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IMPC Asia-Pacific 2022 | Melbourne, Australia | 22–24 August 2022 1204
Strategic and tactical mine planning considering value chain
performance for maximised profitability
R Smith1, F Faramarzi2 and C Poblete3
1. Global GEOVIA Services Director, Dassault Systèmes, Brisbane Qld 4000.
Email: ralph.smith@3ds.com
2. Global Senior Mining Industry Consultant, Dassault Systèmes, Brisbane Qld 4000.
Email: farhad.faramarzi@3ds.com
3. GEOVIA R&D Apps Portfolio Senior Manager, Dassault Systèmes, Santiago, Chile.
Email: cristian.poblete@3ds.com
ABSTRACT
In the minerals industry, ‘value’ is the difference between the expected revenue derived from
saleable minerals and the costs required to liberate them from gangue. In the early 1990s, it was
found that the interconnected nature of mining and minerals processing provides an opportunity to
unlock additional value by breaking from the established practice of silo-based cost minimisation to
focusing on maximising profit across the value chain. The approach of ‘Mine-to-Mill’ was thus
formalised as an operating strategy aiming to improve profitability through leveraging blast intensity
for increased milling throughput. However, today the mining sector not only has to deal with more
complex orebodies at lower grades, but there is also an accelerating need to develop sustainable
capabilities and continuously upgrade its practice for addressing other challenges; of the intensified
global demand for commodities, limited resources, market volatility and also new environmental and
social regulations/responsibilities. Therefore, this level of sophistication necessitates innovative
solutions for effective response to changed situations, hence setting real-time optimising strategies
towards risk mitigation and value maximisation.
The constant challenge for any mining operation whichs to align strategic and tactical objectives.
Strategic Mine Planning is a long-range production planning which aims at maximising the value
from the exploitation of an ore deposit, while Tactical Mine Planning focuses on short-range plans to
maintain operational viability. With recent advances in technology and data analytics, there is an
opportunity to integrate key mining and processing stages. That is, integrating existing isolated mine
production planning and optimisation strategies with the downstream KPIs, assessing performance
through scenario-based simulations, and then dynamically re-optimising production plans for
maximised profitability across the value chain over the mine lifespan.
This paper offers a methodological framework for integrating mine production planning and
downstream process performance. A ‘Holistic Model of Mine Optimisation’ is conceptualised, which
relies on GEOVIA’s capabilities ranging from mineral resources modelling, design and planning to
simulating process plants through evaluation of ‘what-if’ scenarios. A case was exemplified for a Cu-
Ag-Ag deposit, and the potential impact of implementing Mine-to-Mill improvement strategies was
quantified at a strategic level through simulating several scenarios. Improvement ranges of three key
variables of mining rate, milling rate and Cu recovery were considered for analyses which were
based on several reported Mine-to-Mill projects. The results implied the potential to improve the Net
Present Value (NPV) by 15 per cent without deploying Capex only through maintaining Mine-to-Mill
optimisation strategies. This approach offers sustainable solutions for unlocking the potential for
improving the NPV over life-of-mine for green – and brownfield projects through practicing Mine-to-
Mill basics, which essentially would assist with better decision-making by aligning optimisation
objectives across the value chain. The developed approach is proposed, case examples presented,
and implications discussed.
INTRODUCTION
From the early 1900s, mining and processing have been operated as separate silos. The former was
focused on producing ore at a required rate and cut-off grade. The latter’s objective was to process
ore as provided by the upstream. However, the realities of mining and processing confirm a series
of interconnected and sequential stages aiming at removal, transportation and size reduction of ores
for liberating valuable minerals from the gangue. This interconnected nature of the minerals industry
IMPC Asia-Pacific 2022 | Melbourne, Australia | 22–24 August 2022 1205
motivated a paradigm shift from silo-oriented optimisation strategies to value-based, integrated
approaches. The new paradigm demands optimal contribution of each stage towards maximising
overall value across the value chain rather than realising silos’ distinct objectives – widely known as
‘Mine-to-Mill’ since the the early 1990s. Recognition of the importance of drill-and-blast as the first
step in comminution, let to the approach aiming at manipulation of blast designs to produce more
appropriate mill feed size distributions for improved grinding capacity. The Mine-to-Mill approach is
now widely implemented in the mining industry across the globe, with documented productivity gains
in the range of 5–20 per cent (Burger et al, 2006; Hart et al, 2001; Kanchibotla et al, 1999; Scott
et al, 2002; Valery et al, 2019). From the mining perspective, drill-and-blast is an excavation method
for exploiting an ore deposit rather than tailoring feed size distribution for increased milling
throughput. The mining stage is mainly concerned with ‘in time extraction and transportation’ of
certain volumes of material within a time frame, which has to be executed in sequence following a
‘mining plan’. The time frame defines the nature and hence key objectives of mine planning
strategies, commonly known as ‘Strategic’ for long-range and ‘Tactical’ for short- and medium-range
planning.
Today, the mining sector is confronting new challenges in dealing with more complex ore deposits
at lower grades and the elaboration of social and environmental concerns, limiting the effectiveness
of conventional strategies if not nullify. Therefore, the next generation of Mine-to-Mill should adapt
the emerging techniques and technologies in field measurement, ore characterisation, data
analytics, advanced control systems, ore pre-concentration, blending strategies, valourisation of low-
grade dumps, waste management, modelling and simulation in a more sophisticated and inclusive
manner. This offers new opportunities to further improve downstream gains by leveraging more
variables and allows for effective integration of mining and processing stages. With this proviso in
mind, application of Mine-to-Mill should not be limited to downstream gains, but also could be
deterministic in Strategic and Tactical Mine Planning. That is, the development of stochastic analysis
to quantify Mine-to-Mill impacts at strategic and tactical levels, which are deterministic in achieving
economic and strategic targets of mining companies.
Optimisation is a continuous and data-driven endeavour for mining projects, which materialises when
maximum profitability is achieved across the value chain. The measures for an optimised operation
frequently change with an update of information on resource properties and market initiatives, which
ultimately determine the expansion or contraction of mining projects. Therefore, in the presence of
large uncertainties, integrated modelling solutions would accelerate the real-time alignment of
upstream and downstream objectives more interactively. Simulation is an established approach for
exploring the feasibility and impact of ‘change(s)’ on Key Performance Indicators (KPIs) through
analysis of ‘what if’ scenarios. Scenario-based analysis has been widely used in mining and
processing with exemplified applications in optimising upstream (Godoy, 2018; Morales et al, 2019;
Poblete et al, 2016b; Rimélé et al, 2020; Smith et al, 2021) and downstream (Carrasco et al, 2016;
Faramarzi et al, 2018, 2019; Grundstrom et al, 2001; Kanchibotla et al, 1999; Scott et al, 2002) KPIs.
Accordingly, this study reviews several Mine-to-Mill case studies in the Asia Pacific region with the
aim to establish linkages between KPIs of mining and processing disciplines at a strategic level and
improve our understanding of their interdependence through analysis of scenarios. A few published
works have highlighted the interdependence of mine planning and processing, but without
quantifying the impacts on strategic and tactical measures. In this respect, a well-structured study
was conducted at the Mount Isa lead-zinc to explore the flotation performance of ore sourced from
key mining domains (Munro, 1986; Young et al, 1997). This resulted in adopting a pre-concentration
strategy to reject some ore from the mine plan, which lowered the tonnage of ore being mined and
processed. Moreover, Bye (2011) documented case studies mainly centred on spatially modelling
and applying geo-metallurgical/geo-technical attributes and discussed potential benefits to mine
planning and economic optimisation. He pointed out the significance of ore variability on mine
valuation, production schedule and economic gains. In an example, Bye (2011) employed a geo-
metallurgical model to gain further value from geo-metallurgical initiatives by incorporating them into
block models to show the impact of ore variability on the mine plan and identified high-risk periods
to optimise the mine schedule.
