Run-of-mine fragmentation is an important aspect of mine productivity optimization, as it affects all mine-to-mill processes. The current blasting fragmentation calculation methods do not consider the 3D geometric information. Therefore, their calculation results are imprecise. 3D laser scanning is a technique for extracting the 3D geometric information of an object by constructing a 3D point cloud model, with which extra information on the geometrical characteristics of an object could be captured than with the technique of 2D image processing. In this paper, 3D laser scanning technology was utilized for the calculation of the rock blocks on the surface of a muck pile, and the information about the surface blocks was utilized as the samples for the statistical estimation of blasting fragmentation of muck pile (BFMP). Monte Carlo simulation was utilized as the statistical estimation method for the BFMP. In the lab experiment, results from 2D image processing technique and from 3D laser scanning technique combined with statistical estimate were compared with the physical measurements utilizing a water tank, which show that results with 3D laser scanning are more similar to the physical measurement. Finally, the applicability of 3D laser scanning technology combined with statistical methods to the calculation of blast fragmentation was estimated through field tests in Biesikuduke and Santanghu mine, two open-pit coal mines in Xinjiang Province of western China. Results show that the accuracy of the statistical estimation results of BFMP has a particle size deviation of 1–3 cm.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.