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3rd International Geometallurgy Conference 2016

Paper Number: 107

The Business Case for Early-stage Implementation of Geometallurgy – an example from the Productora Cu-Mo-Au deposit, Chile

G.S. King1 and J.L. Macdonald2

1. Principal Mining Engineer, AMEC Foster Wheeler, Level 7, 197 St Georges Tce, Perth, Western Australia. Email: grant.king@amecfw.com 2. Principal Geologist, Mining Technical Solutions Pty Ltd, 768 Canning Hwy, Applecross, Western Australia. Email: lachlan@mintechsol.com

PREVIOUSLY PUBLISHED BY THE AUSIMM IN THE GEOMET 2016 CONFERENCE PROCEEDINGS

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ABSTRACT

The development of a predictive geometallurgical model is a multi-disciplinary and iterative task that involves

both discovery and integration aspects. In this paper, the authors discuss the concepts of geometallurgical

modelling in terms of the underlying relationships that are used to connect geology, metallurgy and economic

value, and how the early-stage preparation of a spatial geometallurgical model, enhances project value and

provides a sound basis for future studies and test work programmes.

Hot Chili’s (copper-gold-molybdenum) Productora Project, which is located in northern Chile, is a porphyry-

copper style deposit that can be exploited using open pit mining and preparation of a saleable concentrate

from fresh ore, as well has heap leaching and SX-EW extraction of copper from oxide ores. To enhance

project value in the Productora PFS, Hot Chili developed a geometallurgical model that was used to predict

the process responses of both sulphide and oxide ores. The Productora model incorporates test results from

mineralogical, metallurgical and comminution testing as well as other studies completed on a suite of

samples collected from the many geological domains of the deposit.

Using Bond Work Index and Abrasion Index proxies in the geometallurgical model, Hot Chili estimated the

variability of sulphide ore throughput and comminution costs. Heap leach acid consumption in oxide ore was

estimated from drill hole calcium concentrations. The model was then used in mine scheduling to identify

high and low throughput or high and low acid ore zones so that the processing of these zones could be

advanced or deferred to improve NPV. Additionally, the cost-benefits of preparing an early-stage

geometallurgy model were quantified through a comparison to a conventional model that was based on test

work averages.

INTRODUCTION

The development of a predictive geometallurgical model is a multi-disciplinary and iterative task that involves

both discovery and integration aspects. In this paper, the authors discuss the concepts of geometallurgical

modelling in terms of the underlying relationships that are used to connect geology, metallurgy and economic

value, and how the early-stage preparation of a spatial geometallurgical model, enhances project value and

provides a sound basis for future studies and test work programmes.

Hot Chili’s copper-molybdenum-gold (Cu-Mo-Au) Productora Project is located 16 km south of the regional

mining centre of Vallenar in Region III of Chile, approximately half way between La Serena and Copiapo

(Figure 1). The Project includes both the Productora tourmaline-breccia hosted Cu-Mo-Au deposit, and the

Alice porphyry hosted Cu-Mo-Au deposit (Figure 2).

To enhance project value in the Productora PFS, Hot Chili developed a geometallurgical model that was

used to predict the process responses of both sulphide and oxide ores. The Productora model incorporates

test results from mineralogical, metallurgical and comminution testing as well as other studies completed on

a suite of samples collected from the many geological domains of the deposit.

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Figure 1. Regional geological setting (image modified from Marschik and Fontboté 2001)

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Figure 2. Productora Project drill hole collars, tenement boundaries, deposit names, and grade polygons.

Yellow shading denotes 0.2%-0.3% Cu mineralisation and red shading denotes >0.3% Cu mineralisation.

Grid projection is WGS84 Z19S.

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GEOLOGY AND MINERALISATION

The Productora Project is located between the Coastal Cordillera and the Atacama fault system (Fig 1).

During the Cretaceous, a thick sequence of andesite and minor sediments (Bandurrias Group) was

deposited in an extensional regime within a volcanic island-arc setting (Moreno and Gibbons (eds) 2007). A

variety of porphyritic formations intruded this sequence, some of which are interpreted to be

contemporaneous with the host volcanic rocks. These porphyritic intrusions are interpreted to be

responsible for most of the alteration and mineralisation in the area, with Jurassic to Late Cretaceous

granodioritic and monzodioritic batholiths cropping out over large areas throughout the Coastal Cordillera

(SERNAGEOMIN, 2003).