In this paper, we document proof of concept of an outcome-based solution developed at GEOVIA
Dassault Systèmes, which expectedly assists the minerals industry with improved operational
viability and sustainability. The proposed solution extends the application of conventional ‘Mine-to-
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Mill’ to the mine planning space. A scenario-based example is developed in the context of Mine-to-
Mill based on considered case studies, demonstrating the potential for unlocking additional value for
the whole mine lifespan by using the most-trusted software package in Strategic Mine Planning
GEOVIA Whittle™.
STRATEGIC VERSUS TACTICAL
In mine planning, Strategic and Tactical encapsulate objectives to be achieved at certain timespans
over the life-of-mine (LOM).
Strategic Mine Planning aims at capturing maximum potential value from the exploitation of an ore
deposit on an annual time scale, generally expressible in Net Present Value (NPV).
Tactical Mine Planning aims at ensuring viability and sustainability of the strategic planning at an
operational level on monthly or even weekly scales. Therefore, it tracks and measures operational
performance regarding its alignment with strategy, and corrective action is taken when required to
minimise the gap between planned versus actual practice.
In the context of Mine-to-Mill optimisation, with extending its application to the realm of mine
planning, we propose approaches as follows:
Strategic Mine-to-Mill
Tactical Mine-to-Mill
Strategic Mine-to-Mill aims at considering and quantifying potential gains from applying Mine-to-Mill
optimisation strategies in long-term, for capturing maximum economic potential of ore reserves. The
key objective would be to establish linkages between strategic KPIs of a mining project (eg NPV,
IRR, and LOM) and performance improvement possibilities in the downstream. This level of
integration allows for a more realistic assessment of overall value by harmonising upstream and
downstream activities at a strategic level.
Tactical Mine-to-Mill centres on the realisation of Strategic Mine-to-Mill by implementing Mine-to-Mill
optimisation strategies in a sustainable manner. In general, Tactical Mine-to-Mill applies
conventional Mine-to-Mill strategies to maximise profitability across the value chain by improving
process performance (eg throughput, target product size, recovery) and to ensure objectives of the
Strategic Mine-to-Mill are fulfilled.
Overall, the conceptualised Strategic and Tactical Mine-to-Mill approaches aim to integrate key
stages exploration, mining and processing and interrogate their interdependence through simulating
‘what-if’ scenarios. To further explain, achieving objectives of Strategic Mine-to-Mill depends on
downstream gains and accounts for reliability the ‘block model’ used for development of a strategic
mine plan. Therefore, the extent of exploration activities, resource modelling and estimation and
mine design considerations are respected. The representativeness of the block model improves
during the LOM through continuous accumulation of orebody knowledge, which assists with fine-
tuning adopted strategies for optimal outcomes. The Tactical Mine-to-Mill uses the block model for
delivering short- and medium-range schedules. It engages drill-and-blast progress, haulage system,
blending strategies, destination and flow of materials of different types. The Tactical Mine-to-Mill
takes place at an operational level, not only accounts for blasting outcomes (ie muck pile
fragmentation and ore loss/dilution) but also it engages timing and characteristics of material flow
across the value chain – which should assist with ore/waste tracking most useful for ore feed quality
and waste management.
WHERE ARE OPPORTUNITIES?
Mine-to-Mill optimisation is a well-established technique with a range of applications for improvement
and optimisation of process performance. It effectively leverages the drill-and-blast practice to
influence downstream stages’ performance by implementing blast-induced changes to ore feed size
distribution (Morrell and Valery, 2001), breakage, and physical properties (Michaux and Djordjevic,
2005). These then translate into changes in crushing and screening performance (Kojovic et al,
1995), AG/SAG mill throughput (Hart et al, 2001; Nielsen and Kristiansen, 1995), and flotation.
Several examples are but are not limited to the influence of blast fragmentation on crushing and
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screening performance (Kojovic et al, 1995), and AG/SAG mill throughput (Dance, 2001; Michaux
and Djordjevic, 2005; Morrell and Valery, 2001; Nielsen and Kristiansen, 1995) and flotation (Valery
et al, 2019). However, as exemplified in this paper, the Strategic and Tactical Mine-to-Mill aims at
real-time re-scale of mining and processing activities for optimal outcomes in response to large
uncertainties introducible to most operations.
A more advanced Mine-to-Mill strategy should incorporate key processing parameters into the mine
planning for improved ‘productivity’ of the whole value chain (mineral reserve-to-metal) over LOM
rather than boosted ‘production’ (ie mill throughput) gains maximised value, ie NPV. This approach
would further support the sustainability of green- and brownfield mining projects via operational
leverages, as further explained below.
Project risk mitigation options at strategic and tactical levels:
To de-risk mining projects, it is necessary that the interaction between key stages across the
value chain being captured in a quantitative manner. This allows for the short and long-term
development of tailored strategies for scaling mining and processing activities which most
reflects on orebody characteristics, hence mitigates ‘unknown’ risks associated with CAPEX
and OPEX. Embedding Mine-to-Mill possibilities into Strategic and Tactical Mine Planning
procedures is a dynamic approach which aims at estimating value over LOM in real-time, so
corrective actions are taken when required for minimising the gap between planned versus
actual practice or improvement strategies implemented when the opportunity is recognised.
Improving sustainability at the operational level:
As aforementioned, Strategic and Tactical Mine Planning leaves the door open to improvement
opportunities which is critical to the sustainability of current and future mining operations. There
is no limit to confronting challenges to the mining sector, which can limit the effectiveness of
conventional strategies if not nullify. Strategic and Tactical Mine-to-Mill has to account for the
integration of emerging technologies towards risk mitigation and value maximisation. As mining
operations adapt the emerging techniques and technologies in practice, surveying and data
analytics, automation and control systems in a more sophisticated manner; then the influence
of such changes/improvements has to be reflected at a strategic and tactical level over LOM.
That is, expressing value as a dynamic measure ‘in practice’, so it requires re-evaluation for
best representing the economic status of a project.
Harmonising upstream and downstream activities:
The upstream activities are mainly concerned with removal of in-situ rock volumes in
sequence, and then depending on transferring them to different destinations based on their
value (ie grade/metal content). The key objective of downstream activities is to extract value
from the material provided by the upstream, which may not necessarily result in maximum
profitability. However, the question is:
o What if, we tailor/scale the upstream activities eg planning, blasting, loading and haulage
to provide the downstream with the material, which gains optimal productivity across the
value chain?
This obviously requires tuning conventional mine plans to account for new considerations and
establish harmony between the mining and processing stages in short, medium and long-
terms. It is important to note that the heterogeneous nature of orebodies introduces large
uncertainties into all quantitative evaluations, design, and predictions (Faramarzi et al, 2020).
The extent of the variability of ore changes across the value chain, and hence its impact. A
better understanding of the flow of material of different types from the pit to the process plant
should assist with better ‘waste/ore tracking’ for ore feed quality and variability management.
More importantly, it helps quantify the impact of ore-induced operational variations
(ie production bottlenecks or poor productivity) on the viability of mining projects in different
timespans.
Waste management and valourisation:
Commodity price volatility is an ever-present and influential factor in decision-making at both
strategic and tactical (or operational) levels, which ultimately determine expansion or
IMPC Asia-Pacific 2022 | Melbourne, Australia | 22–24 August 2022 1208
contraction of mining projects. Strategic Mine-to-Mill might provide a better understanding of
‘value volatility’ over LOM. The development of stochastic analysis for price changes and
accordingly estimating contingencies for difficult periods, or potential gains from the
valourisation of ‘low-grade’ material. This area requires further investigation and development
of robust techniques in future because appropriate management of waste/low-grade materials
is critical to the sustainability of green- and brownfield mining projects.
MINE-TO-MILL CASE STUDIES IN ASIA PACIFIC REGION
Review of selected case studies
The Mine-to-Mill approach has been implemented at many mining operations worldwide, showing
that the capacity and efficiency of crushing and grinding processes are significantly influenced by
run-of-mine (ROM) size distribution, which is driven by the blasting. Through modelling and
simulation, McKee et al (1995) indicated the potential for 20 per cent higher grinding capacity
achievable by tailoring the blast-induced PSD. The practice has resulted in billions of dollars of
additional value to the minerals industry since 1990s, mainly achieved through increased throughput.
