Abstract
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.
Highlights
The modern geometallurgical approach aims to model variability based on correlating metallurgical testwork with rapid small-scale tests and by calibrating metallurgical properties with other features that can be realised from resource drilling [11,24,25]
It is generally recognised that the total sampling value chain error is dominated by the sampling process, which can be in the range of 15% to 60% [37,48,51]
The context of sampling merits attention because metallurgical parameters are a function of geological factors such as grade, lithology, alteration, mineralogy, texture, spatial relationships and specific gravity
Summary
Sampling is a vital component during all stages of the Mine Value Chain (MVC). An assay is the quantitative measurement of the concentration (e.g., mass fraction such as g/t gold) of a metal by a given methodology, for example a 30 g fire assay followed by measurement of gold using an instrumental method (e.g., atomic absorption spectroscopy). This entire process can be challenging in the gold environment and may require special protocols [11,12,13,14]
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