Mining Education Australia Research Projects Review

Transcription

Mining Education Australia
Research Projects Review 2012
Editor: Paul Hagan
Published by:
The Australasian Institute of Mining and Metallurgy
Ground Floor, 204 Lygon Street, Carlton Victoria 3053, Australia
i
© Mining Education Australia 2013
All papers published in this volume were independently peer reviewed prior to
publication.
Mining Education Australia is not responsible as a body for the facts and opinions
advanced in any of its publications.
ISSN: 2201 - 716X
Desktop published by:
Kylie McShane and Claire Lockyer
The Australasian Institute of Mining and Metallurgy
Printed by:
BPA Digital
11 Evans Street, Burwood Victoria 3125, Australia
ii
PReFACe
The 2012 MeA Research Projects Review includes a selection of papers summarising the work undertaken by final year
students in the mining engineering program across the four Mining education Australia (MeA) universities. The papers
were co-authored by students who completed their mining research project in 2012 and their academic supervisor. All
the papers in this volume were peer-reviewed by academics from the MeA member universities.
Preparation of a conference paper is one of the assessment requirements in the Mining Research Project (or
thesis). The project is undertaken over two consecutive semesters in the final year of the MeA undergraduate degree
program. while the learning outcomes are primarily aimed at applying the principles of the scientific process to reallife issues facing the industry, it is also concerned with students applying the principles of project management and
risk management; preparing a proposal with clearly defined objectives and benefits; planning and scheduling tasks
and activities; liaising with people from a range of different backgrounds; and developing both verbal and written
communication skills.
The MeA program is an education initiative between the four major universities in Australia that offer a degree
program in Mining engineering; Curtin University through the western Australian School of Mines (wASM), University
of Adelaide (UoA), University of New South wales (UNSw) and University of Queensland (UQ). The program, which
was launched in 2007, was collaboratively developed by academic staff from each of the partner institutions with
advice from education consultants to ensure the latest in teaching and learning practices were embedded into it.
The program is supported by the Minerals Council of Australia (MCA) who, through its various industry partners, has
helped ensure MeA Graduates will be able to meet current and future challenges of the global minerals industry.
over 190 students across the four MeA universities completed the research project course in 2012 and consequently
many will have completed the degree program. As graduate mining engineers they are now set to begin their
professional careers in the mining industry.
As in previous years, the research projects span a broad spectrum of topic areas, reflecting the range of issues
being faced by the mining industry. The distribution of project categories across MeA in 2012 is shown in Figure 1
while Figure 2 shows the topic distribution at each of the four universities reflecting to some degree the differences in
expertise and focus at each university.
Fig 1 - Distribution of research topic areas across MEA in 2012.
iii
Fig 2 - Distribution of research topic areas by university.
The fourth MeA Student Conference was held in october 2012 at the wASM campus of Curtin University in Kalgoorlie;
a copy of the program is shown in Appendix 1. This year the event was enlarged to a half-day conference with up to
three students selected from each university to present a paper on their research project. The list of presenters is
shown in Appendix 2. The three 2012 prize winners at the conference were:
•
•
•
Casey Costello (UQ)
Brenton Goves (UQ)
Tim Graham (UNSW).
Members of the MeA course team who coordinated the research project are acknowledged for their efforts. This
team included Dr Basil Beamish (UQ), Dr Paul hagan (UNSw), Prof Roger Thompson (wASM) and Associate Professor
Chaoshui Xu (UoA). It is also important to acknowledge the important role of the academic staff who supervised the
students and the professional staff who supported the students in their project. Special acknowledgments are made to
Dr Beamish who will be standing down from his full-time academic role at UQ in early 2013, and Dr Andrew jarosz who is
retiring from wASM also in early 2013. Dr Beamish was a founding member of the course team when it was established
in 2006. he has had a great passion for the course and as a result of his drive and enthusiasm has contributed to the
continual development and improvements in the course that have translated into a better learning experience for
students. Dr jarosz similarly has been a long-term supporter of the course, understanding the importance it plays in
contributing to the education of mining engineers.
Finally, the contribution made by the mining industry and especially those people who assisted the students with
their research projects must be acknowledged. Many of the projects were initiated while students were on vacation
employment which again emphasises the important contributions work experience makes to student education. It is
important that industry recognises the role it plays in contributing to student education and ensuring the continual
supply of high quality graduate engineers.
Paul Hagan
Mining research Project course convenor, and chairman MEa Program leaders committee
iv
MEA Student Conference Organising
Committee
Dr Andrew Jarosz (WASM) – Conference Convenor
Associate Professor Emmanuel Chanda (UoA)
Dr Paul Hagan (UNSW)
Associate Professor Mehmet Kizil (UQ)
Dr Rudra Mitra (UNSW)
Review Panel
Mr Duncan Chalmers (UNSW)
Associate Professor Emmanuel Chanda (UoA)
Dr Chris Daly (UNSW)
Dr Nazife Erarslan (UQ)
Dr Paul Hagan (UNSW)
Dr Adrian Halim (WASM)
Dr Andrew Jarosz (WASM)
Dr Murat Karakus (UoA)
Associate Professor Mehmet Kizil (UQ)
Dr Rudra Mitra (UNSW)
Dr Simit Raval (UNSW)
Associate Professor Serkan Saydam (UNSW)
Professor Warren Seib (UQ)
Associate Professor Chaoshui Xu (UoA)
v
viii
CONTENTS
An Investigation into Semi-Intact Rock Mass
Representation for Physical Modelling Block Caving
Mechanics Zones
P Carmichael and
B Hebblewhite
1
Maximising Production at Brockman 4 by
Minimising Truck Delays at the Crusher
V Collins and M S Kizil
7
Grizzly Modifications at Ridgeway Deeps Block
Cave Mine
C Costello and P Knights
13
Multi-Objective Optimisation of MiningMetallurgical Systems
V Golding, D Jafari,
B Rajopadhyaya, D Sundquist
and E Chanda
19
Evaluating the Performance and Productivity of
Continuous Surface Miners in Iron Ore at Christmas
Creek Mine
B Goves, M S Kizil and W Seib
25
Strategic Project Risk Management for an
Emerging Miner
T K C Graham and S Saydam
31
Metaheuristic Analysis of Current Open Cut Blasting
Fragmentation Prediction Models
A Holland, M Iles and
M Karakus
37
Ventilation Requirement for Electric Vehicles in
Underground Hard Rock Mines – A Conceptual
Study
M Kerai and A Halim
45
Prediction and Modelling of Blast Vibration and its
Effects at Glendell Colliery
M K McKenzie and D Chalmers
51
Modelling Depletion of Queensland Coal Resources
using Historical Production Trends
A Meikle and W Seib
57
Management of Trailing Cables on Electrically
Powered Load-Haul-Dump Units
W Paterson and P Knights
67
The Effect of Joint Properties on a Discontinuous
Rock Mass
M Sherpa and P Hagan
75
83
Author Index
ix
x
an Investigation into Semi-Intact
rock Mass representation for physical
Modelling Block caving Mechanics
zones
P Carmichael1 and B hebblewhite2
aBStract
With the continual advancement of block caving operations comes a need for greater understanding
of the governing physics of the propagation process. Hence, it is desirable that cavability be more
reliably predicted in the future to ensure safety and efficiency is achieved throughout the mining
process.
This paper presents the results of an experimental program that examined the potential use of
semi-intact rock masses in the physical modelling of block caves, to analyse cave propagation
and the associated mechanics zones. The findings of the investigation showed that it is possible
to model caving mechanics zones with a material consisting of gravel, sand and a gypsum plaster
such as Plaster of Paris. The paper also identified the two major limitations of the current material:
similitude and texture of the material. Regarding similitude, both geometrical and dynamic
similitudes were not met in all areas, whilst the texture of the material makes delineation of the
caving mechanics zones impossible at this time. The results of the investigation are presented in
this paper such that they can be used for further refinement of the modelling material and for
immediate preliminary cave propagation modelling.
IntroductIon
Block caving is fast becoming the method of choice for
extracting large, low-grade deposits at depth. It has been
identified by Laubscher (1994) and later by Chitombo (2010)
that block caving is entering a new era of ‘super caves’. As
such it has been recommended by Chitombo (2010) that
extensive research be undertaken in all aspects of block
caving with particular focus on propagation, fragmentation
and particle migration to ensure block caving remains safe
and efficient in the future.
Cave propagation and fragmentation is governed by five
caving mechanics zones, first analysed by Duplancic and
Brady (1999) by way of a seismic and long hole drilling
survey. These various caving zones are indicated in Figure 1.
Extensive time and resources are required to analyse these
zones in the full scale and as such are unavailable to most
operations. Therefore, numerical modelling is currently the
method of choice to analyse cave propagation due to the
current accessibility of computational power; however, the
results obtained are only as accurate as the inputs provided.
As such, there is a requirement for greater understanding
the caving process to be able to provide accurate inputs for
the calibration of numerical models.
It is believed that with continual research and analysis,
the governing physics of the caving process can be fully
Fig 1 - The five regions associated with caving, determined by Duplancic and
Brady (1999).
understood to not only aid in numerical modelling
investigations but also ensure accurate and reliable cave
prediction at existing and new operations in the future.
The primary objective of this research project was to provide
a method by which the industry can study and analyse the
caving process in a controlled environment.
1. The University of New South Wales, Sydney NSW.
2. MAusIMM, The University of New South Wales, Sydney NSW. Email: [email protected]
MINING eDUCATIoN AUSTRALIA – ReSeARCh PRojeCTS ReVIew 2012
1
p carMIchael and B heBBlewhIte
By using readily available resources, low-cost quick-setting
modelling materials were investigated for use in a twodimensional (2D) block cave modelling frame to allow for
prompt analysis of caving mechanics.
It must be noted that the research at this stage was largely
qualitative and subjective due to the preliminary nature of the
investigation.
apparatuS
The main apparatus used in the experimentation was a purpose
built 2D modelling frame, which is shown in Figure 3 The
critical dimensions of the frame are outlined in Tables 1 and 2.
Methodology
preliminary testing
A preliminary analysis was first undertaken to determine
a range of cemented rock masses suitable for large-scale
testing. This preliminary testing was largely qualitative as
the investigators were seeking an indication of material
behaviour rather than accurate material properties.
The materials chosen for the testing were sand, 5 mm gravel,
15 mm gravel, Boral casting plaster and water due to their
availability and low cost.
The preliminary testing involved slump testing
approximately 40 mixtures ranging from zero per cent to
six per cent plaster and zero per cent to 30 per cent sand, using
the moulds shown in Figure 2.
Fig 3 - Two-dimensional physical modelling frame in
the testing orientation.
TABLE 1
Modelling frame internal dimensions.
internal model dimensions (mm)
Height
1250
Width
1000
Depth
188
TABLE 2
Modelling frame critical dimensions.
Fig 2 - Modelling moulds used in preliminary analysis.
Laboratory tests were also undertaken to determine the
strength of the material. By using a 15 cm core mould (10 × the
largest particle) and plaster capping, the material was tested
for its uniaxial compressive strength (UCS).
large-scale testing
Overview
Once the preliminary testing was completed the successful
materials were applied to a newly acquired 2D physical
modelling frame.
The purpose of this testing was to determine the compatibility
of the cemented rock mass to model cave propagation with
varying stress and fault regimes in place.
The procedure involved mixing up approximately 380 kg
of a material for loading into the frame. Once loaded it was
allowed to set for 24 hours. When set an undercut was created
and stresses were applied to induce caving. The resulting
cave propagation was documented and analysed.
With the frame being new and little work having been done
in this area, the process was largely ‘trial and error’ and as
such, the procedure changed for each test undertaken, based
on recommendations from previous tests.
2
Modelling frame critical measurements (mm)
Max drawpoint area
Stress-inducing plate area
56 × 300
1250 × 188
The frame has the ability to rotate in two dimensions to
allow for the placement of faults in the rock mass. It also has
a system by which horizontal stresses can be applied during
the caving process. The stress application system is shown in
Figure 4. It consists of two stress screws on each side, which
act upon a plate and then onto the mixture.
The stresses were measured by cantilever load cells, which
were connected to a National Instruments card for recording
and analysis during testing. This system is shown in Figure 5.
reSultS and analySIS
preliminary testing
From the preliminary tests, the following key findings were
made:
•
sand content had the largest effect on material strength
and behaviour in the first 48 hours due to its water
retention abilities
an InveStIgatIon Into SeMI-Intact rock MaSS repreSentatIon for phySIcal ModellIng Block cavIng MechanIcS zoneS
TABLE 3
Large-scale tests undertaken.
Plaster (per cent)
Fig 4 - Load cell positioning on force plates and amplification system
(Kell, 2012).
Fault (degrees)
Stress regime
-
Free flow gravel
-
Wooden block test
1
-
Low
2
-
High
1.5
-
High
1.2
-
High
1
-
Medium
1
45
Medium
1
-
Very high
1
45
Medium
1
-
Normal (prestressed)
Fig 6 - Comparison of cross-sections of test one and numerical modelling
investigation.
Fig 5 - Load cell monitoring equipment for use with the model.
•
•
•
•
a material of approximately one to two per cent plaster
and zero per cent sand was suitable for large-scale testing
the material forms its own inherent weakness planes,
along which failure occurs
the material does not instantly break into individual
gravel pieces, but rather clumps, allowing for secondary
fragmentation
a material consisting of one per cent plaster and zero per
cent sand had a UCS of approximately 11 kPa.
fracturing increased in size, to be able to be detected visually.
Examples of this fracturing are shown in Figures 7 and 8.
Both faulted tests resulted in similar findings, with the
cave changing direction slightly while passing through the
faulted zone, as shown in Figure 9. This process showed that
the cemented rock mass is suitable for testing caving through
fault zones.
large-scale testing
Eleven tests were undertaken using the developed material
and modelling frame. These tests are outlined in Table 3.
A number of key findings were made from these large-scale
tests.
Firstly, it was identified that, as with full-scale operations, air
gap management was key to the success of the test. In one test
the air gap was not managed, consequently the cave instantly
broke through to the surface. Despite the failure to manage
caving, the cross-section of the propagation struck similarities
with a numerical modelling investigation undertaken by
Vyazmensky (2012), as shown in Figure 6.
Another finding was the identification of fracturing above
the cave back, starting in the seismogenic zone and further
expanding in the zone of discontinuous deformation.
Fracturing could not be visually identified in the seismogenic
zone; however, fracturing was audible. It was not until the rock
mass entered the zone of discontinuous deformation that the
Fig 7 - Fracturing occurring above the cave back.
In the majority of the large-scale tests, an air gap was formed
before stresses were applied to allow caving to take place,
allowing the material to manage its stresses. It was decided
3
p carMIchael and B heBBlewhIte
on material strength and swell factor, which was identified to
be lower than the 20 per cent to 40 per cent experienced in
full-scale operations.
The result of this test was that the material was stronger
during testing and withstood stresses of up to 0.04 MPa as
opposed to the previous rock mass that failed with 0.035 MPa.
It was also identified that the rock mass had a higher swell
factor as the material had fewer voids due to setting under
compression.
Fig 8 - Fracture opening up during the caving process.
Two major issues were also identified during the large-scale
testing and subsequent analysis. The first major issue was the
porosity and texture of the material not allowing for simple
detection of fracturing above the cave back, hence making
it difficult to delineate the five caving mechanics zones.
The second major issue was that similitude was largely not
met during the investigation. Recommendations have been
made to rectify both problems, which are outlined in the
recommendations section.
concluSIonS
The results of this investigation show that it is possible to
utilise a semi-intact rock mass to model cave propagation and
the associated mechanics zones when used in conjunction
with a block cave modelling frame.
However, at this stage the delineation of the caving
mechanics zones is impossible due to the inability to visually
detect fracturing above the cave back. This was a result of
material texture.
Fig 9 - Cave propagation through faulted zone.
in one test to place the material under high stress without
allowing caving, to determine whether the cave would stall,
form wider, form narrower or undergo any other changes
during the caving process. The stresses of the high-loading
test compared to a normal test are shown in Figures 10 and 11
respectively.
It can be seen that the stresses applied were higher than
those normally applied and they were continuously reapplied
throughout the test. The result of this test was that when the
drawpoint was opened the cemented rock mass flowed freely
from the frame, similar to the free-flow gravel test. It was
hypothesised that this was due to the high stress breaking
bonds in the material as it could not de-stress through caving.
It is believed that this is largely due to the material not being
set under stress; therefore, there are significant voids for the
material to move into under stress.
As a recommendation from this test a cemented rock mass
was allowed to set under stress to determine the effect this had
From the preliminary investigations, a cemented rock mass
consisting of one per cent Plaster of Paris, three per cent water
and 96 per cent gravel (5 mm to 15 mm) was deemed suitable
for testing in the 2D modelling frame.
The material showed changes in cave propagation when
subjected to differing fault and stress regimes; however, the
material had two significant flaws, relating to the texture and
similitude.
recoMMendatIonS
Although caving was achieved, it was identified that there are
still a number of flaws in the cemented rock mass. The two
major flaws are outlined below.
The first issue associated with the material is the texture,
more specifically the porosity. Due to the porosity and
dark colour of the material, it is difficult to identify any
small fracturing above the cave back, making it impossible
to delineate the mechanics zones for analysis. As such, it
is recommended that an infill material be investigation to
Fig 10 - Stresses applied during high stress test.
4
an InveStIgatIon Into SeMI-Intact rock MaSS repreSentatIon for phySIcal ModellIng Block cavIng MechanIcS zoneS
Fig 11 - Stresses applied during normal test.
decrease this porosity. A key property of this material is that
it must not increase water retention in the model, as did sand.
This is to ensure the material can set rapidly for timely testing.
The second major issue with the material was a lack of
similitude. It was determined through the literature that
complete dynamic and geometric similitude is not achievable;
however, it should be attempted to replicate similitude in
the areas that most affect the phenomenon being tested. As
such, it is recommended that further work be undertaken
into the area of dynamic similitude concerning the scaling of
material UCS, tensile strength and other properties that have
a significant effect on the caving process.
acknowledgeMentS
The authors acknowledge the support of James Tibbett and
thank the contributions made by Jake Kell, Alison Tibbett and
Kanchana Gamage.
referenceS
Chitombo, G P, 2010. Cave Mining – 16 years after Laubscher’s 1994
paper ‘Cave mining – state of the art’, in Proceedings 2010 Second
International Symposium on Block and Sublevel Caving (ed: Y Potvin),
pp 45-61 (Australian Centre for Geomechanics: Perth).
Duplancic, P and Brady, B H, 1999. Characterisation of caving
mechanisms by analysis of seismicity and rock stress, in
Proceedings 9th International Congress on Rock Mechanics 1999,
pp 1049-1053 (Balkema: Rotterdam).
Kell, J, 2012. Personal communication (undergraduate student):
University of New South Wales, 6 September.
Laubscher, D, 1994. Cave mining - state-of-the-art, in Proceedings
6th Underground Operators Conference 1995 (ed: T S Golosinski),
pp 165-175 (The Australasian Institute of Mining and Metallurgy:
Melbourne).
Vyazmensky, A, 2012. Personal communication
Geotechnical Engineer) Kazakhmys PLC, 14 June.
(Principal
5
6
Mining Education Australia – Research Projects Review 2012
Maximising Production at Brockman 4
by Minimising Truck Delays at the
Crusher
V Collins1 and M S Kizil2
ABSTRACT
The truck and shovel mining method is the most commonly used method in surface mining.
Trucks are loaded by dig units such as shovels, excavators and front-end loaders. Haul trucks
then transport the mined material from the pit to the crusher, where it is broken down and sent
to the plant for further processing. Brockman 4, a surface iron ore operation in Western Australia,
utilises the truck and shovel method of mining. Observations show that the truck cycle is not
operating at its full capacity. This paper investigates ways in which truck delays at the crusher
can be minimised in order to effectively maximise production. Possible solutions identified in
this paper can be used as an interactive tool to further understand the nature of the mine-plant
interface at Brockman 4 and how wait-for-truck and wait-for-crusher delays can be effectively
reduced. This could translate to an annual revenue increase of A$130 M should wait-for-truck
delays be eliminated entirely. This paper also provides a number of suggestions to reduce delays
for both the crusher and the trucks.
Introduction
Due to the high operational cost related to the truck and
shovel method, it is vital that the efficiency of the truck cycle
is maximised. The smallest improvement to the efficiency of
any truck and shovel operation presents the opportunity for
an increase in revenue of millions of dollars.
At Brockman 4, a Rio Tinto Iron Ore mine site in Western
Australia’s Pilbara, observations show that often, numerous
trucks are found queuing at the crusher or there are no trucks
available to supply feed. This paper investigates possible
solutions and ways to minimise wait-for-crusher and wait-fortruck delays, and the effects that implementing such solutions
can have on production and revenue in the long term.
Modular DISPATCH® is a dispatching system used on
all mobile equipment on site to record various parameters
such as the time taken to load, haul, queue and dump. This
data, collected over a six month sample period, was used to
generate a series of inputs into a computer software package
known as TALPAC. TALPAC is a haulage fleet evaluation
system that is used to determine the productivity and related
economics of any truck and shovel haulage system (Runge,
1993). This software package was used to create a series of
haulage simulations that replicate those at Brockman 4, and
whose effect on crusher delays could be further analysed.
Furthermore, a computer system known as Citect is
installed throughout all plant processes on site and effectively
determines when trucks are able to dump in the crusher
by changing a light signal alongside. The possibility of
integrating the Modular and Citect systems at Brockman 4
has been investigated, as the exchange of information across
the mine-plant interface is expected to increase production.
The prime focus of this paper is to minimise wait-forcrusher and wait-for-truck delays by investigating both the
fleet composition and external solutions in order to achieve
this.
Truck and Shovel Operations
Truck and shovel method
The truck and shovel mining method is considered to be
the most predominant and flexible system used in open pit
mining (Holke, 2004; Oberisser, 2010). Despite this, the costs
associated with the truck and shovel mining method account
for more than 50 per cent of the total cost involved with
surface mining (Kizil, Knights and Nel, 2011). It is vital then,
that this component of the mining process is made as efficient
as possible. An example of a truck and shovel production
cycle can be seen in Figure 1.
Truck-shovel matching
In a truck and shovel operation, it is imperative that the
capacities of the dig units are compatible with the capacities
of the truck fleet. Fleet efficiency can be investigated by first
categorising the truck and shovel fleet as being truck-shovel
matched, over-trucked or under-trucked. The term ‘truckshovel matched’ refers to situations where the ideal capacity
and number of haul trucks is available for any given dig unit.
Operations that are ‘over-trucked’ involve a larger number
of trucks than the optimal number, yielding unnecessary
additional costs with no improvement to production.
1. SAusIMM, Graduate Mining Engineer, School of Mechanical and Mining Engineering, The University of Queensland, St Lucia Qld 4072. Email: [email protected]
2. MAusIMM, Associate Professor, School of Mechanical and Mining Engineering, The University of Queensland, St Lucia Qld 4072. Email: [email protected]
7
v collInS and M S kIzIl
in open pit mines. Modular Mining Systems, established in
the United States of America in 1979, created a computerbased DISPATCH® system designed to be used in mining
operations worldwide. The company developed the system
to be used primarily as a large scale mine management/
dispatch system to control the operation of haul trucks in
open pit mines.
‘The primary objectives of DISPATCH® are to maximise
production, minimise rehandle, supply the plant and meet
blending objectives (Olsen and White, 1992)’. To achieve
this, algorithms must be developed that use raw data to
intelligently dispatch the trucks in all situations.
Fig 1 - The truck/shovel production cycle
(modified from: Kizil, Knights and Nel, 2011).
Operations that are ‘under-trucked’ involve a smaller number
of trucks than the optimal number, which adversely affects
production, yielding substantial revenue losses.
Runge (1993), as cited in Kizil, Knights and Nel (2011),
observed that when an incorrect match between trucks and
shovels or the fleet is not being efficiently assigned, the
following operational characteristics resulted:
•
•
•
•
excessive truck queuing times
excessive wait on truck delays
abnormal queuing times at the dump
truck bunching (typically observed with mixed fleet
haulage).
types of dispatch systems
There are three general types of dispatching methods:
1. manual
2. semi-automatic
3. automatic.
Manual dispatching systems involve decision-making,
where trucks are assigned to shovels by pit supervisors.