Many of these are documented, and operational improvements gained are reported. In this section,
we review several Mine-to-Mill optimisation projects commissioned in the Asia Pacific region across
gold, copper, lead and zinc commodities. Each project offers exclusive lessons to learn depending
on their considerations and characteristics, providing a better understanding of possibilities if
embedded in the mine planning context.
Case Study #1 – Cadia Hill Gold Mine, Australia
The Cadia Hill concentrator was commissioned in July 1998, aiming at processing 2065 t/h of
monzonite ore, giving an annual processing rate of 17 Mtpa. Hart et al (2001) reported a range of
strategies for SAG mill capacity debottlenecking and improving overall performance of Cadia Hill
comminution circuit – one of which strategies was Mine-to-Mill optimisation. A reduction in SAG mill
feed size F80 to 70 mm increased throughput between 10–15 per cent. Accordingly, the powder
factor increased from the standard 0.8 to 1.2 kg/m3 for improved fragmentation, which resulted in a
10 per cent higher throughput by improving the SAG feed rate from 2270 to 2505 t/h. More intense
blasting by tightening the drill pattern (Burden × Spacing) produced more ‘fines’, visually evident
during the trial and confirmed by lower pebble recycle rates. A finer PSD from intense blasting
increased SAG mill power draw but resulted in overall lower specific power consumption.
Case Study #2 – Porgera Gold Mine, Papua New Guinea
Progeria Joint Venture and Dyno Nobel identified the SAG mill as a production bottleneck, in
particular when milling hornblende diorite (Lam et al, 2001). The Mine-to-Mill project centred on
optimising blast design for improving the milling rate by altering the feed size distribution. For this
purpose, the blasting powder factor was increased from the standard 0.24 to 0.38 kg/t, which
reduced SAG feed P50 from 75 to 35 mm. Thus, the finer feed boosted the SAG milling rate from
673 to 774 t/h, which equals a 15 per cent increase in SAG milling throughput.
Case Study #3 – Ernest Henry Copper-Gold Mine, Australia
The Ernest Henry Mine concentrator, commissioned in August 1997, with a nominal throughput rate
of 1200 t/h. Strohmayr and Valery (2001) conducted an extensive optimisation program which
included filed surveys, ore characterisation, blast fragmentation modelling, comminution modelling
and simulations. In this Mine-to-Mill project, alternative blast designs in conjunction with a closer
crusher gap (from 130 to 115 mm) improved SAG mill throughput. More intense blasting provided
more favourable feed size distributions as the amount of fines (below 10 mm) in the feed increased
from 18.6 per cent (standard blast practice) to 21.4 per cent in blast designs with higher powder
factors. The study confirmed the potential to increase mill throughput to 12 per cent by altering the
blast designs and primary crusher gap (Strohmayr and Valery, 2001).
IMPC Asia-Pacific 2022 | Melbourne, Australia | 22–24 August 2022 1209
Case Study #4 – KC Gold Mine, Australia
The KC Gold Mine (KCGM) treats ore from the Super Pit at Kalgoorlie. Kanchibotla et al (1998)
explored the interdependence between fragmentation size distribution from blasting and SAG mill
throughput. Standard blast design with powder factor of 0.58 kg/m3 compared to modified designs
with powder factors of 0.66 (Design 1) and 0.96 kg/m3 (Design 2). The simulations indicated that the
SAG mill feed rate of 1250 t/h from standard blasting could be improved up to 1480 t/h, which equals
18.4 per cent higher milling capacity. This study argued possible pitfalls of intense blasting from the
dilution viewpoint and more importantly highlighted the need for the energy balance between the
SAG and ball milling circuits as well as loss of recovery for a changed final grind size.
Case Study #5 – Batu Hijau Copper-Gold Mine, Indonesia
The key objective of commencing Mine-to-Mill at the Batu Hijau Copper-Gold operation was to
modify blasts for improved SAG mill throughput (Burger et al, 2006; McCaffery, 2006). For different
zones of the orebody, regression models were developed to predict throughput separately for 16
domains. In addition to grinding capacity, it was recognised that a domain-based blasting strategy
should also improve loading rates through fragmentation top size reduction. Between 2006 and
2011, extensive orebody characterisation allowed for improvement of blasting and mill throughput
predictive models and coding mill throughput predictive equations into the mine block model, which
was used for short and long-term production planning based on mill throughput prediction to
±2.0 per cent accuracy.
Productivity gains of 10 per cent for loading rates in the pit and 10–15 per cent increases in SAG mill
throughput for individual ore domains were reported. The Sandsloot mine is another example where
a modified blasting strategy was deployed for improving operational performance (Bye, 2006).
Loading rate and milling performance were monitored at this mine between 2001 and 2003, which
demonstrated a significant impact of powder factor on these KPIs by 18 per cent improvement in
average milling rate, and 13 per cent increase in average instantaneous load rate (ore and waste).
Case Study #6 – Mount Isa Lead and Zinc Mine, Australia
The case study of Mount Isa Lead and Zinc Mine demonstrates benefits from applying integrated
mining and processing strategies in a different space through cut-off grade control for improved
flotation performance (Young et al, 1997). It was noticed that determination of ore cut-off grade
without accounting for flotation performance of ore from different sources in the mine results in poor
prediction. Recognising the interconnected nature of mining stages, a new strategy was adopted to
establish the link between head grades of each ore type, concentrate grades, recovery as well as
capital and operating costs for each mined ore resource. Because of alignment of the mining practice
with downstream objectives, 30 per cent of the ‘low-value’ ore was removed from the mine schedule,
which reduced operating costs, while improved recovery of silver, lead and zinc by 5.0 per cent,
5.0 per cent and 2.0 per cent, respectively.
Implications from the case studies
Numerous Mine-to-Mill case studies at mining operations across the globe provide a reliable base
from which to integrate and harmonise the upstream and downstream stages for maximised
controllability and, therefore value. The literature does not reflect long-term outcomes of
implementing Mine-to-Mill at the operations – if the changes were sustained for a while. However,
the authors believe that Mine-to-Mill strategies determined at a specific time frame, need continuous
re-visit because of the thin line between profit and loss in mining industry.
The review of selected case studies commissioned in the Asia Pacific region, provides a base for
the proof of concept by indicating improvement potentials. The conventional Mine-to-Mill studies
imply the potential to improve milling capacity by 20 per cent (from intense blasting), productivity
gains of 10 per cent for loading rates in the pit (from intense blasting) and metal recovery up to
5 per cent (from ore feed quality management). These values vary by site; however, in this paper,
we use the values to quantify the impact of such improvements over LOM at strategic and tactical
levels. The authors acknowledge that there are other improving techniques such as coarse flotation
and Grade Engineering®, application of novel explosives etc, which can significantly enhance
profitability and sustainability of mining operations. In this paper, we mainly focused on conventional
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Mine-to-Mill leverages; however, there is no limit for applying other technologies to improve the
overall practice, all of which are well-settled within the Mine-to-Mill optimisation context developed
in the early 1990s.
METHODOLOGY
The Strategic Mine-to-Mill optimisation comprises several strategic steps required for unlocking
maximum potential economic gains from an ore reserve. The term ‘strategic’ in this concept aims at
aligning the steps with the strategic objectives of the project (Figure 1).
FIG 1 – Strategic Mine-to-Mill and involved stages.
Key requirements of applying any Mine-to-Mill optimisation are as follows:
Data acquisition across the value chain:
Data collection, curation, analysis, and interpretation are important parts of any quantitative
evaluation, and Mine-to-Mill has been no exception. For the diverse nature of mining stages in
practice, a wide range of information has to be analysed in order to quantify the interaction
between them, which also necessitates effective communication between people of different
disciplines/specialisations across the value chain.
