An integrated pit-to-plant approach using technological models for strategic mine planning of copper and gold deposits

Abstract

The strategic mine plan is a crucial step for the success of mining companies, and for its development, it is necessary to use a number of inter-related variables that are usually estimated independently. These variables include operational data that is traditionally isolated in information islands between the different departments in the mine or they are consolidated into individual models. This reduces the holistic view of the deposit, thereby causing a negative impact on the results of the strategic planning itself. In order to improve the process and to maximize the production and/or value of a mining project, there needs to be an integration of the geology, the mine plan, the processing and the geometallurgy data. In order to accomplish this, a new methodology is proposed for the creation of a technological model. This model can be interpreted as the consolidation of the different models required for a better understanding of the geological and technical information of the deposit. This concept was developed and applied at a copper and gold mine site located in Brazil. Based on the evaluation of different blasting and mill productivity scenarios through a pit-to-plant approach, it was possible to obtain operational short-term gains such as a 10.7% increase in the plant production rate and a 2.2% increase in the crusher's feed rate with little or no capital investment.

Full text

307
Rodrigo Augusto Nunes et al.
REM, Int. Eng. J., Ouro Preto, 72(2), 307-313, apr. jun. | 2019
Abstract
The strategic mine plan is a crucial step for the success of mining companies, and for its
development, it is necessary to use a number of inter-related variables that are usually
estimated independently. These variables include operational data that is traditionally
isolated in information islands between the different departments in the mine or they
are consolidated into individual models. This reduces the holistic view of the deposit,
thereby causing a negative impact on the results of the strategic planning itself. In order
to improve the process and to maximize the production and/or value of a mining proj-
ect, there needs to be an integration of the geology, the mine plan, the processing and
the geometallurgy data. In order to accomplish this, a new methodology is proposed
for the creation of a technological model. This model can be interpreted as the con-
solidation of the different models required for a better understanding of the geological
and technical information of the deposit. This concept was developed and applied at a
copper and gold mine site located in Brazil. Based on the evaluation of different blast-
ing and mill productivity scenarios through a pit-to-plant approach, it was possible to
obtain operational short-term gains such as a 10.7% increase in the plant production
rate and a 2.2% increase in the crusher’s feed rate with little or no capital investment.
keywords: strategic planning, pit-to-plant, technological model, copper and
gold, geometallurgy.
Rodrigo Augusto Nunes1,3
http://orcid.org/0000-0002-6613-1551
Giorgio de Tomi2,4
https://orcid.org/0000-0002-7836-1389
Bladen Allan1,5
http://orcid.org/0000-0001-9169-6290
Erbertt Barros Bezerra2,6
https://orcid.org/0000-0002-4303-6201
Ranyere Sousa Silva2,7
https://orcid.org/0000-0003-1034-9495
1Yamana Gold Inc - Technical Services,
Toronto – Ontario – Canada
2Universidade de São Paulo – USP,
Departamento de Engenharia de Minas e Petróleo,
São Paulo – São Paulo – Brasil.
E-mails: 3rodrigo_anunes@hotmail.com,
4gdetomi@usp.br, 5bladenallan@gmail.com,
6erberttbarros@gmail.com, 7ranyere.eng@gmail.com
An integrated
pit-to-plant approach
using technological models
for strategic mine planning of
copper and gold deposits
http://dx.doi.org/10.1590/0370-44672018720060
Mining
Mineração
1. Introduction
The strategic planning of a mine is
a crucial step for the success of mining
companies, since it provides the neces-
sary information for the decision-making
process concerning the development of the
deposit (Silva, 2008). Therefore, in terms
of strategic mine planning, it is important
that the uncertainty related to the orebody
is properly quantied, assessed, and man-
aged (Godoy, 2018). For the preparation
of the strategic mine plan, it is necessary
to use models and data that are often
estimated independently.
The technological model presented
in this manuscript, aims to solve the dis-
connected and many times, inaccurate
view of the deposit. This new approach
consolidates the different data used to gain
a better understanding of the geological,
mine planning and processing data of the
project. Lund and Lamberg (2014) state
that the construction of technological
models can reduce operational risks and
could help to optimize the production,
taking into account sustainability and
socio-economic factors.
The data that is being referred to in
this study includes the resource and struc-
tural models, the geometallurgical model,
the cost model and the operating Selec-
tive Mining Unit (SMU) model, which
is the smallest volume of material that
the classication of ore and waste can be
determined (Sinclair and Blackwell, 2002).
The Geometallurgical model, a
component of the technological model,
allows for a better understanding of the
ore characteristics. The importance of this
increased knowledge base is that by know-
ing the strength, structure, and grade,
there are adjustments that can be made
to the crushing, grinding, and oatation
processes (La Rosa et al., 2014).
Gomes et al. (2016) used a geomet-
allurgical model to support an economic
study considering reserve volumes, prod-
uct quality and operational cost. From the
model a remarkable gain was obtained in
the iron ore reserve and in an operation
cost reduction. Its use is also essential
for strategic mining planning for mines
that seek to optimize the use of mining
resources over the life of mine (Philander
and Rozendaal, 2014). Navarro et al.
(2018) incorporated the geometallurgical
modeling in long-term planning and con-
rmed its great impact on the life of mine.
As noted by Augustin et al. (2017 )
the models should continually be updated
whenever possible, by way of additional
exploratory drill holes, to ensure that a
greater detail of accuracy is achieved.
Nadolski et al. (2015) complement that