The pit supervisor uses their informed opinion on what
assignment is best to be conducted at that point during the
shift (Bonates and Lizotte, 1988; Georgieff, 2006). Manual
dispatching systems are usually only used in small operations.
Semi-automatic dispatching systems incorporate real-time
data into the decision-making process. Data is obtained directly
from mining equipment via wireless communication systems
and the computer systems installed in all the pit equipment
(Bonates and Lizotte, 1988). Data is then transmitted from the
trucks and shovels to the pit controller who can effectively
make the decision (hence the term ‘semi-automatic’) as to
where and how trucks are assigned (Georgieff, 2006).
Automatic dispatch systems involve truck assignments that
are dictated entirely by the computer system. The computer
system analyses its own data in order to compute the best
assignment to be conducted in any circumstance. Once an
assignment has been determined, a system alert is displayed
on the computer screen within the truck and the operator can
see what their next assignment is. This system relies less on
the competency of the pit controller (Georgieff, 2006).
MonItorIng SySteMS In Surface MInIng
Modular dISpatch® production monitoring
It is widely accepted in modern mining operations that truckshovel dispatching by computer-based systems is invaluable
8
Since the start of production in 2010, Brockman 4 has
incorporated the use of Modular DISPATCH® to control
the operation of haul trucks in the pit, and assist the grade
controller in directing or appointing shovels where to dig.
Each haul truck is equipped with a live computer system
that emits wireless signals back to the control centre. The
accuracy of the data obtained from Modular depends on
the competency of the operator and the accuracy that they
provide. Such data includes (Banfield, 2012):
•
•
•
•
•
•
•
material grade, truck ID and number of tonnes per load
block number (source feed) and dump location (dump
feed)
time truck arrives at the excavator
time excavator or shovel starts and stops loading truck
time the truck arrives at the dump
time the trucks starts and stops dumping into the crusher
time spent queuing at the dig unit or dump location.
citect production monitoring
Citect is a software development company who specialises in
the automation and control industry (Schneider Electric, 2012).
Citect has released numerous different software packages, one
of which is known as Citect Ampla. For a particular application,
Citect Ampla can graphically replicate on the control monitor
a representation of the facility showing ‘animation points’
(Schneider Electric, 2012). The facility operator can then
supervise the control of the facility via the screen.
The Citect screen at Brockman 4 allows the crusher circuit
to be displayed on the screen. The dynamic representation
displays the crusher bin level at all times, along with the
‘dump light status’ at the crusher. This dump light status
refers to the signal that trucks can see, and corresponds to the
crusher bin level.
Modular dISpatch® and citect integration
In 2011, an interface between Modular DISPATCH® and
Citect was implemented at Paraburdoo Mine Site, a different
Rio Tinto Iron Ore operation (Rio Tinto Iron Ore, 2011b).
The integration of the two systems between mine and plant
allowed the transfer of information between the two sectors
to aid in real-time amendments or changes to the operation to
make it more efficient. The objective of this entire process was
to minimise wait-for-crusher and wait-for truck (Rio Tinto
Iron Ore, 2011b).
Citect transfers the following information to DISPATCH®
every five seconds (Rio Tinto Iron Ore, 2011a):
the crusher bin level (per cent)
the time until the apron feeder stops running, ie runs out
of feed (seconds)
• the truck dump light status (green/red/flashing red).
Modular DISPATCH® then combines this information with
estimated truck arrival times to make real-time decisions
about (Rio Tinto Iron Ore, 2011a):
•
•
MaXIMISIng productIon at BrockMan 4 By MInIMISIng truck delayS at the cruSher
when to reassign trucks to the run-of-mine (ROM)
stockpile, without compromising feed and hence risking
a wait-for-truck (or wait-for-product delay) on the plant
• when to instruct the ROM loader to feed the crusher so
that plant feed is maintained when no trucks are in close
proximity, but stop the feed in time so that the bin is not
full when the next truck arrives and unable to dump,
inducing a wait-for-crusher delay
• how the queue at the crusher should be managed.
After the Modular-Citect integration was implemented
at Paraburdoo, a number of improvements were observed.
These were:
•
a reduction in truck queue and dump times at the crusher
(166 to 134 seconds per load)
• a reduction in wait-for-truck/wait-for-product delays
(163 to 113 seconds per load)
• an increase in throughput tonnes delivered to crusher
(median shift from 16 861 tonnes to 18 161 tonnes).
The increase in throughput tonnes delivered to crusher
resulted in a reduction in truck queue and dump times
and a reduction in wait-for-truck delays as was expected.
It was also found that the average throughput increased by
approximately 1300 tonnes per shift (Rio Tinto Iron Ore,
2011a). Using a conservative iron ore price of A$100 per
tonne, this corresponded to an increase of approximately
A$94.9 million dollars over the course of the year.
•
data collectIon
Data were collected from Brockman 4 mine site in Western
Australia over a six month sample period. Various data
collected included the following:
modular data (cycle times)
haul routes/strings from Vulcan
daily wait-for-crusher and wait-for-truck delays
maintenance/downtime data
daily production reports
hourly crusher throughput.
The sample period began on 1 January, 2012, at 6.00 am
and ended on the 1 July 2012, at 6.00 am. The sample period
consisted of 180 operating days and 360 shifts.
•
•
•
•
•
•
Fig 2 - Crusher delay breakdown categories.
duration and the portion of which is attributed to wait-fortruck delays which is hightlighted in the grey secind bar of
each month.
Figure 2 also shows the total monthly duration of waitfor-crusher delays gradually decreased over the six month
period. In January, Brockman 4 utilised six Komatsu 730Es
amongst their fleet of Komatsu 830Es and only two Komatsu
730Es by the end of June, 2012. This decreasing trend in waitfor-crusher delays is most likely attributed to less use of
mixed fleet haulage over the sample period. This supports the
literature findings by Kizil, Knights and Nel (2011).
operating standby delay analysis – plant:
wait-for-truck
The operating standby delays for the crusher were further
broken down into their subcategories. The data used was
obtained between 1 January and 1 July, 2012. The pie chart in
Figure 3 shows the respective proportions each subcategory
contributes to the overall operating standby delay duration
over this six month period. From the graph, it is apparent
that wait-for-truck delays constitute the largest proportion
of operating standby delays, a total 62 per cent of the total
duration.
There are three main pits at Brockman 4: Pit 2 (P02),
Pit 3 Central (P03C) and Pit 3 East (P03E), which are
located approximately 7.7, 3.5 and 2.7 km from the crusher
respectively. Brockman 4 operates using 12 hour shifts
with the day shift beginning at 6.00 am and the night shift
beginning at 6.00 pm. Four crews (Team 1, 2, 3 and 4) are
employed on an eight days on and six days off roster and
alternate between dayshift and night shift each swing. The
mine operates 24 hours per day, every day of the year.
reSultS
characterisation of delays – primary crusher
Crusher delays over the six month period were categorised
according to four different delay types: operating delays,
operating standbys, scheduled loss and unscheduled loss.
The total delay duration for each month was then broken
down into the various delay categories, and presented in the
stacked bar graph as seen in Figure 2, which demonstrates
their relative proportions.
Wait-for-truck delays exist as a subcategory of operating
standby delays. The graph in Figure 2 shows a separate
series of stacked bars alongside the total delay durations for
each month. These bars represent the total operating delay
Fig 3 - Operating standby crusher delays.
operating standby delay analysis – mine: waitfor-crusher
When analysing the operating standby delay breakdown
from the production side of the mine-plant interface, it was
expected that wait-for-crusher delays would constitute the
largest portion of delays. The results of the operating standby
9
v collInS and M S kIzIl
delays for the Brockman 4 production truck fleet are shown in
Figure 4. As expected, the wait-for-crusher delays constituted
the largest portion of operating standby delays, representing
44 per cent of the total delay duration.
TABLE 1
Economical analysis.
Month
Potential
utilisation
increase (%)
Potential
production
increase (t)
Potential
revenue increase
@$100/t
January
5
143 389
14 338 900
February
6
143 603
14 360 300
March
4
105 034
10 503 400
April
3
73 680
7 368 000
May
4
99 158
9 915 800
June
4
86 016
8 601 600
TOTAL
Fig 4 - Operating standby truck delays.
key delay periods
Figure 5 shows the average hourly production throughput
for the crusher over the course of the day over the six month
period. From the graph, it was evident that there were
significant production throughput loses at 6 am, 12 pm, 6 pm
and 12 am. These significant production drops were attributed
to shift change (6 am and 6 pm) and production lunch break
(12 pm and 12 am) for both day and night shift. Truck driver
change-outs during shift change led to a recurring window
where throughput ceased. Lunch breaks also contributed to
the significant slumps in production.
65 088 000
1. as the potential revenue increase that could have been
achieved at Brockman 4 in the first six months of 2012
2. the revenue lost at Brockman 4 due to wait-for-truck
delays in the first six months of 2012.
From the analysis conducted, it was found that
approximately A$65 M was lost due to wait-for-truck delays
over the six month period. This potential gain easily justified
the cost involved in implementing various methods and
solutions to achieve this.
talpac analysis – input variables
To run the TALPAC simulations, a series of input data was
required. This data input included:
equipment availability
equipment machine models and rated capacity
haul segment lengths, gradients and curve angles
rolling resistances
speed limits per segment.
Haul road routes, digitised in Vulcan were imported to
TALPAC for haulage simulations using a rolling resistance of
three and the following speed restrictions:
•
•
•
•
•
60 km/h empty
40 km/h loaded on flats
20 km/h downhill/uphill whilst loaded
10 km/h around corners.
For the purpose of this exercise, two Komatsu 730E
were used as the fixed variable for the mixed-fleet haulage
scenarios as this was the number of Komatsu 730Es on site as
of June, the month from which the haul roads were modelled.
The number of Komatsu 830Es was then allowed to run on
‘auto’ in order to determine the ideal number required for
each digger.
•
•
•
•
Fig 5 - Average hourly crusher throughput.
economical analysis – impact of eliminating
wait-for-truck delays
Using the data obtained between 1 January and 1 July, 2012, an
economic analysis was conducted on the impact of eliminating
wait-on-truck delays. By hypothetically eliminating the waitfor-truck delays from the data obtained, the monthly increase
in utilisation of the crusher was determined. Using a crusher
rating of 3600 tonnes per hour, the potential increase in
throughput each month was then quantified.
Using an iron ore price of A$100 per tonne, potential
revenue increases were estimated for each month as shown in
Table 1. These revenue values can be seen from two different
perspectives:
10
truck-shovel matching outcomes
In order to determine the ideal number of trucks for each of
the dig units, parameters for the longest haul route from each
of the three pits was used. From Pit 2, Pit 3 Central and Pit
3 East (P02I, P03C and P03E respectively), the longest haul
routes determined were the 530, 540 and 590 RL (relative
level) respectively.
The series of simulations run using TALPAC involved
a mixed truck fleet consisting of both Komatsu 830Es and
Komatsu 730Es. Each of the different dig units were placed
in each pit one at a time and the ideal number of trucks for
the operation to be considered truck-shovel matched was
conducted. The truck numbers corresponding to non-mixed
fleet haulage can be found in Table 2.
MaXIMISIng productIon at BrockMan 4 By MInIMISIng truck delayS at the cruSher
TABLE 2
Mixed fleet truck numbers (Komatsu 830Es when two Komatsu 730Es
were fixed).
Pit
Loader
Letourneau
L-1850
Shovel Hitachi
5500
Excavator
Hitachi 3600
P02I
11
15
11
P03E
6
6
6
P03C
5
6
5
alleviating wait-for-crusher delays. This material then
provides the loader with feed that it can transfer between
the ROM pad and the crusher when there is another gap in
the truck cycle. An area alongside the crusher approximating
60 000 m3 is already available at Brockman 4. A photo of this
ROM pad can be seen in Figure 6.
Brockman 4 has an annual production rate of 22 million
tonnes ore with 44 million tonnes of waste. When simulations
were run placing each type of dig unit in each of the three pits
and analysing the production rates that could be achieved, the
results shown in Table 3 were achieved.
TABLE 3
Mixed fleet production capabilities.
Mixed
Loader Letourneau
L-1850
Shovel Hitachi
5500
Excavator
Hitachi 3600
P02I
18 742 632
26 668 760
19 795 772
P03E
18 814 835
26 978 261
19 924 772
P03C
19 147 615
27 174 059
20 101 023
As shown in Table 3, the Hitachi 5500 is capable of moving
the largest number of production tonnes. At Brockman 4,
each shovel and every truck mines high-grade, low-grade and
waste. Trucks and shovels are not assigned to one particular
type of material all year round, and thus, it is necessary to
analyse how much material can be moved using the current
fleet.
The required truck numbers given in Table 2 and the
production rate calculated in Table 3 were used in order to
determine the minimum number of trucks required to move
66 million tonnes of material per annum. The smallest number
of trucks required can be achieved if two Hitachi 5500 and
one Hitachi 3600 excavators were used, which will require 21
Komatsu 830E and 2 Komatsu 730E trucks.
Brockman 4 currently has 26 Komatsu 830E and 2 Komatsu
730E trucks. According to the TALPAC analysis, the Brockman
4 operation appears to be over-trucked.
Over-trucking can therefore be considered to be one
of the factors contributing to truck delays at the crusher
and consequently the constantly out-of-sync truck cycle
at Brockman 4. It should be noted that the implications of
having different capacity trucks will inevitably result in
longer queuing times and a more frequent chance of truck
cycle disruptions.
delay reductIon StrategIeS
utilising a loader on run-of-mine
One possible way to alleviate crusher delays is to utilise a
loader on the ROM. If the loader were to remain on the ROM
at all times, it could supply feed to the crusher when no trucks
are in the vicinity of the crusher, thereby minimising wait-fortruck delays.
Conversely, if there is already a truck waiting at the crusher,
the incoming truck can dump on the ROM pad, thereby
Fig 6 - Run-of-mine pad.
The graph in Figure 5 illustrates the key times that the ROM
loader can be further utilised to counteract production slumps.
Therefore, not only can it be used to minimise wait-for-truck
and wait-for-crusher delays, but also to increase significant
production slumps during crew shift change or lunch breaks.
Additionally, having a permanent ROM loader would
help the crusher throughput increase from approximately
3000 tonnes per hour toward the crusher rating of 3600 tonnes
per hour. The approximate cost of incorporating a permanent
ROM loader at Brockman 4 operations can be found in
Table 4, with input from R2Mining (2011) and an estimated
average loader availability of 85 per cent.
TABLE 4
Initial cost associated with ROM loader.
Component
Cost (A$)
Capital cost
$550 000
$550 000
$250/h × 0.85 × 365 × 24
$1 861 500/year
$120 000/year
$240 000/year
Operating costs per year
Personnel to operate
equipment
Cost in first year acquired
Total
$2 651 500
When the potential increase in revenue that can be gained
by eliminating wait-for-truck delays (as determined in
Table 1) is compared to the cost to acquire, operate and man a
loader, it is evident that the acquisition of a permanent ROM
loader is easily justified. Approximately A$65 M can be saved
over a six month period, and thus a potential of A$130 M each
year if wait-for-truck delays are essentially eliminated. The
A$2 651 500 that it would cost to attain and operate a loader
in the first year (and less in subsequent years) is insignificant
and would be well worth the investment.
hopper near crusher
Another option that may help to reduce wait-for-truck and
wait-for-crusher delays is to install a hopper system alongside
the crusher. This system is similar to dumping on the ROM,
except that the ore from the hopper can be transferred to the
crusher instantaneously and does not need to be rehandled.
11
V Collins and M S KiziL
When trucks are unavailable to tip at the crusher, the hopper
system can be activated and material can immediately be fed
at a constant rate until the next truck arrives. If the crusher is
at full capacity, trucks then have the choice to dump on the
ROM or in the hopper and immediately return to the truck
cycle. The cost of implementing a hopper system would be far
outweighed by the potential increase in revenue that could be
achieved using this system. Table 5 outlines the approximate
cost involved with implementing a hopper system with input
from R2Mining (2011).
TABLE 5
Hopper system cost analysis.
Component
250 tonne hopper
Operating costs per year
Operators
Total ($)
450 000
450 000
15/h × 0.90 × 365 × 24
118 260/year
120 000/year
240 000/year
808 260
Similarly, when the potential increase in revenue that can
be gained by eliminating wait-for-truck delays is compared
to the cost to acquire, install and operate a hopper system,
the costs involved are easily justified. These costs were
determined with input from R2Mining (2011) and would be
considered insignificant compared to the A$130 million that
could be saved by implementing it.
Integrating Modular and Citect
After the implementation of the Modular-Citect integration
at Paraburdoo, numerous improvements to the mine-to-plant
process were achieved. The integrated system would be able
to accurately determine when to cue the loader to feed the
crusher and when to cue the truck to dump at an alternative
location. This is the image of production at Brockman 4 in
the near future. If the implementation of Modular and Citect
proves to be feasible after further data analysis, production
increases of up to 1300 tonnes per shift may result. Using a
conservative iron ore price of $100/tonne, this could see
revenue increases of up to A$94.9 M per year.
CONCLUSIONS
By categorising delays from either side of the mine-plant
interface, it was possible to quantify the significance of waitfor-crusher and wait-for-truck delays at Brockman 4 (44 and
62 per cent respectively). The data analysis showed a decrease
in wait-for-truck delays from January to June, 2012 as a result
of gradually eliminating the use of Komatsu 730E trucks.
When comparing these observations to previous research
(Kizil, Knights and Nel, 2011), it could be concluded that the
reduction in wait-for-truck times over the sample period is
attributed to the gradual reduction in mixed fleet haulage
over this time.
By analysing Modular data outputs over a six month period,
it was possible to determine where the production cycle at
12
The economical analysis also determined that if the waitfor-truck delays were removed from the production cycle,
there would be a potential increase in revenue of A$130 M/a
for the company.
Acknowledgements
Cost (A$)
Cost in first year acquired:
Brockman 4 could be improved. By reassessing the truck cycle
at Brockman 4 as a preliminary step towards minimising
delays at the crusher and increasing throughput, it is to be
expected that Brockman 4 will see significant improvements
to their operations. The interdependent nature of mining
operations, including the truck and shovel method, highlights
the importance of maintaining each link in the mine-to-plant
production chain.
The authors acknowledge the support and guidance provided
by Dr Basil Beamish and industry professional Gareth
Banfield.
References
Banfield, G, 2012. Personal communication, 11 May.
Bonates, E and Lizotte, Y, 1988. A computer simulation model to
evaluate the effect of dispatching, International Journal of Surface
Mining, 2:99-104.
Georgieff, D, 2006. Improving truck utilization during shovel delays,
undergraduate thesis (unpublished), University of Queensland,
Brisbane.
Holke, B, 2004. Planning and application of hydraulic shovels in
open pit mines: shovels/truck operation and shovel/crusher
operation, Journal of Mines, Metals and Fuels (0022-2755), 34(4).
Kizil, M, Knights, P and Nel, S, 2011. Improving truck-shovel
matching, in Proceedings 35th APCOM Symposium, pp 381-391 (The
Australasian Institute of Mining and Metallurgy: Melbourne).
Oberisser, H, 2010. Fully mobile IPCC solutions [online]. Available
from: <http://www.mining magazine.com/equipment/fullymobile-ipcc-solutions> [Accessed: 26 April, 2012].
Olsen, J P and White, J, 1992. On improving truck/shovel productivity
in open pit mines, in Proceedings 23rd International Symposium 1992
– Application of Computers and Operations Research (SME: Arizona).
R2Mining, 2011. Australia mine and mill equipment costs [online].
Available
from:
<http://calc2011.au.costs.infomine.com/
Projects/projectlist.aspx> [Accessed 30 September, 2012].
Rio Tinto Iron Ore, 2011a. ER crusher Dispatch/Citect integration
project valuation (unpublished), Paraburdoo Mine Site,
Paraburdoo.
Rio Tinto Iron Ore, 2011b. Paraburdoo Modular-Citect interface:
automated truck reassignment and loader reassignment
(unpublished), Paraburdoo Mine Site, Paraburdoo.
Runge, 1993. Section 6: Truck and loader productivity estimation,
section 7: factors affecting truck and loader productivity, section
10: sample TALPAC output, Planning and Operation of Truck
and loader Mining Systems – Technical Training Course Notes,
Course Ref: 1593, pp 59-81, 83-100, 125-141 (Runge Mining
(Australia) Pty Ltd: Mackay).
Schneider Electric, 2012. Products and Services [online]. Available
from: <http://www. schneider -electric.com/site/home/index.
cfm/ww/> [Accessed 11 May, 2012].
Grizzly Modifications at Ridgeway
Deeps Block Cave Mine
C Costello1 and P Knights2
ABSTRACT
Grizzlies are used in underground mining to act as size separators at the crusher tipping stations.
Acceptable size ore will fall through the apertures and oversize ore will remain on the screen
for secondary breakage. At Ridgeway Deeps block cave mine the rock breaker operating on the
oversize at the grizzlies is causing approximately two hours per shift downtime. This is vastly
affecting production rates. This paper presents the results of an investigation to modify the static
grizzly aperture design to reduce downtime caused by the rock breaker attending to oversize rocks.
This paper reviews existing alternatives to underground grizzlies and proposes a new design. The
new design has taken into consideration the conclusions found through research and data analysis
conducted on the tipple stations at Ridgeway Deeps gold mine. A cost benefit analysis of installing
new grizzlies was conducted considering the remaining life-of-mine of four years.
Introduction
Grizzlies are used in underground mining to act as size
separators at the crusher tipping stations. Acceptable size ore
will fall through the apertures of a selected size, and anything
too big (oversize) will remain on the screen for secondary
breakage. Due to the arrangement of the tipple stations
at Ridgeway Deeps (RWD) the rock breaker cannot act
independently of the LHD’s tipping, so therefore the LHDs
must stop tipping each time the grizzly has a blockage or a
build-up of oversize ore. RWD is suffering from an average
of two hours downtime per shift due to the rock breaker
operation.
An investigation was undertaken to determine alternatives
to modifying the current grizzly and how other block
cave mines have dealt with this problem. As RWD is an
‘experimental’ mine for Newcrest it is important that the
larger neighbouring Cadia panel cave mine base their designs
upon lessons learnt from Ridgeway. This study developed an
alternative design based upon the findings of investigations
into:
••
••
••
••
••
RWD feasibility study
crushers
different shapes of oversize occurring on the grizzlies
frequency of the oversize shapes occurring
time required to break the different shaped rocks.
Material handling system
Overview
The Ridgeway gold-copper mine, owned by Newcrest Mining
Limited, is a block cave (BC) mine which was developed
under a pre-existing sublevel cave (SLC) mine (Brunton,
Sharrock and Lett, 2011). The material handling system (MHS)
is integrated with the existing truck and portal conveying
system to deliver ore to the surface stockpile (Maunsell, 2007).
The primary crushing system consists of two single toggle
jaw crushers (nominal size 2000 × 1500 mm). The significant
changes from SLC (which has a single gyratory crusher) to BC
mining impacted the crusher selection as follows:
•• BC fragmentation – the models predicted significantly
coarser material compared to SLC
•• increased footprint – simulation in the prefeasibility
indicated that two tipping stations approximately 200 m
apart in bypass drives emanating from the southern
perimeter, with run-of-mine (ROM) ore transition,
drawpoints to crusher, performed by four to five LHDs is
required to achieve production
•• geotechnical constraints limit the size of the crusher
chambers and their location relative to the orebody
(Maunsell, 2007).
The feasibility study outlined that from these conditions a
two-stage crushing philosophy was implemented adopting
two jaw crushers fed with preconditioned ore via static
1200 mm square grizzlies with fixed hydraulic rock breakers
at the tipping stations. Alternate crusher types including
gyratory crushers, jaw-gyratory crushers and rotary breaker
type were also considered for primary crusher duties. The
rotary breaker type was deemed unproven for primary
crushing duty for abrasive hard rock application, and
discounted from any comparisons due to lack of relevant
benchmarking applications. Gyratory and jay-gyratory
crushers were found to return low business reliability ratings
(Allman, Dunstan and Syme, 2007).