Access to reliable software packages for modelling and evaluation:
There are numerous technologies available in the industry ranging from simple to
sophisticated, which assist specialists in their everyday decision-makings. GEOVIA Dassault
Systèmes software capabilities (with over 17 000 active users) covers geology modelling,
resource estimation and mine design (GEOVIA Surpac™), Strategic Mine Planning (GEOVIA
Whittle™), Tactical Mine Planning and scheduling (GEOVIA MineSched™). For simulation
purposes, simulation process automation and design optimisation solutions (SIMULIA and
CATIA Products) provide a reliable base for integrating and optimising all mining stages
through the development of ‘what-if’ scenarios. In blasting and processing fields, the
JKSimBlast® and JKSimMet® software packages developed by Julius Kruttschnitt Mineral
Research Centre, The University of Queensland, are among the most widely-used products in
the field, which have been used for decades in Mine-to-Mill projects worldwide. It is evident
that applying Mine-to-Mill at strategic and tactical levels requires suitable tools and
technologies critical to each stage. In this paper, an outcome-based solution is proposed based
on some of the most industry-trusted software packages available in the minerals industry.
Today with the fast progress of technology in computation, measurement, data analytics and
simulation areas, it is expected that more sophisticated technologies are being developed for
modelling, simulating and controlling all stages of ore resource exploitation on platforms in a
real-time manner.
Data requirement
To develop a holistic model of a mining operation, representative data has to be collected sufficiently
across the value chain. Availability and representativeness of data is critical to modelling and process
design. A detailed data-related discussion is beyond the scope of this paper, and could be found
elsewhere (Hustrulid and Kuchta, 1995; Napier-Munn, 2014; Napier-Munn et al, 1996). However,
the nature of data requirements is briefly addressed here. In general, the data required for Strategic
Mine-to-Mill can be categorised depending on its applicability:
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The data required for Strategic Resource Modelling and Assessment:
Resource estimations underpin multi-million-dollar investment decisions made by mining
companies, strategic decisions along with financial, social and environmental considerations.
Geological databases acquired from drill holes are the most common set of data used for
Strategic Resource Modelling and Assessment. The information from drill holes describes the
location of the drill hole collar, the maximum depth of the hole and whether a linear or curved
hole trace will be calculated when retrieving the hole. This information combined with data
acquired from topographic surveys and characterisation test works conducted on retrieved
cores (eg assays, geological, geotechnical and chemical properties of deposit at different
subsurface spatial coordinates) are used to describe an orebody. An outcome of this stage is
a block model, which describes orebodies properties, eg shape, volume and characteristics
through size cubes or cuboids. A block model is critical to all quantitative evaluations in most
mining operations, where upstream and downstream data such as mining, blasting and
metallurgical data can be coded into it to describe impact of mined blocks on operational
performances. GEOVIA Surpac™ is one of the world’s most popular software, supporting open
pit and underground operations and exploration projects.
The data required for Strategic Mine Planning and Design:
The resource block model is the data library, which is used for mine planning and design at
strategic and tactical levels. A wide-range of information is utilised at this stage, which covers
topographic surveys, rock mass and ore geological, mechanical, metallurgical
characterisations, mining and processing capacity, elements prices, and costs imposed across
the value chain over LOM.
The data required for Strategic Blast Modelling and Design:
The essential task to break rock down into a specific size fraction starts at the very beginning
by drilling and blasting operations and continues by subjecting ore to a series of breakage
processes through comminution machines. This proceeds to the point where particles meet
appropriate size criteria for being treated in a beneficiation process. Strategic Blast Modelling
and Design aims at optimal overall productivity, which is reachable if design criteria are aligned
with a project’s strategic objectives. The key data required for this practice could be described
as pre-blast and post-blast. Key pre-blast information required is rock mass properties,
explosives properties, operational and safety considerations. Post-blast data are but not limited
to fragmentation size distribution, backbreak, ground vibration, noise, flyrock, movement and
ore dilution, dust and Nox emissions.
The data required for Strategic Comminution Circuit Modelling and Design:
To model a process plant is to configure a flow sheet, which illustrates units and their
interaction. An initiative of a flow sheet is to model the state of the process. Irrespective of
simulation structures, to develop a ‘base-case’ model, consistent data has to be collected
sufficiently, which can be sourced from site and plant surveys, laboratory experiments, value
chain instrumentation, technical reports etc. Today, some industry-trusted examples of
simulating packages are JKSimMet® (Morrison and Richardson, 2002), MODSIM (King, 1990),
SysCAD (Stepto et al, 1990) and MolyCop Tools (Silva et al, 2015) with a range of useable
unit models incorporated in them. Reliable comminution circuit modelling and design is a
strategic step towards harmonising upstream and downstream activities and is critical to
project economics. The data should be sourced from plant surveys; nonetheless, an alternative
might be considering the operational history of the plant, namely ‘historic or operational data’.
Common data is a requirement of this stage but not limited to the units/equipment design and
operational characteristics, ore properties (eg A, b, ta, SPI, BWi, density, grade, mineralogy),
flow characteristics through streams (eg mass flow, percent solids, size distributions),
operational considerations (eg power draws, mill fractional speed, screen sizes, hydrocylones
pressure, target size), and KPIs (eg throughput, recovery).
The data required for Strategic Waste Management and Valourisation:
Developing a plan for managing current and future waste/low-grade material should be part of
any strategic decision-making. In addition to ore physical, mechanical, metallurgical and
IMPC Asia-Pacific 2022 | Melbourne, Australia | 22–24 August 2022 1212
chemical properties, it would be worth creating a data set including the location of dumped
materials of different kinds by deploying GPS-based and survey techniques, or even track of
material by implanting RFIDs. Such information should assist with generating new block
models of waste dumps for future modelling and assessment practices.
The data required for continuously improving accuracy and precision of strategic stages:
Finally models of mining and processing activities developed based on operational data
represent a narrow time frame and will lose their fidelity over time due to frequent variations
introducible within a mine life cycle. The fidelity of a model can be assessed by comparing the
degree of agreement between actual and predicted values where an error is given as a
quantitative measure of the discrepancy. For the models representing operational stages a
‘mean absolute error’ within ±10 per cent is desirable. As a measure of precision, the Relative
Error (RE) expresses magnitude of error relative to the measured/actual value, which reads:
(%) = 100 × ( )
(1)
Upon collecting more descriptive data by integrating and using more accurate measurement
systems and sensors into mining and processing stages, a more detailed description of an
orebody can be generated. This should assist decision-makers with considering unseen
aspects of their plans, refine, improve or even re-define their strategic and tactical objectives.
It is important to note that not all the information collected is useful. The key objective of data curation
and analysis is preparing ‘relevant’ and ‘reliable’ data. For this purpose, useful data should be
discriminated from the redundant and presented in an appropriate number, format, size and units. In
this process, it is also important to account for misinformation as well as disinformation. The former
considers vital data missed or unavailable, and the latter refers to ‘unexpected’ information, ie
unreliable instrument readouts. Analysis of data is an essential step, which also engages technical
and analytical knowledge of individuals, eg engineers, data analytics specialists. This includes
running several basic tests to ensure ‘consistency’ of the data by establishing relevant correlations
between different values eg ore properties and KPIs. Additionally, when developing a base-case
model, specifically a ‘process plant’ if using a software package eg JKSimMet®, it is vital to check if
the data available aligns with the unit models requirements. For example, the common breakage test
in most South American operations is SAG Power Index, namely SPI (Starkey et al, 1994), however,
this index is not useable in the JKMRC AG/SAG mill model (Morrell and Morrison, 1989) as the
model requires A, b (ore competence indices) and ta (ore abrasion index) values from the JK Drop
Weight Test (JKDWT) (Napier-Munn et al, 1996). Therefore, relevant test works should be
conducted, or the values should be estimated based on available indices using established
correlations. As these breakage tests are extensively used, it has been a common practice for
metallurgists developing correlations between the SPI and JKDWT breakage indices.
