1

Developments in Ore Characterisation for Coarse

Gangue Rejection Amenability

Bernard Agbenuvor, Erica Avelar, Teresa McGrath and Chris Aldrich

WA School of Mines: Minerals, Energy and Chemical Engineering, Curtin University, Australia

COEMinerals, ARC Centre of Excellence for Enabling Eco-Efficient Beneficiation of Minerals,

Australia

ABSTRACT

Coarse gangue rejection (CGR), a method utilized for ore upgrade by physical methods (screens and

density) or sensor-based detection, has become highly topical in the modern mining value chain.

However, selecting an ore suitable for such an approach has been the main setback for its more

comprehensive application. Despite the advancement of technology, characterization for ores amenable

to coarse gangue rejection is still limited, mainly in the gold sector. The current declining nature of ore

grade and the emergence of complex ores have made ore characterization more critical. Currently,

during deposit development and mining operation, ore characterization is an integral component of

the process. It provides a great deal of information about an ore, contributing to overall process

performance prediction and managing uncertainties in mineral processing plants. As for every other

mineral processing unit operation, ore characterization for coarse gangue rejection helps understand

the inherent properties of ore that influence the separability of the product stream. In light of this, this

paper looks at a brief review of ore characterization methods that have been utilized in literature for

identifying ores that are amenable for coarse gangue rejection and assesses the opportunities available

for further improvement to the methodology. The advancement in geometallurgy has seen ore

characterization shifting toward more comprehensive geometallurgical approaches. Identifying

geometallurgical parameters indicating CGR performance before conducting complete CGR

characterization is seen as a vital tool in driving future CGR characterization methods.

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INTRODUCTION

Gangue is primarily unwanted or worthless material coexisting with the metals/minerals of interest

in the in-situ rock. The term gangue can have different meanings, which can change in the various

domains of an orebody. It could mean total waste material (no grade), uneconomic ore material

(below cut-off grade) and to some extent deleterious elements depending on the ore material being

treated. In this paper, gangue would primarily mean low-grade or baren material. Due to the

processing of high-grade ores in the past, much concern was not given to gangue materials since

saleable metal products yield large profits after deductions of expenses. However, the declining trend

of high-grade ore deposits observed over the past decades across principal minerals (i.e., gold,

copper, nickel, zinc, lead) has redirected attention to addressing the issues posed by processing low-

grade ores with a large volume of gangue material (Prior et al., 2012).

The decline in ore feed grade is often made up for by increasing plant throughput as a larger volume

of material has to be mined and treated to achieve the same units of metal compared to treating lower

tonnages of high-grade deposits (Norgate & Jahanshahi, 2010). The implication of such operation

results in higher energy, reagent and water consumption, enlarged plant footprint, and extensive

tailings dam facility. Addressing the implications of consistently pushing plant tonnage has

encouraged the mining industry to investigate the potential benefits of preconcentration before

highly intensive energy and downstream reagent processes to improve overall plant performances

(Murphy et al., 2012). The preconcentration applications of interest concentrate minerals of value by

removing gangue using ore-specific properties before intensive ore processing stages to reduce the

cost of metal production and significantly improve sustainability in the mining sector (Carrasco,

2013). The inherent properties that are exploited can be in the form of physical properties such as

color, density, conductivity or magnetic properties (Wills, 2013) and mineral liberation characteristics

of ore particles (Sutherland & Fandrich, 1996). Until today, screening and DMS, which exploit natural

grade by size deportment properties of ore, remain the most used methods in executing CGR

applications. This has been the case despite the emergence of a range of other technologies, such as

dielectrophoresis, bulk and particle-based sorting (including via optical, X-ray, microwave and

conductivity measurements) and coarse particle flotation (Ballantyne et al., 2012).

The advancement of research in ore amenability characterization and ore feed heterogeneity is on the

rise since CGR applications are gradually being accepted and integrated into existing processing

circuits and greenfield projects. Subsequently, there has been an increased focus on researching

separability of the process feed and understanding the reasons for poor separations, including:

Treatment of unsuitable feed producing no separation streams;

Loss of valuable material in screening due to ore variability; and

Insufficient differential density between gangue and metal/mineral of interest during density

separation.

