Predicting rock fragmentation based on drill monitoring: A case study from Malmberget mine, Sweden

• Published in: Journal of the Southern African Institute of Mining and Metallurgy
• Main Authors: Manzoor, Sohail, Danielsson, M., Söderström, E., Schunnesson, Håkan, Gustafson, Anna, Fredriksson, H., Johansson, Daniel
• Format: Article in Journal/Newspaper
• Language: English
• Published: Luleå tekniska universitet, Geoteknologi 2022
• Subjects: rock fragmentation; measurement while drilling; quick rating system; partial least squares regression; sublevel caving; Geotechnical Engineering; Geoteknik; Luleå; Malmberget; Northern Sweden
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-80805; https://doi.org/10.17159/2411-9717/1587/2022

Fragmentation analysis is an essential part of the optimization process in any mining operation. The costs of loading, hauling, and crushing the rock are strongly influenced by the size distribution of the blasted rock. Several direct and indirect methods are used to analyse or predict fragmentation, but none is entirely applicable to fragmentation assessment in sublevel caving mines, mainly because of the limitations imposed by the underground environment and the lack of all the required data to adequately describe the rock mass. Over the past few years, measurement while drilling (MWD) data has emerged as a potential tool to provide more information about the in-situ rock mass. This research investigated if MWD can be used to predict rock fragmentation in sublevel caving. The MWD data obtained from a sublevel caving mine in northern Sweden were used to find the relationship between rock fragmentation and the nature of the rock mass. The loading operation of the mine was filmed for more than 12 months to capture images of loaded load-haul-dump (LHD) buckets. The blasted material in those buckets was classified into four categories based on the median particle size (X50). The results showed a strongercorrelation for fine and medium fragmented material with rock type (MWD data) than coarser material. The paper presents a model for prediction of fragmentation, which concludes that it is possible to use MWD data for fragmentation predict ion. Validerad;2022;Nivå 2;2022-04-07 (hanlid); Funder: Centre for Advanced Mining and Metallurgy (CAMM 2 ), Luleå University of Technology