In summary, because of the very diverse nature of data generated across the mining value chain,
data preparation for modelling such activities would engage multiple disciplines of geologists,
geotechnical, exploration specialists, mining, blasting and process engineers, and well as data
scientists. Within the context of mine planning, Couzens (1979) advised that it is ‘vital to keep our
objectives clearly defined while realising that we are dealing with estimates of grade, projections of
geology, and guesses about economics – we must be open to change and communicate’.
Software packages
Strategic resource modelling, assessment and mine design – GEOVIA Surpac™
The software supports to open pit and underground operations and exploration projects in more than
120 countries. Key capabilities of GEOVIA Surpac™ are drill hole data management, geological and
block modelling, resource estimation; geo-statistics, drill-and-blast and mine design.
Strategic mine planning – GEOVIA Whittle™
The software is used to evaluate the financial viability and the optimal open pit mining strategy for a
deposit. It is a commercially trusted tool applied in scoping, feasibility, life-of-mine scheduling, and
ongoing re-evaluation of mine plans throughout the production phase. GEOVIA Whittle™ aims at
IMPC Asia-Pacific 2022 | Melbourne, Australia | 22–24 August 2022 1213
optimal scenarios through provides the capability to consider key mining, processing, geological,
geotechnical and financial considerations into any analysis at an annual timescale over LOM.
Tactical mine planning – GEOVIA MineSched™
The software provides scheduling to improve productivity for surface and underground mines of all
sizes and types. GEOVIA MineSched™ aims at realisation of a strategic mine plan by taking into
account operational details at monthly and weekly timescales over LOM. Tactical Mine Planning is
the chain loop that facilitates harmonising the upstream and downstream stages through integration.
The tool provides seamless capabilities in short- and medium-term mine scheduling by accounting
for the strategic mine plan considerations, grade control, blasting sequence, haulage system, mining
and processing capacities, and tracking different types of materials. It allows for the development of
constraint-based scenario analysis to reach optimal and feasible alternatives, which de-risk
realisation of strategic objectives.
Blasting and mineral processing – JKSimBlast®, JKSimMet® and MolyCop Tools®
JKSimBlast® is a user-friendly tool for drill-and-blast design and predicting muck pile PSD based on
blast fragmentation models such as the original Kuz-Ram (Cunningham, 2005) and Crushed Zone
Model (CZM). It is proven that the Kuz-Ram model underestimates the amount of fines (-25 mm) in
the ROM PSD (Comeau, 2018), which significantly affects the downstream processes of crushing,
grinding and flotation. Therefore, the CZM developed by JKMRC is a suitable alternative when
estimating the contribution of smaller size fractions is of prime importance, specifically for Mine-to-
Mill optimisation purposes because the model provides a more realistic estimation of blast-induced
fines (Kanchibotla et al, 1999). In mineral processing, software packages such as eg JKSimMet®
and MolyCop Tools® might be used for developing reliable base-case flow sheets of process plant.
For example, JKSimMet® is software for comminution circuit mass balancing, modelling and
simulating, which benefits from a wide range of validated comminution and separation models
developed by the JKMRC (Napier-Munn et al, 1996).
Design of experiment
Design of experiments consists of carrying out a set of tests that allow the generation of results,
which, when analysed, provide objective evidence to study the behaviour of a process, in this case
Mine-to-Mill optimisation process.
The objective is quantify and analyse the influence of applying Mine-to-Mill optimisation at strategic
and tactical levels – so it centres on overall value maximisation, ie Net Present Value, ‘NPV’. The
Net present value is the present value of the cash flows at the required rate of return of a project
compared to your initial investment. In other words, it considers the ‘Time Value of Money’ in the
assessment of an investment opportunity. Thus, near future cash flows are worth more today than
distant future cash flows. The equation for NPV reads as follows (Fisher, 1930):
NPV
R
k
C
t
t
t
n
1
1
NPV
R
k
C
t
t
t
n
1
1
(2)
Where C is initial capital investment, R is cash flow per period, k is the discount rate and t represents
time. Another economic indicator is the Internal Rate of Return, ‘IRR’ which is used in financial
analysis to estimate the profitability of the potential investment. IRR is a discount rate that makes
the NPV of all cash flows equal to zero in a discounted cash flow analysis. In general, the higher IRR
is, the more desirable an investment is to make.
In this paper, we propose an example based on implications from conventional Mine-to-Mill projects,
which demonstrates the potential application of the GEOVIA Surpac™, Whittle™ and MineSched™
in the implementation of ‘Strategic Mine-to-Mill’ and ‘Tactical Mine-to-Mill’ approaches, which are
conceptualised at GEOVIA Dassault Systèmes in 2021, and introduced in this paper.
The considerations and assumptions of this study are:
The JKSimBlast® and JKSimMet® software packages are not used for the modelling nor for
the analyses given in this paper. However, base-case models of downstream processes are
IMPC Asia-Pacific 2022 | Melbourne, Australia | 22–24 August 2022 1214
critical for linking and harmonising the upstream and downstream activities in an interactive
manner. That is, GEOVIA Whittle™ (Strategic Mine Planning software package) is fed by
downstream KPIs generated from a base-case model of a process plant, which assists with
integrating the upstream and downstream KPIs, and quantify their interactions.
This study does not consider ore loss and dilution in value quantification. However, intense
blasting generally limits control over the outcomes. It increases the risk of undesirable side
effects such as backbreak, ground vibration, air-blast and flyrock, and dilution, which imposes
additional expenses for taking appropriate mitigation measures and control strategies.
However, ore dilution is the phenomenon, which may result in a remarkable loss of value as
reported before (Engmann et al, 2013; Esen et al, 2007; Eshun and Dzigbordi, 2016). It can
directly affect the overall value through ore loss and disposal of waste or less valuable material
to the mill. In this space, blast movement modelling and monitoring systems assist with tuning
blast design parameters (ie timing, pattern, explosives properties and distribution of blast
energy etc) for reduced ore loss.
Review of several implemented Mine-to-Mill projects in Asia Pacific region implies performance
improvement opportunity of up to 20 per cent in milling capacity, 10 per cent in loading rate,
and 5.0 per cent in recovery. The values are used to quantitatively taking into account impact
of such Mine-to-Mill induced results on strategic and tactical mine plans. Accordingly, What if
scenarios will be considered in ±20 per cent, ±10 per cent and ±5 per cent for milling rate,
mining rate and recovery, respectively.
Pre-concentration in mining aims to manipulate ore feed quality by removing low value gangue
material prior to the comminution process. Pre-concentration requires a suite of well-
established techniques and technologies being utilised to exploit differences in physical and
chemical properties of an ore to separate valuable minerals from gangue. Thus, depending on
ore characteristics, a technique based on size, gravity, conductivity, competence, magnetic
susceptibility, thermal reactivity etc, can assist with feed upgrade prior to energy-intensive size
reduction stages – and should be used as an optimising leverage for next generation Mine-to-
Mill projects. GEOVIA Whittle™ allows for grade control (improving ore feed quality) for both
the pit and process plant. Accordingly, the impact of adopting an ore pre-concentration strategy
on the strategic outcomes is quantified, and discussed.
Metal (copper, gold and silver) price and selling costs are assumed constant over LOM.
In this paper, a constant financial model is used for estimating mining and processing costs.
Because of confidentiality considerations, details of financial models used cannot be shared.
However, it is worth noting that implementing Mine-to-Mill practices generally results in more
than doubled drill-and-blast costs, while saving money by improving loading and haulage
efficiency and even safety. On the processing side, an intense blasting practice potentially
could reduce the cost of processing for a given ore type by reducing the process time. To
reflect such changes in cash flow across the value chain, it is required to develop sophisticated
financial models by tracking changes over a significant timespan, which is an area of future
investigation in Strategic Mine-to-Mill planning and optimisation.