In order to promote the application of gangue rejection and improve circuit operation, more attention

is required for the characterization of gangue minerals in addition to the valuable constituents since

gangue constitutes a large proportion of run-of-mine (ROM) material. The characterization of the

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valuable constituent of ore has been widely developed in the literature. This has seen a wide range

of methods used to characterize ore material before process route determination and process

optimization in mineral processing.

Ore Characterization for Mineral Processing

For this review, ore characterization in mineral processing refers to mineralogical and metallurgical

techniques used to assess an ores' intrinsic attributes, response to separation techniques and

economic value. While different characterization methods are well established within the mineral

processing sector, mineralogical characterization has been the chief method for decades. Recent

technological advancements have provided sophisticated tools that have improved mineralogical

characterization and provided a roadmap for advanced process mineralogy (Baum, 2014; Becker et

al., 2016; Lotter, 2011). Modern process mineralogy considers how ore mineralogical properties such

as modal mineralogy, mineral textures, mineral association, mineral chemistry, mineral texture and

mineral liberation impact downstream mineral processing responses (Becker et al., 2016). These

techniques may include X-ray Fluorescence (XRF), X-ray Diffraction (XRD), optical microscopy, SEM-

based automated mineralogy, X-ray Micro-computed Tomography (XCT) and hyperspectral imaging

(Becker et al., 2016; Peterson et al., 2021). Unlike process mineralogy, other established

characterization methods are based on the physical, chemical and metallurgical behavior an ore

material is likely to exhibit if subjected to certain processing conditions. Some key examples of such

characterization methods may include: porosimetry (for porosity and density determination),

hardness tests (for example the JK Drop Weight Test for determining crushing performance and the

Bond Work Index (BWi) for determining ball mill performance), batch flotation test (for flotation

reagent screening and flotation condition optimization), bottle roll test (for determining leaching

kinetics) and the gravity recoverable gold test (GRG, for assessing physical separability of gold using

batch centrifugal concentrators).

In CGR, there have been many established characterization methods associated with a particular ore

type or method of gangue rejection. However, many CGR characterization methods need revision as

some methods can provide inconsistent results due to orebody variability. Given this, the current

review paper focuses on and reviews some of the ore characterization methods used in assessing ore

suitability for coarse gangue rejection irrespective of the breakage mechanism utilized. In addition,

the limitations and robustness of the methods reviewed are highlighted, with the emerging future

direction of ore characterization for enhanced CGR applications being discussed.

COARSE GANGUE REJECTION CHARACTERIZATION METHODS

The stochastic nature of metal deportment behavior of ore could be a key influencing factor in

predicting CGR responses. Due to the random probabilistic nature, a detailed ore characterization

method is required to understand CGR amenable ores' behavior fully. Given this, the paper reviews

some of these CGR Characterization methods used in literature to assess the CGR potential of

different ore types. The methods reviewed in this paper include mainly screening and density-based

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CGR studies found in the literature. For each characterization method, the critical terms of focus are

ore type, mineralization style, breakage mechanism and method of result interpretation. The

associated CGR studies, being laboratory or pilot scale, are categorically discussed in three domains:

geological and mineralogical, metallurgical, and geometallurgical.

Geological Domain

Geological attributes of ores have always been seen to dictate how ores respond to mineral and

metallurgical processing. Similar to CGR, geological characteristics such as lithology, alteration, style

of mineralization, the mineral association have all been used to assess the suitability of ores for CGR.

In a report by Rutter (2017), different geological and mineralization styles were associated with

different CGR methods. Their report based their classification on the drill hole dataset attained across

various ore deposits during the span of the study. These collective datasets were consolidated to form

the Grade Engineering® probabilistic amenability matrix. Despite the dataset lacking comprehensive

geological information, the matrix developed had clear evidence that most geological ores styles

having one or more combinations of a vein, stockwork and breccia style of mineralization were

amenable to CGR. In a similar perspective, findings from work conducted by Bamber (2008) on three

different deposits show that deposits with veins and breccia had a better CGR response than the

deposit with disseminated mineralization style. Although mineralization style and mineral

association are vital, the breakage energy's type and magnitude control the quality of the product

output (Carrasco, 2013). Sutherland and Fandrich (1996) and Hesse et al. (2017) explain the

deportment phenomenon of soft minerals deporting rapidly into fine fractions compared to harder

minerals when ore particles with both compositions are subjected to breakage energy. Hesse et al.