Fragmentation
The predicted cave fragmentation as per the feasibility
study for RWD BC reveals that the initial fragmentation
1. SAusIMM, School of Mechanical and Mining Engineering, The University of Queensland, Brisbane Qld 4072. Email: [email protected]
2. MAusIMM, Head of Division of Mining Engineering, School of Mechanical and Mining Engineering, The University of Queensland, Brisbane Qld 4072. Email: [email protected]
13
c coStello and p knIghtS
was expected to be coarse, becoming finer as the cave
propagates. The crusher size selection was based upon the
coarse prediction to minimise the requirement for secondary
breakage at the drawpoints. The BC fragmentation geometry
was predicted to be 1:0.75:0.5 (x, y, z). Secondary breakage
would be required at the drawpoints for rocks > 3 m3 (y >
1500 mm) (Maunsell, 2007). The cave fragmentation sizing
indicated at the time of the feasibility report that a crusher
feed F80 of approximately 1480 mm. The crusher nominal size
is 1200 mm square, therefore, the grizzlies were implemented
to reduce the chance of bridging at the crusher.
grIzzlIeS
overview
A grizzly is a screen that acts as a scalping device for oversize
material (Darling, 2011). Very coarse material is usually
screened on a grizzly, which in its simplest form consists of
heavy parallel bars set in a frame. The most common use of
grizzlies is the feed to a primary crusher. Clogging is a major
problem, especially on the cross-members that hold the bars
together (Darling, 2011). Square mesh grizzlies are used
to get a more accurate maximum size of material, whereas
rectangular and non-gridded grizzlies can allow material of
larger size to pass through. Heavy duty grizzly bars are cast
from manganese steel and have double tapers.
grizzly design
When designing a grizzly for a specific purpose, the openings
between the grizzly bars should be commensurate with the
size of the receiving hopper where the product has to be
discharged (Gupta and Yan, 2006).
Stationary screens are operated either at horizontal or
inclined planes. A relatively steep installation is preferred for
higher throughputs but the quality of separation is likely to be
affected as the effective aperture and open area are decreased.
During the process of screening, particles either fall through
the apertures or get held back. Obviously larger particles than
the aperture opening cannot pass through (Gupta and Yan,
2006).
Size and shape of particles
A fraction of particles, although smaller than the aperture also
do not pass through the first time they encounter an aperture.
Particles that are elongated, but with cross-section less than
the aperture, will pass through provided they approach the
aperture at an appropriate angle. Figure 1 shows the effect
of shape and size of particles through screening. Both A and
Fig 1 - Particle size and shape at screen surface (Gupta and Yan, 2006).
14
C particles are prevented from passing through, A being
larger in size and C elongated. Particle C will, however, pass
through if rotated to look like particle D. Particle B will always
pass through. Thus both shape and size are of importance
in a screening operation. Particle sizes that are near to the
aperture size are the most difficult to screen. This is defined
as particles having a size 0.75 to 1.5 times the aperture are the
most difficult to screen (Gupta and Yan, 2006).
The passage of each particle will also depend on the
angle at which it reaches the aperture. If the screen were
sufficiently long it could eventually approach the aperture
at the appropriate angle and pass through. If the length was
insufficient then in spite of the particle being smaller than the
aperture it may report as oversize (Gupta and Yan, 2006).
Blinding or clogging of screens during operation is one of the
most contentious and difficult factors that a screen designer
has to face. The most effective way to reduce clogging is to
impart vibratory or circular motion to the screen. To impart
the motion, the screen surfaces are rigidly fixed onto a frame,
the frame in turn is fixed to moving devices that are either
mechanically or electrically driven (Gupta and Yan, 2006).
alternatIveS to grIzzlIeS underground
A primary concern in underground mining is where and how
to handle ‘moveable’ oversize. The oversize can be:
handled at the extraction points
moved to a special gallery for blasting
moved to an orepass equipped with a grizzly and handled
there
• directly dumped into an orepass for later handling.
All variations are used, and each company has its own
philosophy in this regard (Bullock and Hustrulid, 2001).
Palabora BC mine in South Africa and Northparkes BC mine
in New South Wales both have the same crushing level design
as RWD BC mine in that the ore is delivered straight to the
crusher; however, they have no grizzlies.
•
•
•
northparkes block cave mine
As part of the E26 Lift 2 mine, a single jaw-gyratory crusher
was installed. The primary advantage of this size of crusher
over conventional crushers is its ability to accept a maximum
rock size of 3 m3 whilst achieving consistent P80 of 120 mm.
As a result, a grizzly screen is not required on the crusher
feed bin, although a rock breaker is fitted to the crusher to
address periodic blockages in the crusher and on the plate
feeder, which can be seen in Figure 2 (Butcher et al, 2011).
Fig 2 - Northparkes E48 crusher chamber with rock breaker.
grIzzly ModIfIcatIonS at rIdgeway deepS Block cave MIne
Rio Tinto’s philosophy was to minimise the stages in ore
block size reduction, size the crusher to handle the largest
rock size that the LHD could carry, and install a continuous
system ‘rock factory’ concept. It was the first time the jawgyratory crusher was installed in an underground mine. It
was the preferred option as it accepts a larger feed size than
Northparkes’ current crushers and produces a finer product
(Duffield, 2001).
palabora block cave mine
The feasibility report for Palabora depicts the LHDs will be
tipping onto grizzlies at the bottom of an 8 m orepass. The
grizzlies at the tipples have been designed such that any
oversize material would slide to the central breaking area
between the grizzlies. The tips would be monitored by CCTV
and thus the control centre would dispatch one of the mobile
rock breakers to the tip if this should occur. The rock breaker
would then proceed to break the boulder. The broken material
would then fall into the grizzlies on either side of the breaking
area. No allowance was made for delays due to oversize rocks
being removed from the grizzly as it was assumed that rocks
would migrate down the grizzly to the breaking area where
they would be broken by the mobile rock breakers (Palabora
Mining Company Limited, 1996). The ore is then crushed
by a Krupp 1700 mm × 2300 mm double toggle jaw crusher
(Maiwasha, Ngidi and Pretorius, 2010).
The grizzlies were reportedly removed by 2007 due to the
8 m drop height causing excessive damage and production
downtime at the crusher. Oversize is now handled in the
levels (Capes, 2007).
has stopped, hence the downtime. The following occurrences
can activate a red light at the tipple stations:
ROM bin level high (bin greater than 80 per cent full)
grizzly airlock door alarms (door to the bird cage is
opened)
• rock breaker in operation.
Another cause of downtime recorded in Citect is when
maintenance shutdowns occur. At RWD there are two
shutdowns per month. When a shut occurs Citect sometimes
records the rock breaker as operating (out of park position);
however, it is in fact only out of park position for maintenance
rather than actually in operation.
•
•
Whilst carrying out data analysis the following steps were
taken to ensure accuracy of results:
Each time a maintenance shut occurred the downtime data
was discounted as it would incorrectly bias the average.
• If both the high bins and rock breaker in-use alarms were
activated at the same time only the minutes from the high
bins would count. This is because if the bins are full LHDs
aren’t allowed to tip anyway so if the rock breaker is not
the cause of the downtime.
This will allow for accurate quantification of when the rock
breaker is causing downtime. Through analysing data from
13 December 2011 to 13 February 2012 it was concluded that
both the east and west tipples clocked just over four hours of
downtime per shift on average. Figure 3 displays the different
triggers for red lights and the average downtime they caused
per shift for this time period.
•
removal of grizzlies
The removal of the grizzlies at Northparkes and Palabora
has proved successful for both mines (Butcher et al, 2011).
Northparkes made the initial decision to not include grizzlies
at the feasibility stage as the crusher could handle the
largest size rock a LHD could transport. Palabora, similar
to RWD suffered from downtime due to the rock breaker.
Palabora educated their workforce on identifying oversize,
relying upon the capability of their crushers, and the effect
of the impact resulting from an 8 m drop to the ore bin to
assist breakage (Capes, 2007). It can be concluded that the
requirement of a grizzly rests upon the crusher requirements
and specifications.
Typically the maximum rock size the crusher will accept
and crush without bridging is approximately 90 per cent of
the crusher gape for a single toggle machine. The largest jaw
crusher gape is around the 1500 mm mark, which results
in the largest practical rock size that could be accepted by
the crusher of approximately 1350 mm in size. The cave
fragmentation predicts an F80 of 1480 mm, which is greater
than the nominal size (Maunsell, 2007). Therefore, a grizzly
scalping screen is required to guard against damage to the
crusher.
reSultS and dIScuSSIon
estimating downtime
The initial data was collected using Citect to calculate the
total downtime at the tipping stations and the cause of the
downtime. Downtime at the tipple stations has been defined
as any time the red traffic lights are activated. A set of traffic
lights are installed at each access point to the tipples so the
LHDs know when to tip (green light) or wait (red light).
Anytime a LHD is waiting to tip due to a red light, production
Fig 3 - Average downtime caused by three triggers of the red lights
over two months.
It can be seen that the biggest contributor to downtime on
average, is the rock breaker at both the east and west tipples.
On average the rock breaker causes two hours of downtime
per shift. It can be concluded from these findings that in order
to increase productivity the root cause of why the rock breaker
is operating excessively will need to be identified.
It is to be noted that the cause for high bins could be due
to downtime further along the MHS, such as the conveyor or
crusher; however, this is outside the scope of the investigation.
The focus is on what occurs above the grizzly panels.
rock behaviour analysis
In order to design a grizzly that would reduce downtime,
the problem with the current grizzly design first needs to
be identified. To do this data was collected using the CCTV
footage of the tipples. The footage was initially reviewed in
segments of 24 hours for 60 days. Figure 4 displays a snapshot
of the footage that was reviewed.
Different types of analysis carried out using the footage has
determined:
15
across the oversize rock shapes, and is reduced in the
jammed category as the rock breaker merely needs to move
the rocks rather than break any. It can be concluded from
these results that the most problematic rock shape is round
and nesting-round. As these rock shapes also take longer
than one minute and 26 seconds to break, these are the rock
shapes that will be targeted in the modified grizzly design.
As the nesting due to round rocks only occur due to the
initial plug round rock, if the round rock shape problem can
be alleviated there will only be a small fraction of oversize
occurring on the grizzly panels.
tipping style
An investigation regarding if the style of tipping affects
oversize was also conducted. Three tipping styles were
identified:
•
Fig 4 - Snapshot of the east tipple station.
the shapes of the rocks that occur on the panels
the frequency of the shapes of rocks
the location on the grizzly panel each rock shape occurs
the average time required by the rock breaker to break
each shape of rock
• the effect of different tipping styles from the LHD buckets.
The CCTV footage has been examined using different
categories of oversize rocks. The different shapes of rocks
being examined are:
•
•
•
•
round: oversize rock (>1200 mm) that is round in shape
nesting – round: nesting or clogging, caused by a plug
round rock
• nesting – jammed: nesting or clogging, caused by rocks of
acceptable size jamming together upon impact resulting
in a blockage
• slab – oversize (>1200 mm)rock that is of rectangular
shape
• nesting – slab: nesting or clogging, caused by a slab rock.
By understanding the most problematic rock shapes in
the highest frequented area will allow for the most effective
grizzly design. The results from the analysis can be seen in
Figure 5.
•
•
Drop – this is when the LHD stops at the grizzly, raises
the bucket and dumps the load in one position with most
of the material landing in the one area (ie zones 2 and 7 if
the LHD came from the left, refer to Figure 6 for grizzly
zones).
Fig 6 - Grizzly zones.
Spill – similar to drop except the bucket is dumped slower
allowing for the material to move around and spread out
as it is being dumped.
• Launch – occurs when the LHD doesn’t fully stop at the
tipple. The LHD drives in and starts emptying the bucket,
then begins reversing. This causes the material to ‘launch’
over the grizzlies allowing the material to scatter and
distribute more between the holes.
Figure 7 outlines the results indicating the type of tipping
style and the grizzly zone in which the oversize occurred.
•
Fig 7 - Location of oversize relative to the tipping style of the LHD.
Fig 5 - Frequency of rock shapes versus the average time to break them.
It can be seen that nesting due to round rocks and round
oversize rocks are on average the highest frequented rock
shapes. The time to break each rock shape is fairly similar
16
It can be seen that the spilling method was prevalent and
the middle four holes are the most frequented. The data is,
however, inconclusive. There were too many variables to be
considered for this study, including: different operators,
which side of the tipple the LHD approached from, and
whether to only record plug rocks or the loads that contributed to nesting as well. Data was only recorded for six shifts.
grIzzly ModIfIcatIonS at rIdgeway deepS Block cave MIne
trial – minor modification
An initial trial of a modified grizzly was carried out. Two
old grizzly panels were installed on the west tipples. The
old panels consisted of three holes approximately 900 mm ×
1000 mm in size. The bars separating the holes were removed
in order to increase passing size to approximately 3000 mm ×
900 mm. The west tipple with the modified grizzly panels can
be seen in Figure 8.
any component further along the MHS by allowing a greater
rock size through.
fIndIngS froM analySIS
The following conclusions have been made from the initial
data analysis and preliminary trials:
•
•
•
•
•
Fig 8 - Minor modification to grizzly panels.
The purpose of this trial was to:
evaluate that if by increasing the passing size less oversize
would accumulate on the grizzly thereby increasing
productivity
• monitor the effects of potential rock sizes of 2 m2
damaging or bridging on the crusher gape.
Data for this modification was accumulated for 28 shifts
(14 days). It was found that downtime was reduced
marginally by six per cent. Keeping in mind that only half
the grizzly panels were modified, this was an encouraging
outcome. The results can be seen in Figure 9.
•
•
•
•
The rock breaker operation was causing approximately
two hours of downtime per shift.
The cause of the excessive rock breaker operation was due
to oversize on the grizzlies.
The most problematic rock shapes were round oversize
rocks which also cause nesting. These rock shapes also
require more time to break, making them a target in the
modified design. If the round rocks were no longer a
problem it would free 79 per cent of the rock breaker time,
allowing production time to increase by approximately
1.5 hours.
The oversize predominantly occurs in the middle four
holes of the grizzly panels, and this is where the most
wear and tear occurs.
The most common tipping style is spilling; however, due
to data collection methods the tipping style analysis is
inconclusive toward the objectives of this research project.
The initial modified trial found that increasing passing
size helped to reduce downtime, however only by six per
cent.
Concerns regarding large rocks passing through
and damaging or bridging on the crusher gape were
disregarded.
The option to remove the grizzlies altogether was
disregarded due to the crusher requirements.
fInal ModIfIed deSIgn
The final modified design takes into account findings from
the literature review, initial data analysis and the preliminary
trials. Figure 10 displays the modified design.
Fig 10 - Final modified grizzly design (1 and 2 represent transverse and
cross bar designs).
The modified grizzly design encompasses the same area
as the old design so no new excavations are required for
installation. It is a static design with eight apertures similar
to the old design. However, the apertures are square, unlike
the previous holes, which were filleted and can be seen for
comparison in Figure 11.
Fig 9 - Downtime comparison between the new and old grizzly.
It was concluded from this investigation that by increasing
the passing size, less oversize accumulated on the grizzlies,
thereby reducing downtime by not requiring the rock breaker.
It was also found that no damage was done to the crusher or
Removing the fillets allows for greater throughput and
reduced flat surface area in order to alleviate room for nesting
to occur. All the bars are now angled; this has been done
for two reasons, firstly to alleviate nesting by removing flat
surfaces where material can build up and secondly, having
an angled impact zone for the material allows for the material
to potentially be broken up more and moved around before
passing through the apertures. The aperture size has been
17
Investigation into the grizzly zones showed oversize
occurred and the effect of LHD tipping styles was found
irrelevant and had no impact on the final design.
• An initial trial of a slightly modified grizzly found that
by increasing passing size downtime was reduced
marginally.
The modified grizzly design was based on the identified
problems and initial results. The final design has targeted
round rock shapes and nesting through a larger passing size
and eliminating flat surfaces where nesting could occur. A
cost benefit analysis was carried out and it has been found
beneficial to manufacture and trial one modified grizzly
design.
•
Fig 11 - Original grizzly schematics.
increased to 1200 × 1330 mm, this is a 16 per cent increase
in passing size. The maximum rock size passable is 1.27 m3,
which is still under the maximum rock size allowable into
the crusher. The total weight of the grizzly is approximately
22.5 tons. A quote was sourced from an Australian company
for manufacturing the grizzly at a cost of $278 750.
cost benefit analysis
As the remaining life-of-mine is only four years, it is important
to calculate if the cost of implementing a new grizzly will be
beneficial. As the apertures have been made larger a greater
amount of material can pass through. It has been estimated
that there would be an increase of 7.7 per cent passing feed for
the new grizzly. This increase in throughput has been used to
calculate the net present value (NPV) base on the revenue the
new grizzly could make.
Over the remaining four year mine life, the net present
value of replacing the east and west grizzlies with the new
design was estimated at $4.8 M.
concluSIonS
An investigation into the grizzlies at Ridgeway Deeps mine
was undertaken in order to design a modified grizzly design
that will reduce downtime. The chosen research topic has
proved difficult to investigate as the design requirements
are site specific. The dimensions and/or requirement of
a grizzly are based upon the crusher feed requirements.
Preliminary research found that the crushers at RWD can
accept a maximum rock size (90 per cent of the crusher
gape) of 1350 mm. The option of removing the grizzlies was
investigated as it has proven successful for other block cave
mines. It was found, however, that due to the predicted cave
fragmentation and the crusher capacity grizzlies are required.
The original grizzlies consisted of eight round apertures 1200
× 1200 mm in size.
The data gathered produced the following conclusions:
•
•
18
The rock breaker in operation is the biggest contributor to
downtime (approximately two hours per shift).
Analysis of the oversize rocks indicated that round rock
shapes are the most problematic rock as they are the most
frequented type of oversize. Round rocks were also found
to cause the most nesting.
Newcrest Mining Limited has manufactured one modified
design and it will be implemented on the west tipple for trial.
Newcrest will monitor the downtime at the tipple and also
the production tonnes. Further research is recommended to
monitor any change in crusher downtime resulting from the
new design.
referenceS
Allman, A, Dunstan, G and Syme, T, 2007. Ridgeway Deeps feasibility
study, Newcrest Mining Limited.
Brunton, I, Sharrock, G and Lett, J, 2011. Full scale near field flow
behaviour at the Ridgeway Deeps block cave mine, in Proceedings
MassMin 2012 (Canadian Institute of Mining, Metallurgy and
Petroleum: Montreal).
Bullock, R L and Hustrulid, W A, 2001. Underground Mining Methods:
Engineering Fundamentals and International Case Studies, 718 p
(Society for Mining, Metallurgy, and Exploration: Littleton).
Butcher, A, Cunningham, R, Edwards, K, Lye, A, Simmons, J,
Stegman, C and Wyllie, A, 2011. Northparkes Mine [online], The
Australasian Institute of Mining and Metallurgy. Available from:
<http://www.ausimm.com.au/content/docs/northparkes_
example_paper.pdf> [Accessed: 16 March 2012].
Capes, G, 2007. Palabora 2010 visit: Production and secondary break
benchmark, Newcrest Mining Limited.
Darling, P, 2011. SME Mining Engineering Handbook, third edition,
1835 p (Society for Mining, Metallurgy, and Exploration: Littleton).
Duffield, S, 2001. Design of the second block cave at Northparkes E26
Mine, in Proceedings MassMin 2000, pp 35-46 (The Australasian
Institute of Mining and Metallurgy: Melbourne).
Gupta, A and Yan, D, 2006. Mineral Processing Design and Operation –
An Introduction, pp 293–353 (Elsevier BV: Amsterdam).
Maiwasha, C, Ngidi, N and Pretorius, D, 2010. Competent persons
report on the mineral assets of Palabora Mining Company
Limited, South Africa [online]. South Africa, Rio Tinto. Available
from: <http://www.jse.co.za/Libraries/JSE_Documents_and_
Statistics_-_CPR_-_Rio_Tinto/Palabora_Mining_Company.sflb.
ashx> [Accessed: 19 April 2012].
Maunsell, 2007. Ridgeway Deeps Mine – MHS and fixed infrastructurefeasibility study report, Maunsell Pty Ltd, Brisbane.
Palabora Mining Company Limited, 1996. Palabora feasibility
report, Palabora Mining Company Limited.
Multi-Objective Optimisation of
Mining-Metallurgical Systems
V Golding1, D Jafari2, B Rajopadhyaya3, D Sundquist4
and E Chanda5
Abstract
Optimisation of integrated mining and metallurgical systems poses some significant challenges
due to the conflicting nature of business objectives. Traditionally, mine planners would seek to
optimise the net present value (NPV) of a project, but the issue of sustainability in mining requires
consideration be given to a wider range of goals. Standard linear programming can optimise a
system, based on a single objective such as maximising the NPV. However, the single objective
function approach is not adequate to accommodate multiple objectives that are often conflicting
in nature. A mine optimisation model should be formulated in such a way as to include multiple
objectives and applicable constraints. A model that can simultaneously optimise a number of
conflicting objectives is required to handle such complexity in mine planning. Multi-objective
optimisation is a linear programming method, which can handle mine optimisation problems
with multiple goals. In this paper, a weighted goal programming (WGP) approach was used,
whereby each objective function had priority over the other(s), depending on corporate decisions.
The aim was to develop a mine optimisation model that includes energy, water consumption
and equivalent carbon emissions as objective functions. The model is based on WGP and is
used to find the optimum material flow rates in a network model of a large-scale mining and
metallurgical operation. The network system and relevant data is based on a large-scale open pit
mining operation to add realistic values to the generic system. The LINGO software package was
used for the analysis.
Preliminary results show that the model can successfully consider the different objective function
goals, weights and constraints to optimise the given network by changing resource utilisation. The
program is quick to solve and useful for undertaking what-if analyses.
INTRODUCTION
An essential part of the mine planning process is optimising
the net present value (NPV) for the operation (Rafiee and
Asgahri, 2008; Topal and Ramazan, 2012). The NPV essentially
determines the value of the operation and helps to predict
the life of the mining operation. Whilst this is a valuable
tool in the planning process, the optimisation is simplistic in
that it only considers a single objective. This single objective
optimisation (SOO) provides a single, unique solution for the
production schedule of the operation, but does not consider
some of the operational constraints that have the potential to
significantly alter the capacity and practicality of the mining
operation. Such operational constraints could be in the form
of energy consumption, carbon emission and water use. SOO
does not have the capacity to include these factors into the
optimisation strategy.
(Rangaiah, 2008). This optimisation strategy has the capacity
to optimise a number of objective functions, based on specified
variables and constraints, and output a range of optimum
conditions. Whilst the SOO will output a unique optimum
solution in the form of local and global optima, the MOO
will have many optimal solutions; the exception is when the
objective functions are non-conflicting. In this special case only
one unique solution is expected. Real life problems involve
several objectives to be considered simultaneously. Often
these objectives are conflicting, which means that optimum
solution to one objective function might compromise one or
more other objective functions. In this respect, MOO is an
ideal tool for representing a realistic mining operation as it
is able to consider a number of variables within the mining
environment.
Multi-objective optimisation (MOO), also known as multicriterion optimisation, involves finding the optimum values
of decision variables corresponding to all objective functions
A mathematical model will be created and applied to a largescale mining-metallurgical operation in South Australia. The
model will ensure that the cost associated with carbon, energy
1. SAusIMM, Student, School of Civil Environmental and Mining Engineering, University of Adelaide, SA 5005. Email: [email protected]
5. MAusIMM, Director of Teaching, School of Civil, Environmental and Mining Engineering, University of Adelaide SA 5005. Email: [email protected]
19
v goldIng et al
and water consumptions are minimised while maximising the
economic value (NPV).
MultI-oBjectIve optIMISatIon MethodS
There have been many achievements in optimising single
objective functions in the last century. However, MOO has
only been thoroughly studied in the past few decades. Many
complex organisational problems involve multiple decision
functions, which are difficult to model due to a lack of
information. These types of planning problems are modelled
using multi-objective linear programming (MOLP).
There are two major methods for solving MOO problems:
generating and preference-based method. The preferencebased method requires inputs and suggestions from the
decision makers (DMs). The decision maker is a person or
group of people assigned with the task to accept the best
optimal solution, based on their experience and knowledge.
The generating method involves finding the solution’s space
to the MOO hoping that one will be the optimal solution,
without any inputs from the DM. These two methods are
further divided into subgroups, illustrated in Figure 1
(Rangaiah, 2008).
