Material fingerprinting as a potential tool to domain orebody hardness and enhancing the prediction of work index

Conference paper (2021)

Authors

J.R. van Duijvenbode Resource Engineering - CEG
L. M. Cloete AngloGold Ashanti South Africa, Johannesburg
M. Shishvan Resource Engineering - CEG
Mike W.N. Buxton Resource Engineering - CEG
Research Group
Resource Engineering (CEG) (TU Delft)
Copyright: © 2021 J.R. van Duijvenbode, L. M. Cloete, M. Soleymani Shishvan, M.W.N. Buxton
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Published Date

2021

Language

English

Reuse Rights

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Faculty

Civil Engineering & Geosciences

Department

Geoscience and Engineering

Research Group

Resource Engineering

Abstract

Geochemical and mineralogical datasets from Tropicana Gold Mine, Australia, have been used to define ore fingerprints. VNIR/SWIR spectral data were represented by four normalised wavelength regions and were clustered to form spectral classes. Sequentially, these spectral class proportions within a block and collocated XRF data were clustered to from material types (fingerprints). The material types were related to an Equotip-BWi correlation. These correlations can be used to extrapolate a hardness signature and generate a BWi proxy for different blocks. The combined fingerprints and BWi proxy can assist as a tool for enhancing the prediction of comminution behaviour. They can explain specific domain-related hardness variations. For example, one material type could be separated into a softer (~15-18 kWh/t), and harder (>20 kWh/t) material blend. This was accomplished using the commonly overlooked VNIR region at 605 nm. This outcome has significance for blending strategies.

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