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Minerals
Special Issues
Sampling Across the Mine Value Chain
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Sampling Across the Mine Value Chain

A special issue of Minerals (ISSN 2075-163X).

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 39776

Special Issue Editors

Dr. Simon Dominy

E-Mail Website
Guest Editor
Camborne School of Mines, University of Exeter, Penryn, Cornwall TR10 9FE, UK
Interests: economic geology; mineral exploration; orebody knowledge; geometallurgy; mining geology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hylke J. Glass

E-Mail Website
Guest Editor
Camborne School of Mines, University of Exeter, Penryn, Cornwall TR10 9FE, UK
Interests: geometallurgy; resource modelling; mine sequencing; mineral process design; sampling
Special Issues, Collections and Topics in MDPI journals
Dr. Kim Esbensen

E-Mail Website
Guest Editor
1. KHE Consult, Aldersrogade 8, 2. sal, 2100 Copenhagen Ø, Denmark
2. Geological Survey of Denmark and Greenland (GEUS), Oester Voldgade 10, 1350 Copenhagen C, Denmark
Interests: sampling/theory of sampling; geochemistry/geoanalysis; process analytical technology; multivariate data analysis; chemometrics
Dr. Dominique François-Bongarçon

E-Mail Website
Guest Editor
Agoratek International Consultants Inc., Vancouver, BC, Canada
Interests: sampling/theory of Sampling; QA/QC; geostatistics; due diligence studies
Dr. Ralph Holmes

E-Mail Website
Guest Editor
CSIRO, Melbourne, VIC, Australia
Interests: sampling/theory of sampling; iron ore processing; on-stream analysis

Special Issue Information

Dear Colleagues,

Sampling is a critical component throughout the entire mine value chain. It includes sampling of both in situ and broken material for exploration, resource, and grade control and geoenvironmental, metallurgical and geometallurgical purposes; sampling is also a critical success factor in the analytical laboratory. The analytical data produced must be fit for purpose to contribute to mineral resources/ore reserves reported in accordance with the 2012 JORC Code or other international reporting codes. Quality assurance/quality control is critical to maintaining data integrity through documented procedures, sample security, and monitoring of precision, accuracy, and contamination. Samples and their associated assays are key inputs in important decisions throughout the Mine Value Chain.

The theory of sampling (TOS), though it has far wider applications today, was originally developed in the 1950s by Dr. Pierre Gy to improve sampling performance within the mining industry. TOS defines and provides guidelines for the reduction of sampling errors, which may lead to uncertainty and create an unnecessarily enlarged overall measurement error. TOS attempts to break down this error into a series of contributions along the sampling value chain (e.g., the planning to assay measurement process). Errors are additive throughout the sampling process and generate both monetary and intangible losses. The aim is only to collect fit-for-purpose representative samples to accurately describe the material in question.

Despite a wealth of knowledge available on correct sampling principles, it is surprising how little attention and resources are often dedicated to collecting representative samples. Often, practitioners appear to be satisfied as long as some material is collected and delivered to the laboratory for analysis. Nevertheless, unless the samples are representative, the whole measurement process is flawed from the outset, and no amount of re-analysis can fix the problem. Consequently, companies, corporations, and organizations stand to lose millions of dollars in terms of poor investment decisions, wasted resources, poor plant performance, poor product quality control, and income from product sales. Sampling, therefore, needs to be given the attention it deserves to ensure that the samples extracted are representative so that meaningful and defensible decisions can be made based on their analyses. This Special Issue aims at collecting informative contributions from the entire mine value chain.

Dr. Simon Dominy
Prof. Dr. Hylke Glass
Dr. Kim Esbensen
Dr. Dominique François-Bongarçon
Dr. Ralph Holmes
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Minerals is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Theory of Sampling
  • Exploration sampling
  • Sampling for resource/reserve estimation
  • Mine grade control sampling
  • Geoenvironmental sampling
  • Metallurgical and geometallurgical sampling
  • Sample preparation, testing and assaying
  • Quality assurance/quality control
  • Mathematical modelling of sampling systems
  • New developments in sampling, sample preparation and blending equipment
  • Future technologies
  • Case studies

Published Papers (6 papers)