(2017) gave a diagrammatical explanation (Figure 1) to this phenomenon, showing how ore material

of different mineralization and mineral association can be exploited through varying breakage

mechanisms. This phenomenon was described as selective comminution, which is a method of CGR.

Figure 1 Different style of mineralization behavior under different types of breakage mechanism NB: Black-

mineral of interest and White-gangue mineral (after Hesse et al., 2017)

It is worth mentioning that the CGR studies cited in this review have, in one way or another, sought

mineralization style of deposits to explain the various CGR responses achieved. The presence of vein,

stockwork and breccia mineralization styles has been demonstrated as good way to identify CGR

potential. However, the approach can be limited if an ore has a metal/mineral of interest occurring

not only in those mineralization styles but also in a disseminated style of mineralization. Reports

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from the literature have shown that the disseminated style of mineralization tends to exhibit a lower

degree of natural grade by size deportment. Ores of such mineralization typically require fine

grinding to fully liberate the metal/mineral of interest from gangue material (Bamber, 2008). It is

essential then to consider the mineralization style as a first indicator when evaluating the CGR

potential of an orebody. The confirmation of this indicator can then be provided using a metallurgical

method that can quantify the CGR potential of an orebody.

Metallurgical Methods

Metallurgical ore characterization methods provide processing data that can be analyzed to predict

the behavior of the ore in specific processing conditions. In CGR applications, two main metallurgical

CGR evaluation methods have been identified. These methods include the deportment (metal

recovery vs mass recovery) curve and CRC ORE's Response Ranking curves (similar the Henry II

(enrichment ratio) curve (Drzymała, 2006)).

Figure 2 (a) Metal deportment Curve (b) Response Ranking Curve

The deportment curve (Figure 2(a)), also referred to as the Mayer (II) upgrading curves (Drzymała,

2006), is used to access ore's propensity to CGR (Carrasco, 2013). It has been established that the

farther the distance of the deportment curve extends above the reference (45° line = no deportment),

the more suitable an ore is to CGR. The grade deportment curves have been used widely in CGR

applications. It has been the go-to method since it is direct and fast for first-stage screening and

interpretation of CGR responses (Bowman & Bearman, 2014; Huang, 2019). However, the ability to

distinguish between similar deportment curves and dissociate the effect of mass recovery was

lacking. These limitations are seemingly addressed by the Response Ranking (RR) curves (Figure

2(b)). The RR curve is used in ranking ores on the scale of 0 to 200, with increasing RR value denoting

better separability of mineral of interest from gangue material (Carrasco et al., 2016; Walters, 2016).

The metal deportment curves and the RR curves both can accommodate input results from different

types of ores and from varying CGR methods. Among these methods is the Gangue Rejection

Amenability Test (GRAT). The GRAT is a characterization method designed to demonstrate the

advantage of assessing ores' response to CGR through size and density separations (McGrath et al.,

2018). The optimum gangue rejection is achieved based on the flexibility of cut size, density or

(a) (b)

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combinations of both. Like the single-stream CGR method, the performance of the GRAT

characterization method has been reported to link to the quality of the feed material, which is highly

dependent on the natural deportment behavior of the ore deposit (McGrath et al., 2020).

Aside from the widely applied deportment and RR curves, integration methods with cumulative

particle size distribution curves (Figure 3(a)) and the degree of separation plot (Figure 3(b)) have also

had limited application in CGR characterization. Both methods are associated with the selective

comminution (a method of CGR) research activities of the Institute of Mineral Processing Machines

and Recycling Systems Technology at TU Bergakademie Freiberg (Hesse et al., 2015).