Project Geology

The Productora Project deposits are hosted in the (lower Cretaceous) Bandurrias Group, which is a thick

volcano-sedimentary sequence comprising intermediate to felsic volcanic rocks and intercalated sedimentary

rocks. Dioritic dykes intrude the volcano-sedimentary sequence at Productora, typically along west- to

northwest-trending late faults, and are interpreted to represent sub-volcanic feeders to an overlying andesitic

sequence not represented in the project area.

The Bandurrias Group dips gently (15-30°) west to west-northwest and is transected by several major north

to northeast-trending fault zones, including the Productora Fault Zone, which coincides with the main

mineralised trend. These faults are interpreted to be sympathetic to the nominally parallel but distal Atacama

fault system. In the Productora deposit, these major fault zones are commonly associated with extensive

tectonic breccia that hosts Cu-Mo-Au mineralisation. Later faults cross-cut and offset the volcano-

sedimentary sequence together with the Productora (and sub-parallel) major faults. Late faults have a

generally west to north-westerly strike and while generally narrow, are locally up to 20 m wide.

The volcano-sedimentary sequence at Productora is extensively altered, particularly along major faults and

associated breccia zones, which have a distinctive alteration zonation. The distribution of alteration mineral

assemblages and spatial zonation suggest a gentle northerly plunge for the Productora mineral system,

disrupted locally via vertical and strike-slip movements along late faults. These late faults appear to be

trans-tensional and nominally normal to the distal Atacama fault system. The depth of oxidation varies

across the project, from 30 to 100 m.

Mineralisation – Productora Deposit

The Productora chalcopyrite-dominant mineralisation has two contrasting styles. The predominant style is

characterised by north to northeast trending, sub-vertical 2 m to 5 m wide feeder stocks at depth, which

increase in width to in the order of 50 m in the high-grade mineralisation zones near the upper surface of the

tourmaline-rich breccia. These wider brecciated zones vary in orientation. The central lodes tend to be sub-

vertical but with an upper flex in wider mineralised zones so that the lodes dip approximately 70° towards the

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west. In contrast, the shallower flanking lodes, dip shallowly away from the central lodes. There are also a

few locally steeply east dipping lodes.

Secondary and relatively lower-grade mineralisation at Productora occurs as flat lying manto-like horizons in

the southern, far northern and far eastern flanks. This manto-like mineralisation appears to be locally

focused along flow-top volcanic breccia and also intercalated, weakly-foliated volcanic and sedimentary

rocks. Lodes within the manto-like horizons are typically shallow-dipping 20° to 30° to the east or west and

are enclosed by lower grade mineralisation. Also, relative to the Productora breccia mineralisation, manto-

like mineralisation typically exhibits elevated levels of iron (in hematite or magnetite) and calcium (in calcite).

Gold and molybdenum mineralisation is associated with the copper mineralisation.

Mineralisation – Alice Deposit

The Alice porphyry deposit is located immediately beneath an extensive, pyrophyllite-rich advanced argillic

lithocap, with a porphyry stock of quartz diorite to granodiorite composition. The chalcopyrite-dominant

mineralisation at Alice is hosted in a porphyry with sheeted and stockwork quartz veinlets, within additional

locally disseminated background mineralisation. Gold mineralisation is closely associated to copper

mineralisation. Molybdenum (in molybdenite) mineralisation is associated with possibly late vein networks.

Post mineralisation albitisation locally lowers or destroys mineralisation.

PROJECT STATUS

In March 2016, following a drilling campaign that tested near-resource extensions and resulted in the

discovery of the Alice deposit, Hot Chili prepared a Prefeasibility Study (PFS) into project development. Hot

Chili subsequently reported to the Australian Stock Exchange (ASX) a total “higher grade” resource estimate

of 236.6 Mt grading 0.48 % Cu, 135 ppm Mo, and 0.10 g/t Au (Hot Chili, 2016). Currently the Productora

Project drill hole database includes 893 holes for a cumulative length of 245,327 m, which includes 212,692

m of RC drilling and 32,636 m of diamond drilling.

