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Application of Data Analytics Techniques to Establish Geometallurgical Relationships to Bond Work Index at the Paracutu Mine, Minas Gerais, Brazil

1
Lassonde Institute of Mining, University of Toronto, 170 College St., Toronto M5S 3A3, ON, Canada
2
Kinross Gold Corporation, 25 York St, 17th Floor, Toronto M5J 2V5, ON, Canada
3
Harquail School of Earth Sciences, Laurentian University, Sudbury P3E 2C6, ON, Canada
*
Author to whom correspondence should be addressed.
Minerals 2019, 9(5), 302; https://doi.org/10.3390/min9050302
Received: 13 April 2019 / Revised: 9 May 2019 / Accepted: 10 May 2019 / Published: 16 May 2019
(This article belongs to the Section Mineral Processing and Metallurgy)
PDF [4505 KB, uploaded 16 May 2019]

Abstract

Analysis of geometallurgical data is essential to building geometallurgical models that capture physical variability in the orebody and can be used for the optimization of mine planning and the prediction of milling circuit performance. However, multivariate complexity and compositional data constraints can make this analysis challenging. This study applies unsupervised and supervised learning to establish relationships between the Bond ball mill work index (BWI) and geomechanical, geophysical and geochemical variables for the Paracatu gold orebody. The regolith and fresh rock geometallurgical domains are established from two cluster sets resulting from K-means clustering of the first three principal component (PC) scores of isometric log-ratio (ilr) coordinates of geochemical data and standardized BWI, geomechanical and geophysical data. The first PC is attributed to weathering and reveals a strong relationship between BWI and rock strength and fracture intensity in the regolith. Random forest (RF) classification of BWI in the fresh rock identifies the greater importance of geochemical ilr balances relative to geomechanical and geophysical variables.
Keywords: geometallurgy; principal components analysis; K-means cluster analysis; random forest; compositional data geometallurgy; principal components analysis; K-means cluster analysis; random forest; compositional data
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Bhuiyan, M.; Esmaieli, K.; Ordóñez-Calderón, J.C. Application of Data Analytics Techniques to Establish Geometallurgical Relationships to Bond Work Index at the Paracutu Mine, Minas Gerais, Brazil. Minerals 2019, 9, 302.

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