Therefore, it is important to have an algorithm to solve these
multi-criterion fractional problems. Kornbluth and Steuer
(1981) have used goal programming and multi-objective
scalarisation programming to solve multi-criterion fractional
objective functions. There are several papers on solving
multi-objective fractional functions using either GP or MOLP;
however, very few have utilised both methods collectively.
Methodology
A network was adapted from a typical large-scale mining
operation that encompasses a comprehensive miningmetallurgical system. That is, from the extraction of a raw
material through to a smelted and refined saleable product.
network model
The modelling of the mining-metallurgical system involved
identifying nodes where a decision must be made about ore
utilisation. Nodes are also required where a constraint must
be applied, such as the limitations of a smelter or refinery.
Processes that do not require a decision to be made, or a
constraint implemented, can be simplified by combining
associated costs. The revised network for the hypothetical
mine is presented in Figure 2.
The operation has been modelled for a one-year period.
This allows a yearly production schedule to be generated that
will satisfy the annual targets for carbon production, water
consumption and energy use.
Stockpiles have not been considered as an ore source
although they could be easily added to the network. The
primary purposes of stockpiles are short-term grade control
and processing beyond the life of mining, which is not
applicable to the time frame being modelled.
Model formulation
The network represents the flow of ore from one node to
another. The ore is extracted, crushed, processed and then
concentrated or smelted into a refined product.
Fig 1 - Multi-objective optimisation methods (Rangaiah, 2008).
Multi-objective goal programming
Jones et al (2010) has described multi-objective goal
programming (GP) as a model that is based on achieving
certain goals assigned by the decision maker. The decision
maker will put forward preferences, which are used as tools
to solve the decision functions. GP is classified into two major
subsets, known as weighted goal programming (WGP) and
lexicographic goal programming (LGP) (Tamiz, Jones and
Romero, 1998). The WGP assigns weights to the unwanted
positive and negative deviations of the decision maker, based
on their importance. LGP, also known as pre-emptive goal
programming, relies on the existence of the priority levels.
The priority levels are the objective functions to be optimised
and each priority level will have negative and positive
deviation from the goal target. Each objective function will
have priority on one or more of the other objective functions.
Weighted goal programming has been chosen to formulate
the optimisation model.
Multi-objective linear fractional goal
programming
Some real life modelling problems are best dealt with in terms
of fractions such as investment, costs, profit per share, etc.
20
If we consider the amount of copper within each arc of
the network, we can estimate an equivalent quantity of ore
by using the average grade of the deposit. By doing this,
the material flowing along the arcs between nodes can be
represented as a tonnage as shown in Figure 3.
In Figure 3 ‘X’ represents the equivalent amount of ore in
tonnes and ‘C’ represents the coefficient associated with costs
of carbon, energy, water or the economic value of the arc. The
source ‘i’ could represent an ore source (a mine) and the sink
‘j’ a concentrator (Chanda, 2004).
decision variables
The variable Xij as described above represents the tonnes of
equivalent ore from source ‘i’ to sink ‘j.’ These are the decision
variables used in the optimisation model. The number of
decision variables corresponds to the number of arcs within
the described network. For the chosen network, each of the
decision variables is described below.
objective functions
Using the described decision variables, a number of objective
functions can be defined. This will allow the MOO analysis
to be formulated. For the purposes of this optimisation, the
objective functions will focus on carbon emissions, energy
use, water use and economic value.
X0,1: Tonnage of ore extracted from the pit
Multi-Objective Optimisation of Mining-Metallurgical Systems
Fig 2 - Revised mining network model.
X0,2: Tonnage of ore hoisted from underground
XR,3: Combined tonnage of pit and underground ore (ie from
ROM pad) going to concentrator
X3,4: Equivalent tonnage of ore in tails transported to Counter
Current separator (hydrometallurgy)
X4,5: Equivalent tonnage of ore in underflow from
hydrometallurgical plant transported to tailings storage
facility
X4,6: Equivalent tonnage of ore in overflow from
hydrometallurgical plant sent for solvent extraction
X6,7: Equivalent tonnage of ore contained within U3O8 solvent
X7,8: Equivalent tonnage of ore contained within U3O8
concentrate transported to market
X6,9: Equivalent tonnage of ore contained in copper solvent
transported to refinery
X9,10:: Equivalent tonnage of ore in refined copper
transported to market
X2,3: Tonnage of ore extracted underground and transported
to concentrator
X3,11: Equivalent tonnage of ore contained in concentrate
leach
X11,12: Equivalent tonnage of ore from concentrate leach
transported to smelter
X11,14: Equivalent tonnage of ore in copper concentrate
X12,13: Equivalent tonnage of ore in smelted material
transported to refinery
X13,10: Equivalent tonnage of ore from refined copper
cathode transported to market
X13,15: Equivalent tonnage of ore extracted into Anode slimes
X15,16: Equivalent tonnage of ore from gold and silver bullion
� Source
X ij C
ij
� Sink
Fig 3 - Network representation of material flow.
Minimisation of carbon cost
The first objective function to be minimised is the amount
of carbon produced in the entire system. The amount of ore
passing through the process is optimised such that carbon
emission through whole system is minimised:
MinZ1^Xh = / Cij # Xij i,j
(1)
where:
Cij represents the cost of equivalent carbon emissions in the
form of a dollar value per equivalent tonne of ore.
Xij represents the material flowing from the respective
source ‘i’ to sink ‘j’ in terms of equivalent tonnes of ore.
Minimisation of energy usage
The second objective function is the amount of energy
consumed in the mine. Again, the amount of energy used per
tonne of ore through different arcs is known.
21
V Golding et al
MinZ 2 ^Xh = / Eij # Xij i,j
(2)
where:
Eij represents the cost of energy use (per kwh) in the form of
a dollar value per equivalent tonne of ore.
Minimisation of water usage
The third objective function is the minimisation of water
through the system. The water consumption per tonne of ore
passing through the different process in the system is known.
The optimisation will produce the tonnages of ore passing
through the various nodes, which will aim to optimise the
water usage in the system.
(3)
Wij represents the cost of water use (per litre) in the form of
a dollar value per equivalent tonne of ore
source ‘i’ to sink ‘j’ in terms of equivalent tonnes of ore
Maximisation of economic value
The final objective function is the maximisation of the
economic value of the ore through the system. This function
will counter the three previous cost functions creating a more
realistic scenario. The model will thus be analysing trade-offs
such as sacrificing profit to decrease costs. This will optimise
the value per tonne of ore through the system.
(4)
where:
Vij represents the present value ($) in the form of a dollar
value per equivalent tonne of ore
Constraints
In order for the model to reflect a real situation it must
produce results within a realistic range. That is, it should
have appropriate flows of ore through the network, and it has
to simulate the environment that a normal mine can operate
under. In order to ensure that this is achieved a number of
constraints are implemented. These include non-negativity
constraints, production constraints and flow constraints. The
use of these constraints will be explained further.
Non-negativity constraints
Material flowing through nodes in the network represents ore
as it travels through the stages of mineral processing in the
mine. It does not make sense for ore to travel backwards across
the arcs. That is, the amount of material moving through each
path must be positive. This non-negativity constraint applies
to all of the decision variables as described above.
Production constraints
The amount of material passing through the network cannot
exceed the capabilities of the mining operation or processing
facilities. Constraints were placed on the relevant nodes in
22
Further constraints are necessary to ensure that the model
produces a solution that is realistic and satisfies the conditions
of the optimisation process. One example is the definition of
flow constraints, which need to be set so that mass is balanced
within the network. This ensures that the amount of material
entering the system, ie extracted through mining, must equal
the amount of material exiting the system through various
paths. These include not only the relevant markets, but also
outlets such as the tailings storage facility (TSF) or similar
waste products.
Modelling
where:
MaxZ 4 ^Xh = / Vij # Xij i,j
Further constraints were determined for the capability of the
processing systems in the mine. An upper limit was estimated
for facilities such as the concentrator, smelter and refinery.
Additional constraints
MinZ 3 ^Xh = / Wij # Xij i,j
order to ensure that the optimised conditions are realistic in
terms of the mining operation. The underground and open pit
capacities were estimated at 10 Mt/a and 50 Mt/a, respectively.
The multi-objective linear optimisation is analysed and
evaluated using the linear programming software called
LINGO. This software package is a comprehensive tool
designed to make building and solving linear, nonlinear and
MOO faster, easier and more efficient.
Weighted goal programming (WGP) was determined to
be the most suitable method for optimising this system. It
provides flexibility in that it allows a decision-maker, ie a
mining emgineer to assign preferential weights to the goal
functions that are of higher importance, hence, minimising
the cost (Tamiz, 1998).
Goal functions
The summation objective functions are converted to equations
by adding the deviation variables (ni, pi) and the goal. The
goals are based on hypothetical data for a typical operation.
The target values are $90 M, $150 M and $3 M for carbon,
energy and water cost objective functions respectively. They
represent costs that the company does not want to exceed in a
year of operation. The goal for economic value was assumed
to be A$2.43 B. Equation 5 shows the four goal functions, ie
carbon, energy, water and economic value.
/ Cij # Xij + n1 - p1 = 90 000 000
i,j
/ Eij # Xij + n2 - p2 = 150 000 000
i,j
/ Wij # Xij + n3 - p3 = 3 000 000
i,j
/ Vij # Xij + n4 - p4 = 2 430 000 000
i,j
(5)
Achievement function
It is required to assign weights to the different deviation
variables, based on the importance of their goal functions.
For instance, the energy goal function has greater importance
over the other costs due to its higher overall cost, therefore,
p2 will have a higher weight assigned to it. Similarly, p1 will
have lower weight and p3 will have the lowest weight. The
deviations are scaled by the goal assigned to their respective
goal functions. The achievement function is the sum of all the
unwanted deviations, which is minimised (see Equation 6).
Min f^ xh = w1 # `
1
# p1 + w2
90 000 000 j
1
# p2 + w3
150 000 000 j
1
#`
# p3 + w4
3 000 000 j
1
#`
# n4
2 430 000 000 j
#`
(6)
Multi-Objective Optimisation of Mining-Metallurgical Systems
where:
a value of zero, which means that the target goal has been
perfectly reached.
w1, w2, w3 and w4 are the weights assigned to the
The water objective function was underachieved with an n3
value of $682 421. In other words, money has been saved from
water use. The goal could thus be reduced even further to
$3 000 000 – $682 421 = $2 317 579. The value of ore has been
overachieved as the p4 value is $1 412 376 000. This means that
we have increased the value of the system in comparison to
our goal value. It must be noted that this economic value is
determined from addition across the network, so it does not
correspond to a real profit.
corresponding goal functions
p1, p2 and p3 are negative deviations of the carbon,
energy and water goal functions
n4 is the positive deviation of the economic value, which
will act to maximise this objective
Results
The LINGO output represents the result of the optimisation
process. The achieved objective value is 0.1021345, which
represents the sum of the deviations from the set goals. This
objective value is very small, which means that the goals
assigned to the carbon, energy and water objective functions
are almost achieved. In other words, the system is working
and the mining operation can make profits as well as achieve
these targets.
Data validation
The outputs from LINGO were compared to a hypothetical
mining operation as shown in Table 2. The estimated tonnages
for each of the relevant nodes were converted to equivalent
tonnages of ore, so that it can be compared directly to the
outputs from LINGO.
The amount of infeasibilities is zero in this case, which
means that no constraints or variables are violated. LINGO
has run four cycles to achieve this result, which corresponds
to the four criteria in the optimisation. The total number of
variables is equal to 21 and all the constraints and equations
are linear in nature. The total number of constraints used in
optimisation is 43.
Discussion
The mining, metallurgical and smelting/refining components
of a mining operation were represented in a network flow
diagram as shown in Figure 2. The network displays a number
of different streams that produce different saleable products
in accordance with the described mining operation.
From the data validation, values appear to be within a
reasonable range of a typical large-scale open pit operation;
however, a number of shortcomings or biases are observable.
The most significant of these appears to be a bias towards
minimisation of cost, which may have been brought about
by an insufficient weighting of the revenue function. In
this respect, the object functions focused mainly on the
minimisation of cost, with respect to carbon emissions and
water and energy usage. The revenue generated was not
given appropriate weighting in this optimisation process.
This is mostly highlighted in the fact that a lot of material was
sent to the TSF instead of being passed on to flow through the
The values of the decision variables are also produced,
which represents the amount of ore passing through each arc.
All the variables and their resulting values are provided in
Table 1.
The value column shows the deviation from the target value
in dollar terms. The p1 value of $9 192 105 means that the
goal for the carbon function is overachieved and the system
is unable to reach this target. The optimisation will achieve a
carbon function goal of $90 000 000 + $9 192 105 = $99 192 105.
The p2 and p3 deviations are zero, which means that the goals
have been underachieved. The energy objective function has
TABLE 1
LINGO modelling results.
Variable
Ore (mt)
Reduced cost Variable
Value ($)
Reduced cost Variable
Weight
Reduced cost
X0,1
50.0
0
P1
9 192105
0
W1
1.00
0
X0,2
10.0
0
P2
-
0
W2
1.00
0
XR,3
53.3
0
P3
-
0
W3
1.00
0
X3,4
6.5
0
P4
1 412 376 000
0
W4
1.23
0
X3,11
46.8
0
N1
-
0
X4,5
1.0
0
N2
-
0
X4,6
5.5
0
N3
682 421
0
X6,7
2.5
0
N4
1
0
X6,9
3.0
0
X7,8
2.5
0
X9,10
3.0
0
X11,12
10.0
0
X11,14
36.8
0
X12,13
10.0
0
X13,10
6.0
0
X13,15
4.0
0
X15,16
4.0
0
23
V Golding et al
TABLE 2
Comparison to a hypothetical mining operation.
Node
Min (t)
Mid (t)
Max (t)
Converted
(t)
Open pit
50 000 000
50 000 000
50 000 000
50 000 000
Underground
10 000 000
10 000 000
10 000 000
10 000 000
Concentrator
61 000 000
71 434 800
81 869 000
60 000 000
Smelter and acid
plant
14 000 000
16 155 970
18 311 940
25 000 000
Refinery
12 500 000
16 520 250
20 540 500
29 000 000
Tailings storage
facility
45 000 000
45 375 000
45 750 000
4 000 000
Hydrometallurgy
6 500 000
9 864 280
13 228 560
8 000 000
Cu Conc
35 000 000
35 000 000
35 000 000
33 000 000
Cu Cathode
6 000 000
8 155 973
10 311 945
29 000 000
U3O8 concentrate
2 500 000
2 500 000
2 500 000
53 800 000
Au and Ag
bullion
4 000 000
5 875 000
7 750 000
45 300 000
refinery and smelter. These nodes have high values associated
with carbon and energy use, but also contribute significantly
to the amount of valuable material extracted. This is especially
true for the production of gold and silver bullion.
Conclusion and recommendations
A MOO strategy was successfully implemented to describe
a comprehensive mining-metallurgical system. The
optimisation process focused on the minimisation of the
costs associated with carbon emission and water and energy
consumption, whilst also incorporating the revenue generated
by the end saleable products.
LINGO was found to be an extremely efficient program,
capable of handling the model and the associated number
of constraints. It is believed that this method of optimisation
is ideal for a mining operation to model a system in terms
of flow of material through relevant nodes or processes.
Furthermore, the model is capable of addressing a number
of objective functions, or optimising criteria, in order to
realistically model the expected cash flow of the mine.
24
The LINGO model output results that are within an
acceptable range of an existing mining operation (see Table 2).
The objective value was found to be 0.1 dollars, which suggests
that most of the targets set by the decision-maker have been
achieved. In terms of the individual objective functions, the
carbon goal was overachieved by $9.1 M. The energy goal
was satisfied within a reasonable amount. The water goal was
under achieved by $0.7 M and the economic value goal was
over achieved by $1.4 B.
More detailed information from a mining operation will
assist with data validation. The actual application of the
model will need to be tailored so as to be site-specific for the
mining operation in question. Weighting can be altered, with
respect to the objective function, to represent the different
operating conditions of the mine in question and the goals
required. A limitation of this method is that the model is
affected significantly by the goals set by the decision-makers.
For this reason it is important for these goals to be set with a
high level of confidence by a competent person(s).
REFERENCES
Chanda, E K, 2004. Network linear programming optimisation of
an integrated mining and metallurgical complex, in Orebody
Modelling and Strategic Mine Planning, pp 13-19 (The Australasian
Jones, D and Tamiz, M, 2010. Practical Goal Programming, International
Series in Operations 11, Research and Management Science 141, DOI
10.1007/978-1-4419-5771-9_2, Springer Science Business Media,
LLC.
Kornbluth, J S H and Steuer, E, 1981. Multi objective linear fractional
programming, Management Science, 27:1024-1039.
Rafiee, V and Asghari, O, 2008. A heuristic traditional MIP solving
approach for long term production scheduling in open pit mine,
Journal of Applied Sciences, 8(24):4512-4522.
Rangaiah, G P, 2008. Process optimisation, multi objective
optimisation, techniques and application in chemical engineering,
Advances in Process Systems, 1:1-12.
Tamiz, M, Jones, D and Romero, C, 1998. Goal programming for
decision making: An overview of the current state-of-the-art,
European Journal of Operational Research, 111(3):569-581.
Topal, E and Ramazan, S, 2012. Strategic mine planning model using
network flow model and real case application, International
Journal of Mining, Reclamation and Environment, 26(1):29-37.
Evaluating the Performance and
Productivity of Continuous Surface
Miners in Iron Ore at Christmas Creek
Mine
B Goves1, M S Kizil2 and W Seib3
ABSTRACT
Continuous surface mining involves milling, crushing and loading iron ore in one systematic
process. Whilst the method is relatively new in the iron ore industry, it is also beneficial in
selective mining of orebodies with challenging geologies. This paper details an evaluation of the
performance and productivities of continuous surface miners in iron ore mining at Christmas
Creek Mine in Western Australia. The study involved ten months of data collection and analysis to
successfully complete the project. In total, 31 surface mining machines were examined involving
five different machine models. The five machine models investigated were the Wirtgen SM2200,
Wirtgen SM2500, Wirtgen SM4200, Vermeer T1255 and Vermeer T1655. The data is comprised
of two different types of analysis concerning operational and instantaneous production reports.
The operational production data objective was based on five key criteria including productivity,
performance, influence of material type, availability, utilisation and overall unit cost. The objective
of the instantaneous data was to correlate instantaneous productivities with material type. This
resulted in the construction of plots using mapping software packages.
Introduction
Christmas Creek Iron Ore Mine is located approximately
110 km north east of Newman in the Pilbara region, Western
Australia. The deposit is hosted within the Nammuldi
Member at the base of the Marra Mamba Iron Formation
(Clout and Rowley, 2009). Selective mining is used to extract
the iron ore at Christmas Creek, utilising the continuous
surface miners that are innovative to the region and the iron
ore industry. With advancements in technology, continuous
surface miners offer an alternative to traditional orebody
extraction techniques. The load and haul process is the
method primarily used in open cut mining applications.
Whether surface miners are used in conjunction with load
and haul practices, or as a completely different mining
method, the potential advantages may directly affect the
economic viability of a deposit. Selective mining poses
significant difficulties concerning the profitability of a
mine, especially, in the current market where large-scale
deposits with small stripping ratios are preferred. With the
utilisation of surface mining methods, thin seam mining of
iron ore bands, not only increases the economic viability
of a project, but assists with mine planning, drill and blast
design, transportation, processing and flexibility. Surface
miners allow high quality products to be excavated with
economic efficiency even from technically challenging
mineral deposits (Wirtgen GmbH, 2010). Additionally the
method can dramatically improve run-of-mine (ROM)
quality, reduce dilution and increase mining reserve. The
material is excavated and crushed in one operation before it
is loaded onto trucks, saving further handling, transportation
and process costs (Williams, Mendelawitz and Castle, 2007).
With this in mind, the current surface mining equipment
at Christmas Creek mine is not being actively tracked with
regard to production rates, locations and the type of geological
material. An analysis and evaluation will allow a baseline of
performance for any future improvement projects, and ensure
effective tracking of the surface miner productivities.
The study was limited to comparing five machine types at
Christmas Creek Iron Ore Mine. The Wirtgen surface miners
evaluated included the SM2200, SM2500 and SM4200. The
Vermeer surface mining machines evaluated included the
T1255 and T1655.
Mining Method and Equipment
The surface mining method at Christmas Creek involves
continuous surface miners cutting the material to ground rather
than utilise the direct loading conveyor system implemented
at Cloudbreak mine. Whilst trials have been conducted on the
conveyor system, as well as a harvesting method, this paper is
only concerned with the conventional method.
1. The University of Queensland, St Lucia Qld 4072. Email: [email protected]
2. MAusIMM, Associate Professor, School of Mechanical and Mining Engineering, The University of Queensland, St Lucia Qld 4072. Email: [email protected]
3. FAusIMM(CP), Adjunct Professor, School of Mechanical and Mining Engineering, The University of Queensland, St Lucia Qld 4072. Email: [email protected]
25
B Goves, M S Kizil and W Seib
Fig 1 - Cross-section of mining process (Walker, 2011).
The operation is separated into an overburden crew and
surface mining crew. The two crews are unlikely to work in the
same pit at any given time due to increased traffic and safety
issues. The top of surface miner (TOSM) is the key factor in the
mining profile. Material above this line is drilled and blasted
before overburden removal takes place through hydraulic
excavators and haul trucks. Figure 1 illustrates the mining
process and cross-section profile. The orebody is located below
the TOSM and is milled in strips using the surface miners.
Hump cuts are initiated to provide a smooth flat running
surface for the surface miners to run on. The surface miners
mill the ore whilst separating any waste layers. The material is
mined in situ alleviating the need for drill and blast.
Once the stripping process has been completed, the broken
material is then ‘walked’ by geologists to confirm and mark
up blocks of ore and waste. The designs are uploaded on
to dozers’ computer to ‘push up’ the material into suitable
piles for loading out. Front-end loaders are used to load haul
trucks, which then dump at a designated ROM pad or waste
dump. In order to effectively utilise the surface miners, a
minimum of three strips are required in a rotational system.
This includes a milling strip, a strip being loaded out and a
strip that has finished being loaded and is cleared.
Wirtgen and Vermeer surface miners were analysed
throughout the project. Both manufacturers utilise a
mechanical cutting drum to fragment the rock. The key
difference between the two is the opposite cutting directions.
The Wirtgen machines cut upwards compared with the
Vermeers, which cut top-down. A Wirtgen SM2500 machine
is illustrated in Figure 2 equipped with a conveyor system.
Depending on the machine configuration, surface miners can
cut from 2.20 to 4.57 m wide and at a depth of 0.10 to 0.71 m.
The differences in size, weight, horsepower and cutting
dimension deliver advantages and disadvantages for each
machine model.
Methodology and Data Analysis
Operational production data
The operational production data involved a large amount
of data collection and analysis. The data was collected for
10 months on a daily basis from July 2011 to April 2012. It
was recorded and captured by the surface miner operators,
before compilation into Microsoft Excel spreadsheets. Each
individual spreadsheet included:
•• day shift and night shift for a particular day
•• every surface mining machine that was operated during
that day
26
Fig 2 - Wirtgen SM2500 at Christmas Creek mine (Truong and Nguyen, 2012).
•• SMU hours, working hours, standby time, operating
delays, weather delays, unscheduled maintenance and
scheduled maintenance
•• the amount of picks, tool holders and fuel used
•• the tonnages milled
•• the area of mining (pit/strip/flitch)
•• the ground conditions that were encountered and any
additional comments.
This information was obtained via email and then compiled
into monthly summary spreadsheets before analysis could
continue. Due to the large amount of data, a significant
period of time was spent assembling the information into
readable and comprehensive data sets. It is estimated that
approximately 600 to 800 spreadsheets were used in the
analysis.
Instantaneous Production Data
The instantaneous production data was collected for a
number of different shifts for the Wirtgen surface miners. The
instantaneous productivity of a surface miner is defined as
the production rate at a certain point in time. This rate also
details the immediate GPS position of the machine along with
an estimate of the type of material the machine is milling. The
data was recorded and captured through access to the Trimble
GPS on-board the surface miners. On average three to four
points are recorded every two seconds when operating. The
production reports contain information relating to:
•• easting, northing, Z RL (x, y, z) position of the surface
miner
•• GPS points for both ends of the cutting drum
•• GPS tolerances
•• the speed the machine is operating at (km/h)
•• the design and location the machine is operating in (pit/
strip/flitch)
•• which gear the machine is operating in and the direction
of travel.