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Research

20 pages, 2963 KiB  
Article
Sampling Broken Ore Residues in Underground Gold Workings: Implications for Reconciliation and Lost Revenue
by Simon C. Dominy, Hylke J. Glass and Richard C.A. Minnitt
Minerals 2022, 12(6), 667; https://0-doi-org.brum.beds.ac.uk/10.3390/min12060667 - 25 May 2022
Viewed by 1602
Abstract
The underground mining process typically results in some of the metal inventory remaining as a broken residue within mine workings. Up to 0.5 m of broken ore may be left on the floors of development drives and in stopes. It is possible that [...] Read more.
The underground mining process typically results in some of the metal inventory remaining as a broken residue within mine workings. Up to 0.5 m of broken ore may be left on the floors of development drives and in stopes. It is possible that this broken ore contains 5% or more of the original metal in the ore reserve, which will have a material effect on reconciliation and project economics. Broken ore remaining in the mine may have been subject to enhanced milling during the mucking process, yielding enhanced liberation of the economic minerals of interest. Given that the material in question is already broken, the sampling strategy will be based on digging trenches or pits into the mine floor to extract a pre-determined mass of material for assay. The sampling of stope floors will most likely be based on grab sampling. Application of the theory of sampling is a key aspect of ensuring that evaluation is effective. Gy’s equation for the fundamental sampling error can be used to determine an optimum sample mass, and to inform subsequent steps in preparation for assaying at given confidence limits and precision. This paper presents a discussion and case study. Full article
(This article belongs to the Special Issue Sampling Across the Mine Value Chain)
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21 pages, 3312 KiB  
Article
The Grouping and Segregation Error in the Rice Experiment
by Richard Minnitt
Minerals 2022, 12(3), 335; https://0-doi-org.brum.beds.ac.uk/10.3390/min12030335 - 08 Mar 2022
Cited by 2 | Viewed by 1944
Abstract
The grouping and segregation error, one of the seven sampling errors defined by Pierre Gy, is related to the combined effects of gravity and characteristics of the target analyte such as particle size, density, shape, and moisture content of the particulate materials being [...] Read more.
The grouping and segregation error, one of the seven sampling errors defined by Pierre Gy, is related to the combined effects of gravity and characteristics of the target analyte such as particle size, density, shape, and moisture content of the particulate materials being sampled. Kinetic energy acting on particulate materials that are moved, flow, transported, or stockpiled causes the spatial distribution of fragments relative to one another to change. The grouping and segregation error is identified, quantified, and measured in relative sampling variance terms by comparing the sampling variability due to fractional shovelling (scooping) with that using a Jones riffle splitter. The relative sampling variance of low concentrations, approximately 0.01%, of steel balls, lead balls, and flakes of tungsten carbide in the host substrate indicates that, in this specific sampling space, the grouping and segregation error is primarily a function of particle density. Conclusions from the experiments are that components of the grouping and segregation error, namely the grouping factor and segregation factor, can be identified, measured, and mitigated. Whereas the grouping and segregation error has historically been considered to be less than the fundamental sampling error, these experiments suggest that it can be up to four times the fundamental sampling error depending on the density of the segregated materials. Full article
(This article belongs to the Special Issue Sampling Across the Mine Value Chain)
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46 pages, 12856 KiB  
Article
Determination of Gold Particle Characteristics for Sampling Protocol Optimisation
by Simon C. Dominy, Ian M. Platten, Hylke J. Glass, Saranchimeg Purevgerel and Brian W. Cuffley
Minerals 2021, 11(10), 1109; https://0-doi-org.brum.beds.ac.uk/10.3390/min11101109 - 10 Oct 2021
Cited by 4 | Viewed by 4690
Abstract
Sampling, sample preparation, and assay protocols aim to achieve an acceptable estimation variance, as expressed by a relatively low nugget variance compared to the sill of the variogram. With gold ore, the typical heterogeneity and low grade generally indicate that a large sample [...] Read more.
Sampling, sample preparation, and assay protocols aim to achieve an acceptable estimation variance, as expressed by a relatively low nugget variance compared to the sill of the variogram. With gold ore, the typical heterogeneity and low grade generally indicate that a large sample size is required, and the effectiveness of the sampling protocol merits attention. While sampling protocols can be optimised using the Theory of Sampling, this requires determination of the liberation diameter (dℓAu) of gold, which is linked to the size of the gold particles present. In practice, the liberation diameter of gold is often represented by the most influential particle size fraction, which is the coarsest size. It is important to understand the occurrence of gold particle clustering and the proportion of coarse versus fine gold. This paper presents a case study from the former high-grade Crystal Hill mine, Australia. Visible gold-bearing laminated quartz vein (LV) ore was scanned using X-ray computed micro-tomography (XCT). Gold particle size and its distribution in the context of liberation diameter and clustering was investigated. A combined mineralogical and metallurgical test programme identified a liberation diameter value of 850 µm for run of mine (ROM) ore. XCT data were integrated with field observations to define gold particle clusters, which ranged from 3–5 mm equivalent spherical diameter in ROM ore to >10 mm for very high-grade ore. For ROM ore with clusters of gold particles, a representative sample mass is estimated to be 45 kg. For very-high grade ore, this rises to 500 kg or more. An optimised grade control sampling protocol is recommended based on 11 kg panel samples taken proportionally across 0.7 m of LV, which provides 44 kg across four mine faces. An assay protocol using the PhotonAssay technique is recommended. Full article
(This article belongs to the Special Issue Sampling Across the Mine Value Chain)
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38 pages, 4201 KiB  
Article
Integrating the Theory of Sampling into Underground Mine Grade Control Strategies: Case Studies from Gold Operations
by Simon C. Dominy, Hylke J. Glass, Louisa O’Connor, Chloe K. Lam and Saranchimeg Purevgerel