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An integrated pit-to-plant approach using technological models for strategic mine planning of copper and gold deposits
REM, Int. Eng. J., Ouro Preto, 72(2), 307-313, apr. jun. | 2019
these models require constant adjustment,
with the insertion of the geotechnical,
geological, metallurgical and other op-
erational data from the plant, which will
also serve to calibrate predictive models.
The development process for the
preparation of the technological model
in question, used the work undertaken
at a large-scale open pit copper and gold
mine of sulde ore located in Brazil, as a
reference. The process includes crushing,
grinding, and otation. The saleable prod-
uct is a copper-gold concentrate.
In the studied mine, the ore comes
from different mining faces feeding the
plant at the same time. The technological
model created, tracked the ore charac-
teristics back to its original mining face.
One of the main objectives of the techno-
logical model was to determine the plant
performance by the ore domain on an
industrial scale and, to adjust the blasting
parameters aiming to increase the plant
throughput but keeping the metallurgical
recoveries at similar or higher levels. This
concept of pit-to-plant optimization has
been in use at other mine sites around
the world where different processes of
integration and optimization were applied
(Jankovic and Valery, 2011).
2. Materials and methods
As a starting point for the model,
the different unit operations of the mine
and plant were characterized. Blasting
designs, mine and plant sampling, and
operational testing were carried out.
The data obtained was used to develop
the specific models for the prediction
processes of blasting, crushing, and grind-
ing. These models were the basis of the
construction of the technological model.
Figure 1 summarizes the components of
the technological model.
Figure 1
Components of the Technological Model.
The methodology used for the
preparation of the technological model
is based on the following steps:
• Characterization and delin-
eation of areas based on their geo-
logical structure, strength, and cor-
relation with their lithological and
structural domains.
• Establishing operating restric-
tions such as the stability of benches,
the presence of water, SMU size, ore
dilution allowance, the blast pile char-
acteristics, the size of the equipment,
and the size/power of the crushers/mills,
in addition to any other bottlenecks in
the process.
• Dening the main requirements
of the subsequent steps to plan for each
geological domain in order to meet their
specic requirements.
• Use of software, mathematical
models, and process simulation.
• Implementation and monitoring
of the dened operational strategy (suit-
able plans for each domain followed by
the ideal adjustment for the crushing
and grinding cycle).
• Analysis and management of
data and results.
• Project implementation and
maintaining the benets obtained.
The above methodology is simpli-
ed in the Figure 2.
Figure 2
Project flow chart.
The rst step required an audit
to be carried out during the drilling
and blasting operations. This was
necessary in order to obtain data, pic-
tures, and other observations that are
required to provide enough informa-
tion for the creation of the fragmenta-
tion models. Once the information is
obtained, the blasting techniques can
be tailored to the rock characteris-
tics, which will allow for substantial
improvements in the downstream
operations (Kanchibolta et al., 2015).
Simulations of the blasting plan
were executed with both the soft and
hard ore. Various parameters were
changed depending on the simula-
tion. Compared to the base case these
variations included, a slight increase
in bench height, an increase in hole
diameter, a change in spacing, sub
drilling, hole depth, stemming, and an
increase in the column charge.
In the second phase of the prepa-
ration of the technological model,
laboratory tests, image analyses, data
processing, and mathematical models
were consolidated. In order to opti-
mize the ROM fragmentation from
the mine blasting, a fragmentation