The instantaneous productivity is dependent on several
key constraints and variables. The width and depth of the cut
must be fixed for a given strip. The density must also be fixed
to accurately calculate tonnage per hour. The key variable
is the speed the machine is milling at in a given strip. The
instantaneous productivity is calculated by:
P = S × CW × CD × ρ
(1)
evaluatIng the perforMance and productIvIty of contInuouS Surface MInerS In Iron ore
where:
P
is the instantaneous productivity
S
is the speed (metres/hour)
CW
is the cutting width (metres)
CD
is the cutting depth (metres)
ρ
is the density (kg/m3)
reSultS
operational production results
Productivity of surface mining fleet
Calculating the productivity of the surface miners was one
of the key pieces of analysis throughout the duration of the
project. Figure 3 portrays a comparison of the five machine
models and their average production rates. The targeted
production rates also highlight whether a machine made
target or not.
was lower than expected, there was an upward trend and
improvement from an initial reading of 444 t/h before
finishing with 528 t/h.
The Vermeer T1655 machine produced results on par
with its target of 1700 t/h, with an average of 1699 t/h. The
machine met its target in the months of September, October
and November 2011, before a downward trend developed
at the beginning of 2012. The highest monthly average was
2137 t/h in September 2011 and the lowest being 635 t/h in
March 2012. The unusual result in March dropped the project
average significantly, triggering an investigation into the
machine’s availability, utilisation, operational delays and
maintenance.
When comparing all of the machines, the SM2500s produced
the most impressive results in terms of improvement,
where a sharp increase was observed to almost triple the
production rate by project end. The SM4200s achieved the
highest production rates above target and consistently
achieved positive results. The SM2200 machines operated
at approximately a quarter the productivity of a SM4200.
Finally, the T1255s consistently struggled to meet the targeted
rates and operated at approximately a quarter the production
rate of a T1655.
In combination with the productivity, the performance of all
the surface mining machines was calculated. Figure 4 details the
performance comparison. The SM2500 and SM4200 produced
positive performances of approximately 13 per cent and 27 per
cent respectively. The SM2200 and T1255 machines produced
results below the expected level at -8 per cent and -17 per cent
respectively. The Vermeer T1655 produced a result that was on
par with its target. All of the machine models except for the
T1655 observed an upward trend in performance from the
beginning of the study to the end. The improvement trend was
due to a combination of factors concerning:
•
Fig 3 - Productivity comparison of surface mining fleet.
The Wirtgen SM2200 machines averaged a production rate
of 579 t/h, which was slightly lower than the targeted 630 t/h.
Over the five-month period from July to November 2011, the
machines met target only twice and almost reached target in
October. A trend analysis, revealed a gradual improvement in
productivity. A recording of 386 t/h in July before finishing
with three months averaging over 600 t/h highlighted the
improvement. It was concluded that had there been a larger
period of data collected, there may have been more consistent
results observed. Due to the decommissioning of the SM2200s
in December, data would need to have been sourced earlier
than April 2011.
•
•
•
a better understanding of the limitations and capabilities
of the machines
improvement of selected preconditioning of the orebody
enhanced knowledge and understanding of geological
model
development of operator skills and efficiency.
Influence of material type
This analysis detailed the average production rates when
a machine was operating in a certain material type or rock
hardness. As expected, there was a correlation between
material type and productivity, as highlighted by the three
linear trend lines in Figure 5. Based on the average production
rate of 579 t/h, it is estimated that the SM2200 machines were
operating on average in medium material. The SM2500s were
The Wirtgen SM4200 machines produced the highest
productivities of all the machines analysed. The average
production rate was 2161 t/h, which was significantly
above the targeted value of 1700 t/h. The machines were
implemented in December after the decommissioning of the
SM2200s and studied through to April. A sharp improvement
was observed from averaging 1138 t/h in December to
finishing with an average of 2198 t/h in April. The highest
monthly average was 2451 t/h in January.
The Vermeer T1255 machines recorded the lowest
productivity rates and struggled to meet targets. An average
of 458 t/h was recorded with a target rate of 550 t/h. The
machines were analysed for a period of six months meeting
target only in February 2012. Although the productivity
Fig 4 - Performance comparison of surface mining fleet.
27
B goveS, M S kIzIl and w SeIB
Fig 5 - Comparison of material type productivity for Wirtgen surface miners.
The total rostered time for each machine model was broken
down into a comparison of working, standby, operational
delay, unscheduled maintenance and scheduled maintenance.
This comparison is shown in Figure 6 where T1255 machines
achieved the highest amount of working time. Considering
they had the worst performance at -16.7 per cent, this suggests
that the target rates were too high at 550 t/h. The SM2200
machines had the lowest working time at 24 per cent, resulting
in large periods of operational delay and unscheduled
maintenance at 36 per cent and 27 per cent respectively. This
excessive amount of downtime was the cause for not meeting
targets and its -8.1 per cent performance. The most productive
machine the SM4200, had a working time of 37 per cent. By
reducing its excessive operational delay a further increase
in performance would prevail. Overall, across all 31 surface
miners, the working time was 34 per cent, standby eight per
cent, operational delay 18 per cent, unscheduled maintenance
21 per cent and scheduled maintenance 18 per cent.
estimated to have milled medium to medium hard material.
The SM4200 machines operated in medium soft material,
based on an average production rate of 2161 t/h.
The smaller SM2200 and SM2500 machines almost
doubled their production rates when moving from hard to
soft material. This was displayed in Figure 5 by the 343 t/h
and 543 t/h in hard material compared with 622 t/h and
1043 t/h respectively in soft material. Based on the result
of 2021 t/h in hard material for the SM4200s, a production
rate of approximately 4000 t/h is expected in soft material.
This correlation between material type and productivity,
suggests the smaller Wirtgen machines are more sensitive
to fluctuations in rock hardness when looking at production
rates. The bigger SM4200 machines are less susceptible to
variation in geology, suggesting a more robust machine
configuration. The result suggests the SM4200 machines can
obtain much higher and consistent production rates when
milling different types of rock without having an adverse
or detrimental effect on the productivity. Depending on the
type of material the SM4200 can operate between two to
four times greater than the SM2500. The SM2200 operates at
approximately two-thirds the production of a SM2500.
Further analysis of the results exposed unusual values
in medium hard material for SM4200 and in soft material
for SM2200 machines. The values do not follow the linear
relationship and are significantly lower than the linear trend
line. If the correlation were correctly modelled a value of
around 2040 t/h would be expected in medium hard material
for the SM4200. A value of above 800 t/h would be expected
for the SM2200 in soft material.
The differences in the linear trend are attributed to the smaller
periods of data analysed. The SM4200 and SM2200 were both
analysed for five months due to decommissioning and arrival
of new machines. The SM2500 machines had comprehensive
data from the full 10 month study and exhibit an almost
perfect linear line. Human errors would also be evident due
to the manual process involved in recording and capturing the
data. This estimation process provides an idea of the material
type operated in, as majority of the iron ore specified by the
geological model, is in the medium to hard range.
Availability and utilisation
The availability is defined as the percentage of time a machine
is mechanically available for work. The utilisation represented
the percentage of scheduled machine hours that were
productive. Finally the effective utilisation is the percentage
working time per total calendar time. The effective utilisation
is also described as the product of availability and utilisation.
28
Fig 6 - Comparison of rostered time for surface mining fleet.
When comparing all the machine models on a month-bymonth basis, a number of important trends were observed, as
illustrated in Figure 7. One key area to improving productivity
is by limiting the amount of unscheduled maintenance. From
July to April a distinct reduction from 40 per cent down to
17 per cent in unscheduled maintenance was achieved. By
more than halving this downtime, the machines saw a rise
in productivity and performance. The months of November
through to February experienced increased operational delay.
In particular, the month of January with almost 50 per cent of
total rostered time attributed to operational delay. This was
due to the summer months where increased cyclone activity
and dust storms were prevalent. Scheduled maintenance
was fairly constant at 17 per cent and represented proactive
measures taken on the machines. This resulted in limiting the
unscheduled maintenance.
The average availability, utilisation and effective utilisation
were calculated with a number of key observations found.
Table 1 shows that the T1255 had the highest utilisation and
effective utilisation, however produced the lowest results in
terms of performance, further suggesting that the target rate
of 550 t/h was potentially too high. A cause for concern is
the underutilisation of the SM4200. Whilst the machine had
the highest availability at 76 per cent, it was only utilised
49 per cent of the time. This resulted in 37 per cent effective
utilisation, which requires improvement considering the
SM4200 machines achieved the highest production rates.
Overall, when considering all machine types in the study,
evaluatIng the perforMance and productIvIty of contInuouS Surface MInerS In Iron ore
Fig 7 - Comparison of rostered time on a monthly basis for surface mining fleet
from July 2011 to Apr 2012.
TABLE 1
Average availability, utilisation and effective utilisation for the surface
mining fleet.
Machine
Availability (%) Utilisation (%)
Effective
utilisation (%)
SM2200
64
45
24
SM2500
55
59
32
SM4200
76
49
37
T1255
68
64
43
T1655
66
48
32
Average
61
59
34
Fig 8 - Monthly trends in availability, utilisation and effective utilisation for
surface mining fleet.
a cost per tonne basis, the results indicate that the two largest
machines are preferred over the smaller machine models.
The overall unit cost was also calculated regarding material
type. This provided a cost of operating in different strengths
of rock hardness for the Wirtgen surface miners. In Figure 10,
different line types correspond to unit cost and production
rates for the same machines. The increase in rock hardness
produces an increase in overall unit cost. When perfectly
modelled, a linear relationship should be observed as per the
red trend line for the SM2500 machines.
the availability was 61 per cent, utilisation 59 per cent and
effective utilisation 34 per cent.
The availability, utilisation and effective utilisation were
then examined on a month-by-month basis for all the
machines. A number of key trends were found as illustrated
by the coloured trend lines in Figure 8. The availability
experienced a positive trend from just above 40 per cent to in
excess of 60 per cent by the end of the study. The utilisation on
the other hand, experienced a downward trend. Utilisation of
70 per cent was observed at the beginning of the study before
falling to around 50 per cent by the project end. Finally the
effective utilisation was fairly constant and was maintained at
around 35 per cent for the ten month study.
The analysis of overall unit cost is aimed at quantifying
which models are preferred on a cost per tonne basis. The
study required the total operating and maintenance (O&M)
cost to be calculated in order to determine the cost per milling
hour. The cost was inclusive of overheads, supervision,
depreciation, interest, fitter labour, components, oil and
grease, tool holders, fuel and the operator’s salary. However,
it was not inclusive of picks. The most expensive piece of
machinery based on total O&M cost was the SM4200 surface.
The least expensive machine was the T1255 surface.
Using the average production rates and the total O&M
cost, the overall unit cost was determined for each machine
model. This is shown in Figure 9. The study found that the
T1255 machine was the most expensive, whilst the largest
machines the T1655 and the SM4200 were cheaper by 63 per
cent. Figure 9 details the most expensive to least expensive
machines on the x-axis. The analysis suggests a relationship
between production rate and cost per tonne. An increase in
production rate results in a decrease in overall unit cost. On
Fig 9 - Comparison of overall unit cost for surface mining fleet.
When milling in hard material the SM4200 machines were
over three times cheaper than the SM2500 and 4.5 times
cheaper than the SM2200. In soft material, the SM2200 was
1.5 times more expensive than the SM2500 machine. The
SM4200 was a further three times cheaper in soft material
compared with the SM2200 machines. It is conclusive that
the SM4200 machines were significantly cheaper to operate
than the other two Wirtgen machines in a wide range of
materials. The costing analysis suggests that when milling
below the targeted rates, the cost per tonne will be higher.
This is associated with operating in harder rock strengths,
which leads to increased wear and tear on the machine. If the
cost of picks were included in this study, it is predicted that
the overall unit cost would see a further rise.
Instantaneous production results
Instantaneous parameters
The instantaneous productivity calculation involved using a
number of parameters. The analysis uses the forward speed
of the machine as the key variable in combination with the
29
B goveS, M S kIzIl and w SeIB
TABLE 3
Instantaneous operating ranges for SM2200.
Material
Speed range (m/min)
Very hard
0 - 1.7
Hard
1.7 - 5
Medium
5 - 6.7
Soft
6.7 - 10
TABLE 4
Fig 10 - Comparison of material type overall unit cost for surface mining fleet.
cutting dimensions and density. The density used was
2.8 t/m3 based on the rock properties at the Fortescue deposits
(Hutchins, 2012). For the calculation to work correctly, the
cutting width and depth of a machine is fixed for a given strip.
Table 2 details the cutting dimensions used for each of the
Wirtgen surface miners in the calculation.
As the forward speed of the machine is the principal
TABLE 2
Surface miner cutting dimensions for instantaneous productivity calculations.
Machine
Cutting width (mm)
Cutting depth (mm)
Wirtgen SM2200
2200
300
Wirtgen SM2500
2500
500
Wirtgen SM4200
4200
650
determining factor, it was necessary to establish the
penetration rate or the amount of metres per minute a machine
could operate at. Using industry research and trials conducted
at the Fortescue deposits (Hutchins, 2012), operating ranges
were calculated for this particular shift. Tables 3 to 5 show the
operating ranges for the SM2200, SM2500 and SM4200 surface
miners.
concluSIonS
The paper investigated two types of data with regard to
several different surface mining machines. The SM4200 and
T1255 were the largest machines and produced the highest
production rates. The best performance was recorded by the
SM4200 and the SM2500. However, the worst performed
machines were the T1255 and SM2200 over the project
duration. There was a distinct linear correlation between
material type and productivity. The SM2200 and SM2500
followed the linear trend, whilst the larger SM4200 machine
experienced smaller fluctuations in productivity as the
material type varied. In terms of efficiency, a decrease in
utilisation was an alarming trend, as the availability steadily
increased over project duration. An assessment of the total
rostered time exposed a target rate too high for the T1255
machines. However, an improvement in unscheduled
maintenance downtime by more than half was promising.
Regarding the overall unit cost, the larger T1655 and SM420
were the cheapest machine to operate, whilst the smaller
T1255 was the most expensive on a dollar per tonne basis.
The instantaneous productivity calculations resulted in
the construction of plots for three shifts. Areas of high and
low productivity were identified with corresponding zones
30
Material
Speed range (m/min)
Very hard
0 - 1.7
Hard
1.7 - 5
Medium
5 - 8.3
Soft
8.3 - 11.7
TABLE 5
Material
Speed range (m/min)
Very hard
0 - 3.3
Hard
3.3 - 6.7
Medium
6.7 - 15
Soft
15 - 25
of material hardness. Sections of tramming and erroneous
points were observed and were excluded from the analysis.
An important correlation was found between instantaneous
productivity, speed of the surface miner and material type.
acknowledgeMentS
The authors would like to thank the continued support
of Downer EDi Mining’s technical services department in
Brisbane and at the Christmas Creek Mine site. Additionally,
the author would like to thank Fortescue Metals Group,
Western Australian Surface Mining and UEA for providing
the data. The author gratefully acknowledges the advice and
information provided by Jim Hutchins.
referenceS
Clout, J M F and Rowley, W G, 2009. The Fortescue Metals Group
story – From exploration to the third largest iron ore producer in
Australia, in Proceedings Iron Ore 2009, pp 63-71 (The Australasian
Hutchins, J, 2012. Personal communication, 9 August.
Truong, B and Nguyen, A, 2012. Xmas Creek Operation, workshop
delivered at the 2012 Engineers and Surveyors Conference,
Mackay, March 2012.
Walker, B, 2011. Surface mining – An overview, presentation, CC
Operations.
Williams, J, Mendelawitz, D and Castle, M, 2007. Applicability of
using Wirtgen surface miners as a mining tool for iron ore, in
Proceedings Iron Ore 2007, pp 427-432 (The Australasian Institute
of Mining and Metallurgy: Melbourne).
Wirtgen GmbH, 2010. Job report surface mining: iron ore mining in
Australia: surface miners are main mining equipment [online],
Germany, Wirtgen Group GmbH. Available from:<http://www.
wirtgengroup.de/en/aktuelles_und_presse/presseberichte/
uebersicht/Presseartikel_detail_4703.html> [Accessed: 10th May
2012].
Strategic Project Risk Management for
an Emerging Miner
T K C Graham1 and S Saydam2
Abstract
Development of a new minerals project represents a time of high strategic risk for a mining
company, particularly in the case of an emerging mining company; one in the process of making
the transition from predominantly an exploration organisation to a full-scale mining organisation.
A number of methods have been identified as having the potential to assist these companies in
understanding the risk of their projects. A case study based the Cockatoo Coal’s Baralaba Coal
Expansion Project (BCEP), was used as the basis to evaluate three quantitative scenario analysis
methods; these being: sensitivity analysis, program evaluation and reviewing technique (PERT)
and Monte Carlo simulation.
The study found that the Monte Carlo simulation provided the most comprehensive results
combined with the flexibility to be selective in terms of output data. By contrast, the PERT
approach appeared to be a simplified modelling method with potentially unreliable and biased
results; whereas the sensitivity analysis provided limited results for the purpose of analysing the
prospects of and risks in the project. Furthermore, a model for risk management in a minerals
development project has been suggested. This highlights the unique stages through the life of the
project, their respective milestones and the deliverables required. The Monte Carlo simulation has
subsequently been applied to this model and recommendations provided for which deliverables
should be used for risk analysis for the project.
Introduction
Emerging Miners are mining companies that are at the
development stage; these companies have proved up mineral
reserves and are undergoing the process of advancing into
production. This transformation from explorer to producer is
characteristically a high risk phase of a company’s life cycle.
There is generally quite a high level of investor, management
and corporate pressure to succeed in bringing the projects
from scoping to construction and operations; as well as a
distinct lack of operating experience hampering the ability to
achieve these goals. Additionally, it is quite common for these
companies to run, what could be classified as, medium to high
levels of risk. Such companies are not faced with the capital
allocation issues of the larger, more established companies;
and having a mine may be more desirable for shareholders
than having no mine at all (Stirzaker, 1997).
Emerging miner – Cockatoo Coal Limited
In 2005 Cockatoo Coal Ltd, an Australian coal exploration
company, was formed and listed on the Australian Stock
Exchange. The company began with only one exploration
lease, however it has since grown a substantial portfolio
containing five Coal exploration leases throughout
Queensland and New South Wales as well as one small-scale
operating mine (Cockatoo, 2012).
Cockatoo is a prime example of the modern ‘Emerging
Miner’, with multiple projects undergoing development and
a goal to grow these into operational mines within the next
five years. It is a company with big aspirations, and equally
large risks.
In early 2012, Cockatoo identified the unique nature of its
position as an emerging company. The company also realised
that if it was to succeed in making an effective transformation
to full-scale mining operations then it would need to have a
thorough understanding about how to manage its projects
successfully, and how to understand and manage its various
risks in the process.
These business risks became more apparent in 2012 when
the Australian mining industry appeared to contract slightly
leading to a slump in share prices across the market and as
a consequence a 70 per cent reduction in the Cockatoo share
price between February 2012 and September 2012. As such
Cockatoo recognised that in order to ensure the success of
its projects, and with them, the company, it must implement
new initiatives to identify where risks lie and how best to
manage and mitigate them. The topic of this paper was thus
inspired by this company with the intention to assist with
their objectives.
Research objectives
With reference to the issue of risk in an emerging miner’s
project development system, the objective of this study was to
analyse potential techniques for incorporating risk effectively
1. GAusIMM, The University of New South Wales, Sydney NSW. Email: [email protected]
2. MAusIMM, The University of New South Wales, Sydney NSW. Email: [email protected]
31
t k c grahaM and S SaydaM
into a project, and to provide recommendations on how they
could be integrated into a project management structure.
project rISk ManageMent theory
As defined by Dobie (2007), the general concept of project
management is the disciplined process in the planning,
organising, monitoring and controlling of all aspects of
a project. This concept can be expanded upon further by
using nine functional elements to serve as the basis of the
project management framework. These elements and their
relationships are shown in Figure 1.
Importantly, in Figure 1, it must be noted that the
relationship of most relevance to this study is that between
Risk and the four key elements, and how a variation in one
element can alter the inherent risk in the project.
Fig 2 - Mine project stages (Chinbat, 2011).
Fig 1 - Elements in project management (Dobie, 2007).
the mine development project
For the purpose of this study, a mine development project has
been defined as a number of stages separated by milestones.
This concept is illustrated by Chinbat (2011) in Figure 2 and
emphasises that, before each stage is passed, a review must be
undertaken to conclude whether the stage met its objectives, if
there is adequate prospects to continue to the next stage, and
what the best approach to the subsequent stage is.
Scenario analysis of mine project options
It is at the stage gates concluding the stages shown in Figure 2
that a decision has to be made about what the future direction
for the project will be. Importantly, there are a number of
options available to management for the progress of the
project at these points. These range from delay, terminate
and abandon through to expand, shrink, revise and proceed
(Eschenbach et al, 2007). In order to summarise how these
options fit into the mine project structure, a decision tree has
been constructed as shown in Figure 3. This tree runs through
the feasibility and prefeasibility stages of a mine development
project and demonstrates what options are available to
management at the stage gate completing each stage.
It is at these decision points that a new problem is raised,
‘Which of the above options is the best for our project at this
stage?’
In order to assist with this problem, this paper proposes
that scenario analysis could be employed. The intention
of scenario analysis is to thoroughly analyse each of the
options at the stage gate (from a time, cost, quality and scope
perspective) so that they can be accurately understood and
compared objectively, so that the ‘best’ option can be selected.
The purpose of this process is to introduce reliability into
32
Fig 3 - Options in a mine development project.
the information used by decision makers. By having more
reliable and accurate data, it is proposed that there will be
a reduced chance of uncertainty and thus risk of the project
going ‘wrong’ in the future.
It is at this point that attention now turns to how this
scenario analysis should be undertaken. In order to answer
this, five scenario analysis methods have been identified for
the potential to assist in this application. These are listed in
Table 1 and all take a quantitative approach to model the
outcome, and additionally, uncertainty in the outcome of a
project.
StrategIc project rISk ManageMent for an eMergIng MIner
TABLE 1
Scenario analysis methods.
Event tree analysis (probabilities)
Sensitivity analysis
Monte Carlo simulation
Risk adjusted discount rate
Program evaluation and reviewing technique (PERT)
coMparISon of ScenarIo analySIS
MethodS
In order to compare the methods found in Table 1 they
have each been applied to the Cockatoo Coal Baralaba
Coal Expansion Project. This is a project currently at the
completion of the Bankable Feasibility Study (BFS) with
the intention to expand the small Baralaba Coal Mine from
750 kt/yr to 3.5 Mt/yr through the addition of two new
pits and an associated wash plant. As such, to evaluate and
compare the above methods, an analysis has been undertaken
at the BFS stage gate analysing the option to proceed through
construction stage. This has been undertaken on both the
schedule (time) and budget (cost) for this option.
Furthermore, of the above five methods, three have been
selected for the comparison; sensitivity analysis, PERT and
Monte Carlo simulation. The other two have been deemed
either too limited in their scope to be applicable across all
stages of a minerals project (risk adjusted discount rate) or
un-achievable with the limited level of information available
(event tree analysis).
Sensitivity analysis
The sensitivity analysis has been used to model a variation in
each of the input variables in both the schedule (duration) and
the budget to see what kind of a variation will be produced in
the overall outcome (total).
This analysis produced some results of minor relevance to
the research’s objectives. It has identified which jobs in the
schedule the overall duration is most sensitive to (shown
through Figure 4). However, these results are limited as they
have not given any indication about what the actual expected
outcome is likely to be. This is further exaggerated in the case
of the budget analysis (not shown) which only indicated the
obvious conclusion that the total budget sum is most sensitive
to a variation in the larger budget items – an outcome which
provides little assistance to managers attempting to make a
decision about which option to pursue.
program evaluation and reviewing technique
The program evaluation and reviewing technique (PERT)
simulation makes use of three input values for each of the
variables – a best case, worst case and most likely outcome.