Geochemical Databases

Hot Chili has routinely completed multi-element geochemical analysis of drill hole samples from Productora.

To date 165,377 samples have been assayed via four-acid digestion and subsequent inductively coupled

plasma atomic emission spectroscopy (ICP-OES) to determine the concentrations of 33 elements. Hot Chili

has also assayed 14,054 samples from key exploration ‘type-sections’ using inductively coupled plasma

mass spectrometry (ICP-MS) to determine the concentrations of 48 elements. This geochemical database

allows Hot Chili to model deportment and alteration for exploration targeting and geometallurgical proxies for

development studies.

Both Productora and Alice deposits have undertaken dedicated metallurgical drilling, with the subsequent

samples subjected to conventional metallurgical test work. Included in the test work, the majority of samples

underwent geochemical assaying, which was combined with metallurgical results to discover metallurgical

proxies within the geochemical database.

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GEOMETALLURGY DEVELOPMENT OVERVIEW

Hot Chili investigated the correlation between the geochemical data and metallurgical data and developed

equations to predict metallurgical test results from geochemical proxies.

The designed sulphide comminution circuit was separately modelled to relate circuit performance with

metallurgical data. This allowed geochemistry to predict the performance of the comminution circuit using a

throughput equation. Furthermore, since comminution performance could be related to processing costs,

geochemistry was able to predict processing cost using a circuit power equation and a circuit media

consumption equation. These three equations formed the basis of the sulphide geometallurgical model

applied to mine scheduling and economic analysis.

The comminution circuit for the oxide process involved a three-stage crushing and screening circuit with no

milling. This relatively coarse comminution circuit would be largely unaffected by variable metallurgical

hardness or grinding properties. Instead, the geochemistry was able to predict the acid consumption of

oxide ore within the heap leach, which is a major cost component of the oxide processing cost.

Geometallurgy – Sulphide Ores

Bond Work Index (BWi) is a key metric determined from metallurgical comminution testing. BWi is is a

measure of the resistance of the material to crushing and grinding. Using BWi it is possible to determine the

specific energy requirement to grind a defined mass of material. BWi also allows the throughput rate of a

defined mass to be calculated for a designed (fixed) mill power supply.

The Productora PFS completed a number of metallurgical tests on the Productora diamond cores, including

BWi. The BWi test samples underwent multi-element analysis so that multivariate correlations between BWi

and the suite of element concentrations could be investigated. The regression process involved excluding

low concentration elements first (due to high measurement error) then a trial and error, step-wise approach

of assessing regression fit with and without certain elemental data. The final regression equations were

selected on the basis of goodness of fit, experience with similar deposits, and consistency of the regression

inputs to the known alteration mineralogy.

Hot Chili found that in the main body of the Productora deposit, aluminum and potassium were the preferred

regression predictors for BWi, with a squared correlation coefficient (R²) value of 0.75 (refer Equation 1).

Equation 1

��� = 0.9796�� + 1.5071� + 3.3686

Hot Chili tested the predictor performance for the five BWi results available for the Habanero and Alice zones

and found (refer Figure 3) that for both zones the BWi results were poorly predicted. Consequently, Hot Chili

used average BWi results from the test work in these two domains.

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Figure 3. Comparison of BWi test work values against predicted BWi values (Alice and Habanero values are

included to compare against fit).

Using Equation 1, BWi was calculated for the drill hole composites using the geochemical database. BWi

was then estimated into the transitional and fresh domains of the Productora Resource Model using inverse

distance (squared) estimation. The exceptions were the Habanero and Alice zones, where the average BWi

from test work was applied.

Figure 4 contains a 3 dimensional image of the mineralised sulphide blocks coloured by the estimated BWi

value. The Habanero domain in the north is distinguished by its high BWi values. The variability of BWi

appears to be related to a combination of proximity to structure and whole rock alteration. Apart from

Habanero, higher BWi values are spatially associated with cross-cutting faults.

