Gravity Gold 2010

165 Pages·2010·40.22 MB·English

by  Dominy, Simon (Eds.)

GRAVITY GOLD 2010
21-22 September 2010
BALLARAT, VICTORIA
The Australasian Institute of Mining and Metallurgy
Publication Series No 8/2010
A u s i mm
THE MINERALS INSTITUTE
Edited by Dr Simon Dominy
Published by:
The Australasian Institute of Mining and Metallurgy
Level 3,15 - 31 Pelham Street, Carlton Victoria 3053, Australia
i
©The Australasian Institute of Mining and Metallurgy 2010
All papers published in this volume were refereed prior to publication.
The Institute is not responsible as a body for the facts and opinions advanced
in any of its publications.
ISBN 9781 921522 291
Desktop published by:
Kylie McShane and Olivia Tet Fong
The Australasian Institute of Mining and Metallurgy
Printed by:
BPA Digital
11 Evans Street, Burwood Victoria 3125, Australia
ii
FOREWORD
Multidisciplinary teams are a distinct feature of our global mining industry. Gravity Gold 2010
aims to bring together geologists, engineers and metallurgists.
As a result of the Global Financial Crisis, there are now great corporate pressures to reduce costs
and increase efficiencies and all within a framework of safe operations and environmental and
economic sustainability. In the face of such increasing challenges, we need to redirect our work
assiduityto cost-effectiveness and efficiency, technical competency and innovation. Optimisation
of our activities, particularly of mining and metallurgical recovery has become ever more critical.
Gravity Gold 2010 aims to explore the issues of mining operations that are characterised by
coarse-gravity gold mineralisation with an often high-nugget effect. These deposits usually
require strong geological control, selective mining methods and apply gravity-based processing
technology. Key areas of common interest include: understanding the orebody; resource and
reserve estimation; predicting grade and metallurgical variability; optimising mining and
metallurgical extraction; definition of project risk and economics; tailings and waste handling;
and environmental sustainability.
This two day conference will share problems and learning experiences in a multidisciplinary
environment within the rural setting of the Central Victorian goldfields. We have put together a
program of some 20 papers covering issues related to flotation; gravity concentration; leaching;
metallurgical testing; selective mining and stope cleaning; plant optimisation; orebody variability;
safety and security; and sampling. Case studies include projects in Africa, Australia and North and
South America.
I hope that the proceedings volume, together with the oral presentations, discussions with the
presenters and the open forum, will provide insights to challenge the way we do business.
The organisation of a conference requires the support of a number of people. I would like to
take this opportunity to thank everyone who has helped on the Organising Committee, without
their dedication and enthusiasm the conference could not have taken place.
On behalf of the Organising Committee, we would like to thank the authors and presenters
of the technical papers for their high quality contributions. I would particularly like to thank the
band of reviewers who undertook the task of maintaining the quality of the papers.
Finally I would like to acknowledge the contribution provided by industry organisations and
companies throughout Australia and overseas, for sponsorship, the trade exhibition and for
supporting delegates to attend this meeting.
I hope that all delegates will participate in the conference technical and social sessions and meet
and engage with old and new friends, thus ensuring that the ideas, solutions and technologies
presented will be of benefit to all.