Minerals 2019, 9(4), 238; https://0-doi-org.brum.beds.ac.uk/10.3390/min9040238 - 17 Apr 2019
Cited by 3 | Viewed by 7443
Abstract
Grade control aims to deliver adequately defined tonnes of ore to the process plant. The foundation of any grade control programme is collecting high-quality samples within a geological context. The requirement for quality samples has long been recognised, in that these should be [...] Read more.
Grade control aims to deliver adequately defined tonnes of ore to the process plant. The foundation of any grade control programme is collecting high-quality samples within a geological context. The requirement for quality samples has long been recognised, in that these should be representative and fit-for-purpose. Correct application of the Theory of Sampling reduces sampling errors across the grade control process, in which errors can propagate from sample collection through sample preparation to assay results. This contribution presents three case studies which are based on coarse gold-dominated orebodies. These illustrate the challenges and potential solutions to achieve representative sampling and build on the content of a previous publication. Solutions ranging from bulk samples processed through a plant to whole-core sampling and assaying using bulk leaching, are discussed. These approaches account for the nature of the mineralisation, where extreme gold particle-clustering effects render the analysis of small-scale samples highly unrepresentative. Furthermore, the analysis of chip samples, which generally yield a positive bias due to over-sampling of quartz vein material, is discussed. Full article
(This article belongs to the Special Issue Sampling Across the Mine Value Chain)
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25 pages, 4052 KiB  
Article
Variographic Assessment of Total Process Measurement System Performance for a Complete Ore-to-Shipping Value Chain
by Karin Engström and Kim H. Esbensen
Minerals 2018, 8(7), 310; https://0-doi-org.brum.beds.ac.uk/10.3390/min8070310 - 23 Jul 2018
Cited by 4 | Viewed by 4072
Abstract
Variographic characterisation has been shown to be a powerful tool to assess the performance of process measurement systems, using existing process data. Variogram interpretation enables decomposition of variabilities stemming from the process and measurement system, respectively, allowing to determine if measurements are able [...] Read more.
Variographic characterisation has been shown to be a powerful tool to assess the performance of process measurement systems, using existing process data. Variogram interpretation enables decomposition of variabilities stemming from the process and measurement system, respectively, allowing to determine if measurements are able to describe the true process variability with sufficient resolution. This study evaluated 14 critical sampling locations, covering a total of 34 separate measurement systems, along the full processing value chain at Luossavaara Kiirunavaara limited company (LKAB), Sweden. A majority of the variograms show low sill levels, indicating that many sub-processes are well controlled. Many also show low nugget effect, indicating satisfactory measurement systems. However, some notable exceptions were observed, pointing to systems in the need of improvement. Even if some of these were previously recognized internally at LKAB, the use of variographic characterisation provide objective and numerical evidence of measurement system performance. The study also showed some unexpected results, for example that slurry shark-fin and spear sampling show acceptable variogram characteristics for the present materials, despite the associated incorrect sampling errors. On the other hand, the results support previous conclusions indicating that manual sampling and cross belt hammer samplers are leading to unacceptably large sampling errors and should be abandoned. Such specific findings underline the strength of comprehensive empirical studies. Based on the present compilation of results, it is possible to conduct rational enquiry of all evaluated measurement systems, enabling objective prioritization of where improvement efforts will have the largest cost–benefit effect. Full article
(This article belongs to the Special Issue Sampling Across the Mine Value Chain)
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45 pages, 4572 KiB  
Article
Integrating the Theory of Sampling into Underground Mine Grade Control Strategies
by Simon C. Dominy, Hylke J. Glass, Louisa O’Connor, Chloe K. Lam, Saranchimeg Purevgerel and Richard C.A. Minnitt
Minerals 2018, 8(6), 232; https://0-doi-org.brum.beds.ac.uk/10.3390/min8060232 - 29 May 2018
Cited by 13 | Viewed by 18943
Abstract
Grade control in underground mines aims to deliver quality tonnes to the process plant via the accurate definition of ore and waste. It comprises a decision-making process including data collection and interpretation; local estimation; development and mining supervision; ore and waste destination tracking; [...] Read more.
Grade control in underground mines aims to deliver quality tonnes to the process plant via the accurate definition of ore and waste. It comprises a decision-making process including data collection and interpretation; local estimation; development and mining supervision; ore and waste destination tracking; and stockpile management. The foundation of any grade control programme is that of high-quality samples collected in a geological context. The requirement for quality samples has long been recognised, where they should be representative and fit-for-purpose. Once a sampling error is introduced, it propagates through all subsequent processes contributing to data uncertainty, which leads to poor decisions and financial loss. Proper application of the Theory of Sampling reduces errors during sample collection, preparation, and assaying. To achieve quality, sampling techniques must minimise delimitation, extraction, and preparation errors. Underground sampling methods include linear (chip and channel), grab (broken rock), and drill-based samples. Grade control staff should be well-trained and motivated, and operating staff should understand the critical need for grade control. Sampling must always be undertaken with a strong focus on safety and alternatives sought if the risk to humans is high. A quality control/quality assurance programme must be implemented, particularly when samples contribute to a reserve estimate. This paper assesses grade control sampling with emphasis on underground gold operations and presents recommendations for optimal practice through the application of the Theory of Sampling. Full article
(This article belongs to the Special Issue Sampling Across the Mine Value Chain)
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