Base-case development
A block model of copper-gold-silver deposit with two dominant rock types is used for developing a
base-case model for quantifying the impact of downstream changes on overall value, NPV. For this
purpose, GEOVIA Whittle™ was used to integrate mining throughput with downstream KPIs of mill
throughput and recovery, which are the focus of this paper. It is also worth highlighting that this study
specifically deals with mining production rate, process throughput and recovery as the three key
KPIs that were frequently considered in previous Mine-to-Mill projects. However, these KPIs are
significantly determined by geology, mineralogy and geo-mechanical properties of orebodies and
quantifying the impact of such variables on mine plans is beyond the scope of this article. It is worth
adding that it benefits from the ‘Throughput Factor’ as a useful leverage to account for the milling
rate of different rock types (influenced by ore competence and feed PSD), which makes it most
appropriate for Mine-to-Mill investigations. Operational constraints/considerations can be set to
account for likely bottlenecks between mining and processing for any given period. Furthermore,
IMPC Asia-Pacific 2022 | Melbourne, Australia | 22–24 August 2022 1215
GEOVIA Whittle™ allows for accounting variable values for different times for any given input eg
mining and milling rates, recovery, financial variables and grade targets as business objectives and
hence adopted strategies may require refining changes over time (See Figure 2).
FIG 2 – Mining and processing constraints set-up interface – GEOVIA Whittle™.
The optimal pushbacks were selected through the ‘directional mining’ approach, which evaluates
profitability through testing several mining starting points. That is, depending on orebody shape and
distribution of grade within it, adopting an appropriate starting point will impact NPV as it controls
‘metal yield’ from any part of an orebody over time. Poblete (2016a) comprehensively describe the
approach, and then estimated its influence on NPV.
In this study, we tested directions of North (0°), North-east (45°), East (90°), South-east (135°), South
(180°), South-west (225°), West (270°) and North-west (315°), out of which the ‘East’ alternative
resulted into the best outcome compared to the others.
The 3D Lerchs-Grossmann algorithm is used for pit optimisation, which accounts for block values,
mining precedence and is capable of fining 3D outline with the highest possible value. For the
Scheduling practice, GEOVIA Whittle™ benefits from Milawa algorithm, which combines feasible
schedules into careful economic forecasting for improved NPV. Details on Lerchs-Grossmann and
Milawa algorithms are beyond this paper’s scope and is comprehended elsewhere (Lerchs, 1965).
Figure 3 showing mining directional and phases (pushbacks) optimised for Cu-Au-Ag deposit.
IMPC Asia-Pacific 2022 | Melbourne, Australia | 22–24 August 2022 1216
FIG 3 – Pit expansion towards East using directional mining approach (Left) and the four optimal
mining phase (pushbacks) selected for scheduling purposes (right) – GEOVIA Whittle™
The pushbacks should satisfy mining and processing constraints, and produce the highest NPV from
moving volumes of earth in sequence. Figure 4 shows the base-case schedule which illustrates the
amount of material of different type and their destination over 17 years of mine life. Failure to provide
enough ore feed results in decreased profitability, therefore strategies like having stockpiles for
compensating ore feed shortage for difficult periods are helpful to maintain productivity over LOM
and ensure production balance between the upstream and downstream stages.
FIG 4 – Strategic scheduling: material movement over LOM, Base-Case
Figure 5 shows the scheduling for tonnage removal from each pushback. Obviously, in the 17th year
of LOM, removed tonnages from the pit is zero, indicating that the ore reserve is fully exploited. The
ore feed required by the process in the last two years of LOM (years 16th and 17th) is provided by
the stockpiles.
0
5
10
15
20
25
30
35
0
10
20
30
40
50
60
70
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Period (Year)
Tonnage in Stockpiles Tonnage from Mine to Processing
Tonnage from Stockpiles to Processing Tonnage from Mine to Stockpiles
Tonnage Waste
IMPC Asia-Pacific 2022 | Melbourne, Australia | 22–24 August 2022 1217
FIG 5 – Strategic scheduling: tonnage of pushbacks mined over LOM, Base-Case.
Scenario-based simulations
Three key areas of improvement by adopting a Mine-to-Mill approach are mining rate, milling rate
and recovery. Based on documented values from reviewed case studies, the variation of values by
10 per cent, 20 per cent and 5 per cent were considered improvement opportunities compared to
the base case. However, a poor drill-and-blast practice can be deleterious and limit value. Therefore,
sensitivity analysis of individual key variables (26 scenarios) and combinations of them (8 scenarios)
were conducted in the context of Mine-to-Mill.
Table 1 provides further details on development of scenario-based simulation runs for sensitivity
analysis of individual key variables by using GEOVIA Whittle™.
TABLE 1
Ranges and levels of key variables used in GEOVIA Whittle™ (26 scenarios).
Description Variation Scenarios Unit Minimum Base-case Maximum
Mining
Capacity/Rate
± 10.0% @
2.5% Levels 8 t/h 5137 5708 6279
t/d 123 288 136 986 150 685
Milling
Capacity/Rate
± 20.0% @
5.0% Levels 8 t/h 1826 2283 2740
t/d 43 836 54 795 65 753
Cu Recovery ± 5.0% @
1.0% Levels 10 % 78 83 88
Four scenarios were considered for evaluating potential losses and gains from optimising key
variables of mining and milling rates as well as recovery in a Mine-to-Mill context:
Scenario 27: Mining Rate = -10%, Milling Rate = -20%, Cu Recovery = -5%
Scenario 28: Mining Rate = -5%, Milling Rate = -10%, Cu Recovery = -2.5%
Scenario 29: Mining Rate = +5%, Milling Rate = +10%, Cu Recovery = +2.5%
Scenario 30: Mining Rate = +10%, Milling Rate = +20%, Cu Recovery = +5%
Four additional scenarios were assessed with only accounting for the mining and milling rates, and
assuming Cu recovery is constant:
Scenario 31: Mining Rate = -10%, Milling Rate = -20%, Cu Recovery = 0.0%
Scenario 32: Mining Rate = -5%, Milling Rate = -10%, Cu Recovery = 0.0%
0
10
20
30
40
50
60
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Period (Year)
Tonnage of Push-Back 1 Mined Tonnage of Push-Back 2 Mined
Tonnage of Push-Back 3 Mined Tonnage of Push-Back 4 Mined
IMPC Asia-Pacific 2022 | Melbourne, Australia | 22–24 August 2022 1218
Scenario 33: Mining Rate = +5%, Milling Rate = +10%, Cu Recovery = 0.0%
Scenario 34: Mining Rate = +10%, Milling Rate = +20%, Cu Recovery = 0.0%
RESULTS, ANALYSIS AND DISCUSSION
Three areas of potential gain from implementing changes in drill-and-blast practice were studied and
their impact on NPV was quantified by simulating several scenarios using the Strategic Mine
Planning and Optimisation software package, GEOVIA Whittle™. Accordingly, generated variations
in overall value were recorded. This provides a quantitative figure of potential gains from Strategic
Mine-to-Mill under several operational conditions, and how upstream and downstream processes
may interact; hence NPV is impacted. This section centres on presenting such results and
developing a discussion on the technical and economic aspects of the simulated scenarios.
For a given example of Cu-Au-Ag deposit, three key variables of mining rate, milling rate and
recovery were analysed to quantify their impact at a strategic level. The range of variation for each
variable was adopted from several Mine-to-Mill case studies in Asia Pacific region.
Figure 6 provides a brief summary of how implementing Mine-to-Mill optimisation strategies
potentially changes the Net Present Value, NPV in the long-term without deploying CAPEX. In the
context of Mine-to-Mill and its improvement potentials, the simulation outcomes suggest recovery
and mining rate as the most effective leverages the overall value and then the milling rate. It is
important to note how failure in maintain optimal mining and milling practices (here blast-induced
impact on the downstream stages) may diminish profitability over life-of-mine.
FIG 6 – NPV changes compared to the base-case NPV for all considered scenarios.