Dr Simon Dominy FAuslMM(CP), Snowden Group
Conference Chair
vii
C O N T E N TS
Keynote Addresses
Heterogeneity, Sampling Errors and the Nugget S C Dominy, IM Platten 3
Effect in Gold Ores - Implications for Evaluation, andRCAMinnitt
Exploitation and Extraction
Where is the Risk in Coarse Gold Projects? WΒ Edgar 19
Gravity Gold - The Process of Change S Gray 21
Stope Cleaning - Historical Methods and Future M Tuck 25
Developments
Papers
Gravity Gold Recovery in Obuasi Mines J Κ Afidenyo, J Bernasko 29
and J Osei-Owusu
Coarse Gold Separation at Bendigo Mining's M Braaksma and Ρ Gray 41
Kangaroo Flat Operation
Development of the Sao Chico Gold Project, Brazil Κ Chappie 49
The Use of Nuclear Tracers to Evaluate the Gold R Clayon 51
Recovery Efficiency of Sluiceboxes
Minimum Industry Gold Room Security Standards D Connelly 63
Selection Sizing and Developing the Optimum D Connelly 69
Gravity Gold Circuit for Your Project
Redevelopment of the Morning Star Gold Mine G Curnow, Ρ Hepburn- 81
Brown and Ε O'Grady
Determining Gold Particle Size in Gravity Ores for S C Dominy, IM Platten 83
Sampling and Metallurgical Characterisation - and Y Xie
Discussion and Test Protocol
Predicting the Benefit of Gravity Recovery Prior to M Fullam 97
Flotation
Is the Python Right for Me? Python Amenability Τ Hughes, Κ Donaldson 101
Test Work and Β Murphy
xiii
Geology and Definition of the Wattle Dam Coarse R Hutchison 109
Gold Deposit
The Role of Gravity and Intensive Cyanidation in Ν Katsikaros and Ρ Davies 119
Processing Preg-Robbing Gold Ores
Effect of Gravity Recovery on Overall Plant CNaudι 121
Recovery, Sadiola - A Case Study
Safety Culture and Resilience Engineering - M Pillay, D Borys, D Else 129
Exploring Theory and Application in Improving and M Tuck
Gold Mining Safety
Maximising Payable Gold in Base Metal C Potorieko, A Duran 141
Concentrators and R H Cuttriss
Recovery of Gold from Gravity Tailings by Flotation J Rowe and S Sidrak 149
Modelling the Effect of Gravity Gold Recovery on W Ρ Staunton and AR Box 155
Leaching Performance
Resue or Split Face Firing - Trial Results from M Tuck and Ρ Ganza 159
Hillgrove Mine
In Plant Sampling for Gravity Recoverable Gold Β Watson 165
Author Index 167
xiv
Keynote Addresses
Heterogeneity, Sampling Errors
and the Nugget Effect in Gold
Ores - Implications for Evaluation,
Exploitation and Extraction
S C Dominy1-2,1 M Platten3 and R C A Minnitt4
ABSTRACT
Sampling errors result from heterogeneities within the lot under study. Within a rock mass in
situ heterogeneity results in the geological nugget effect, which accounts for most of the natural
variability. In broken rock, constitution and distribution heterogeneities are important. A major
result of the constitution heterogeneity is the Fundamental Sampling Error (FSE) which is the
smallest residual error that can be achieved. It cannot be eliminated, but can be minimised and
reflects the fact that the broken rock sampling process is never completely free of error. Together
with other sampling errors, FSE contributes to the total nugget effect. The FSE for a sampling
protocol can be evaluated using the so-called Gy equation. Its practical applications to low-grade
gold ores have often been unsuccessful due to problems linking the FSE and minimum sampling
mass and the definition of key parameters such as the liberation diameter. Well-designed sample
protocols minimise the total sampling error and hence the nugget effect. Reduction of the
nugget effect is a key requirement for all sampling programs whether related to exploration and
resource evaluation, metallurgical test sampling, grade control or metallurgical plant sampling.
This contribution reviews the relationship between heterogeneity, FSE and nugget effect in gold
deposits; and emphasises the need for FSE management throughout the mine value chain.
INTRODUCTION
Poorly designed sampling protocols can result in elevated collection is followed by sample reduction in both mass and
project risk by artificially increasing grade variability fragment size to provide an assay charge or test sample.
(ie the nugget effect). Sample collection, preparation and
The sampling process can be particularly challenging in the
assay protocols that are based on the characteristics of the
gold environment (Sketchley, 1998; Pitard, 1993) and no more
ore type, together with Quality Assurance/Quality Control
so than in the presence of coarse (gravity) gold ores (Royle,
(QAQC) systems, will reduce the nugget effect. Reporting
1989; Dominy et al, 2000; Dominy and Petersen, 2005;
codes (eg JORC, SAMREC, etc) require the Competent Person
Cintra er al, 2007). From a sampling perspective, coarse gold
to consider the quality and implication of sampling and
is considered to be greater than 100 μπι in size (Dominy et al,
assaying programs. High sampling errors beget higher project
2000; Dominy, Platten and Xie, 2010). Where it constitutes
uncertainty; despite this fact sampling does not receive the
more than ten per cent of all gold present, problems are likely
attention it deserves.
to be encountered during sampling and assaying.