These are then substituted into the PERT formula to produce
a weighted average estimate of the outcome. Subsequently
this method has been applied to both the Baralaba Coal
Expansion Project construction schedule and budget with a
weighted average outcome being produced for each of these
factors, and the overall results (showing the variation) found
in Table 2.
TABLE 2
Results of program evaluation and review technique analysis.
Schedule duration
Budget total
Original
PERT
1491 days
1515 days (101.6%)
100%
100.2%
This method has, in some regard showed an improvement
over the sensitivity analysis; this is as an actual estimate for
the outcome that has been produced, a result which could
be used by management in better understanding the project
and applying appropriate contingency in planning. However,
these results produced do have some limitations. Firstly, they
do not have a high statistical reliability – only one iteration
has been undertaken and as such they are reliant wholly on
the assumptions made through the PERT formula to produce
a result. Secondly, the PERT method has been shown to be
susceptible to bias through the use of subjective estimates
for the three input values; this means that it is possible for
management to be grossly misled by the outputs of this
technique (Klingel, 1966).
Monte carlo simulation
Monte Carlo simulation uses a process whereby each of the
independent variables (job durations in the schedule/items in
the budget) is allocated a distribution function. A simulation
is then undertaken with the input values generated randomly
from within these probability distributions for each of the
inputs.
This method has subsequently been applied (through the
use of Palisade’s @Risk MC Simulation Program) to both the
construction schedule and budget and a 1000 to 5000 iteration
simulation been carried out.
Resulting from this technique, a density histogram has
been produced. Shown in Figure 5 is the density histogram
for the construction budget. It can be seen this has effectively
formed a normal distribution of results which can resultantly
be used to produce a mean, medium and mode result; as well
as various other distribution data such as confidence intervals
and percentiles – all of which can be applied in the calculation
of project contingency and budgeting. Similar results were
produced for the construction schedule (not shown).
Furthermore, as shown in Figure 6, this technique (through
the software package used) has the ability to produce its
own sensitivity analysis – in this case for the sensitivities in
the construction schedule – effectively making the previous
stand-alone sensitivity analysis void.
Fig 4 - Major sensitivities in Baralaba Coal Expansion Project schedule.
As such the Monte Carlo simulation has shown a greater
flexibility and reliability in the results produced, which could
be used by management in the decision making process, than
both the Sensitivity Analysis and PERT simulation.
33
t k c grahaM and S SaydaM
of ‘deliverables’ (Project Development Institute, 2012). A
deliverable for the purpose of this model comprises of all
the elements/factors produced in each of the respective
stages and are used as inputs to the stage gate review. For
the purpose of a mine development project, it is additionally
proposed that these deliverables both address the outcomes
of the previous stage, as well as an analysis of the options for
future progression into further stages.
Fig 5 - Monte Carlo simulation – budget results.
The emphasis of the guide is to roughly highlight the areas
of uncertainty at each stage gate in a minerals development
project and to provide guidance on the most appropriate
method to be undertaken in order to understand the impact
these uncertainties could have on the outcome. As such,
a summary of the mine project process and associated
deliverables is found in Figure 7.
Fig 6 - Monte Carlo sensitivities – schedule results.
risk scenario analysis methods – discussion of
results and findings
Resulting from this comparison, the Monte Carlo simulation
method has been deemed the most valuable method for use
in evaluating the options in a mine development project. It
provides a greater level of detail, accuracy and reliability in
the information in its results, which could thus be applied
by management in making the correct or most appropriate
decisions. However it must be noted, that there is an inherent
risk associated with its use. If the analysis is not carried out
correctly and improper distributions are used, it is highly
likely that inaccurate results are produced. This can lead to
misleading information or poor decisions being made.
As Mooney (1997) notes, one of the key problems in
conducting Monte Carlo Simulation is determining which
distribution function a random variable in the population
should assume. Additionally as McCabe (2003) explains,
experts are very comfortable estimating the most likely values
of activity duration, but are not as experienced at estimating
lower and upper limits. This inaccuracy could lead to errors
in the results and thus the integrity of the simulation. None
the less, this method, utilised properly, has the ability to best
assist managers (compared to the other methods) in making
decisions regarding the options in a project.
project rISk ManageMent guIde
The final outcome of this study was to construct a system to
provide guidance for Emerging Miners on how Monte Carlo
simulation could be employed to decrease risk in the decision
making process of a mine development project.
In order to do this, the stage gate model discussed
previously has been expanded to include the third category
34
Fig 7 - Proposed flow diagram summarising stages and deliverables for
analysis in a mine project.
For example, using Figure 7 at the prefeasibility stage gate it
is recommended through this system that the resource should
be mostly defined and the mineable properties understood.
Therefore the major variables which are a cause for uncertainty
are based primarily on financial, infrastructure, logistical
and approval variables. Risk modelling using Monte Carlo
simulation can now be undertaken at this stage looking at
these variables in order to make the best recommendations on
the financial and business position associated with the project.
Further risks should also be identified regarding the schedule
of on-going activities. This should include an analysis of
identified hazards which could impact on the progression
of the project as well as a scenario analysis of the Feasibility
study schedule and budget, thus assisting in the accurate
planning and allocation of resources for on-going studies.
concluSIonS
The presence of uncertainty has been recognised as a primary
factor leading to the existence of risk in an Emerging Miner’s
Strategic Project Risk Management for an Emerging Miner
minerals development project. This uncertainty results from a
number of limiting characteristics unique to this class of miner,
and can potentially have a detrimental impact on the ability
of the company to identify and pursue the best options in a
project. Subsequently this paper has been focused on assisting
emerging miners (characterised through the Cockatoo Coal
case study) understand this risk, reduce uncertainty within a
project and consequently lead to better decisions being made.
The following conclusions have been produced and,
furthermore, are recommended for implementation into the
project management system for both Cockatoo Coal and other
existing or future emerging mining companies:
•• the use of the mine project stage and stage-gate theory is
recommended as a tool for managing the general project
progress
•• a real options modelling system is recommended for use
in identifying all the options available to decision makers
at the corresponding stage-gates
•• the Monte Carlo simulation method has been identified as
the top technique for analysing these options in order to
make a decision on the best path to pursue
•• finally, a guide has been produced to provide
recommendations on which deliverables at each of the
stage gates should be analysed through the use of Monte
Carlo simulation.
It is hoped that by applying this system, companies in this
phase of their life will be able to better appreciate the risk in
their projects and thus account for and mange it better in their
business decisions.
Acknowledgements
The authors wish to acknowledge the assistance provided by
the Cockatoo Coal senior management team for their support
and provision of resources and data vital for this study;
Andrew Lawson, Luca Rocchi, Steve Jukes, and particularly
Dane Stewart for his assistance and supervision in this
process.
References
Chinbat, U, 2011. Risk analysis in the mining industry, in Risk
Management in Environment, Production and Economy (ed:
M M Savino), pp 103-122 (InTech: Croatia).
Cockatoo, 2012. Cockatoo Coal Limited [online], Available from:
<http://www.cockatoocoal.com.au> [Accessed: 12 March 2012].
Dobie, C, 2007. A Handbook of Project Management – A Complete Guide
for Beginners to Professionals (Allen and Unwin: NSW).
Eschenbach, T, Lewis, N, Henrie, M, Baker, I V E and Hariman, J C,
2007. Real options and real engineering projects, Engineering
Management Journal, 19(4):11-19.
Klingel, A R, 1966. Bias in PERT project completion time calculation
for a real network, Management Science, 13(4):194-201.
McCabe, B, 2003. Monte Carlo simulation for schedule risks, in
Proceedings 2003 Simulation Conference: Driving Innovation, Winter
Simulation Conference, New Orleans.
Mooney, C Z, 1997. Monte Carlo Simulation (Sage Publications: USA).
Project Management Institute, 2012. Stage-Gate – your roadmap for
new product development [online], Available from: <http://
www.prod-dev.com> [Accessed: 15 October 2012].
Stirzaker, M, 1997. Feasibility studies - Risks and sensitivities, in
Proceedings MINDEV 97 – The Third International Conference of
Mine Development (The Australasian Institute of Mining and
Metallurgy : Melbourne).
35
36
Metaheuristic Analysis of Current Open
Cut Blasting Fragmentation Prediction
Models
A Holland1, M Iles2 and M Karakus3
ABSTRACT
Open cut mining operations require blasting optimisation through appropriate rock fragmentation
to minimise cost and maximise efficiency of the entire mining process. A good blast fragmentation
prediction model is critical for blasting optimisation. This paper addresses shortcomings of the
current models through a critical comparison of their accuracy. It then proposes a new model
based on genetic programming (GP), which takes into consideration a more comprehensive
set of parameters related to blast design, explosive properties and rock mass description. Data
used to derive the model was collected from blast trials conducted in conjunction with Orica
Mining Services. The fragmentation of the material after blasting was estimated using Orica’s
image analysis software, Powersieve 3®. The current prediction models compared included the
Kuz-Ram, corrected Kuz-Ram and the Kuznetsov-Cunningham-Ouchterlony (KCO) model using
the three-parameter Swebrec function. It has been found that inclusion of the Kuz-Ram correction
factor offers a reasonable improvement in the accuracy of prediction for both the Kuz-Ram and KCO
models. Orica’s blasting prediction software, Blast Design Assistant®, was used as an additional
tool to evaluate both the accuracy of the Swebrec function to predict fragmentation, and its capacity
as the underlying model of a software program. The GP approach predict 80 per cent passing size
of the muck pile with a high level of accuracy at CITIC Pacific’s Sino Iron magnetite operation
in Western Australia. Further validation of the proposed equation is required to determine its
effectiveness at other mine sites and as a general blasting fragmentation prediction model.
INTRODUCTION
Blasting in mining has been used for centuries to effectively
and efficiently break rock for excavation. The fragment size
after blasting has a major impact on the performance of all
aspects of the mining operation and on the economics of the
mine and mill (Marton and Crookes, 2000). The term ‘rock
fragmentation’ is used to describe the range of sizes, including
the distribution, of rock portions after blasting. Fragmentation
size affects all downstream processes, from load and haul
(including immediate digging), crushing and grinding, through
to processing. Improved fragmentation through blasting can
increase diggability, thereby increasing productivity of loaders
and excavators, reduce the need for secondary breakage of
oversize material, increase mill throughput and decrease
energy consumption in crushing (Hudaverdi, Kulatilake and
Kuzu, 2010). Therefore, the ‘mine to mill’ approach has gained
popularity, optimising blast design for the profitability of the
overall operation, rather than individual processes (Hudaverdi,
Kulatilake and Kuzu, 2010). Each operation has an optimum
fragment size, which when achieved by blasting, will result in
the greatest efficiency of the operation. Tools and technologies
that can predict the performance of individual blasts make
it possible to ensure that the optimum fragment size will be
achieved every time (Marton and Crookes, 2000).
Prediction tools make it possible to adopt real-time
adjustments to the blast design as rock properties vary
between blast areas. Factors affecting the outcome of a blast
are grouped into three main categories: blast design variables,
rock properties and explosive properties. Rock properties can
vary significantly throughout the orebody and overburden
material, but are not variables that can be controlled by the
blast design engineer. The blast design, and to a lesser degree
the explosive properties, are variables, which can and should
be changed to achieve the desired fragmentation of the
resulting muck pile.
Fragmentation Prediction Models
Blast fragmentation models were reviewed in chronological
order to observe developments in parameter inclusion,
complexity and practicality.
Kuznetsov (1973) developed the fundamental equation
relating mean particle fragment size and applied blast energy
per unit volume of rock. Later, a standardised comparison
of ammonium nitrate/fuel oil (ANFO) to trinitrotoluene
(TNT) was incorporated, yielding the following equation
1. SAusIMM, Graduate Mining Engineer, School of Civil, Environmental and Mining Engineering, University of Adelaide, SA 5005. Email: [email protected]
2. SAusIMM, Graduate Mining Engineer, School of Civil, Environmental and Mining Engineering, University of Adelaide, SA 5005. Email: [email protected]
3. MAusIMM, Lecturer, School of Civil, Environmental and Mining Engineering, University of Adelaide, SA 5005. Email: [email protected]
37
A Holland, M Iles and M Karakus
for the prediction of the mean fragment diameter, XM (cm)
(Aghababaei et al, 2009; Kuznetsov, 1973):
XM = BI * K-0.8 Qe 1/6
`S
115
ANFO
19/30
j
(1)
where:
A = 0.06 # ^ RMD + JF + RDI + HF h BI: Lilly’s Blastability Index (7 for medium hard rocks; 10 for hard, but highly fissured, rocks; and 13 for very hard, weakly fissured rocks)
K: powder factor, kg/m3
Qe:
mass of TNT equivalent in energy to the explosive
charge in one blast hole, kg
SANFO: relative weight strength of the explosive to ANFO
Rosin and Rammler’s (1933) historic empirical distribution
was obtained from crushed coal data. The Rosin-Rammler
formula was used to predict the fragment size distribution of
blasted rock in the form:
X n
RM = 1 - e- c Xc m (2)
RM:
proportion of material passing the screen
X:
screen size, cm
XC:
characteristic size through which 63.2 per cent of
particles pass, cm
n:
index of uniformity
In 1983, Cunningham published a historic paper outlining
his empirical fragmentation model, combining the Kuznetsov
and Rosin-Rammler formulae with a unique algorithm
deriving the coefficient of uniformity, n (Ficarazzo and Morin,
2006). First rearranging the Rosin-Rammler Equation (2) gave:
(3)
Cunningham (1983) identified that substituting the mean
passing size XM from Kuznetsov’s equation, with R=0.5
(assuming 50 per cent of material passes through the mean
size), into Equation 3 yielded:
XM
XC =
n 0.693
(4)
In addition to this, Cunningham (1983) suggested an
equation estimating the blasting parameter, n:
0.5
n = ` 2.2 - 14 B j` 1 + S j ` 1 - W j` L j D 2 2B
B H
where:
B:
burden, m
S:
spacing, m
D:
borehole diameter, mm
W:
standard deviation of drilling accuracy, m
L:
total charge length, m
H:
bench height, m
TABLE 1
Rock factor parameters (Aghababaei et al, 2009).
RMD (rock mass description)
Powdery/friable
10
Vertically jointed
JF*
Massive
50
JPS (vertical joint spacing)
<0.1 m
10
0.1 m to MS
20
MS* to DP*
50
Dip out of face
20
Strike perpendicular to face
30
Dip into face
40
RDI (rock density influence)
RD, rock density (t/m3)
RDI = 25RD* -50
A fragmentation curve can be plotted with values for the
characteristic size XC and uniformity index n (Aghababaei et
al, 2009).
X
XC =
n - ln ^ 1 - R h
M
(6)
JPA (joint plane angle)
where:
38
Another important recommendation by Cunningham (1983)
was the adaptation of Lilly’s Blastability Index, BI, shown in
Table 1, to better describe the rock factors in Kuznetsov’s
model (Aghababaei et al, 2009). Attempting to account for
density, mechanical strength, elastic properties and structure,
the equation for rock factor, A, is:
(5)
HF (hardness factor (GPa))
HF = Y/3
If Y<50
HF = UCS*/5
If Y>50
*
Meaning
Unit
MS
Oversize
m
DP
Drilling pattern size
m
Y
Young’s modulus
GPa
Uniaxial compressive
strength
MPa
UCS
JF=JPS+JPA
Whilst the Kuz-Ram fragmentation model has been
fundamental in the development of fragmentation theory,
there are some important drawbacks. A major issue with the
Kuz-Ram model is inadequate parameter inclusion. Due to
the simplified nature of the Kuz-Ram model, some important
blasting aspects are not explicitly accounted for, including
geomechanical properties and blast initiation timing.
The Kuz-Ram model generally gives poor prediction
for the fines fraction. In addition, there is no upper limit to
fragmentation size, which is highly unrealistic.
In 2004, Spathis identified a technical error in the original
Kuz-Ram model. The error arose from the assumption that
the mean passing size from Kuznetsov’s equation could be
equated with 50 per cent passing size in the Rosin-Rammler
Equations 3 and 4. The correct expression for mean fragment
size XC was in fact presented in Kuznetsov’s original paper as:
XC =
XM
C` 1 + 1 j
n
(7)
MetaheurIStIc analySIS of current open cut BlaStIng fragMentatIon predIctIon ModelS
The upper limit of the fragmentation size (XMAX) is defined
as the minimum of the in situ block size, the blasthole spacing
or the blast hole burden.
where:
is the gamma function Γ(n) = (n - 1)!
Γ
This corrected expression was shown to reduce errors by
179 - 105 per cent for uniformity index n in the range 0.8 2.2. The error partially explained the underestimation of fines
from the original Kuz-Ram model (Spathis, 2004).
To apply to the Kuz-Ram model, a multiplying factor g(n)
was created as a ratio of the two XC functions, to be applied in
the calculation of XM :
ln ^ 2h1/n
g (n) =
C^ 1 + 1/nh
(8)
In 1999, two models were published by the Julius
Kruttschnitt Mineral Research Centre (JKMRC) to address the
issue of poor fit of the Kuz-Ram model to actual sieved blast
data: the crush zone model (CZM) and the two-component
model (TCM) (Ouchterlony, 2005). These models used
multiple applications of the Kuz-Ram model to describe
the fragmentation distribution. These two models were
considered more realistic, as they contain more parameters
than a single application of the Kuz-Ram model; however they
are considerably more complex, and may require data input
from sample blasts (Ouchterlony, 2005). The CZM model
has also attracted criticism for the incorrect assumption that
almost all fines are generated in the blast crush-zone.
The Swebrec function was developed to address the residual
flaws of the Kuz-Ram and JKMRC models. Ouchterlony
(2005) fitted the Swebrec function to hundreds of sets of
blasted fragmentation data, with excellent results.
The Swebrec function, depicted in Equation 9, generates a
fragmentation curve of mass percentage passing a given sieve
size, shown in Figure 1.
In addition, a third parameter was introduced in the
Swebrec function: the curve-undulation parameter, b,given
in Equation 12. The curve-undulation parameter results in an
inflection point on the fragmentation curve (in log-log space),
allowing for very accurate non-linear fines prediction.
X
b = = 2 ln 2× ln e MAX oG n
X50
n
is Cunningham’s uniformity index.
The improvement in fragmentation prediction by the
Swebrec function led to the development of a new model:
the Kuznetsov-Cunningham-Ouchterlony (KCO) model.
The KCO model was created by combining the Kuznetsov
equation for X50 with the Swebrec function describing particle
distribution. The KCO model offers distinct improvements
over the Kuz-Ram model, with better prediction capacity in
the fines and a finite upper limit to block size. Blast design
variables are included via the curve undulation parameter,
b, which uses n in its expression. The use of the Kuznetsov
equations ensures rock mass properties and powder factor are
also accounted for.
The g(n) factor, which is applied to the Kuz-Ram model
to offer the corrected Kuz-Ram, could also be incorporated
in the KCO model. The factor is applied to both models in
Kuznetsov’s formula, given in Equation 13. The Swebrec
function already offers a vast improvement for prediction in
the fines region; therefore, it may not be necessary to include
this factor in the KCO model.
(9)
where:
XMAX
is the upper limit of the fragmentation size
X50
is the fragmentation size of 50 per cent passing
b
is the curve undulation parameter
j
(13)
19/30
j
In the current fragmentation prediction models, many
physically significant parameters are not accounted for.
Inclusion of more parameters was expected to increase the
accuracy of fragmentation prediction, specifically considering
the Kuz-Ram and corrected Kuz-Ram, KCO and Swebrec
models; stemming, subdrill and explosive distribution,
density of explosives, tensile strength, Posission’s ratio of
rocks, joint aperture and joint plane area are not included in
these models.
The inclusion of the g(n) factor into the KCO model is a
relatively new advancement and, as such, its use in prediction
models requires further investigation.
The 50 per cent passing size is defined as in Kuz-Ram:
ANFO
ANFO
19/30
reSearch gapS
Z R
Vb_-1
X
]
S ln c MAX m W b
]
Wb
X
P^ x h = [ 1 + S
S
XMAX W `
]
S ln e
Wb
]
S
X50 o W b
\
T
Xa
`S
`S
115
Although the Swebrec function has improved the prediction
model for blast fragmentation, it has not incorporated any
further parameters than those that have been included
since the development of the Kuz-Ram model in 1983. The
inclusion of further parameters may be necessary to offer a
more realistic and reliable prediction model. Incorporating
more parameters also offers a more tailored prediction model,
specific to each mine site.
Fig 1 - Fragmentation curve generated by Swebrec function
(Ouchterlony, 2005).
115
(12)
where:
X50 = g^nh AK-0.8 Qe 1/6
X50 = BI # K-0.8Qe 1/6
(11)
XMAX = min (in situ block size, S, B)
(10)
Another identified ‘gap’ in the inputs to these models is
the description of maximum fragment size. The expression
39
a holland, M IleS and M karakuS
for XMAX is a simple selection of the minimum value of in
situ block size, burden or spacing. In reality, the maximum
fragment size will be much more variable, especially in
the case of highly fractured rock masses. A comprehensive
understanding of rock mass structure, and all in situ rock mass
characteristics is, therefore, pivotal for blast fragmentation
modelling. All rock properties used as inputs have potential
to be improved with better understanding of the rock mass.
Identifying a new expression for XMAX, or suggesting methods
for collection of more detailed geological data, is outside the
scope of this research paper.
Methodology
A series of trial blasts were undertaken to provide a data set,
which was used for the three main areas of research:
1. Blast Design Assistant® (BDA) analysis
2. development of a new prediction equation using genetic
programming
3. comparison of current models, BDA and the newly
proposed equation and investigation into the benefit of
the g(n) factor.
The blasts were conducted at CITIC Pacific’s Sino Iron
Project, located at Cape Preston, 100 km south west of
Karratha, in the Pilbara region of Western Australia. Results
were collected from nine specifically designed trial shots,
with assistance from Orica Mining Services. The actual
fragmentation of the material after blasting was estimated
using Orica’s image analysis software Powersieve 3®.
Blast design assistant analysis
This phase of the research involved the use of Orica’s blasting
prediction software, BDA. The software utilises the threeparameter Swebrec function to predict the fragmentation
distribution of a blast, based on various blast design and
geological inputs. The program requires the same input
parameters required by the Swebrec function.
BDA also uses a ‘base case’ to improve predictions.
Information required for the base case includes all blast
design, explosive and rock property inputs for a single blast
area, as well as the ‘result’ or actual fragmentation distribution
of that blast, which is imported from Powersieve 3®.
Direct output from the program includes the 20 per cent,
50 per cent, and 80 per cent passing sizes, a graphical
fragmentation distribution, illustrated in Figure 2 and the
Swebrec parameters: b, XMAX and X50. It is these output variables
that were used to compare the accuracy of BDA’s predictions
against the actual values obtained from Powersieve 3®. The
BDA software served as a tool to evaluate both the accuracy
of the Swebrec function to predict fragmentation, and its
capacity as the underlying theoretical model of practical,
user-friendly software.
genetic programming analysis
The research addressed blast design, explosive and rock
property parameters that are absent in current prediction
models. These variables have significant physical impact on
the fragmentation outcome of a blast and, therefore, their
inclusion into prediction models is recommended. Using all
current design parameters, as well as additional parameters,
an equation to predict the 80 per cent passing size of the muck
pile after blasting was developed using genetic programming.
Model comparison
Finally, a model comparison was carried out by comparing
each model to the actual fragmentation results from site
and calculating the prediction error. The models compared
in this research were the Kuz-Ram, corrected Kuz-Ram and
KCO (using the three-parameter Swebrec function with and
without the inclusion of the g(n) correction factor), the BDA
software and the newly proposed equation. The models were
compared in their ability to predict the 80 per cent passing
size.
Fig 2 - Blast Design Assistant® output, the base case and predicted fragmentation curves.
40
Metaheuristic Analysis of Current Open Cut Blasting Fragmentation Prediction models
The trials also assisted to clarify the effect of the inclusion of
the g(n) factor in the KCO model. A conclusion as to whether
the g(n) factor improves the accuracy of the KCO model was
not expected as the number and variety of trials was limited.
However, this trial laid the foundation for this investigation.