Figure 3 (a) Size distribution integration method and (b) Ore separation degree method for evaluating CGR

(Hesse et al., 2017) NB: Q3 is the cumulative particle size distribution

The integration method applies the area under a curve integration approach to cumulative passing

distribution (Figure 3(a)). Here the selectivity of ores is determined by the area between the

distribution of the valuable component and the gangue content in the feed material (SF). Then

selectivity of the comminuted product is determined by calculating the area between the distribution

of the valuable component and the gangue component in the product material (SP). The difference in

the SP and the SF is quantified as the selectivity of the ore (SZ). The higher the value of SZ, the more

suitable an ore is for selective comminution. In addition to the integration method, Hesse et al. (2017)

also proposed the ore separation degree method. The method estimates the difference between

valuable and gangue component recoveries in the product stream. However, suppose the valuable

component concentration is known for feed, product and waste streams. In that case, an equation

similar to that of the mass balancing equation given in Wills (2013) could be used to estimate the ore

separation degree. The ore separation degree is plotted against the log of sieve sizes (separation cut)

used in screening the product material (Figure 3(b)). From the plot, a separation cut can be obtained

for the optimal ore separation degree.

Unlike the deportment and RR curves, the integration and the degree of separation methods are

mainly limited to selective comminution-based CGR approaches. While it is expected that these

methods could be used to also evaluate and quantify ore amenability to preferential size by size

deportment, just like the deportment and RR curves, their application has not been as widely

(a) (b)

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implemented as compared to the deportment and RR curves, with the latter being used on a large

scale to evaluate CGR potential across the mining value chain. In addition, the deportment curve can

be used to infer CGR amenability quickly rather than computing multiple areas under the curve

equations associated with the integration method. The approach involved in establishing these

detailed metallurgical CGR evaluation methods can be time-consuming and costly. Moreover, it can

produce varying CGR responses even within ores of the same deposit. This has seen recent CGR

characterization methods aim to incorporate geometallurgical principles to understand rock

characteristics' role in the CGR phenomenon.

Geometallurgical Method

The area of geometallurgy has been an emerging area for the past several decades, bridging the gap

between geology and metallurgy. The bridged space helps identify geological and mineralogical

features that link to mineral processing behavior for a holistic approach to sustainable metal

production (Lund & Lamberg, 2014). As this review paper has established, the strong connection

between geology and metallurgy on the amenability for CGR also has promoted the incorporation of

geometallurgy into the related CGR characterization methods. Table 1 summaries some related

geometallurgical CGR studies.

An initial study from Carrasco (2013) on applying geometallurgical testing protocols to CGR

characterization showed variable results. These results observed across the range of testing protocols

indicated that not all geometallurgical characterization test work could be applied to CGR. The

review then identified a more direct approach with a focus on ore particles in the work of Hesse et

al. (2017), Pérez-Barnuevo et al. (2018) and Bacchuwar et al. (2020). The common theme in these works

is utilizing geological and mineralogical attributes to associate CGR.

Hesse et al. (2017) provided the Quantitative Microstructural Analysis (QMA) method for

characterizing ore. The QMA method was aimed at analyzing and quantifying mineral characteristic

features using the mathematical petrography approach. The QMA approach collects comprehensive

data in a volume percentage of minerals, grain size, shape, distribution, roughness, orientation and

space-filling degree for all mineral groups within the ore particle. The outcomes from Hesse et al.

(2017) showed that the QMA method in conjunction with other physical properties could influence

choosing the suitable comminution device and input parameters to achieve selective comminution

for an ore. Their method requires further development despite the promising results as only a limited

range of ore types were tested. Furthermore, no clear evidence was established correlating their

resulting selective comminution-based CGR responses to the mineral characteristic, the comminution

device and the input parameters.

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Table 1 Summary of Related Geometallurgical CGR Characterisation

In the Pérez-Barnuevo et al. (2018) study, drill core ore textures were recognized and classified for

Canada's Mont-Wright iron ore deposits. Each texture group was processed through comminution

and heavy liquid separation (also known as DMS-based CGR). The response of each drill core texture

was then mapped against their processing performance. The outcome of Pérez-Barnuevo et al. (2018)

work suggested that ore texture can be used as a geometallurgical indicator to infer the expected

processing response of a particular mine zone during core logging. Although their characterization

approaches provided a site-specific library that can serve as a reference when making future process

predictions, the use of qualitative visual mineralogical parameters limits its application for modeling

purposes.