Sampling is thus a vital component to all stages of a mining
The inherent nature of the mineralisation, in particular
project and includes the sampling of in situ material and
the nature and distribution of the gold particles, will control
broken rock (Carrasco, Carrasco and Jara, 2004; François-
sampling ease or samplability. Within the coarse- (>ιοο μπι)
Bongarçon, 2004; Pitard, 2005, 2007; Dominy, 2007;
to fine-gold (<ιοο μπι) spectrum, the samplability of a
Minnitt, 2007; Holmes, 2008). Poor sampling produces
deposit ranges from relatively simple for fine-grained
hidden financial losses related to low quality resource/reserve
estimates, the misclassification of ore and waste in the mine disseminated gold particles, through to difficult for coarse
and poor mass balances in the plant (Carrasco, Carrasco and gold mineralisation. There is strong evidence to show that
Jara, 2004; Minnitt, 2007). The aim of any protocol must relationships exist between increased gold grade and larger,
be to extract a representative and reproducible sample that potentially clustered gold particles (Dominy and Platten,
accurately describes the material in question. Primary sample 2007; Dominy, Xie and Platten, 2008b).
1. FAuslMM(CP), Executive Consultant and General Manager (UK), Snowden Mining Industry Consultants Limited, Abbey House, Wellington Way, Brooklands Business Park, Weybridge Surrey
KT130TT, England. Email: sdominy<8>snowdengroup.com
2. Adjunct Senior Principal Research Fellow, School of Science and Engineering, University of Ballarat, Mount Helen, Ballarat Vic 3353.
3. Senior Principal Consultant, Snowden Mining Industry Consultants Limited, Abbey House, Wellington Way, Brooklands Business Park, Weybridge Surrey KT13 OTT, England.
Email: [email protected]
4. JCI Professor of Mineral Resources and Reserves, School of Mining Engineering, University of Witwatersrand, Private Bag 3, Wits 2050, South Africa. Email: [email protected]
GRAVITY GOLD CONFERENCE / BALLARAT, VIC, 21 - 22 SEPTEMBER 2010 3
S C DOMINY, I M PLATTEN AND RCA MINNITT
Part of the total nugget effect is directly related to the throughout the mine value chain are presented in Dominy
sampling process, that is, to the mass of the field sample (2010a and 2010b), Holmes (2005 and 2008), Minnitt (2010)
taken; the effectiveness of collection; sample preparation (eg and Pitard (2005).
crushing, pulverising and splitting); the mass of subsamples
(samples for pulverising and assay); and analysis. THE NUGGET EFFECT
This paper reviews the relationship between heterogeneity,
Defining the nugget effect
FSE and nugget effect; and emphasises the need to reduce
FSE and hence nugget effect throughout the mine value chain. Nugget Effect is a quantitative t term describing
Challenges are put in the context of evaluation, exploitation the inherent variability between samples at very small
and extraction of hard rock deposits. This paper principally
separation distances; though in reality has a wider remit than
addresses the challenges of gold deposits, however the
just differences between contiguous samples. It is effectively
discussions are generally applicable to other precious metals,
a random component of variability that is superimposed on
native copper, molybdenite and galena bearing deposits that
the regionalised variable and is defined in a variogram as the
may show complex distribution and delayed particle size
reduction. percentage ratio of nugget variance to total variance (the sill).