The results of simulated scenarios are given in Figure 7 for individual variables. Figure 8 and Table 2
summarise the results for the combined scenarios, which account for potential improvements or
decrease of mining rate, milling rate and recovery at the same time, indicating extreme possibilities
for potential gains and losses.
As it is implied in Figure 7, for the given example, 10 per cent improvement in mining rate/capacity
through ‘implementing and maintaining’ quality drill-and-blast practices over life-of-mine can
translate into approximately 6.0 per cent increase in the NPV, while failure to move earth volumes
in sequence may impose a loss of 9.0 per cent in NPV over LOM. Mining rate changes resulted in
±5.0 per cent.changes in IRR. Increases in mining capacity should generally be accompanied by a
reduction in costs due to economy of scale. However, if the original fleet is maintained and this
improvement is associated with improved performance of load-and-haul operation as a result of a
finer muck pile fragmentation (eg if the shovels were the bottleneck for mining and any improvement
in their performance results in shorter waiting times for trucks), it should imply an extra cost also
associated with the mine cost, specifically associated with drill-and-blast costs – this additional cost
should cushion the increase in value. On the other hand, the increase in this mining capacity offers
IMPC Asia-Pacific 2022 | Melbourne, Australia | 22–24 August 2022 1219
the possibility of providing better quality ore available over time, to be sent to the process plant, and
postponing the lower quality material by sending it to the stockpile, which would contemplate its re-
handling costs.
FIG 7 – Results of sensitivity analysis of individual key variables by using GEOVIA Whittle™.
Base
-10%
-7.5%
-5%
-2.5%
+2.5% +5% +7.5% +10%
0
10
20
30
40
50
60
4,600
4,800
5,000
5,200
5,400
5,600
5,800
6,000
6,200
6,400
IRR (%)
NPV ($) Millions
Mining Rate (t/d)
NPV IRR
Base
-20%
-15% -10%
-5% +5% +10% +15% +20%
0
10
20
30
40
50
60
4,600
4,800
5,000
5,200
5,400
5,600
5,800
6,000
6,200
6,400
2,283 1,826 1,941 2,055 2,169 2,397 2,511 2,626 2,740
IRR (%)
NPV ($) Millions
Milling Rate (t/h)
NPV IRR
Base
-5%
-4%
-3%
-2%
-1%
+1%
+2%
+3%
+4%
+5%
0
10
20
30
40
50
60
4,600
4,800
5,000
5,200
5,400
5,600
5,800
6,000
6,200
6,400
83 78 79 80 81 82 84 85 86 87 88
IRR (%)
NPV ($) Millions
Cu Recovery (%)
NPV IRR
Run # Mining
Rate (t/d)
NPV
($M)
IRR
(%)
Mine
Life (y)
Base 136 986 5442 49.92 16.58
1 123 288 5007 44.55 17.28
2 126 712 5133 45.83 17.00
3 130 137 5236 47.14 16.76
4 133 562 5344 48.55 16.69
5 140 411 5539 51.31 16.46
6 143 836 5609 52.37 16.40
7 147 260 5690 53.42 16.35
8 150 685 5779 54.46 16.30
Run # Milling
Rate (t/h)
NPV
($M)
IRR
(%)
Mine
Life (y)
Base 2283 5442 49.92 16.58
9 1826 5160 47.51 19.89
10 1941 5247 48.39 18.89
11 2055 5306 48.86 18.01
12 2169 5398 49.5 17.22
13 2397 5476 50.28 16.08
14 2511 5498 50.46 15.66
15 2626 5515 50.56 15.37
16 2740 5529 50.66 15.29
Run #
Recovery
Cu (%)
NPV
($M)
IRR
(%)
Mine
Life (y)
Base 83 5442 49.92 16.58
17 78 4969 47.32
16.58
18 79 5063 47.85
16.58
19 80 5158 48.37
16.58
20 81 5253 48.89
16.58
21 82 5347 49.4 16.58
22 84 5537 50.43
16.58
23 85 5631 50.93
16.58
24 86 5726 51.44
16.58
25 87 5821 51.94
16.58
26 88 5915 52.44 16.58
IMPC Asia-Pacific 2022 | Melbourne, Australia | 22–24 August 2022 1220
FIG 8 – Results of sensitivity analysis of combinations of key variables by using GEOVIA Whittle™.
TABLE 2
Details of simulated scenarios for combinations of key variables.
Run # % Changes
(M:M:R)
Mining
Rate (t/d)
Milling
Rate (t/h)
Recovery
Cu (%)
NPV
($M) ($M)
Relative NPV
Changes (%)
IRR
(%)
Mine
Life (y)
Base 0:0:0 136 986 2283 83 5442 0 0.0 49.92 16.58
27 -10:-20:-5 123 288 1826 78 4403 -1039 -23.6 40.77 20.21
28 -5:-10:-2.5 130 137 2055 80 4894 -548 -11.2 45.17 18.14
29 +5:+10:+2.5 143 836 2511 86 5980 538 9.0 54.66 15.36
30 +10:+20:+5 150 685 2740 88 6418 976 15.2 58.91 14.30
31 -10:-20:0 123 288 1826 83 4832 -610 -12.6 43.00 20.20
32 -5:-10:0 130 137 2055 83 5166 -276 -5.3 46.61 18.14
33 +5:+10:0 143 836 2511 83 5686 244 4.3 53.04 15.36
34 +10:+20:0 150 685 2740 83 5910 468 7.9 56.06 14.30
NOTE: Mining Rate: Milling Rate: Recovery = (M:M:R)
The milling rate/capacity suggests the potential to impact the NPV between -6.0 per cent and
+2.0 per cent, which its dollar equivalence range would be -$282 M and +$87 M (See Figure 7). The
process plant is generally the bottleneck of the value chain, so its limit determines the metal yield,
and hence cash flow. It is worth noting that conventional Mine-to-Mill optimisation mainly aims to
improve the milling throughput by performing high-energy blasts to tailor ore feed PSD in favour of
SAG milling (generate more <10 mm fines). However, it is of prime importance to ensure power
balance between the SAG and ball milling stages. The finer fragmentation feed may result in
overloading the ball mill-cyclone circuit, and consequently impose pressure on circulating load –
limiting throughput. For such operational scenarios, enlarging cyclone cut size (Product P80) might
reduce circulating load – however, the impact on recovery should be evaluated. As expected, the
milling rate is most effective on life-of-mine in longer term if milling rate improvements could be
maintained.
The Cu recovery directly accounts for metal yield/saleable product, and the result suggest dramatic
influence of recovery on NPV over life-of-mine. In this example, ±5.0 per cent changes in recovery
resulted in NPV varying between -10 per cent and +8.0 per cent, equivalent to half a billion dollar in
17 years of operation (See Figure 7). The simulation scenario indicate 1.0 per cent change in
recovery means $95 M over LOM, which is $5.6 M per annum for the given example – which
highlights importance of applying grade control and ore loss reduction strategies across the value
chain.
Base
0
10
20
30
40
50
60
70
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
0:0:0 -10:-20:-5 -5:-10:-2.5 +5:+10:+2.5 +10:+20:+5 -10:-20:0 -5:-10:0 +5:+10:0 +10:+20:0
IRR (%)
NPV ($) Millions
Percent Changes (Mining Rate : Milling Rate : Recovery)
NPV IRR
IMPC Asia-Pacific 2022 | Melbourne, Australia | 22–24 August 2022 1221
Applying a Mine-to-Mill optimisation approach at strategic level, should aim at delivering operational
improvements. Not only limited to milling throughput, but also in mining rate as well as recovery.
Figure 8 and Table 2 show the results of scenarios that account for Mine-to-Mill gains in three key
areas of production with varying degrees/percentages. Compared to the conventional Strategic Mine
Planning (Base-Case), the results suggest the potential to improve NPV up to $976 M over 14 year
life-of-mine, by adopting a Mine-to-Mill at a strategic level without the need to deploy additional
CAPEX. However, poor performance of mining, milling and beneficiation stages may be associated
with risk of diminishing NPV by $1.039 B over an extended LOM of 20 years.
