Predictive Geometallurgy: An Interdisciplinary Key Challenge for Mathematical Geosciences

Abstract

Predictive geometallurgy tries to optimize the mineral value chain based on a precise and quantitative understanding of: the geology and mineralogy of the ores, the minerals processing, and the economics of mineral commodities. This chapter describes the state of the art and the mathematical building blocks of a possible solution to this problem. This solution heavily relies on all classical fields of mathematical geosciences and geoinformatics, but requires new mathematical and computational developments. Geometallurgy can thus become a new defining challenge for mathematical geosciences, in the same fashion as geostatistics has been in the first 50 years of the IAMG.Geometallurgy, from the Greek words for earth (geia), metal (metallo) and work (ergon), can be understood as the exploitation of a metallic ore based on a precise understanding of its geoscientific characteristics. Geometallurgy is hence a cooperation field for geoscientists and mineral processing engineers, something which has occurred in virtually all mining operations. A modern understanding of geometallurgy, what we could call predictive geometallurgy, proposes a quantitative approach to the subject. In rough terms, that requires optimizing the ore processing based on automated mineralogy and microstructure characterisation of the ore, coupled with geometallurgical tests. These are tests conducted at several scales (from lab to plant) along which the actual ore is processed in realistic conditions in order to study the differential behaviour of the several ore and waste mineral phases, and thus the enriching potential of the ore through the processes considered.

Drill-Core Mineral Abundance Estimation Using Hyperspectral and High-Resolution Mineralogical Data

“…The automated character of the approach can be later used on mine sites provided that hyperspectral drill-core scanning is available to support the geologists in the core-logging procedure, as well as training samples characterized by high resolution methods of mapping mineral distributions, such as SEM-based image analyses. The derived mineralogical parameters such as modal mineralogy and mineral association can additionally prove useful past exploration stages as they are essential in defining geometallurgical domains [37].…”

Performance Improvements during Mineral Processing Using Material Fingerprints Derived from Machine Learning—A Conceptual Framework

“…These concepts, related to geometallurgy, mine to mill tracking, and reconciliation, are not new. Geometallurgy relies predominantly on mineralogy and intergrowth analysis from small datasets [1,13,14]. The novelty of the proposed concept is that routinely acquired material attributes are taken without making a (direct) interpretation.…”

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

“…Multielement geochemical data is a type of compositional data and the constraints related to its compositional properties have been documented in geometallurgical studies [3,4,8,13]. Compositional data is a special form of data that is non-negative, carries information in the ratios of its variables and sums to some constant [44].…”

“…Such information can include multielement geochemistry, geomechanical parameters and petrophysical properties [2]. Analysis of geometallurgical data is an essential basis for establishing geometallurgical relationships, but this can be challenging given complex multivariate relationships in the datasets and omnipresent spatial variability within an orebody [3,4]. Complex multivariate relationships can be a product of geometallurgical variable interactions, non-normal underlying data distributions or non-linear functions of the processing performance variable.…”

“…These data structures can also occlude underlying geometallurgical relationships. Challenges also include non-additive geometallurgical variables, for example, comminution indices; variables with different measurement units; and datasets with variables belonging to different sample spaces, such as compositional and non-compositional data [4,[6][7][8][9]. A proper geometallurgical data analysis must account for all of these data complexities to be effective in establishing geometallurgical relationships.…”