Deposits that possess nugget effect values above 50 per cent
SAMPLING ERRORS and particularly above 75 per cent are the most challenging to
evaluate (Dominy et al, 2003). The magnitude of the nugget
Sampling errors are defined in the Theory of Sampling ('TOS')
effect is related to:
as promulgated by the works of Pierre Gy (Gy, 1979,1992 and
1998). They are relevant to all sampling applications, including • in situ heterogeneity of the mineralisation,
those used during evaluation, exploitation and extraction. The • sample support,
key errors are (Gy, 1979; Pitard, 1993 - Table 1): • sample density,
• Fundamental Sampling Error (FSE), • sample quality, and
• Grouping and Segregation Error (GSE), • assay procedures.
• Delimitation Error (DE), Through the mine value chain, optimised sampling protocols
aim to reduce the total nugget effect thereby also reducing:
• Extraction Error (EE),
• Weighting Error (WE), • the total sampling variance,
• Preparation Error (PE), and • the skewness of the assay distribution, and
• Analytical Error (AE). • the number of extreme assay values.
The FSE and GSE are irreducible random errors related to
Composition of the nugget effect
the inherent heterogeneity and characteristics of the material
being sampled and can only be minimised through optimised The nugget effect reflects the proportion of nugget variance
sampling protocols. The other errors arise as a consequence of present in a data set - in effect the random variability. This
the physical interaction between the material being sampled variability has two principal components; (a) geological or in
and the technology employed to extract the sample and result situ nugget effect (GNE) and (b) sampling nugget effect (SNE).
in a sampling bias. Provided the correct sampling technology
and procedures are employed the sampling bias can be The GNE component relates to the microscopic differences
totally eliminated. All errors are cumulative and contribute in composition throughout the mineralised zone/domain - it
to the total sampling error, which in turn contributes to the is in effect the in situ constitution heterogeneity (Platten and
nugget effect. FSE and GSE are likely to contribute between Dominy, 2003). This principally refers to the distribution of
approximately 50 per cent and 90 per cent and EE to AE up to single grains or groups of gold particles distributed through
25 per cent of the total sampling error.
the ore to larger continuous zones. Variability is most serious
Theoretical discussions on sampling errors can be found where there are very small-scale, low continuity structures
in Gy (1979, 1998) and Pitard (1993). Practical implications such as high-grade gold carriers within the main structure
TABLE 1
Definition of sampling errors.
Sampling error Error definition
Fundamental Sampling Error (FSE) The FSE results from grade heterogeneity of the broken lot. Out of all sampling errors, the FSE does not cancel
out and remains even after a sampling operation is perfect. Experience shows that the nugget effect can be
artificially high because sample weights are not optimised
Grouping and Segregation Error (GSE) Relates to the error due to the combination of grouping and segregation of rock fragments in the sampled lot.
Once a rock volume is broken, there will be segregation of particles whether this be in a surface stockpile or
laboratory pulp
Delimitation Error (DE) The DE results from an incorrect shape of the volume delimiting a sample
Extraction Error (EE) The EE results from the incorrect extraction of a sample. Extraction is only correct when all fragments within the
delimited volume are taken into the sample
Weighting Error (WE) WE relates to collecting samples that are of a comparable support. Samples should represent a consistent mass
per unit
Preparation Error (PE) Refers to issues during sample transport, preparation (contamination and losses), and unintentional and
intentional human error
Analytical Error (AE) Relates to all errors during the assay and analytical process
4 GRAVITY GOLD CONFERENCE / BALLARAT, VIC, 21 - 22 SEPTEMBER 2010
HETEROGENEITY, SAMPLING ERRORS AND THE NUGGET EFFECT IN GOLD ORES
or vein-lets within wall rocks (Platten and Dominy, 2003; HETEROGENEITY
Dominy and Platten, 2008). The dissection and displacement
of a previously continuous high-grade vein by later barren Defining heterogeneity
events will also lead to a high GNE. A clear indication of the
In situ heterogeneity
GNE is where two halves of a drill core are assayed and show
order of magnitude or more differences in assay grades. It is At the in situ level (eg collection of a sample as drill core or
not impossible for the GNE to account for up to 50 per cent of from a mine development face) heterogeneity is based on
the total nugget effect. the nature of gold particle size and distribution (Platten and
Dominy, 2003; Dominy and Platten, 2007; Pitard, 2007;
The SNE component is related to errors induced by
Dominy, Xie and Platten, 2008b). Evenly disseminated
inadequate sample size (FSE and GSE), sample collection
mineralisation will have lower heterogeneity (eg Carlin style
(DE, EE and WE), preparation methods (PE) and analytical
mineralisation) compared to more erratically distributed
procedures (AE). In some instances, the SNE is the dominant
styles of mineralisation that have a higher heterogeneity (eg
part of the total nugget effect and reflects a high FSE and non-
mesothermal veins). In situ heterogeneity leads to the in situ
optimal protocols. or geological nugget effect (GNE - Platten and Dominy, 2003;
The contribution of GNE and SNE to the total nugget effect Pitard, 2007).
