Geometallurgical Modeling at Olympic Dam Mine, South Australia

Boisvert, Jeff B.; Rossi, Mario E.; Ehrig, Kathy; Deutsch, Clayton V.

doi:10.1007/s11004-013-9462-5

Cited by 50 publications

(23 citation statements)

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“…Geostatistical simulation is widely used in the evaluation of mineral resources and ore reserves to map geological heterogeneity at different spatial scales and to assess the uncertainty in the unknown values of coregionalized variables, such as the grades of elements of interest, petrophysical properties of the subsoil, or geometallurgical properties (work index, acid consumption, metal recoveries) (Boisvert et al, 2013;Rossi and Deutsch, 2014). Its practical implementation requires specifying a stochastic model, which describes the spatial distribution of the coregionalized variables (what should be simulated), and an algorithm, which aims at constructing realizations of the prescribed model (how it should be simulated) (Chilè s and Delfiner, 2012;Lantué joul, 2002).…”

Section: Introductionmentioning

confidence: 99%

Comparing sequential Gaussian and turning bands algorithms for cosimulating grades in multi-element deposits

Paravarzar

Emery

Madani

2015

Comptes Rendus. Géoscience

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Artículo de publicación ISIStochastic simulation is increasingly used to map the spatial variability in the grades of elements of interest and to assess the uncertainty in the mineral resources and ore reserves. The practical implementation requires specifying a stochastic model, which describes the spatial distribution of the grades, and an algorithm to construct realizations of these grades, viewed as different possible outcomes or scenarios. In the case of the Gaussian random field model, a variety of algorithms have been proposed in the past decades, but their ability to reproduce the model statistics is often unequal. In this paper, we compare two such algorithms, namely the turning bands and the sequential algorithms. The comparison is hold through a synthetic case study and a real case study in a porphyry copper deposit located in southeastern Iran, in which it is of interest to jointly simulate the copper, molybdenum, silver, lead and zinc grades. Statistical testing and graphical validations are realized to check whether or not the realizations reproduce the features of the true grades, in particular their direct and cross variograms. Sequential simulation based on collocated cokriging turns out to poorly reproduce the cross variograms, while turning bands proves to be accurate in all the analyzed cases.Chilean Commission for Scientific and Technological Research 113008

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Section: Introductionmentioning

confidence: 99%

Comparing sequential Gaussian and turning bands algorithms for cosimulating grades in multi-element deposits

Paravarzar

Emery

Madani

2015

Comptes Rendus. Géoscience

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“…Geometallurgy is a recently used term that refers to the characterization and understanding of the relationship of mineralogy and mineral textures to processing attributes (Lund et al 2014), the characterization of metallurgical properties at a fine scale (Kuhar et al 2011), and the spatial distribution and scaling of rock and metallurgical properties (Keeney and Walters 2011;Boisvert et al 2013). The motivation for geometallurgy is the belief that increased knowledge and an integrated approach considering the nature of the mineral deposit and processing methodology will lead to better mining and processing decisions to maximize the value of the mining operation.…”

Section: Introductionmentioning

confidence: 99%

“…Numerous authors including Carrasco et al (2008), Newton and Graham (2011), van den Boogaart et al (2013), Boisvert et al (2013), and others have proposed geostatistical simulation for modeling metallurgical properties as no requirement is placed on linear averaging. Simulation approaches also correctly model the joint uncertainty between variables at the cost of increased computational time compared to typical estimation algorithms such as ordinary kriging or multigaussian kriging.…”

Section: Introductionmentioning

confidence: 99%

“…Simulation approaches also correctly model the joint uncertainty between variables at the cost of increased computational time compared to typical estimation algorithms such as ordinary kriging or multigaussian kriging. In the Olympic Dam expansion study by Boisvert et al (2013), principal component analysis was used to generate uncorrelated variables which were simulated over the domain using sequential Gaussian simulation with a subsequent regression fit to predict plant performance. Redundant and unrelated variables were first eliminated to avoid over fitting.…”

Section: Introductionmentioning

confidence: 99%

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Spatial Modeling of Geometallurgical Properties: Techniques and a Case Study

et al. 2015

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High-resolution spatial numerical models of metallurgical properties constrained by geological controls and more extensively by measured grade and geomechanical properties constitute an important part of geometallurgy. Geostatistical and other numerical techniques are adapted and developed to construct these high-resolution models accounting for all available data. Important issues that must be addressed include unequal sampling of the metallurgical properties versus grade assays, measurements at different scale, and complex nonlinear averaging of many metallurgical parameters. This paper establishes techniques to address each of these issues with the required implementation details and also demonstrates geometallurgical mineral deposit characterization for a copper-molybdenum deposit in South America. High-resolution models of grades and comminution indices are constructed, checked, and are rigorously validated. The workflow demonstrated in this case study is applicable to many other deposit types.

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“…To the authors' knowledge, all studies conducted on predictive geometallurgy by mathematical geoscientists (Bye 2011;Boisvert et al 2013;Rossi and Deutsch 2014;Hosseini and Asghari 2015;Ortiz et al 2015;Deutsch et al 2016) consisted on appropriately predicting the secondary properties at each block of a mining block model, and proposing the mining and processing engineers to conduct their mine planning and plant scheduling based on those properties instead of on metal grades. The first step (Vann et al 2011) is the geometallurgical analysis of the ore body with respect to its primary properties.…”

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confidence: 99%

Predictive Geometallurgy: An Interdisciplinary Key Challenge for Mathematical Geosciences

Boogaart

Tolosana‐Delgado

2018

Handbook of Mathematical Geosciences

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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.

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Geometallurgical Modeling at Olympic Dam Mine, South Australia

Cited by 50 publications

References 14 publications

Comparing sequential Gaussian and turning bands algorithms for cosimulating grades in multi-element deposits

Comparing sequential Gaussian and turning bands algorithms for cosimulating grades in multi-element deposits

Spatial Modeling of Geometallurgical Properties: Techniques and a Case Study

Predictive Geometallurgy: An Interdisciplinary Key Challenge for Mathematical Geosciences

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