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Figure 4. Oblique View showing Productora and Alice PFS pit designs and mineralised blocks coloured by

BWi value.

Bwi/Grade Relationships

Hot Chili found that for the Productora project there is a weak positive correlation between copper grade and

BWi (refer Figure 5, left) and a weak negative correlation between grade and predicted throughput (refer

Figure 5, right). This indicates that grade and throughput act to counter each other. As available metal

value increases, the rate at which value can be realised decreases, however, the relationship is weak. For

example, for the key grade range of 0.25% to 0.65% Cu, increasing grade corresponds to a fall in throughput

of less than 5%.

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Figure 5. Comparison of Cu grade with estimated BWi (left) and Throughput (right). The weighted boundary

of half the data (P50) is indicated by the circle, the linear line of best fit is also plotted.

Conventional BWi versus Geometallurgical BWi

The conventional approach as defined by the authors, is to calculate BWi or other metallurgical parameters

as an average of the metallurgical test work results for a given domain. The Productora PFS calculated a

conventional BWi for sulphide ore of 17.4 kWh/t, which was used for the design and sizing of the

comminution circuit.

Figure 6 contains histograms of block estimates of BWi and throughput rate in the Productora block model.

The histogram data has excluded model blocks outside the pit designs and BWi values below 10

(unrealistically low). The geometallurgical mean BWi of 16.75 compares favourably with the conventional

BWi of 17.4. Likewise, the geometallurgical mean throughput of 1,910 t/h compares favourably with the

conventional designed throughput of 1,750 t/h. The differences in means are due to the geometallurgical

means incorporating spatial and volume weighting, while the conventional means are independent of

weighting.

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Figure 6. BWi and throughput values from ore blocks inside the Productora pit design.

Geometallurgy – Oxide Ores

A significant portion of the oxide ore is scheduled to be mined in the first three years, driven by the mining

required to access sufficient stocks of sulphide ore. This generates a large stockpile of oxide ore whose

processing is deferred. Estimating the acid consumption in the geometallurgical model improves the ability

to schedule value using the oxide stockpiles because oxide ore is identified as low or high cost, as well as

low or high grade. Specifically, the model facilitates earlier processing of material with higher profit margins

to enhance NPV.

Bottle-roll and column leach tests were performed to determine the leachability and acid consumption of the

oxide ores using seawater under pH 2 conditions. Figure 7 contains cross plots of acid consumption

estimates versus calcite and calcium for ores from the main Productora zone. The left side plot reveals that

there is a strong positive correlation between the acid consumption and calcite, while the right side plot

shows a strong correlation between calcium and sulphuric acid consumption. Acid consumption estimates

for the Productora Main Pit range from 7 to 29kg/t (average of 17kg/t), with separate domains of acid

consumption of 13 kg/t for oxides and 19 kg/t for oxide/transition ore.

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Figure 7. Acid consumption comparisons between Calcite (metallurgy drilling) and Calcium (resource /

exploration drilling). Note different horizontal scales.

The Hot Chili geochemical database does not include calcite, but the available calcium (Ca %) assays and

acid consumption have a strong, and similar positive correlation to that determined between calcite and acid

consumption. In particular the slope and intercepts of the regression lines plotted for each plot in Figure 7

have similar orders of magnitude, albeit the calcium regression has a lower slope and higher intercept.

Hot Chili applied the metallurgical regression equation derived from calcite by substituting calcium from the

geochemical database as a proxy for calcite (refer Equation 2). This approach is conservative because

calcite contains only 40% calcium. Calcium is potentially present in a number of other ore minerals, thereby

overstating the amount of calcite present. Equation 2 will subsequently tend to over predict acid

consumption and leaching cost.

Equation 2

��������������� = 16�� + 5.04

Hot Chili tested the performance of the acid consumption calcium prediction on accessor domains (refer to

Figure 8).

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Figure 8. Comparison of Acid consumption against Ca% assay values from botlle roll tests. Note outlier

high Ca% but low acid consumer “Southern Manto” samples. Other samples are from various other domains

at the Productora deposit.

