Abstract
The availability of fragmentation models for underground mine-to-mill optimization is fundamental, given that fragmentation models establish which blasting parameters can be manipulated to produce a desired particle size distribution. Given the ever-changing conditions in which mining takes place (geology, stress conditions, mining procedures, etc.), any applicable model must be flexible, adaptable, and dynamic to provide a range of predictions with acceptable accuracy. This paper discusses the results and model validation of using multivariate regression analysis to analyze blast fragmentation data from an underground aggregate operation. These are the initial results with respect to the development of a probabilistic model for underground bench blasts that predicts the resultant particle size distribution.
Original language | English |
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Title of host publication | 58th US Rock Mechanics / Geomechanics Symposium 2024, ARMA 2024 |
ISBN (Electronic) | 9798331305086 |
DOIs | |
State | Published - 2024 |
Event | 58th US Rock Mechanics / Geomechanics Symposium 2024, ARMA 2024 - Golden, United States Duration: Jun 23 2024 → Jun 26 2024 |
Publication series
Name | 58th US Rock Mechanics / Geomechanics Symposium 2024, ARMA 2024 |
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Conference
Conference | 58th US Rock Mechanics / Geomechanics Symposium 2024, ARMA 2024 |
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Country/Territory | United States |
City | Golden |
Period | 6/23/24 → 6/26/24 |
Bibliographical note
Publisher Copyright:Copyright 2024 ARMA, American Rock Mechanics Association.
ASJC Scopus subject areas
- Geochemistry and Petrology
- Geophysics