Results
For each of the nine trial shots, the following blast design and
explosive properties were collected from site and entered into
BDA:
••
••
••
••
••
••
burden (m) and spacing (m)
number of holes and their diameters (mm)
bench height (m) and subdrill (m)
stemming (m)
total mass of explosive (kg)
explosive density (g/cm3).
The spacing, dip and dip direction of joint sets and bedding
planes were also required by the software. The geological
properties were entered for each of the ten ore zones present
across the trial area:
••
••
••
••
••
••
uniaxial compressive strength (UCS) (MPa)
tensile strength, σt (MPa)
Young’s modulus, Y (GPa)
Poisson’s ratio
density (t/m3)
P and S-wave velocities (m/s).
Each of the nine blast areas were used as the base case
scenario to predict the fragmentation distribution of all eight
remaining blast areas, resulting in 72 different predictions.
The Swebrec parameters (b, X50 and XMAX) were predicted
by BDA and then substituted into the Swebrec function
(Equation 9) to generate the predicted fragmentation
distribution for each shot.
The actual fragmentation
distribution for each shot was obtained from Powersieve 3® and
the error could then be obtained at each increment of sieve
size and a total average error calculated. A summary of the
errors of the BDA prediction against the actual fragmentation
distribution is displayed in Table 2.
The accuracy of the fragmentation distribution predicted by
the BDA program was also assessed using the 20 per cent,
50 per cent and 80 per cent passing sizes. These values were
predicted and output directly from the program and, hence,
could be compared to the actual percentage passing sizes
obtained from Powersieve 3®; the error calculated is given
Table 3.
Genetic programming analysis
This section of analysis was conducted using GPLAB, a genetic
programming toolbox for MATLAB, developed by Silva
and Almeida (Karakus, 2011). Genetic Programming (GP)
automatically creates several computer programs (CP), which
are composed of functions and terminals to solve a particular
problem. The functions in this case were standard arithmetic
operators: f(x) = {×, ÷, −, +, √, sin, cos, log, power, exp}. The
terminals are the independent input values that the CPs use,
along with the functions (arithmetic operators), to arrive at
the solution or ‘result’, in this case the 80 per cent passing size.
The terminal set included all parameters used in the current
prediction models, with the addition of stemming, subdrill,
explosive density, tensile strength and Poisson’s ratio.
TABLE 2
Summary of average prediction errors for Swebrec fit.
BDA
fit
base
case
1
Blast design assistant prediction
Average
error
(%)
2
3
4
5
6
7
43.1
26.3
41.3
18.9
60.4
45.7 80.9 22.2
1
3
4
5
6
7
4.9
10.0
3.9
3.9
1.9
11.0
8.8 16.5
1
2
4
5
6
7
0.5
12.2
1.8
1.5
3.0
7.9
6.6 13.1
4.7
1
2
3
5
6
7
9
6.2
2
3
4
0.8
8
8
8
8
9
9
9
1.6
1.5
2.4
7.6
6.2 12.6
4.4
1
2
3
4
6
7
9
0.4
10.4
5.1
3.8
4.5
9.2
8.0 13.9
1
2
3
4
5
7
8
9
0.8
14.0
4.5
2.7
5.4
4.5
1.3
2.7
7.9
1
2
3
4
5
6
8
9
6
7
0.2
8
1.6
9
5.2
3.7
6.1
5.3
2.0
1.7
8.7
1
2
3
4
5
6
7
9
14.4
5.1
3.2
6.1
4.9
1.4
1.2
8.4
7
8
9.8
6.4
6.1
7.3
4.1
14.8
1
0.0
8
7.3
2.2
12.0
5
42.4
2
3
4
5
6
17.0
13.1
15.9
8.9
23.3 20.5 29.5
5.4
6.0
5.6
17.2
Initially there were 18 parameters all of which were required
to be included at least once in the equation that is generated
by the program. Dimensionless variables were made by
dividing parameters of the same units of measurement with
each other. The result parameter (80 per cent passing) was also
made dimensionless by dividing it by joint plane spacing, both
in meters.
Additionally, charge length was removed as a parameter
as this was accounted for by stemming, subdrill and bench
height. Similarly, charge per blast hole was removed as this
was used in the calculation of powder factor.
Some variables were simply added or subtracted to one
another, which further reduced the number of input variables
and improved the chance of the program using all variables in
the generated equation.
The aforementioned changes reduced the total number of
input variables from 18 to 7, which represented 16 individual
parameters.
An iterative approach was taken: for each attempt the
terminals were manipulated, input into GPLAB and an
equation was generated using the data, to predict the
result parameter (P80/JPS). This equation was examined for
parameter inclusion, overall simplicity and function fitness.
The seventh attempt proved to be successful as all variables
were employed in the equation offered by GPLAB, given
in Equation 14, and the fitness (sum of absolute difference,
SAD) of 0.008919 demonstrates an excellent fit between the
predicted P80 from the generated equation and the actual P80.
As the result used in GPLAB was P80/JPS, the generated
equation is simply multiplied by joint plane spacing to obtain
the equation, which predicts the 80 per cent passing size.
41
TABLE 3
Summary of average prediction errors based on percentage passing.
Average error (%) per shot
1
2
3
4
5
6
7
8
9
Total
average
error (%)
P20
53.7
32.8
27.3
26.9
32.4
62.7
41.1
94.7
27.8
44.4
P50
51.3
24.2
21.1
21.6
21.1
33.8
33.8
36.2
27.6
30.1
P80
45.9
20.2
18.6
17.9
19.9
21.0
27.3
21.0
41.4
25.9
Blast
Z
]]
x5
[ f log x1× log > ln x3 x7.e Hp
o
P80 = ln ]] e 2
10 e
x4
\
×JPS
_
x2 . log x6b
10 b
x4
`
b
b
a(14)
Using logarithmic rules, Equation 14 can be simplified to:
x5
x3
Go
P80 = ln )eeln x1×e ×x7×0.627× ln =ln
x4 x2 × ln x6 JPS
×0.434 ×
1
x4
(15)
where:
|1 = Burden
Spacing
|2 =
Stemming
+ cos (Joint Plane Angle)
Hole Length
| 3 = Drillhole diameter
Hole length
|4 =
Powder Factor + Rock Density
Explosive Density Powder Factor
|5 =
|6 =
Re lative Weight Strength
100
Young' s Modulus + Tensile Strength
Uniaxial Compressive Stength
|7 = Poisson' s Ratio
The new equation was intended to provide a simple tool
for a quick analysis of the predicted P80 of a blast. Engineers
can simply input the design and rock property variables to
determine the P80 that will be achieved for each blast.
Model comparison
The final step in the data processing was to compare the
accuracy of the Kuz-Ram, corrected Kuz-Ram, KCO model
and the developed model, using the actual field data. Each of
these models required specific inputs, some of which called
for manipulation of gathered field data, or assumptions.
The BDA predictions were included in this comparison
as an additional test of the Swebrec function. The P80 as a
direct output from BDA was averaged, and the P80 was also
calculated from the averaged output Swebrec parameters. The
error in prediction was calculated for each blast area and the
average absolute error for each prediction model was used to
rate the models by accuracy (see Table 4). The calculated P80
values are tabulated in Table 5.
42
TABLE 4
Average absolute error for each prediction method.
Model
Absolute error (%)
Swebrec
100
Kuz-Ram
93
Kuznetsov-Cunningham-Ouchterlony:
Swebrec with g(n) factor
57
Corrected Kuz-Ram
51
Blast Design Assistant (parameters averaged and P80
calculated)
50
Proposed model
9
Blast Design Assistant (P80 averaged)
5
ANALYSIS and INTERPRETATIONS
The accuracy of BDA as a fragmentation prediction tool was
measured in two ways, through the overall average error of
the fragmentation curve and through the percentage passing
parameters.
Overall prediction error
The overall prediction error was found to be quite low
(5.37 per cent - 7.34 per cent) for most cases, and significantly
higher when predicting with a base case of blast area
29 (17.23 per cent error) and blast area 60 (42.35 per cent
error). The consistently high errors when predicting from an
area could be an indication that the actual fragmentation data
input collected from the CITIC Pacific Sino (PS) Iron project
site in June 2012 may not be accurate.
Percentage passing errors
The percentage passing errors were averaged over each blast
area and decreased from P20 to P50 and P80, with respective
errors 44.4 per cent, 30.1 per cent and 25.9 per cent. These
errors are quite high, and could stem from a number of factors.
The actual percentage passing values were read directly from
the Powersieve 3® graph, a process fraught with human error.
The precision of the readings is likely to be low, and random
in the magnitude of the error. The Powersieve 3® program is
also assumed to contain some inherent error in estimating
percent passing size. This error can be minimised by thorough
preprocessing of images, and a good understanding by the
user of the programs functions. It should be noted that the P80
errors were consistently the lowest, indicating P80 is the most
accurate parameter to predict. This reinforces the use of P80 as
the prediction variable in the generated model.
Metaheuristic Analysis of Current Open Cut Blasting Fragmentation Prediction models
TABLE 5
Actual and predicted P80 values.
Blast
area
80% Passing Size, P80 (m)
Actual
Kuz-Ram
Corrected
Kuz-Ram
Swebrec
KCO
BDA (parameters
averaged)
BDA (P80
averaged)
Proposed
model
1
0.310
0.449
0.348
0.464
0.361
0.262
0.216
0.250
2
0.230
0.448
0.350
0.464
0.364
0.355
0.230
0.229
3
0.190
0.350
0.272
0.364
0.284
0.303
0.190
0.171
4
0.240
0.399
0.312
0.414
0.325
0.362
0.240
0.237
5
0.225
0.439
0.342
0.454
0.356
0.279
0.253
0.230
6
0.215
0.462
0.361
0.478
0.375
0.372
0.215
0.226
7
0.200
0.495
0.384
0.511
0.399
0.375
0.200
0.224
8
0.200
0.414
0.323
0.429
0.336
0.363
0.199
0.152
9
0.250
0.462
0.361
0.478
0.375
0.239
0.250
0.242
Proposed model
The new equation, developed via genetic programming in
GPLAB to predict the 80 per cent passing size of a muck pile,
has incorporated additional parameters compared to current
models and hence offers a robust description of the factors
that affect blast outcomes. In theory, this equation should,
therefore, offer highly accurate predictions in various rock
types, with a variety of design parameters. Presently, the
equation offers highly accurate predictions of the 80 per cent
passing size for the geological conditions specific to CITIC
Pacific’s magnetite site. This is to be expected as the field
data that was input and utilised by GPLAB to develop the
equation came from this site. Therefore, validation of the
newly proposed model against a wide variety of field data
will be beneficial to evaluate the equation’s merit.
The equation’s use of 18 different parameters, which describe
a myriad of factors affecting fragmentation from blasting, is
vigorous and should be encouraged. This is a development on
the present position of fragmentation prediction modelling.
The equation, although initially appearing complex, is a
straightforward substitution of variables (χ1 – χ7) and their
corresponding design, explosive or rock property parameters,
all of which are readily available at a mine site. Often, this
information is required for other purposes and is, therefore,
being recorded at site anyway. Therefore, collection of the
input information required by the P80 equation will not only
be readily available, but should not interfere with current
operations nor create more work.
When used with everyday computer applications such
as Microsoft Excel, the formula for the equation can be set
up so that the design parameters are simply entered into
corresponding cells, offering a quick and easy prediction.
The equation can be utilised to check the proposed design
of a shot to determine the resulting P80 of the muck pile. If
the prediction does not satisfy the target P80 required by
downstream processes, changes to the blast design can be
made immediately until the target P80 is reached. All members
of the drill and blast team can simply and quickly enter the
parameters of the proposed design into the spreadsheet
containing the prediction equation. The equation is, therefore,
highly practical and efficient.
Model comparison
The prediction error for the 80 per cent passing size was
compared for each model. The Kuz-Ram, corrected Kuz-Ram,
Swebrec and KCO models consistently over-estimated the P80
size, while the two BDA predictions and the generated model
both over- and under-estimated the P80 size.
It was expected that the Swebrec function would provide
a highly accurate prediction; however, the results suggest
otherwise, showing the Swebrec function to have the worst
error (100 per cent) of all the models. Therefore, the inputs were
investigated and the XMAX, XM and b values were examined to
try to identify where the high error stemmed from. Both the
b and XM values were calculated from the same data as used
in the Kuz-Ram model. The XMAX value, however, was taken
as the minimum value of burden and spacing, as the in situ
block size was not known. The XMAX values for the Swebrec
and KCO models are much higher than the actual XMAX, taken
from CITIC Pacific Sino Iron site. The XMAX values predicted
from each base case in BDA were averaged, and were also
quite high. As the XMAX value is a direct input into the Swebrec
equation, this large over-estimation would account for some
of the overall prediction error.
It is pleasing to note that the corrected Kuz-Ram and KCO
model have increased accuracy due to the inclusion of the
g(n) factor. In both cases (corrected Kuz-Ram versus KuzRam and Swebrec versus KCO) the error was almost halved
using the g(n) factor. This is a promising result, and should be
developed on with further research to clarify the effect of the
g(n) factor on prediction models.
The results showed that the direct P80 size prediction from
BDA was the most accurate of all the models. An average
error of only five per cent was found over nine sets of blast
data. This showed that BDA is a good prediction tool, and
makes good use of the Swebrec function. The program also
includes most of the available parameters. The program itself
proved to be interactive and user-friendly. Also worth note
is that BDA has two other modelling functions: HelFire for
damage, and DMC for the cast and heave of a muck pile.
The model generated in this research showed high accuracy
in predicting the P80 size of the trial data, but this is due to the
model being constructed from this data. Unfortunately, more
data was not able to be collected to validate the model with a
new data set. Given a longer time frame, more field data could
be collected as it became available, and used to validate the
results found.
CONCLUSIONS
A new model predicting the 80 per cent passing size was
developed using genetic programming and field data.
43
It incorporates 18 independent parameters to represent
the variables that affect blast fragmentation: blast design,
explosive and rock properties. Five new parameters, which
are not used by earlier models: stemming, subdrill, explosive
density, Poisson’s ratio and tensile strength were introduced
in this model. All input parameters for the newly developed
model can be easily obtained from site, adding to the ease of
practical application of the equations. The equation offers a
convenient tool, which allows for immediate adjustments to
proposed blast designs to ensure the target P80 is achieved.
A comparison study was carried out on the prediction of
80 per cent passing size, using the Kuz-Ram, corrected KuzRam, Swebrec and KCO models, the BDA program and the
new prediction equation developed in this research. The data
obtained from site was used as inputs in the study. Results
from the BDA were the most accurate, followed by those from
the newly developed equation. Of the remaining models, the
results from the Swebrec function had the lowest accuracy
of prediction. This could be due to the difficulty experienced
in obtaining an accurate value for the significant parameter
XMAX. The inclusion of the g(n) factor into the Kuz-Ram and
KCO models proved to be worthwhile as the accuracy of
prediction improved significantly.
REFERENCES
Aghababaei, H, Gheibie, S, Hoseinie, S H and Pourrahimian, Y,
2009. Modified Kuz-Ram fragmentation model and its use at the
Sungun Copper Mine, International Journal of Rock Mechanics and
Mining Sciences, 46:967-973.
44
Cunningham, C, 1983. The Kuz-Ram model for prediction of
fragmentation from blasting, in Proceedings First International
Congress in Rock Fragmentation by Blasting, Lulea, 2:439-453.
Ficarazzo, F and Morin, M A, 2006. Monte Carlo simulation as a tool
to predict blasting fragmentation based on the Kuz-Ram model,
Computers & Geosciences, 32:352-9.
Hudaverdi, T, Kulatilake, P H S W and Kuzu, C, 2010. Prediction
of blast fragmentation using multivariate analysis procedures,
International Journal for Numerical and Analytical Methods in
Geomechanics, 35:1318-1333.
Karakus, M, 2011. Function identification for intrinsic strength and
elastic properties of granitic rocks via Genetic Programming
(GP), Computers and Geosciences, 37:1318-1323.
Kuznetsov, V M, 1973. The mean diameter of fragments formed by
blasting rock, Sov Min Sci, 9:144-8.
Marton, A and Crookes, R, 2000. A case study in optimizing
fragmentation, in Proceedings of The Australasian Institute of Mining
and Metallurgy, 1:35-43.
Ouchterlony, F, 2005. The Swebrec© function: Linking fragmentation
by blasting and crushing, Trans Inst Min Metall, Mining Technology,
114:A29-A44.
Rosin, P and Rammler, J, 1933. The laws governing the fineness of
powdered coal, Journal of the Institute of Fuel, 7:29.
Spathis, A T, 2004. A correction relating to the analysis of the
original Kuz-Ram model, International Journal for Blasting and
Fragmentation, 8(4):201-5.
Ventilation Requirement for Electric
Vehicles in Underground Hard Rock
Mines – A Conceptual Study
M Kerai1 and A Halim2
ABSTRACT
In the past five years, electric power price in Australia has increased significantly and is likely to
continue to increase in the foreseeable future. This can make a mine uneconomic to operate.
One option to reduce underground mine power consumption is to reduce ventilation power
consumption. The electrical power required for the ventilation system is one of the major
components of the total mine electrical power consumption, which typically represents more than
one-third (de la Vergne, 2003). To reduce ventilation power consumption, ventilation requirement
must be reduced. One option to do this is to replace diesel vehicles with electric ones. An electric
motor produces zero emissions and only emits one-third the heat of an equivalent diesel engine.
Airflow specification can therefore be less (Marks, 2012).
Ventilation requirement for an underground hard rock mine is determined based on the engine
power of diesel vehicles used in the mine. The current Australian regulatory requirement for a
diesel vehicle ranges from 0.05 to 0.06 m3/s per kW engine power. However, no such requirement
stated for an electric vehicle.
This paper evaluates the ventilation requirement of an electric vehicle operating in an
underground hard rock mine.
Introduction
In the past five years, electric power price in Australia has
increased significantly and is likely to continue to increase in
the foreseeable future. This can make a mine uneconomic to
operate.
The electrical power required for the ventilation system for
a mine is one of the major components of the total electrical
power consumption, which typically represents more than
one-third (de la Vergne, 2003). To reduce ventilation power
consumption, ventilation requirement must be reduced.
One option to achieve this is to replace diesel vehicles with
electric ones. An electric motor produces zero emissions and
only emits one-third the heat of an equivalent diesel engine.
Airflow specification can therefore be less (Marks, 2012).
Currently ventilation requirement in Australian underground hard rock mines is determined by multiplying the
rated diesel engine power of all vehicles with the regulatory
airflow requirement. For example, 0.05 m3/s per kW rated
engine power is the requirement in Western Australia Mines
Safety and Inspection Regulation (WAMSIR) 1995, regulation
10.52 (6) (WA Government, 1995). Currently, there is no
such requirement for electric vehicles in Australia. The only
regulation concerning ventilation for electric vehicles is
WAMSIR 1995 regulation 9.34 which states that a minimum
air velocity of 0.25 m/s is maintained in all underground
areas in the mine where vehicles or locomotives powered
by electricity is used. However, this means that the airflow
quantity will be different depending on the dimension of the
area where they work. For example, a 14 tonne LHD unit
with motor power of 178 kW will have airflow quantity of
4 m3/s in a 4 m × 4 m heading and 6.25 m3/s in a 5 m × 5 m
heading. This means that the smaller heading will have higher
temperature than the larger one. This is not the right approach
as the amount of heat produced by an electric vehicle depends
on the size of the motor, not on the dimension of the heading.
The airflow quantity should therefore be governed by the size
of the motor.
Methodology
The first step of this research was to quantify the contaminant
produced by an electric heavy vehicle, which is heat.
The measurement to quantify heat produced by an electric
heavy vehicle was carried out in Rio Tinto’s Northparkes
Copper-Gold mine in New South Wales (NSW). This mine
was selected as it exclusively uses electric vehicles, which
are electric load haul dump (LHD) units, as production
equipment. The LHD unit is Sandvik LH514E. Data collected
were Dry bulb (DB) temperature, wet bulb (WB) temperature,
Barometric pressure, airflow quantity (air velocity and airway
dimension). By using psychometric equations, the amount
of heat produced by an electric LHD can be calculated.
In addition to this, power consumed by the unit was also
determined by measuring current, voltage and power factor.
1. Western Australian School of Mines (WASM), Curtin University, Kalgoorlie WA.
2. MAusIMM, Western Australian School of Mines (WASM), Curtin University, Kalgoorlie WA. Email: [email protected]
45
M Kerai and A Halim
This is to validate a popular assumption that heat produced
by an electric vehicle is equal to the power consumed by it.
The literature review found that this assumption had never
been validated prior to this research.
Ventsim Visual simulations were conducted as the next
step. The aim of these simulations was to determine the total
airflow quantity that is required in order to have Wet Bulb
(WB) temperature at the deepest part of the mine less than
or equal to 30°C. Although the common design limit used is
28°C, this limit is considered to be out of date since it was
determined based on the working condition in the old days
where the application of air conditioned cabin vehicles was
not extensive (Van den Berg, 2012). Today, air conditioned
cabin vehicles are common in mines in developed countries.
These vehicles assist heat stress management by providing
microclimate cooling, ie the vehicles’ operator spend most
of his/her time in cool environment. Due to this, Rio Tinto’s
Resolution Copper project in USA uses design limit of 30°C
(Van den Berg, 2012).
Ventsim Visual models of two currently operating hard
rock underground mines in Kalgoorlie were used with
their known fleet of machinery. The first is a deep mine
which extends to over a kilometre deep and the second is a
shallow mine which extends to 600 m deep. The models were
generated using two distinct geothermal parameters, namely
Kalgoorlie (cool strata) and Mount Isa (hot strata). These
parameters can be altered in Ventsim Visual software. One
input of a thermodynamic simulation using Ventsim Visual
is the heat emitted by an electric vehicle. For this purpose, it
was essential to determine the ratio between the heat emitted
and total electric motor output of the LH514E. The heat
emitted was measured as 145 kW and the total motor output
of this LHD is 178.7 kW. This results in a ratio of 81 per cent.
However, since this ratio varies in every electrical equipment,
a conservative approach was taken in the simulation.
Therefore, it was assumed that the amount of heat emitted
from every conceptual electric vehicle is equal to its motor
output.
heat. However, Ventsim Visual is unable to calculate sensible
and latent heat from a fuel cell. Hence, the assumption above
was used.
QUANTIFYING HEAT EMITTED BY A MINING
ELECTRIC HEAVY VEHICLE
As described before, the first step of this research was to
quantify the contaminant produced by an electric heavy
vehicle, which is heat.
The measurement to quantify heat produced by an electric
heavy vehicle was carried out in Rio Tinto’s Northparkes
Copper-Gold mine in New South Wales (NSW). This mine was
selected as it exclusively uses electric vehicles as production
equipment. Northparkes’ heavy electric machinery fleet is
mainly comprised of electric Load Haul Dump (LHD) units,
specifically the Sandvik LH514E. The machine is operating on
a delivered voltage of about 1,000 V. It is equipped with three
electric motors, one drive motor (132 kW), one pump motor
(45 kW), and one fan motor (1.5 kW). The LH514E carries an
arrow head design bucket with a capacity of 7 m3.
To determine heat emitted by an LH514E, the following
parameters were measured upstream and downstream of the
LHD operating in a production drive, refer to Figure 1:
•• dry bulb (DB) temperature
•• wet bulb (WB) temperature
•• barometric pressure.
It was then necessary to determine the motor output of
every conceptual electric vehicle. The only way to do this is to
compare the power output of a diesel vehicle and an electric
vehicle that have same workload. The only large vehicle that
is manufactured as a diesel and as an electric is Sandvik’s
LH514 LHD unit. The motor output of the LH514 electric is
70 per cent of its diesel equivalent (Sandvik Mining, 2012).
Therefore, it was assumed that every conceptual electric
vehicle in the fleet has motor power of 70 per cent of its
diesel equivalent. With this assumption, the existing mining
diesel fleet was converted into a conceptual electric fleet by
multiplying their engine power with 70 per cent.
The final step was running Ventsim Visual simulations.
After thermal parameters and heat emitted from conceptual
electric vehicles were set, the primary fan was adjusted with
various values of fixed quantity that is sufficient enough
to have the WB temperature less than or equal to 30°C at
the deepest part of the mine. This fixed quantity was then
subtracted with leakage and fixed facilities quantities and
then divided by the total fleet output power to obtain unit
requirement as m3/s per kW output. Refrigeration plant was
simulated if required.