Hot Chili found that the predictive model performed poorly on Alice deposit, Eastern Oxide, and the Southern

Manto areas did not correlate well using the Equation 2 predictor, probably due to the different geological

and mineralogical nature of these external domains. This result was not unexpected due to pre-existing

domain differences.

Acid consumption for the Productora Main Pit was calculated using Equation 2 and estimated into the ore

reserve block model using an inverse distance squared algorithm. For the domains of Alice, Eastern Oxide

and Southern Manto acid consumption was set to average domain acid consumption from test work. Figure

9 is an example image of the acid consumption proxy value.

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Figure 9. Plan view of acid consumptions in oxide resource blocks across the Productora and Alice deposits.

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Discarded Proxies

As discussed above, Hot Chili investigate several other geometallurgical proxy variable, but most were

discarded due to lack of data confidence and/or correlation weaknesses. For example, Ai and A*b

correlations were investigated, but discarded in favour of the more robust BWi regression predictors.

CONVERTING PROXIES TO PROCESSING COSTS

Following identification, calibration and estimation of geometallurgical predictors, Hot Chili next investigated

how changes in the key drivers affected processing costs.

DMCC Pty Ltd (DMCC) prepared a spreadsheet model of the sulphide processing flowsheet that was

designed to allow testing of variations in the maximum SAG mill throughput and the ball mill product size.

Note an implicit assumption of this flowsheet model is that processing throughput would be SAG limited, but

not ball mill limited. DMCC (2015) stated that this is a reasonable assumption because:

“Most copper and gold circuits tend to be less sensitive to grind size. Subsequently, tonnes/capacity

override any benefit gained from superior recovery from a fine grind size, and copper circuits are generally

SAG limiting. In cases where grind size is very sensitive, then the circuit capacity is constrained by the

product size distribution to flotation or leach. In such circuits, the ball mill may become limiting. This often

occurs when the Bond Work Index rises well above the design.”

Figure 10 contains a schematic of the modelled SABC circuit flow sheet, and this model is consistent with

many porphyry copper processing operations. The key components include a SAG mill operating in SABC

circuit configuration, such that the SAG mill discharge is trommel-screened with overflow pebbles reporting

back to the mill via the pebble crusher. A trommel screen underflow is pumped to the cyclone classification

section that is in closed-circuit with ball mills.

Figure 10. SABC schematic flow sheet.

The equipment required for a nominal throughput of 14 Mt/a was modelled as:

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• A single 40x20 ft SAG mill fitted with a 20 MW Gearless Mill Drive motor,

• Two MP1000 or equivalent pebble crushers at 750 kW each, and

• Two 22x37 ft ball mills fitted with 10 MW pinion-driven mills,

where the mill length dimensions are based on an equivalent grinding length (EGL).

Using the methodology of Lane et. Al. (2013) as a basis, DMCC modelled the specific energy value (termed

the SAG Circuit Specific Energy or SCSE) for the “standard” circuit at Productora. The SCSE formula

developed in the model related the SCSE (kWh/t) to BWi and is stated in Equation 3:

Equation 3

��� = !1.55��� " 8.4417#�����$� �$%&'

where SCSE is the motor power and the applied energy is 0.93 or 93%, assuming 7% energy losses through

the mill drive train.

The SCSE equation is also used to calculate the total energy of the SAG and Ball mills and Pebble crusher.

Since the SAG mill is limiting, the underlying assumption is that energy consumption by the Ball mill and

Pebble crusher is constant and that variation occurs within the SAG mill. The specific energy of each

component modelled over a range of BWi values for the conventional flow sheet is illustrated in Figure 11.

Figure 11. Specific energy as a function of BWi in the standard SABC circuit at Productora

Mintrex prepared separate operating costs estimates for the sulphide process and oxide process with each

cost centre further divided into fixed and variable cost categories. Of the variable costs, the power and

consumable costs were modelled to determine specific energy and media consumption as function of BWi

and Ai. These were then converted into a variable cost ($/t) functions of BWi and Ai (for a defined energy

and media cost) as follows:

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Equation 4

(�)$%���� = 0.2067��� " 1.6051

Equation 5

��+,���,$������� = 0.01733 - 700���. " 0.07542

Equation 6

����,���,$������� = !0.0794��/.012#!1.667��� " 19.367#

While the BWi variable was estimated into each block of the resource model, the variable Ai was domained

more globally as Ai=0.27 for the Alice zone, and Ai=0.305 for the Productora zone. The equations above

represent the finished input into the scheduling and financial modelling process.

