Importance of good sampling practice throughout the gold mine value chain

Dominy, Simon C.

doi:10.1179/1743286315y.0000000028

Cited by 17 publications

(58 citation statements)

References 30 publications

(111 reference statements)

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“…Previous drilling utilised 1 m half core samples, which were crushed and pulverised in their entirety and subsequently 30 g taken for fire assay. This approach is considered inappropriate for coarse gold mineralisation as it is prone to high FSE (±35% in this case) and risks high GSE during splitting of the pulp [23,24,27].…”

Section: Previous Sampling Protocolmentioning

confidence: 99%

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Geometallurgical Study of a Gravity Recoverable Gold Orebody

et al. 2018

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Sheeted vein gold deposits are often characterised by multiple sub-parallel veins and free-milling coarse gold. Inherent mineralisation heterogeneity results in grade and process parameter variability, which increases project risk if not quantified. Measured grade variability is often exacerbated by poorly designed sampling and testwork protocols. Protocols that are optimised within the framework of the Theory of Sampling (TOS) to suit the ore type, together with quality assurance/quality control systems, will reduce variability and provide fit-for-purpose results. Geometallurgy can be broadly split into two key approaches: strategic and tactical (or operational). The strategic approach focuses on the whole orebody and long-term life-of-mine view, whereas tactical geometallurgy relates to a more short-to medium-term view during mining. The geometallurgical approach requires spatially distributed samples within a deposit to support variability modelling. Diverse attributes from core logging, mineralogical/textural determination and small-scale tests are used to measure variability. This contribution presents a case study that emphasises an early-stage strategic geometallurgical programme applied to a gravity recoverable gold (GRG) dominated deposit. It exemplifies how data can be acquired from a well-designed and planned programme to support resource estimation, a pre-feasibility study, trial mining and fast-track to production. A tactical geometallurgical programme is embedded into the mine operation.

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Section: Previous Sampling Protocolmentioning

confidence: 99%

“…The original drilling programme applied the traditional coarse-gold sampling paradigm of half core crushed, pulverised and a 30 g fire assay is flawed [13,23,24,27]. The approach is flawed and prone to high FSE, together with high GSE, DE and EE particularly when the assay charge is scooped from the pulp.…”

Section: Sampling Testwork and Assayingmentioning

confidence: 99%

Geometallurgical Study of a Gravity Recoverable Gold Orebody

et al. 2018

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“…In the case of gold, this implies that the sample should contain an identical grade to the original material, not only overall grade, but also on a size-by-size basis. Given gold's relatively low abundance in most mineralisation, this can be a difficult criterion to meet [1,34,52].…”

Section: Representative Samplingmentioning

confidence: 99%

“…It includes the sampling of in-situ material and broken rock for geological, metallurgical (including plant balances), geometallurgical and geoenvironmental purposes [1][2][3][4][5].…”

Section: Sampling Along the Mine Value Chainmentioning

confidence: 99%

“…A direct approach to d 95Au is recommended, which can include a combination of particle size determination via detailed core logging and underground/surface rock observation, optical and automated microscopy, X-ray tomography, and crush-screen-concentration [1,42,[44][45][46]. Characterisation studies allow the practitioner to set the sampling expectations across a number of d 95Au -grade scenarios.…”

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confidence: 99%

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Towards Representative Metallurgical Sampling and Gold Recovery Testwork Programmes

et al. 2018

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When developing a process flowsheet, the risks in achieving positive financial outcomes are minimised by ensuring representative metallurgical samples and high quality testwork. The quality and type of samples used are as important as the testwork itself. The key characteristic required of any set of samples is that they represent a given domain and quantify its variability. There are those who think that stating a sample(s) is representative makes it representative without justification. There is a need to consider both (1) in-situ and (2) testwork sub-sample representativity. Early ore/waste characterisation and domain definition are required, so that sampling and testwork protocols can be designed to suit the style of mineralisation in question. The Theory of Sampling (TOS) provides an insight into the causes and magnitude of errors that may occur during the sampling of particulate materials (e.g., broken rock) and is wholly applicable to metallurgical sampling. Quality assurance/quality control (QAQC) is critical throughout all programmes. Metallurgical sampling and testwork should be fully integrated into geometallurgical studies. Traditional metallurgical testwork is critical for plant design and is an inherent part of geometallurgy. In a geometallurgical study, multiple spatially distributed small-scale tests are used as proxies for process parameters. These will be validated against traditional testwork results. This paper focusses on sampling and testwork for gold recovery determination. It aims to provide the reader with the background to move towards the design, implementation and reporting of representative and fit-for-purpose sampling and testwork programmes. While the paper does not intend to provide a definitive commentary, it critically assesses the hard-rock sampling methods used and their optimal collection and preparation. The need for representative sampling and quality testwork to avoid financial and intangible losses is emphasised.

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Uncertainty Quantification in Mineral Resource Estimation

Lindi,

Aladejare,

Ozoji

et al. 2024

Nat Resour Res

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Mineral resources are estimated to establish potential orebody with acceptable quality (grade) and quantity (tonnage) to validate investment. Estimating mineral resources is associated with uncertainty from sampling, geological heterogeneity, shortage of knowledge and application of mathematical models at sampled and unsampled locations. The uncertainty causes overestimation or underestimation of mineral deposit quality and/or quantity, affecting the anticipated value of a mining project. Therefore, uncertainty is assessed to avoid any likely risks, establish areas more prone to uncertainty and allocate resources to scale down potential consequences. Kriging, probabilistic, geostatistical simulation and machine learning methods are used to estimate mineral resources and assess uncertainty, and their applicability depends on deposit characteristics, amount of data available and expertise of technical personnel. These methods are scattered in the literature making them challenging to access when needed for uncertainty quantification. Therefore, this review aims to compile information about uncertainties in mineral resource estimation scatted in the literature and develop a knowledge base of methodologies for uncertainty quantification. In addition, mineral resource estimation comprises different interdependent steps, in and through which uncertainty accumulates and propagates toward the final estimate. Hence, this review demonstrates stepwise uncertainty propagation and assessment through various phases of the estimation process. This can broaden knowledge about mineral resource estimation and uncertainty assessment in each step and increase the accuracy of mineral resource estimates and mining project viability.

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Importance of good sampling practice throughout the gold mine value chain

Cited by 17 publications

References 30 publications

Geometallurgical Study of a Gravity Recoverable Gold Orebody

Geometallurgical Study of a Gravity Recoverable Gold Orebody

Towards Representative Metallurgical Sampling and Gold Recovery Testwork Programmes

Uncertainty Quantification in Mineral Resource Estimation

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