provides an intimate link between sampling and geostatistics The GNE is reduced by collecting larger and more closely
(François-Bongarçon, 2004). spaced samples (hence more samples). In situ sample mass
requirements to achieve a given level of precision can be
Nugget effect, sample support and
estimated using a Poisson approach, where the rare gold
volume-variance particles are assumed to have a Poisson distribution (Dominy,
1997; Dominy, Platten and Xie, 2010).
The term 'support' embodies the notion that samples have a
specific size, mass, shape, orientation or any other physical Broken rock heterogeneity
characteristic that is common to them. Samples are usually
In the broken rock condition, heterogeneity is defined as the
collected at specific supports; for example 1 m lengths of
nature of a lot under which all the contained fragments are
diamond drill core. These samples represent average values
non-identical. In this case, two types of heterogeneity are
measured over specified geometrical shapes oriented in
defined: (a) the Constitution Heterogeneity (CH) and (b) the
specific directions. As the size of the sample increases, it will Distribution Heterogeneity (DH). CH and DH contribute to
tend to encompass more of the structures that give rise to the sampling nugget effect (SNE), through the FSE and GSE.
variability (eg GNE). In particular, it is likely to include more
The CH is the heterogeneity inherent to the composition
small-scale structures and/or gold particles. (eg gold grade, etc) of each fragment making up the lot. The
The total nugget variance is inversely proportional to the greater the difference in composition between each fragment,
the higher will be the CH of the lot (CH). Mixing and
volume of the sample. In general, the variance (as nugget L
homogenisation has no effect on CH. The CH is the principal
variance) of samples decreases as their volumes increase,
cause of the FSE, which occurs when a sample is selected from
although not in a linear manner (the volume-variance
a broken lot. Gold ores often show the ultimate heterogeneity,
relationship). Other regionalised variable parameters such as
where relatively few fragments in a lot contain large quantities
spatial variance and geostatistical range remain unaffected. If
of gold.
the sample size (eg core diameter) is increased indefinitely the
CH, relates to the differences in composition for a given
limiting value of the variance is the spatial variance, this will
number of fragments (N,.):
remain virtually unaffected. Increasing the sample length will
reduce the contribution from spatial variance, but only when
CH = N„.Σίβ, - a,)2 M* / a,2. M,2 (2)
the geostatistical range is small. When the geostatistical range L
is large, then long samples are required to reduce the variance.
where:
However, a point is soon reached at which the benefit from any
a is the content of a fragment
reduction in variance is offset by the reduction in selectivity (
a, is the average content of the lot
arising from long sample lengths.
Mj is the mass of a fragment and M the mass of the lot
L
Relationship between nugget effect and
The DH represents the difference in average composition of
sampling errors
the lot from one place to the next in the lot; it is responsible
The total nugget effect (TNE) is composed of a series of for the irregular distribution of grade and values in groups of
individual errors that are characteristic of both the in situ fragments of broken ore. The DH can be influenced by large
gold distribution and sampling quality issues. It can be differences in density and fragment composition. DH of a
summarised as: given lot (DN, ) is controlled by CH,, the spatial distribution of
fragments and the lot shape.
