404 XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3
samples, either from drill core or bulk samples and there-
fore, given the use of samples for the usual standard test,
this leads to an increase in sample mass required. There is
a vast amount of industry knowledge and experience on
largely well understood classifications of ore bodies and tra-
ditional liberation and extraction pathways. This industry
knowledge allows the study team to undertake benchmark-
ing of limited testwork results and give the investors greater
comfort in the final design. This depth of industry knowl-
edge is not yet available for sorting.
Where possible, samples and data can be shared
between preconcentration and other testing requirements.
Ballentyne et al. (2020) describes the process of using assay
data from drill core to develop yield (release) curves. This
approach is suited to situations where the preconcentration
is undertaken using sensors that measure grade. If the sort-
ing relies on other non-grade characteristics of the particles,
i.e., colour, or other proxies, then this approach would need
to be augmented with additional data and testwork to relate
the proxies back to grade.
In terms of the overall economic assessment of a full
mining-processing system incorporating preconcentration
versus standard scenarios without preconcentration, this is
still an active field of research. Redwood and Scott (2016)
considered such evaluations and presented a method for
the system optimization, given the use of preconcentra-
tion. At the time of work, the method used an Enterprise
Optimization method from Whittle Consulting, whereby
the mining block models are contained within Geovia and
the Whittle Prober Enterprise Optimization algorithm
was used to model the mine-process system with schedules
generated cash-flows over the life of the mine and there-
fore NPV. A further, more detailed description, of the full
mining production planning and equipment selection,
with preconcentration, was later published von Wielligh et
al. (2020), whereby the steps involved in the mining plan-
ning activity are examined.
In terms of production scheduling for systems con-
taining preconcentration, work by Levinson et al. (2023)
and Sotoudeh et al. (2021) both investigate mathematical
methods to optimize the schedules.
Regardless of the type of optimization used, a key
consideration in any mining study is that economic sen-
sitivities can be quickly and easily processed in order to
support the direction of the study. There are a number of
considerations specific to the level of study—i.e., Order of
Magnitude (OoM) Pre-Feasibility Study (PFS), Feasibility
Study (FES), but also the type of study, namely, Greenfields
versus Brownfields.
In a mining studies environment, the complexities can
be broadly grouped into four categories, see Table 2.
In order to achieve a project business case in a reason-
able timeframe, the amenability testing for sorting and
investigation into the structural geology must commence as
soon as practicable in the study. Mine designs must be able
to flex throughput rates to accommodate long term changes
in sorting performance, as with any process that deals with
long term structural change, such as declining head grade
or increasing problematic handling materials. The mine
design will also need to consider the disposal strategy for
coarse waste streams that are likely to require being stacked
as low grade material (sorting is rarely ‘yes/no’, rather ‘yes/
maybe’ or ‘maybe/no’). These waste streams may be of a
rock size and tight size distribution that makes the stock-
piles non trafficable, similar to rejected SAG mill pebbles.
Source: Morales et al., 2019
Figure 3. Usual progression from mine block model to production scheduling
samples, either from drill core or bulk samples and there-
fore, given the use of samples for the usual standard test,
this leads to an increase in sample mass required. There is
a vast amount of industry knowledge and experience on
largely well understood classifications of ore bodies and tra-
ditional liberation and extraction pathways. This industry
knowledge allows the study team to undertake benchmark-
ing of limited testwork results and give the investors greater
comfort in the final design. This depth of industry knowl-
edge is not yet available for sorting.
Where possible, samples and data can be shared
between preconcentration and other testing requirements.
Ballentyne et al. (2020) describes the process of using assay
data from drill core to develop yield (release) curves. This
approach is suited to situations where the preconcentration
is undertaken using sensors that measure grade. If the sort-
ing relies on other non-grade characteristics of the particles,
i.e., colour, or other proxies, then this approach would need
to be augmented with additional data and testwork to relate
the proxies back to grade.
In terms of the overall economic assessment of a full
mining-processing system incorporating preconcentration
versus standard scenarios without preconcentration, this is
still an active field of research. Redwood and Scott (2016)
considered such evaluations and presented a method for
the system optimization, given the use of preconcentra-
tion. At the time of work, the method used an Enterprise
Optimization method from Whittle Consulting, whereby
the mining block models are contained within Geovia and
the Whittle Prober Enterprise Optimization algorithm
was used to model the mine-process system with schedules
generated cash-flows over the life of the mine and there-
fore NPV. A further, more detailed description, of the full
mining production planning and equipment selection,
with preconcentration, was later published von Wielligh et
al. (2020), whereby the steps involved in the mining plan-
ning activity are examined.
In terms of production scheduling for systems con-
taining preconcentration, work by Levinson et al. (2023)
and Sotoudeh et al. (2021) both investigate mathematical
methods to optimize the schedules.
Regardless of the type of optimization used, a key
consideration in any mining study is that economic sen-
sitivities can be quickly and easily processed in order to
support the direction of the study. There are a number of
considerations specific to the level of study—i.e., Order of
Magnitude (OoM) Pre-Feasibility Study (PFS), Feasibility
Study (FES), but also the type of study, namely, Greenfields
versus Brownfields.
In a mining studies environment, the complexities can
be broadly grouped into four categories, see Table 2.
In order to achieve a project business case in a reason-
able timeframe, the amenability testing for sorting and
investigation into the structural geology must commence as
soon as practicable in the study. Mine designs must be able
to flex throughput rates to accommodate long term changes
in sorting performance, as with any process that deals with
long term structural change, such as declining head grade
or increasing problematic handling materials. The mine
design will also need to consider the disposal strategy for
coarse waste streams that are likely to require being stacked
as low grade material (sorting is rarely ‘yes/no’, rather ‘yes/
maybe’ or ‘maybe/no’). These waste streams may be of a
rock size and tight size distribution that makes the stock-
piles non trafficable, similar to rejected SAG mill pebbles.
Source: Morales et al., 2019
Figure 3. Usual progression from mine block model to production scheduling