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Fragmentation measurement using image analysis
Particle size distribution of the ROM
is one of the parameters used in calibrating
the fragmentation model. Prior studies of
operations around the world have indicated
that image analysis is a good and practical
method for this purpose. Photographs of
the piles formed after the veried blasting
and primary crushing were processed using
the Split-Desktop software (Split Engineer-
ing, 2017). Each image contains an object
for scale. For this project, two Styrofoam
balls were used and are pictured in Figure 3.
Figure 3
Example of a rendered
image from the Split-Desktop software.
Blast Tracking
Electronic tags were used to track the
blasted material, enabling the verication
of when the material enters the crushing/
grinding cycle. Therefore, the complete
sampling of the plant could be performed
using the audited blasted material. Samples
collected from the conveyor belts and the
main streams for laboratory tests deter-
mined the breaking characteristics of the
material. Specic models were developed
for the blasting, crushing, grinding, and
classication processes by using the audit-
ed blast data, sampling, and testing. Figure
4 shows the electronic tag with a diameter
of 60mm and a height of 30mm used in the
study as well as the typical installation of
the electronic tag system with an antenna
positioned above the conveyor belt.
Figure 4
The Reinforced Electronic Tag
and the antenna above the conveyor belt.
In total, 274 electronic tags were
used in the survey data. They were put
in the stem, in the piles formed after
detonation, and in the feed of each
primary crusher. In order to obtain the
data for calibrating the mathematical
models for the different equipment in
the circuit, sampling was performed in
the crushing and grinding circuit. Op-
erational data was also collected during
the sampling to determine the produc-
tion rates and power consumption of
each piece of equipment listed in Table
1. The sampling points are shown in the
owchart in Figure 5. Once the sampling
was complete, the data was balanced and
modelling was performed in the JKSim-
Met software (JKTech, 2014).
model was developed. This model is
sensitive to the main parameters that
affect the blasting performance. The
fragmentation model was then cali-
brated with the data obtained from
the observed blasting during the audit
process and used to predict changes
in the particle size distribution of
the ROM.
Equipment Description
Primary
Crushers •MMD Sizer S100-20, 400kW
•Metso C160, 220kW
SAG Mill 1 mill with an internal diameter of 10.01m and 4.95m of EGL, 11000 kW,
10.34rpm
Pebble Crusher 2 Metso HP800, 600kW
Ball Mill 1 mill with an internal diameter of 7.09m and 11.94m of EGL, 12500 kW,
12.19rpm
Cyclones
•Cluster SAG: 6 Multotec HC900-L20 cyclones, with a 360mm equivalent entry
diameter, a 340mm vortex, and a 170mm apex
•Cluster Ball: 8 Krebs GMAX 33 cyclones, with a 390mm equivalent entry diam-
eter, a 305mm vortex, and a 180 mm apex
Tab l e 1
List of the circuit
comminution equipment.

