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25-014
Are We Testing Enough in Feasibility Studies?
Marcos de Paiva Bueno
Geopyörä Oulu, Finland
Leonardo Ribeiro Lara
Geopyörä Oulu, Finland
Thiago de Almeida
Geopyörä Oulu, Finland
Rajiv Chandramohan
Ausenco Vancouver, Canda
Greg Lane
Ausenco Brisbane, Australia
ABSTRACT
This paper explores the impact of the significant difference
between the number of geochemical assays conducted for
grade estimation and the number of comminution tests
performed for throughput and performance forecasting.
Both grade and throughput are key factors in determin-
ing the project. We analysed 154 feasibility study reports to
assess the impact of sample frequency for comminution test
work on project revenue.
This disparity in size between assay and comminution
data arises because geological and resource-grade risks can-
not be managed without extensive geochemical testing,
whereas comminution risks are traditionally mitigated
through design methods and the use of contingencies. This
approach is partly due to the relatively high cost and large
sample requirements associated with traditional comminu-
tion tests.
Our analysis demonstrates that insufficient com-
minution sampling and testing leads to uncertainties in
throughput forecasts, creating significant financial risks.
Data from the feasibility studies show that a 1% change in
mill throughput can result in up to a 5% variation in Net
Present Value (NPV), underscoring the sensitivity of proj-
ect economics to operational performance.
Building on previous work, we reference studies that
reveal how low frequency of comminution testing can
increase uncertainty in mill throughput forecasts and
increase financial risks. We propose an approach that
optimises the number of comminution samples tested to
enhance forecast accuracy and reduce NPV variance. By
adopting this approach, mining projects can improve finan-
cial reliability and mitigate risks associated with underper-
formance in SAG and AG mill-based operations.
Keywords: Comminution, Testing, performance, AG/SAG
mills
INTRODUCTION
Due to its high capital and operating costs, the commi-
nution circuit is a critical bottleneck in many mining
operations. The principal constraint on plant throughput
in AG/SAG (autogenous or semi-autogenous) mill-based
circuits is typically the capacity of the AG or SAG mill. The
poor performance significantly undermines the project’s
Net Present Value (NPV) (Lane et al., 2017). This paper
examines feasibility study data from recent years, focused
on projects with AG/SAG mills, to explore whether insuf-
ficient comminution testing impacts throughput forecasts
and, consequently, project economics.
A critical aspect under investigation in this paper is the
potential inadequacy of comminution sampling and test-
ing methodologies during the early phases of project devel-
opment. Morrell (2019) addressed this issue by proposing
a classical statistical framework to estimate the required
number of samples. His approach considers key parameters
such as test results’ coefficient of variation (CV), desired
confidence levels, and acceptable accuracy ranges. While
this method provides a systematic starting point, it relies
on classical statistical assumptions, such as randomness and
normal distribution, which often do not hold true for geo-
logical properties like ore hardness. Ore hardness is typi-
cally spatially structured and shaped by complex geological
processes, making it poorly suited to simplistic statistical
models. This paper argues that such limitations necessitate
integrating geostatistical techniques and spatially distrib-
uted sampling strategies to more accurately capture ore-
body heterogeneity and improve the reliability of design
and throughput forecasts.
Other studies have proposed alternative strategies to
address sampling needs for geometallurgical variability tes-
twork. Couët et al. (2015) emphasize the importance of
cost-effective, high-resolution sampling by integrating low-
cost proxy tests with select high-precision measurements
25-014
Are We Testing Enough in Feasibility Studies?
Marcos de Paiva Bueno
Geopyörä Oulu, Finland
Leonardo Ribeiro Lara
Geopyörä Oulu, Finland
Thiago de Almeida
Geopyörä Oulu, Finland
Rajiv Chandramohan
Ausenco Vancouver, Canda
Greg Lane
Ausenco Brisbane, Australia
ABSTRACT
This paper explores the impact of the significant difference
between the number of geochemical assays conducted for
grade estimation and the number of comminution tests
performed for throughput and performance forecasting.
Both grade and throughput are key factors in determin-
ing the project. We analysed 154 feasibility study reports to
assess the impact of sample frequency for comminution test
work on project revenue.
This disparity in size between assay and comminution
data arises because geological and resource-grade risks can-
not be managed without extensive geochemical testing,
whereas comminution risks are traditionally mitigated
through design methods and the use of contingencies. This
approach is partly due to the relatively high cost and large
sample requirements associated with traditional comminu-
tion tests.
Our analysis demonstrates that insufficient com-
minution sampling and testing leads to uncertainties in
throughput forecasts, creating significant financial risks.
Data from the feasibility studies show that a 1% change in
mill throughput can result in up to a 5% variation in Net
Present Value (NPV), underscoring the sensitivity of proj-
ect economics to operational performance.
Building on previous work, we reference studies that
reveal how low frequency of comminution testing can
increase uncertainty in mill throughput forecasts and
increase financial risks. We propose an approach that
optimises the number of comminution samples tested to
enhance forecast accuracy and reduce NPV variance. By
adopting this approach, mining projects can improve finan-
cial reliability and mitigate risks associated with underper-
formance in SAG and AG mill-based operations.
Keywords: Comminution, Testing, performance, AG/SAG
mills
INTRODUCTION
Due to its high capital and operating costs, the commi-
nution circuit is a critical bottleneck in many mining
operations. The principal constraint on plant throughput
in AG/SAG (autogenous or semi-autogenous) mill-based
circuits is typically the capacity of the AG or SAG mill. The
poor performance significantly undermines the project’s
Net Present Value (NPV) (Lane et al., 2017). This paper
examines feasibility study data from recent years, focused
on projects with AG/SAG mills, to explore whether insuf-
ficient comminution testing impacts throughput forecasts
and, consequently, project economics.
A critical aspect under investigation in this paper is the
potential inadequacy of comminution sampling and test-
ing methodologies during the early phases of project devel-
opment. Morrell (2019) addressed this issue by proposing
a classical statistical framework to estimate the required
number of samples. His approach considers key parameters
such as test results’ coefficient of variation (CV), desired
confidence levels, and acceptable accuracy ranges. While
this method provides a systematic starting point, it relies
on classical statistical assumptions, such as randomness and
normal distribution, which often do not hold true for geo-
logical properties like ore hardness. Ore hardness is typi-
cally spatially structured and shaped by complex geological
processes, making it poorly suited to simplistic statistical
models. This paper argues that such limitations necessitate
integrating geostatistical techniques and spatially distrib-
uted sampling strategies to more accurately capture ore-
body heterogeneity and improve the reliability of design
and throughput forecasts.
Other studies have proposed alternative strategies to
address sampling needs for geometallurgical variability tes-
twork. Couët et al. (2015) emphasize the importance of
cost-effective, high-resolution sampling by integrating low-
cost proxy tests with select high-precision measurements