Abstract:

Rock texture is, arguably, one of the most essential geo-metallurgical attributes. Numerous studies have shown that textures influence both mechanical and metallurgical characteristics of the rock. From a mineral processing perspective, the degree of mineral liberation is a critical parameter that needs to be optimized with respect to comminution energy requirements. In this study, we explore the link between texture of the rock and its liberation profile under non-preferential breakage conditions. A series of Monte-Carlo simulations was carried out to compute liberation profiles for a number of synthetic binary 2D textures using the Voronoi tessellation masking technique to simulate rock texture breakage. Based on the obtained results, a generalized model for describing volumetric grade-by-size distributions is proposed. The developed model utilizes a mixture of Beta distributions to calculate grade distribution profiles as a function of a dimensionless scale parameter that relates spatial textural characteristics to the fragments (particle) size distribution, thus, enabling the development of predictive correlations. Application of the developed approach for predicting mineral liberation profiles using rock texture image analysis is discussed.

Biography of the presenter: Jeremy Gottheil has previous completed his B.A.Sc. in Chemical Engineering at the University of Ottawa, in 2018. He is currently enrolled in the University of Ottawa Master of Applied Science program, under the supervision of Dr. Andrew Sowinski, where his project focuses on the efficiency of mining operations. This project was done in collaboration Dr. Andriy Plugatyr and Mr. Christopher Baxter of the National Research Council (NRC) High Efficiency Mining (HEM) team.

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Andrew Sowinski

Biography