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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.
Highlights
A geometallurgical relationship intrinsically links rock properties—i.e., mineralogy, texture, geochemistry and physio-mechanical properties—to its processing behavior
The dataset used in this study includes (1) Bond ball mill work index (BWI); (2) point load strength index (PLSI) and (3) rock quality designation (RQD) as two geomechanical variables; (3) magnetic susceptibility (MAGSUSC), a geophysical variable; and (4) multielement geochemical data
The analysis of geometallurgical data is an essential part of the geometallurgical modelling process
Summary
A geometallurgical relationship intrinsically links rock properties—i.e., mineralogy, texture, geochemistry and physio-mechanical properties—to its processing behavior. Understanding these relationships is key to building 3D spatial models that relate mineral processing performance to the physical variability in an orebody [1]. Geometallurgical data include measures of processing performance indicators and information on the orebody characteristics that can potentially be linked to these indicators. Geometallurgical data analysis may fail to filter out or capture hidden data structures that result from the physical variability in the orebody on different scales. These data structures can occlude underlying geometallurgical relationships. Challenges include non-additive geometallurgical variables, for example, comminution indices; variables with
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