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An integrated pit-to-plant approach using technological models for strategic mine planning of copper and gold deposits
REM, Int. Eng. J., Ouro Preto, 72(2), 307-313, apr. jun. | 2019
Figure 5
Sampling points in the grinding cycle.
Mass balance and adjustment models.
After the completion of the mass
balancing, the mathematical models of all
the processing units were calibrated accord-
ing to the Anderson and Awachie model
for the primary crushers in the JKSim-
Met software version 6.0 (JKTech, 2014)
and Whiten (Whiten and White, 1979).
The objective was to model the grinding
cycle at the time of sampling. The perfect
blend model and the variable rate model
were adjusted to the ball and SAG mills,
respectively. The cyclones were adjusted
using Nageswararao’s model (Nageswar-
arao, 1995).
Using the balanced ow data, that
includes the mass ow rate, the percentage
of dry solids, and the particle size distribu-
tion, the models could be adjusted with
respect to the parameters of each specic
operation. Other information taken into
account were the characteristics of the
ore, the dimensions of the equipment, and
the information related to the operation
during sampling, which includes the load
and speed levels of the mills, the energy
consumption, and the operating pressure
of the cyclones. Historical data was also
considered to ensure that the results are
consistent and that they represent the oper-
ation of the cycle as accurately as possible.
3. Test results and discussion
In total there were 24 blasting simula-
tions carried out. Eleven of those simulations
were performed on soft ore, and the remain-
ing thirteen, which are shown below, were
carried out on hard ore. Once the simula-
tions were completed, one of the thirteen
(base case) was selected to be applied to the
blasting plan in the eld. The hard ore simu-
lations are presented in the Table 2 below.
The use of electronic tags in the
blasted material proved to be effective in
tracking the ore from the mine to the plant
and assured that the audited blasted mate-
rial was fed to the plant during sampling.
During the 34-hour monitoring period, 131
electronic tags out of the 274 were detected.
The spatial coordinates of each detected
electronic tag are shown in Figure 6.
Tab l e 2
Blasting scenarios for hard ore.

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Figure 6
The positioning of
each electronic tag detected.
After the plant sampling was
complete and the experimental data
was collected, the model could be
created. The mass balancing results
compared well to the experimental
data, indicating that the sampling
data is valid for further analysis. In
general, the models developed in
JKSimMet (JKTech, 2014) were well
adjusted with respect to the data, and
are considered appropriate for long-
term simulation studies that are to be
used in strategic mine planning. The
experimental and modeled data can
be compared in Table 3 below.
Mine Modelling Solids, t/h P80, mm % Solids
Exp Mod Exp Mod Exp Mod
feed 2026 2026 157.6 157.6 94.6 94.6
SAG Prod. 2484 8.73 70
O/S 458 458 54.2 51.7 98.5 98.9
Sieve SAG U/s 2026 1.351 1.21 65.2 65.7
Pebble Crusher Prod. 458 19.1 19.4 99 98.9
feed 2026 0.836 1.21 46.1 47.6
Cyclone SAG O/f 768 0.196 0.196 35.3 32.8
U/f 1258 2.593 2.62 64.7 65.7
Ball Mill feed 5411 1.68 70
Prod. 5411 0.803 0.811 72.3 70
feed 5411 2.625 0.811 71.9 61.5
Ball Cyclone O/f 1258 0.177 0.177 34.4 37
U/f 4153 2.187 1.333 76.5 76.9
Tab l e 3
Experimental and modeled
data from the main streams.
The results of the rock blast-
ing simulations demonstrated the
importance of improving the results
of fragmentation, especially when
analyzing the productivity of an
integrated comminution process for
harder lithology. There was a 37%
reduction in the production rate of
the SAG (2026 to 1269 t/h) when the
circuit supply consisted only of hard
material. Under this same condition,
increasing the powder factor to 2.78
kg/m3 resulted in an increase in the
mill’s productivity by 10.7%. This
result directly relates to the ner par-
ticle size distribution obtained from
the increased energy in the rocks from
the blasting.
The SAG cycle improves with an
increase in the load (the percent of mill
volume occupied by media plus voids)
from 8% mill balls (measured after
grinding) to 10-13% mill balls. These
changes allowed the grinding circuit
to increase to within 2-4% of the
productivity achieved with the largest
open area. In order to better utilize the
installed power of the pebble crushers,
SAG mill grates with larger openings
were installed. When comparing the
scenario to the ner ROM, the simu-
lation resulted in a 14% increase in
pebble generation, while the feed rate
increased by 2.2%. It is important to
note that by increasing the opening of
the grate, the grate life is shortened.
Therefore, it was suggested to nd
alternatives in order to minimize the
wear on the grate.
As an outcome of this method-
ology, the technological model has
shown to be adherent to the plant
results and operational data collected
after many reconciliation processes.
This methodology is still being used
at the mine, which constantly updates
the model with the new input data.