TNE = GNE + [£{FSE + GSE + DE + EE + WE + PE} For a given number of fragment groups (N), DH, is given
G
+ AE] (1) as:
The errors FSE to PE are applied to each sampling stage, DH, = N T(aj - a, )2 M2 / a,2. M,2 (3)
G n
from sample field collection to subsampling prior to assay -
errors are cumulative. Good procedures in the laboratory do where:
not cancel out a poor quality field sample or inappropriate a, is the content of a group, a, the average content of
primary split. the lot
GRAVITY GOLD CONFERENCE / BALLARAT, VIC, 21 - 22 SEPTEMBER 2010 5
S C DOMINY, I M PLATTEN AND RCA MINNITT
M is the mass of a given group. 2 χ 104, it is important to determine its order of magnitude
n
DH leads to GSE, though it is also related to FSE since (Pitard, 1993). The value of HIL should be expressed with no
a group of fragments will bear an inherent FSE. From a more than two significant figures (Gy, 1992).
practical perspective, DH is not measured and GSE is kept to
L Determining heterogeneity
a minimum through accumulating as many small increments
to form the composite sample as possible. The 'Gy 50/100 Piece' Experiment
The determination of HI, is of major concern when applying
Heterogeneity invariant
the FSE equation. The method for its indirect determination
A measureable approximation of CH, is the Heterogeneity is based on the 'Gy 50/100 piece' experiment, also known as
Invariant (HI) for the lot, which is the product of the sampling the Heterogeneity Test (HT - Gy, 1979; Pitard, 1993; 2004).
L The data can be processed to estimate the value of Κ and the
constant (K) and the nominal fragment size d:
n liberation size of the gold (d).
L
For coarse d ores (eg a stockpile or mill feed), 50 to 100
HI = CH.M/N (4) n
L L L F individual large fragments of 1.5 kg to 2.5 kg are picked one
by one and each assayed to extinction. For finer ores (eg blast
hole and RC chips or laboratory jaw crusher product), 50 to
HIL = K-D„B (5)
100 groups of 50 individual fragments are picked one by one.
Each group of 50 fragments are each assayed to extinction.
where:
Figure 1 demonstrates the heterogeneity of a high-nugget
M is the mass of the lot
L gold system, through the large number of low-grade samples
The term M,/N, is the average fragment mass. The HI,
t (<3 g/t Au) and only a few high-grade samples (between 36 g/t
is effectively the constant factor of CH and is a numerical Au and 1292 g/t Au). The calculated Κ value was 350 g/cmls.
measure of how heterogeneous a sample lot is, having the In this case, whilst the HT indicated a variable ore, extensive
dimensions of mass in g and is inversely proportional to grade processing of bulk samples proved that the gold particle
(Gy, 1992). The value of HI, is defined as the Ί/iooth of the size was very coarse (~i cm) and the Κ value closer to
sample mass that would generate a relative sampling standard 193000 grammes/cm15.
deviation equal to ten per cent at the 95 per cent confidence The methodology is based on the hypothesis that the grade
level' (Gy, 1992). Gy reports that values of HI, range from of a size fraction and the proportion M /M (where M is
La L (/i
108 grammes for a finely ground high-grade concentrate to the mass of the size fraction and M, is the mass of the lot)
19 000 grammes for an alluvial gold ore (Gy, 1992). The of this size fraction in the lot, varies minimally between the
fact that the major component of the sampling variance is size fragments (Gy, 1979; Pitard, 1993). It is then possible to
proportional to HI leads to two conclusions. Firstly, it is calculate HI, from the formula:
L
necessary to rely upon effective estimation of the HIand
L
secondly, when a factor can take values ranging from 108 to HI = g [Σ (a - a)7a2. M* /M] (6)
L q Q Q q Q
FIG 1 - Heterogeneity curve resulting from a 'Gy 50 Piece' experiment (2.5 kg samples) from a high-nugget gold-quartz reef in Australia.
6 GRAVITY GOLD CONFERENCE / BALLARAT, VIC, 21 - 22 SEPTEMBER 2010