Having modelled the energy requirements of the ore to be ground to 150 microns, we then consider the

change in circuit throughput with respect to BWi – again driven by the limiting SAG mill, which is supplying

energy (motor power) at a constant rate. Mintrex were able to determine throughput (t/h) as a function of

BWi (refer below) for the nominal process design.

Equation 7

34%��&4��� = 113829���56.076

The throughput equation is illustrated in Figure 12 below and is essentially derived from applying the SCSE

equation for the SAG mill defined earlier to the Productora “standard” circuit.

Figure 12. The regression equation for the Throughput function of BWi

The useful output of the throughput function is that variable throughput can be incorporated as a driver in the

mine scheduling process.

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PIT OPTIMISATION AND DESIGN

Hot Chili prepared a geometallurgical model to serve as the basis for PFS financial modelling, with the goal

of identifying possible project fatal flaws, and where identified, reduce and manage project risks. The model

also provided a means to test positive upside risks in the mine scheduling process. In particular, Hot Chili

considered that the ability to be able model blending different ore types based on variable geometallurgical

characteristics provided a mitigation against scheduling the process plant to operate outside reasonable

engineering tolerances of ±10% and thereby ensure that the mine schedule would be linked to optimal plant

performance.

Comparison of Conventional and Geometallurgical Optimisations

In general a mine planner has two options for identifying an optimum pit shell being a conventional cost

methodology approach and a geometallurgy cost methodology. Hot Chili found when comparing these two

approaches for Productora, that there were no significant differences in the pit shell shapes. This confirmed

that geometallurgical variability tends to add value in the scheduling process, whereas for the same given

metal prices and recoveries, the overall grade and distribution, tend to control pit shape.

Hot Chili developed production cost scenarios to compare the conventional metallurgy design values with the

costs based on the geometallurgy modelling. For both scenarios, Hot Chili prepared optimum pit shells

using the Lerchs-Grossmann algorithm (LGA), which included contract mining costs and sustaining capital

costs. Table 1 is a listing of the key results from this comparative study1.

Area Item Conventional Geometallurgy

Mine (Mt) 563.3 572.5

Strip ratio 2.70 2.68

Mining cost (US$/t) 1.87 1.87

Flotation Plant (Mt) 126.6 131.0

(%Cu) 0.45 0.44

(Mlb) 1,035 1,053

(Mlb equiv¹) 1,199 1,221

Heap Leach (Mt) 25.7 24.6

(%Cu) 0.42 0.42

(Mlb) 128 123

(Mlb equiv) 1,326 1,344

Table 1. Comparison of Lerchs-Grossmann pit shells for Geometallurgy and Conventional scenarios.

¹ equivalent included gold and molybdenum credits.

1 It should be emphasised that these figures were preliminary results (using early cost inputs) and not the

final schedule quantities (e.g. ore).

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Hot Chili found that for Productora LGA pit shells, the key differences between a geometallurgical and

conventional approach were that the geometallurgical approach could profitably process:

• 4.4Mt more sulphide ore (131.0 Mt compared to 126.6 Mt)

• 1.1 Mt less oxide ore (24.6 Mt compared to 25.7 Mt).

In total, the geometallurgical approach produced 18 Mlb more copper metal equivalent pounds (1,344 Mlb

compared to 1,326 Mlb), which represents a 1.4%.increase. These outcomes validated the use of a

geometallurgical approach, whose application indicated a slightly larger ore inventory based on lower

average costs and more value recovered.

Comparison of Conventional and Geometallurgy Schedules

Using the same ultimate pit design, Hot Chili prepared two schedules with the first being a fixed-throughput,

conventional schedule and the second a variable-throughput, geometallurgy schedule (refer to Figure 14).

The definition of ore and waste was identical for both schedules and was based on the average

geometallurgical processing costs, which were slightly lower than the conventional costs. Subsequently, both

schedules treat the same quantity of ore and metal tonnes.