Research by Bacchuwar et al. (2020) using High-Resolution X-ray Micro-tomography (HRXMT) and

3D image analysis tools for gangue rejection prediction builds on the characterization method of

Pérez-Barnuevo et al. (2018). Bacchuwar et al. (2020) provide a systematic image processing technique

for CT images where voxel count (representing density) of specified mineral classes were identified

and used to estimate the theoretical density-recovery curve. The theoretical curve was found to be

reasonably representative when it was compared to the experimental density-recovery curve. Even

though the results were encouraging, there was no statistical validation of the results and the study

was limited to just one size fraction (-1.7+1.18 mm). In addition, the theoretical gangue rejection

Commodity

&

Location

CGR

Process Data Collected

Analytical

Technique

Data

Interpretation

Geometallurgical

Influencing factor

Identified

Related

Reference

Precious Metal

(Au)

Telfer Au-Cu mine,

WA, Australia

Screening Size, bulk

mineralogy

Sizing, chemical

assay, XRD

Mass vs metal

recovery,

Preconcentration

Factor (Upgrade

Ratio)

Lithology inferred

from geochemical

information

Carrasco

(2013)

Precious Metal

(Au)

Ballarat CGT,

Victoria, Australia

DMS

Size, bulk

mineralogy,

texture

Sizing, chemical

assay, XCT

Separation density

vs

recovery/rejection

XCT voxel count

(representing

density) to predict

CGR response

Bacchuwar

et al. (2020)

Base Metal (Pb-Zn)

Hermsdorf ,

Erzgebirge

Mountains,

Germany

Screening

Size, bulk

mineralogy,

texture, structure,

Vickers hardness,

fracture toughness

Sizing, QMA,

Vickers

indentation,

Ore separation

degree Microstructural

Information

Hesse et al.

(2017)

Bulk Commodity

(Fe)

Mont-Wright,

Quebec, Canada

DMS

Size, bulk

mineralogy,

texture, liberation,

mineral association

Sizing, chemical

assay, ore

microscopy, auto

SEM-EDS

Grade vs recovery Site specific

texture library

Pérez-

Barnuevo et

al. (2018)

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curve, the added benefits of the HRXMT characterization method is the provision of the grain size

distribution for texture description, grain shape, particle damage state and exposed grain surface

area. These added features make the HRXMT a suitable CGR characterization concept that can be

incorporated into the studies of Carrasco (2013), Hesse et al. (2017) and Pérez-Barnuevo et al. (2018),

provided that the robustness of the HRXMT is improved. The combination of these studies provides

the platform to investigate opportunities for improved CGR ore characterization and, ultimately, for

increased application in the mining sector.

OPPORTUNITIES FOR FUTURE IMPROVEMENT OF CGR CHARACTERIZATION

The topic of CGR is likely to increase in popularity over time, especially as ore gets lower in grade

and more complex in nature. However, the adoption of the practice by mining companies embracing

remains limited though it is anticipated that enhancing CGR characterization methods to include less

expensive, quicker results and more geometallurgical focused outcomes would promote uptake of

the technology. In order to increase confidence, the approach could utilize both a CGR

characterization method and the application of sophisticated statistical tools such as multivariate

statistics. Carrasco (2013) provided a method that used principal component analysis (PCA) to study

the influence of core logging information (such as geochemical composition, equotip hardness) on

metal deportment behavior. This approach looked at investing the influences of rock attributes on

gold deportment. Despite the inability to identify an attribute to explain the metal deportment

behavior, it was evident that the approach could be extended to other geological or mineralogical

features such as texture. Furthermore, the method seemed to have the ability to produce the

correlation with rock attributes which can eventually be used as a geometallurgical index or

indicator.

The development and application of a geometallurgical index can first examine how an ore could

perform when subjected to a particular CGR method. Pérez-Barnuevo et al. (2018) proposed the site-

specific geometallurgical indicator using rock textures, but unlike in this visualization method study,

the utilization of texture would require an automated feature recognition approach. The automated

recognition approach allows features to be obtained and used in CGR analysis since the visualization

method could be subjective and open to human error. The use of automated feature recognition was

used in the work of Bacchuwar et al. (2020). However, the method did not include quantification of

texture features that could potentially be included in their theoretical gangue rejection model.