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REM, Int. Eng. J., Ouro Preto, 72(2), 307-313, apr. jun. | 2019
Simulated Scenarios Sampling 100% Hard Ore CP F 07 3.5" Grates Ball load
SAG Optimization Units Base Case Base Case PF 2.78 CP D 07 12% Open Area 10% 13%
Production Rate t/h 2026 1269 1405 2530 2585 2630 2690
∆ in Production Rate % 10.7 2.2 3.9 6.3
F80 of Feed mm 157.6 142.4 102.1 102.7 102.7 102.7 102.7
Feed Passing Through 12.5 mm %31.7 16.9 29.8 42.6 42.6 42.6 42.6
Power Consumed by the SAG kW 9053 9053 9053 9053 9053 9560 10400
Pebble Production t/h 458 412 404 534 610 577 491
Power Consumed by the Pebble Crusher kW 690 658 640 770 854 804 684
Sieve Undersize P80 mm 1.21 1.55 1.48 1.29 1.44 1.56 1.64
Partition of the SAG cyclone %39.7 40.1 33 38.6 39 38.4 37.6
Cyclone SAG Overflow of P80 mm 0.196 0.179 0.200 0.197 0.191 0.194 0.199
Cyclone SAG underflow of P80 mm 2.615 3.610 2.894 2.808 3.104 3.174 3.113
Ball Mill Power Consumption kW 11201 11201 11201 11201 11201 11201 11201
Rolling Charge %330 287 369 327 329 340 353
Ball Cyclone Overflow of P80 mm 0.177 0.176 0.188 0.208 0.214 0.219 0.224
Grinding Circuit Product of P80 mm 0.184 0.177 0.192 0.204 0.205 0.209 0.214
Total Power Consumed kW 20994 20912 20894 21024 21108 21565 22285
Specific Energy Consumption kWh/t 10.3 16.5 14.9 8.3 8.2 8.1 8.1
Operational WI kWh/t 14.5 22.7 21.5 12.4 12.2 12.3 12.5
The results of the simulated scenarios with different mill loads are presented in Table 4.
Tab l e 4
Simulated scenarios with the optimization of the SAG.
4. Conclusion
The methodology for the implemen-
tation of the technological model proved
to be a viable option in this scenario, and
has the potential to be applied to different
projects with similar grinding processes.
For the studied mine, the technological
model which was an outcome of the
assessment was incorporated into the
short and medium term mine planning
schedules. It was formulated as a block
model and was used together with the
mine planning software. The different
technological variables presented in this
model enabled the engineers to simulate
different blasting and plant productiv-
ity scenarios, supporting the decision
making process for the development of
the deposit. As a result, it is possible to
improve the efciency of the plant with
little or no capital investment by opti-
mizing the breaking and fragmenting
mechanisms through blasting, crushing,
and grinding. This resulted in:
• A reduction of the top size of the
ROM, which allowed the primary crusher
to operate with a higher feed rate by 2.2%,
a reduced opening, and to feed the SAG
with a smaller top size.
• Operating the SAG with more
nes (<10mm) by increasing the powder
factor to 2.78kg/m3, increased the plant
production rate by 10.7% while using the
same power.
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