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Figure 14 - Comparison of Geometallurgy and Conventional scenario schedules and financial results.

The most notable effects of the using the geometallurgical schedule is:

• Improvement in the project financial metrics, in particular a 13% increase in NPV and a 5% reduction

in capital intensity.

• Higher throughput in the first three years, and higher overall throughput.

• Shorter mine life for lower fixed costs overall and a lower average variable cost due to lower energy

consumption.

• A lower throughput than the conventional schedule in Year 6 and 7, when ore from the Habanero

zone is being processed (high BWi, high grade).

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THE BUSINESS CASE FOR EARLY-STAGE GEOMETALLURGY

The business case for early adoption of geometallurgical modelling in the Productora examples are as

follows:

• The early additional expense of multi-element analysis to build the geochemical database cost USD

2.2M. The initial application was exploration targeting.

• The geochemical database provided an opportunity for geometallurgy at the PFS stage. For the

Productora Project PFS, geometallurgy added 13% (USD 25M) to the NPV.

• A specific NPV positive outcome is not guaranteed. The business case for early-stage adoption is in

bringing forward and realising something closer to the “true” processing variability and its inherent

risks and opportunities.

• The geometallurgical modelling identified risks hidden by conventional assumptions. Discovery

during the PFS provided the opportunity to react for a more robust project outcome.

• Without the geochemical database, a conventional approach has limited optionality and delays

knowledge (quantitative or qualitative) to later studies or mining.

• Proxy models allow future drilling to target areas of mining, milling or economic risk, ensuring that

future studies and drilling are efficient and timely. Identified areas of high BWi (e.g. Habanero) or

high acid consumption allow future studies to lower risk in these areas.

CONCLUSIONS

Geometallurgy can be defined as the integration of geological, mine planning, extractive metallurgy and

economic information to maximise the Net Present Value (NPV) of mining project, while concurrently

minimising technical and operational risks.

Early-stage adoption of spatial quantitative geometallurgical models is underpinned by an investment in full

multi element analysis of all exploration to infill resource definition drilling. Early-stage adoption of spatial

quantitative geometallurgical models can provide positive economic outcomes, by providing spatial

representation of early metallurgical drilling

For the Productora project, Hot Chili prepared a geometallurgical model to create a predictive model for the

sulphide and oxide mineral processing plants, by utilising results from mineralogical, metallurgical and

comminution test work and studies completed on a suite of samples among the various geological / ore

domains and to create optimised pit shell designs. This approach has identified the key risk and

opportunities for the project and is a focus of future planning for DFS work and eventual project

implementation

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ACKNOWLEDGEMENTS

The authors would like to acknowledge Hot Chili Ltd for the support for this paper, Angela Escombe for

discussions and data coding for relating the weathering and copper mineral species domaining, as well as

Ian Kerr, David Readett and Leon Lorenzen from Mintrex Pty Ltd. Mark Murphy for reviewing the final draft

and incisively making the paper ‘get to the point’.

REFERENCES

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Republic of Chile; Confidential Report to Hot Chili Limited, July 2012. Internal Hot Chili report.

DMCC Pty Ltd, 2015. Hot Chili: Productora copper project - Revised comminution circuit calculations for Hot

Chili’s 14 Mt/y Productora project. Internal Hot Chili report.

Escolme, A, Berry, R and Hunt, J, 2016. Predicting Ore Classification using Geochemistry at the Productora

Cu-Mo-Au Deposit, Chile in 3rd International Geometallurgy Conference 2016.

Fox, K, 2000. Fe-Oxide (Cu-U-Au-REE) Mineralization and Alteration at the Productora Prospect. MScThesis

Hot Chili Limited, 2016. “Hot Chili delivers PFS and near doubles Reserve at Productora” in ASX release

www.hotchili.net.au on 2nd

March, 2016.

Lane, G., Foggiatto, B., Bueno, M., 2013. Power-based comminution calculations using Ausgrind, in

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Macdonald.J L. 2014. Productora Resource Revision 2 Report. Internal Hot Chili report.

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Chile in Economic Geology, vol 96, 2001, pp. 1799-1826.

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