Current advancements in technology and computer power have provided the avenue for more

accurate extraction of ore image features and texture quantification using computer-aided algorithms

and artificial intelligence (AI) (Fu & Aldrich, 2019). The approach has been studied quite extensively

in the literature (Fu & Aldrich, 2019; Guntoro et al., 2020; Lund et al., 2015; Voigt et al., 2019). Such

work shows that texture can be quantified effectively using methods such as Association Indicator

Matrix (AIM), Gray-Level Co-occurrence Matrices (GCLM), Local Binary Pattern (LBP) and

Convolutional Neural Networks (CNN). The quantification of such features is advantageous since

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the quantified value can be used in a mathematical model such as geometallurgical models as

compared to the qualitative or visualization method.

With the current state of CGR characterization, AI and machine learning tools could improve the

understanding of CGR responses. It would further provide a better platform for linking the geological

and mineralogical features to CGR responses. The resulting quantified responses can then be

incorporated into geometallurgical models. The work by Rezvani et al. (2019) is a typical example of

the new direction of CGR characterization. This research presented image analysis tools as a method

to produce liberation spectrum for synthesized coarse particles. The result obtained suggested a good

prediction of a liberation spectrum. It was then proposed to extend the work to actual coarse particles,

which can have application in CGR.

Lastly, the literature reviewed for CGR shows the application of geochemical analysis, optical

microscopy and XCT based automated mineralogy techniques. However, there is a range of process

mineralogy techniques also available to be used. Such analyses could be incorporated in emerging

techniques such as TIMA, LIBS, and hyperspectral imaging. In developing such methods, it would

be essential to consider their pros and cons, especially in terms of cost, accessibility, robustness,

detection range, mineral discrimination level, and data interpretation.

CONCLUSIONS

Selected techniques of Coarse Gangue Rejection (CGR) and some related innovations have been

presented and discussed in this paper. The paper reveals the comprehensive approaches in

characterizing ores amenable to CGR across geological, metallurgical and geometallurgical domains.

The discussion resulting from the literature shows that the geological method of CGR

characterization was insufficient in giving the overall CGR behavior of ore and thus requires a

metallurgical method for further confirmation. The metallurgical method has proven to provide a

broad range dataset that describes the CGR potential of an ore. The paper identified that the metal

deportment propensity of ore varies among and within deposits. Given this, the geometallurgical

CGR methods discussed in the literature was found to be the way forward since it provides early

indication for inferring CGR amenability of ores. These findings led to the paper emphasizing the

importance of developing geometallurgical indicators using sophisticated statistical tools and state-

of-the-art computer algorithms, which were lacking in previous studies. The new pathway for the

CGR characterization method identified in the review presents the opportunity to assess the ore

suitability to coarse gangue rejection and even predict the gangue rejection performances before

complete characterization is undertaken to establish operating parameters. The methodology could

give mining companies much more confidence to accept the CGR applications when presented to

them. Finally, the review recommends the need for future CGR characterization methods to focus on

establishing gangue rejection indicators since they are becoming more prevalent in the current ROM.

ACKNOWLEDGEMENTS

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The authors acknowledge the funding support from the Australian Research Council for the ARC

Centre of Excellence for Enabling Eco-Efficient Beneficiation of Minerals, grant number CE200100009,

and the sponsors of the Amira P420G Project (AngloGold Ashanti, Australian Gold Reagents,

FLSmidth, Gekko Systems, Gold Fields, Kemix, Newcrest Mining, Newmont Corporation, Northern

Star Resources, Orica, Solvay and St Barbara), Corem and Curtin University.

NOMENCLATURE

3D Three-Dimensional

AI Artificial Intelligence

AIM Association Indicator Matrix

CGR Coarse Gangue Rejection

CRC ORE Cooperative Research Centre for Optimising Resource Extraction

CT Computerized Tomography

CNN Convolutional Neural Networks

DMS Dense Medium Separation

GCLM Gray-Level Co-occurrence Matrices

GRAT Gangue Rejection Amenability Test

LBP Local Binary Pattern

LIBS Laser-Induced Breakdown Spectroscopy

MLA Mineral Liberation Analyser

Q3 Cumulative particle size distribution

QEMSCAN Quantitative Evaluation of Minerals by Scanning electron microscopy

ROM Run-Off-Mine

SF Selectivity of feed material

Sp Selectivity of comminution product

SZ Selectivity of ore material

TIMA TESCAN Integrated Mineral Analyzer

XRF X-ray Fluorescence

XRD X-ray Diffraction

XCT X-ray Microcomputed Tomography

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