It is important to note that Strategic Mine-to-Mill optimisation can only be realised in the presence of
tactical plans. The tactical plans are tailored to deliver improvements at an operational level. With
respect to the significant influence of metal yield/recovery on NPV, proper grade control and ore loss
reduction strategies should be considered across the value chain to manage grade variability and
maintain recovery over time with the objective to maximise orebody utilisation. This requires several
techniques, including measurement and analytics, Strategic Geological Modelling, blast movement
monitoring and control for reduced ore dilution, ore pre-concentration and blending, comminution
and classification. Exploring the results of all scenarios, highlights that maximised profitability is only
achievable if the ore reserve is efficiently utilised in sequence and maintaining quality practices in
mining, milling and flotation over LOM. With For this purpose, operational KPIs should be optimised
and continuously improved over time, following development of new solutions, technologies and
techniques (ie Mine-to-Mill approach) and their long-term impact being described quantitatively. At
an operational level, Tactical Mine-to-Mill aim at realising strategic objectives through plans of
implementation in medium- and short-term time frames.
An area of interest for future work should be integrating energy consumption estimates into strategic
LOM evaluations, specifically the amount used at the downstream stage. To further discuss the
matter of importance, Cohen (1983) estimates that 30–50 per cent of total plant power draw, and up
to 70 per cent for hard ores is consumed by comminution. In minerals industry, energy is not
consumed to best possible advantage in the comminution equipment and efficiency is only of the
order of 0.1–2 per cent considering the energy required to generate new surface area relative to
mechanical energy input (Fuerstenau and Abouzeid, 2002; Tromans, 2008). It has been identified
that power efficiency for crushers varies between 70–80 per cent compared with single particle
breakage in a drop weight tester (Morrell et al, 1992), while the value for ball mills and SAG mill is
about 30 per cent and 40 per cent, respectively (Musa and Morrison, 2009). It was noted by Tromans
(2008) that the limiting energy efficiency under compression varies between 5–10 per cent
depending on the value of Poisson’s ratio and under the uniaxial tension does not exceed
66 per cent. He argued that efficiencies of 5–10 per cent will not be achievable in practice, because
the strain energy within a considerable region of a compressed particle makes no contribution to the
fracture process and dissipates in forms of heat and kinetic. Thus, understanding fundamental
limitations inherent in mechanical breakage of rock is the key to explore more efficient alternatives
(Napier-Munn, 2015). Measuring the extent of competence variability within an ore domain should
help to more accurately scale the size of quipment, estimate required energy and also better
understand the nature of observed variation in the mill performance whether it is ore-induced or has
an operational/technical origin. As an example, the Extended Drop Weight Testing (EXDWT)
approach allows to consider particles pre-breakage physical properties, eg mass, density,
dimension, orientation, colour, roundness, textural features etc, and investigate their possible link to
particles competence based on the concept of ‘tn-family per particle’. This capability offers the
potential for the first time to account for more sources of variation, thus more closely estimate the
true competence heterogeneity within a sample of ore (Faramarzi, 2020; Faramarzi et al, 2020).
Overall, energy consumption is important to the mining industry and is likely to become more so
because of increasing expense, as well as new regulatory and market interventions (Napier-Munn,
2015), therefore estimating energy usage at a strategic level should be beneficial in decision-making
and establishing more sustainable alternatives for efficient utilisation ore reserves.
This study, specifically underlined the NPV improvement potential that could be achieved only by
quality, value-driven drill-and-blast practices within Mine-to-Mill. Although blasting plays a
deterministic, proven role in improving downstream performances; for best utilisation of an ore
reserve, innovative techniques should be applied across the value chain, backed by more
IMPC Asia-Pacific 2022 | Melbourne, Australia | 22–24 August 2022 1222
sophisticated financial models which could effectively reflect economic consequence of changes
over life-of-mine.
CONCLUSIONS
The Strategic Mine-to-Mill optimisation as conceptualised in this paper, centres on long-term impact
of adopting and maintaining optimisation strategies. Application of Mine-to-Mill at strategic and
tactical levels would suggest opportunities for a step change improvement in the controllability of
mining and processing performances, and substantially improved economics of operations.
In this paper, the potential impact of adopting Mine-to-Mill improvement strategies on the Net Present
Value (NPV) was estimated for a Cu-Au-Ag deposit. Three key variables of mining, milling, and
recovery rates were analysed within potential ranges for performance improvement, reported from
Mine-to-Mill studies.
For the given case study, analysis of outcomes indicates that, maintaining value-driven strategies
could result in up to +15 per cent higher NPV relative to the base-base. Although, recovery was
found to be the most influential variable on NPV (by 8.0 per cent); however, based on the results,
only mining and milling performance improvements through blasting could generate 7.9 per cent
more value relative to the base-case. It is concluded that developing such analyses at a strategic
level and integrating operational possibilities into calculations should assist with better understanding
of potential gains and losses quantitatively. This would also be useful for establishing more robust
risk management strategies in presence of uncertainties (ie commodity price, ore property)
introducible over LOM. It is concluded that sustainable ore reserve utilisation is achievable if
upstream and downstream activities becoming ‘mutually informative’ and their KPIs shared in real-
time for harmonising activities for maximised overall value. Future study should focus on addition of
more levels of sophistication, some would be as follows:
Stochastic analysis of the impact of ore variability at a strategic level: Although, ore variability
is frequently debated as a major source of uncertainty in process performance, most of the
current ore testing methods do not capture the variability within ore samples and process
performance. Predictions are based on using average values for ore characteristics. It is
required to use ore characterisation approaches which are designed for measuring the extent
of variability inherent to the orebodies (ie mineralogical, textural and breakage characteristics
of orebodies as key drivers to process performance KPIs such as recovery and throughput)
and estimate the extent of ore-induced operational variability. This should assist with
developing more realistic plans at the Tactical Mine-to-Mill stage in light of diagnosing likely
bottlenecks across the value chain over LOM as ore properties change, although this remains
a focus of future work.
Development of ‘more’ sophisticated financial models for value-chain studies: While it is helpful
to integrate Mine-to-Mill into strategic considerations, it is equally important to develop
sophisticated financial models which can reflect on implemented changes.
Accounting for the impact of ore loss/dilution in long-term analyses: High-energy blasting
generally limits control over the outcomes, therefore might be associated with risk of ore loss
through dilution. Blast-induced ore dilution is the phenomenon that can directly affect the
overall value, NPV over time, and performance by feeding the mill with less valuable material.
While it is important to reflect on ore dilution at a strategic level, it is also critical to set-up risk
mitigation measures through blast movement modelling and monitoring systems for tuning
blast design parameters (ie timing, pattern, explosives properties and distribution of blast
energy etc) for reduced ore loss.
Application of operational history of the plant, namely ‘historic or operational data’: Process
plant historian (PI) data are indicative of the state of a process (commonly used for
tactical/operational evaluations), but some challenges with ‘operational’ (PI) data have been
inconsistency, redundancy, unreliable instrument readouts and large variations (with unknown
sources and amounts). However, careful curation and analysis of PI data could assist with
providing insightful information on process performance, constraints and bottlenecks. This
would assist with developing strategic evaluations as well as risk assessment in light of
operational constraints.
IMPC Asia-Pacific 2022 | Melbourne, Australia | 22–24 August 2022 1223
ACKNOWLEDGEMENTS
The authors would like to acknowledge Dassault Systèmes for sponsoring this work, which made it
possible for the authors to present this paper at IMPC Asia Pacific 2022. Dassault Systèmes provides
software solutions that allow customers to create innovative new products and services using virtual
experiences and enables business and people with 3DEXPERIENCE universes where they can
imagine sustainable innovations capable of harmonising product, nature, and life. The IMPC Council
and AusIMM are sincerely acknowledged, for their effort in putting the IMPC Asia Pacific 2022
conference together in Australia.
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