation distribution. The model presented is aimed to benefit from simplicity in application and also to be efficient in description of the fine to coarse part of the blast rock fragments. In fact, the gamma based model (KC-KUM), gives a unit distribution curve, which is made up of two separate equations, covering different fragment sizes in the muck pile. The generated values of the fines to central part of the equation and the central to coarse part of the equation are connected at the 40 per cent grafting point. For validation, eight production blasts, which were specially designed for this purpose, were carried out at an iron ore mine (Jalal-Abad iron ore mine/Iran). Results achieved by KC-KUM and two of the most well-known models. Kuz-Ram and Swebrec, are compared with measured data. Moreover, to assess the models’ efficiency in presenting the best distribution curve, a Logarithmic error method is chosen. Based on the achievements. KC-KUM has the least deviations by the least absolute log error of 0.52, whereas the values obtained for the Swebrec and Kuz-Ram models were 0.79 and 1.34 respectively. Furthermore, the models were applied to predict X80 for 12 production blasts carried out at Gol-e-Gohar iron ore mine/Iran. These predictions were compared with the results from these trials. The coefficient of determination (R2) and root mean square error (RMSE) are calculated. Consequently, the highest R2 was obtained for KC-KUM at 0.837 with the least RMSE of 11.31. On the other hand, R2 and RMSE are equal to 0.832 and 20.19 for Swebrec; and 0.813 and 19.75 for Kuz-Ram respectively. The results obtained suggest the superiority of the KC-KUM model over the other two models in assessment and prediction of blast fragmentation in a reliable way.

CITATION:

Faramarzi, F, Ebrahimi Farsangi, M A and Mansouri, H, 2015. Prediction of rock fragmentation using a gamma-based blast fragmentation distribution model, in Proceedings 11th International Symposium on Rock Fragmentation by Blasting, pp 685–692 (The Australasian Institute of Mining and Metallurgy: Melbourne).