It was assumed that these vehicles are battery powered,
which do not produce moisture. Hence all the machinery will
be inputted as a point source of sensible heat. Although an
electric vehicle can have fuel cell as its power source, fuel cell
produces moisture and therefore emits sensible and latent
46
Fig 1 - Locations of LH514E heat quantification measurements.
In addition to these, airflow quantity that flows in the
production drive was measured by measuring air velocity
and drive cross sectional area. Using these measurement
results and psychometric equations, the amount of heat that
was emitted by the unit could be calculated.
The measurements were taken while the LHD unit had
come to a halt. Unlike diesel vehicles, there is no difference
between idling and full throttle as the electric motor always
runs at constant speed. This is because when accelerator pedal
is pressed, the motor is engaged to the drive wheel and when
the pedal is released, the motor is disengaged from the drive
wheel.
Ventilation Requirement for Electric Vehicles in Underground Hard Rock Mines – A Conceptual Study
Various instruments were required in order to complete the
measurements. They were supplied by the Western Australian
School of Mines (WASM). The Kestrel4000 pocket weather
meter was used to measure the Dry Bulb (DB) and Wet Bulb
(WB) temperatures, the Druck DPI 740 digital barometer was
used to measure the barometric pressures, Alnor RVA501
digital anemometer was used to measure air velocity, and the
Leica Disto was used to measure dimension of the airway.
The main aim of the measurements is to have sufficient and
reliable data regarding the heat emitted by the LHD unit.
Therefore, the measurements were undertaken as close as
possible to the LHD unit in order to prevent the data being
interfered from other heat sources present in an underground
hard rock mine such as ground, the concrete pavement, the
caved ore in draw points, and water in the production drive.
However, it was also understood that there is a high safety
risk for personnel to get close to a running electric LHD unit.
Therefore, a Risk Assessment (RA) and a Job Safety Analysis
(JSA) were conducted before the measurements were taken.
There is a popular assumption that heat emitted by an
electric vehicle is equal to the power consumed by the vehicle.
In order to validate this assumption, the current, voltage and
power factor were also measured.
Power consumption by the unit was calculated using the
following equations:
E = (√3 × V × I x cosθ)/1000
where E = power consumption (kW) V = voltage (V), I =
current (Amp), and cosθ = power factor
Once these calculations were done, comparison between
power consumed by the LHD unit and heat emitted by it
could be done and the assumption could be validated.
Measurements were carried out across five points at the
front and rear of the LHD unit to minimise the errors. Results
of these measurements are shown in Table 1.
As shown in Table 1, the variations between the values of
each point do not deviate by a huge amount. The air quantity
in the drive was measured as 17.2 m3/s Using these results,
heat emitted by the electric LHD was determined using
psychometric equations included in an Excel spreadsheet.
Table 2 shows the heat emitted by the LHD for different trials.
The average generated from Table 2 was then compared
with the power consumed by the LHD. Power consumption
was calculated as 147 kW. The voltage, current and power
factor for the LHD was measured as 1000 Volts, 100 Amps,
and 0.85 respectively.
The heat generated (145 kW) and power consumption
(147 kW) is very similar and the variation between these
Table 2
Heat emitted by LH514E.
Trial
Heat (kW)
1
161.8
2
156.6
3
138.8
4
128.3
5
139.2
Average
145.0
values is due to the measurement errors. This finding validates
assumption that heat generated by an electric vehicle equals
to its power consumption.
VENTSIM VISUAL SIMULATIONS
Simulation of the deep mine
The mine fleet (existing diesel and conceptual electric) of the
deep mine is listed in Table 3.
Not all vehicles listed in Table 3 are present inside the mine
at the same time. The simulation was run with the vehicles
listed in Table 4 operating in the mine at the same time. This
is the typical operational situation in this mine. Jumbos and
longhole drills were not included as they run with their small
electric motor while drilling. Their motor output is so small
and therefore can be negligible.
As described before, two distinct geothermal parameters
were used in this simulation, namely Kalgoorlie (cool strata)
and Mount Isa (hot strata). This is to analyse the impact of two
distinct geothermal conditions that are present in Australian
mining areas upon the ventilation requirement of electric
vehicles. Table 5 and 6 list the geothermal parameters for cool
and hot strata respectively.
In order to address the shortage of airflow in its deepest
block, this mine utilises a booster fan. Table 7 shows the
conceptual electric vehicle fleet that is used in the deepest
block of the mine. This fleet was based on the actual diesel
fleet used in this mining block. The electric motor output was
estimated based on the 70 per cent diesel power assumption
described before. The fixed quantity of the booster fan was
adjusted along with that of the primary fan in order to find
total quantity that will cause WB temperature less than or
equal to 30°C in the deepest part of the mine.
As with the fixed quantity in the primary fan, this fixed
quantity was then adjusted to exclude leakage quantity and
then divided by the total motor output shown in Table 7 to
Table 1
Data collected at Northparkes Mine.
Trial
Front of load haul dump
End of load haul dump
DB (°C)
WB (°C)
Barometric pressure (Pa)
DB (°C)
WB (°C)
Barometric pressure (Pa)
1
21.00
16.20
106 197.00
18.90
13.40
106,205.00
2
21.50
16.30
106 189.00
18.90
13.60
106,201.00
3
21.30
16.00
106 117.00
18.90
13.60
106,202.00
4
21.20
16.20
106 181.00
19.00
14.00
106,211.00
5
21.50
16.20
106 209.00
18.90
13.80
106,217.00
Average
21.30
16.18
106 178.60
18.92
13.68
106 207.20
47
M Kerai and A Halim
Table 3
Deep mine fleet.
Unit
Diesel engine power (kW)
Electric motor output (kW)
Fleet size
Total electric output (kW)
Light vehicle
98
68.6
12
823.2
Light truck (stores) – long
221
154.7
1
154.7
Light truck (stores) – short
176
123.2
1
123.2
Charge up rig
104
72.8
1
72.8
IT
152
106.4
2
212.8
Jumbo
110
77
2
154
Production drill
104
72.8
2
145.6
Medium load haul dump
231
161.7
2
323.4
Large load haul dump
321
224.7
5
1123.5
Small truck
485
339.5
6
2037
Large truck
548
383.6
5
1918
Grader
152
106.4
2
212.8
Water cart
152
106.4
2
212.8
Shotcrete rig
82
57.4
1
57.4
Concrete agitator
170
119
Table 4
Vehicles operating in the deep mine at the same time.
Equipment
Fleet size
2
238
TOTAL
7809.2
Table 6
Mount Isa (hot strata) geothermal parameters.
Parameters
Values
IT
1
Rock thermal conductivity
Large truck
4
Rock thermal diffusivity
Grader
1
Rock temperature at surface/portal
Water cart
1
Geothermal gradient
19.92 °C per km vertical meters
Large load-haul-dump
1
Airway wetness factor
10%
Small truck
2
Average surface barometric pressure
Light vehicle
5
Average airways age
1.23 W/m°C
0.55 × 10-6 m2/s
Parameters
Rock thermal conductivity
Rock thermal diffusivity
25°C WB, 35°C DB
Table 7
Electric vehicle fleet in the deepest block of the deep mine.
Values
1.75 W/m°C
98.5 kPa
9 years
Average surface temperatures
Table 5
Kalgoorlie (cool strata) geothermal parameters.
28°C
Unit
0.75 × 10-6 m2/s
Motor output
(kW)
Fleet size
Total output
(kW)
23°C
Light vehicle
68.6
5
343
Geothermal gradient
8.5°C per km vertical meters
Charge up rig
72.8
1
72.8
Airway wetness factor
10%
IT
Rock temperature at surface/portal
106.4
1
106.4
77
1
77
Average surface barometric pressure
98 kPa
Jumbo
Average airways age
9 years
Production drill
72.8
2
145.6
Large load-haul-dump
224.7
2
449.4
Average surface temperatures
23°C WB, 35°C DB
Small truck
339.5
1
339.5
obtain unit ventilation requirement in m3/s per kW. No fixed
facilities quantity was subtracted from this fixed quantity as
the fixed facilities are located above this block. This value was
then compared with that obtained from the fixed quantity
in the primary fan. Both fixed quantities were then varied
in the model until WB temperature of 30°C was reached in
the deepest part of the mine and both produced similar unit
ventilation requirement.
Large truck
383.6
5
1918
Grader
106.4
1
106.4
Water cart
106.4
1
106.4
Shorcrete rig
57.4
1
57.4
Concrete agitator
119
1
119
TOTAL
3840.9
48
Ventilation Requirement for Electric Vehicles in Underground Hard Rock Mines – A Conceptual Study
It was found that the mine located in Kalgoorlie (cool strata)
requires a total quantity of 400 m3/s in its primary fan and
172.5 m3/s in its booster fan. A spot cooler of 500 kW(R) was
required in the deepest block.
The fixed quantity in the primary fans takes into account
the 20 m3/s requirement for fixed facilities (fuel bay and
magazine) and the leakage factor of 25 per cent. Hence the
quantity required for the fleet had to be adjusted accordingly
as shown below:
Total quantity = 400 m /s
3
Quantity without leakage = 400/1.25 = 320 m3/s
Quantity for vehicle fleet (less quantity for fuel bay and
magazine) = 320 - 20 = 300 m3/s
The total output power of the conceptual electric vehicles
is noted to be 7809.2 kW. Hence, by simply dividing the
required quantity of 300 m3/s by the total power output of
7809.2 kW, the unit requirement is 0.0384 m3/s per kW electric
motor output.
For the fleet used in the deepest block, the quantity that
was allocated for the fleet was calculated by excluding 15 per
cent leakage factor from the booster fan fixed quantity. It was
found that the fleet quantity is 172.5/1.15 = 150 m3/s. The unit
requirement was calculated by dividing this quantity by the
total output power of the conceptual electric vehicles used in
this block (3840.9 kW). The requirement is 0.0391 m3/s per
kW, which is very similar with that calculated from the fixed
quantity in the primary fan. It was therefore concluded that
a requirement of 0.04 m3/s per kW applies to every electric
vehicle used in this mine. This is 20 per cent less than the
requirement for the diesel fleet which is 0.05 m3/s per rated
kW diesel engine power.
For the mine that is located in Mount Isa (hot strata), it
was found that the requirement is similar with that in the
mine located in Kalgoorlie (0.04 m3/s per kW). However, a
4.3 MW(R) surface refrigeration plant has to be utilised. This
simulation shows that refrigeration plant is required in a deep
mine located in hot strata. Without this plant, ventilation
quantity will have to be increased significantly. However,
this will result air velocity in the decline beyond manageable
limit. Therefore, it is inevitable that refrigeration plant has to
be utilised in a deep mine located in hot strata.
Simulation of the shallow mine
The mine fleet at this mine is listed below in Table 8 with the
vehicles operating in a mine at the same time listed in Table 9.
The shallow mine is a series ventilation circuit in which each
level (ore drive) is ventilated by auxiliary fan and layflat duct.
In addition to heat from the vehicles listed on Table 9, eight
90 kW auxiliary fans and one 180 kW decline development
auxiliary fan were turned on in the model to simulate heat
emitted by them. This reflects the typical condition in this
mine where eight ore drives and decline development face
are active at a time. The average airway age in this mine was
three years.
The shallow mine located in Kalgoorlie (cool strata) requires
a total quantity of 70 m3/s at its primary fan without any
refrigeration plant. This quantity includes 20 per cent leakage
factor. Hence the ventilation requirement was obtained by
adjusted this quantity to exclude leakage and then dividing
Table 8
Shallow mine fleet.
Unit
Diesel
engine
power (kW)
Electric
motor
output (kW)
Fleet
size
Total
electric
output (kW)
Light vehicle
98
68.6
5
343
Charge up rig
66
46.2
2
92.4
Small LHD
123
86.1
2
172.2
Medium LHD
142
99.4
2
198.8
Large LHD
298
208.6
2
417.2
Large truck
600
420
1
420
Medium truck
392
274.4
1
274.4
IT
82
57.4
2
114.8
Shotcrete rig
82
57.4
1
57.4
Concrete agitator
100
70
1
70
TOTAL
2160.2
Table 9
Vehicles operating in the shallow mine at the same time.
Unit
Fleet size
Large truck
2
IT
1
Light vehicle
3
Small load haul dump
2
the adjusted quantity by the total output power of 2160.2 kW.
This resulted in required quantity of 0.027 m3/s per kW electric
motor output. It was therefore concluded that requirement of
0.03 m3/s per rated kW electric motor output applies to this
mine, which is 40 per cent less than the current requirement of
0.05 m3/s per rated kW diesel engine power.
The shallow mine located in Mt Isa (hot strata) required
a total quantity of 120 m3/s at its primary fan without
any refrigeration plant. The higher requirement reflects
the additional heat load from the strata. Therefore, the
requirement was calculated as 0.046 m3/s per kW electric
motor output, which is eight per cent less than the current
requirement of 0.05 m3/s per rated kW diesel engine power.
The requirement would be less if a refrigeration plant was
utilised. However, given that the mine is only 600 m deep, it
was assumed that utilising refrigeration plant in this mine is
not financially feasible.
CONCLUSIONS
This study has found an indication that utilising electric
vehicles will require less ventilation and therefore will save
some primary fan power cost. The reduction of ventilation
requirement varies between mines located in cool and hot
strata, and between deep and shallow mines. This is because
mines located in hot strata have more heat load than that
located in cool strata due to the larger heat emitted by the
strata. Deep mines have more heat load than shallow mines
as heat emitted by the strata increases along with depth. A
refrigeration plant must be used in deep mines located in
hot strata. Utilising refrigeration plant reduces the quantity
requirement; however, it comes with increasing cost of
operating and maintaining the plant.
49
M Kerai and A Halim
The popular assumption that heat emitted by electric vehicle
equals to its power consumption was validated in this study
RECOMMENDATION FOR FUTURE WORK
The simulation done in this study was only based on two
mines. As each mine is unique and has its specific ventilation
circuit, vehicle fleet, and geothermal parameters, a similar
simulation of other mines might produce different results. It
is recommended that similar simulation is done in different
mines in order to support the indication found in this study.
This study was done with assumption that all electric
vehicles are powered by battery since Ventsim Visual is
unable to calculate latent heat produced by fuel cell. It is
recommended that the software is upgraded to include
capability to calculate latent heat produced by fuel cell, and
therefore is able to simulate heat emitted by fuel cell powered
vehicles.
ACKNOWLEDGEMENTS
This study required visiting Rio Tinto’s Northparkes mines,
which was made possible through the funding from Mining
Education Australia (MEA). The authors would like to thank
Prof Peter Knight, Executive Director of MEA and Mrs Paulette
50
Schmidt, Finance Officer and Executive Assistant of MEA for
arranging the visit to Northparkes mine, Mr Eddy Samosir,
Ms Claudia Vejrazka, and Mr Mat Allan from Northparkes
mine for their assistance during the visit.
REFERENCES
De la Vergne, J, 2003. Hard Rock Miner’s Handbook [online].
Available
from:
<http://www.altomines.com/pdfs/
HardRockMinersHandbook.pdf> [Accessed: 30 March 2012].
Marks, J R, 2012. Airflow specification for metal and non-metal
mines, in Proceedings of 14th US/North American Mine Ventilation
Symposium (eds: F Calizaya and M Nelson), pp 191-195,
(University of Utah: Salt Lake City).
Sandvik Mining, 2012. Specification of LH514 and LH514E [online].
Available from: <http://www.mining.sandvik.com> [Accessed:
15 August 2012].
Van den Berg, L, 2012. Personal communication regarding
refrigeration plant design limit, Senior Consultant and Manager
– Ventilation, Snowden, 25 September.
Western Australian (WA) Government, 1995. Mines Safety
and Inspection Regulation 1995 [online]. Available from:
<http://www.mirmgate.com/docs/compliancegate/wa/
MineSftyAndInspectionRegs1995_05-c0-01.pdf>
[Accessed:
30 March 2012].
Prediction and Modelling of Blast
Vibration and its Effects at Glendell
Colliery
M K McKenzie1 and D Chalmers2
Vibration management is an integral part of maintaining best practice standards at Glendell Coal’s
truck and shovel operation in the Hunter Valley of New South Wales. This mining operation is
adjacent to several infrastructure items. A site vibration model was developed, to explore economic,
engineering and community impacts. Due to the proximity of public infrastructure to the mine it is
unlikely that blasting would be able to continue in the future without modifying its blast designs.
A blast vibration model was required to manage the effect of blasting operations on stakeholders.
Charge weight scaling laws provide an ideal tool for this application, but it needed to be carefully
tailored to local site conditions to achieve best results. By analysing vibration measurements from
previous blasts at the mine in conjunction with other data such as geological conditions and blast
design features, it was possible to optimise the charge weight scaling laws. These predictions
where used to evaluate vibration management for the mine, in terms of blast design and financial
consequences.
Consequently, a site optimised vibration model was created for the mine site with derived values
for site constants that account for various blasting scenarios. The model provides Glendell Colliery
with a predictive tool for blast design and was used as the basis for a number of recommendations
to improve future blast designs. The model also indicated that the final pit shell may need to be
modified with a resultant loss of coal reserves, an important finding as any modification to the
final pit shell changes the economics of the operation and will also require modification to the
mine operations plan.
Introduction
Blasting is an integral aspect of almost all modern surface
mining operations. However, despite its importance, blasting
has the potential to create a large source of dust, noise and,
importantly, vibration. If not well designed, blast vibration
can be a source of concern for nearby stakeholders in the
vicinity of mining operations due to the potential effects
on dwellings and infrastructure, as well as causing general
annoyance in communities. The Hunter Valley region of
New South Wales (NSW) is the most productive thermal coal
producing region in Australia. The valley is also home to horse
studs and wineries that support a thriving tourism industry.
This mixture of land uses causes local mining operations to be
held to some of the highest operational standards in the world
(Richards and Moore, 1995).
This paper provides technical justification for establishing
a vibration model to accurately predict vibration levels from
blasting at Glendell Colliery, to enable a more thorough
consideration of the resulting effects and implications.
Glendell Coal is an open cut mining operation in the Hunter
Valley of NSW. It is part of the Mt. Owen Complex and is
owned and operated by Xstrata Coal NSW (XCN). The deposit
is mined using conventional truck and shovel methods,
featuring large scale production blasts. The Great Northern
Rail Line bounds the operation on the southern side of the final
pit shell, presenting special complications. At this stage there
is no blasting deed in place between Australian Rail Track
Corporation (ARTC) and Glendell Coal. This means that full
scale production blasting can only be conducted at distances
greater than approximately 500 M from the rail line to ensure
compliance with a 25 mm/s vibration limit (Davidson, 2012).
In consideration of these concerns, this paper will provide
an assessment of the current state of knowledge in the field
of blast vibration prediction whilst exploring the potential
effects on the Glendell mining operation.
Vibration Modelling
Prediction of vibration has traditionally been performed
using charge weight scaling laws or some derivative of such.
These laws make their base predictions on the assumption
that the peak particle velocity (PPV) caused by a blast will be
the same as that generated if a single blast hole containing a
charge equal to that of the maximum instantaneous charge
(MIC) was detonated at the centroid of the area (Blair, 2004).
Recent times have seen an increase in use of wave form
models, which take each individual blast hole and model the
sum of the orthogonal wave components. These individual
wave forms can then be superimposed after applying the
appropriate delays to form a complete model of the blast.
1. SAusIMM, Graduand Mining Engineer, The University of New South Wales, Sydney.
2. Senior Lecturer, The University of New South Wales, Sydney. Email: [email protected]
51
M k MckenzIe and d chalMerS
One drawback of superposition models is that they assume
the ideal case in which vibration radiates uniformly in
all directions. This is almost certainly not the case as any
geological structures present changes in stratum and the
screening effect of shot ground will affect the propagation
of blast waves. While the limitations of scaling laws are well
recognised, they are still widely used throughout the mining
industry. The use of the charge weight laws is particularly
prevalent amongst site-based personnel as, once established,
they are simple to use and the required data is easily sourced.
These laws expressed in their typical form (in Equation 1
contain two site-specific values: a site constant (K) and
exponent (β).
PPV = K e
where:
D
MIC
-b
o
(1)
mm/s
D:
distance from the charge, metres
MIC:
Max Instantaneous Charge, kg
In the preliminary stages of a vibration prediction, the
formula can be applied using values typical for the relevant
type of mining. However, using these so-called ‘standard
values’ is fraught with danger as their upper and lower limits,
shown in Table 1 can produce wildly different outcomes
when they are varied by a small amount.
TABLE 1
Typical values for site laws (constants) in coal mines (source: Richards and
Moore (1999)).
Site Value
K
β
Upper limit
500
-3.0
Lower limit
10000
-1.4
Typical value in coal mines
1600
-1.6
SIte vIBratIon Model
The investigation of vibration behaviour at Glendell used a
large amount of data from past blasts. Before this data was
used, identifying possible outliers and then sorting data into
different categories for assessment was required. Groupings
trialled were based on factors such as lithology, monitoring
location and blast horizon.
For each of the considered data configurations, statistical
analysis of key parameters was required to ensure the
integrity of the data being used, as well as to maintain
the representative nature of the model being created. For
quantitative comparison, regression analysis was performed,
which was then used to derive values to be used in the charge
weight scaling formulae. After deciding on the structure of
the vibration models and deriving the constants, the results
were plotted. This step was important, not only because visual
representations are a useful communication aid, however as it
also provided a useful quality assurance mechanism.
Using this vibration model, the consequences for Glendell
can be assessed to determine the impact from an operational,
blast design, community and environmental perspective.
Having taken into account the results of the regression
analysis and investigated the types of blasting that typically
occur at the mine site, the structure of the vibration model
was decided. The choice of constant values was based on
both accuracy, and operational considerations. The constant
values are generally skewed towards the mining horizons
that contribute most significantly towards annual waste
movements because some horizons account for a much
greater portion of waste movements than others, as illustrated
in Figure 1 where waste movements are stated in units of
millions of bench cubic metres (Mbcm).
Given the dependence of scaling formulae on appropriate
values of the site constant and site exponent being used, the
derivation of these values should be a priority. To derive
values for these constants data from past blasts is required.
The information gathered should include the maximum
charge per delay, coordinates of both the blast and monitoring
points and the PPV at these monitoring locations.
The PPV can then be plotted versus the scaled distance for
each blast and a curve fitted. The decision of what values to
use for the constants will vary upon the criteria set out for
the prediction. Some scenarios will call for a best-fit case,
calculated using a least-squares method or similar. Given
the potential consequences often associated with exceedance,
Humphreys (2012) suggests a conservative approach will
usually be taken, such as 95 per cent less than or in especially
sensitive cases 100 per cent less than. An important aspect of
the conservative approach is deciding what data to exclude
as outliers, as these methods take no account of directional
influences on vibration. Thus, it is dangerous to exclude
unusually high results, as they can be the most indicative of
what conditions will produce high vibration levels.
A useful technique to improve the robustness of the site
exponent estimate is to place a series of vibration monitors
in a concentric line away from a blast. This method of
gathering data, suggested by Gilbert (2012), is an excellent
way of estimating the attenuation characteristics of the rock
mass as it provides multiple data points all while keeping the
directional influence component constant.
52
Fig 1 - Waste movements during study period.
The rationale being that compromise is a necessary part of
such a model, but the negative effects of the compromises made
can be mitigated by ensuring it comes at the expense of the
horizons that are less important to the overall mine schedule.
The values for the constants used in the model varied in
the range from 1587 to 3668 for K, and -1.34 to -1.72 for β. A
lithology-based approach was found to give the best result in
most cases.
The following formulae are recommended for use in the
described situations at Glendell.
power lines
For the monitoring sites ‘Power line 1’ and ‘Power line 2’:
PPV = 3668 e
D
MIC
-1.72
o
mm/s
(2)
predIctIon and ModellIng of BlaSt vIBratIon and ItS effectS at glendell collIery
Sandstones and conglomerates
D
PPV = 1652 e
MIC
-1.34
o
mm/s
(3)
operations
claystone, siltstone and mudstone
PPV = 1587 e
D
MIC
-1.44
o
mm/s
(4)
conSequenceS
The vibration model developed for Glendell was used to
consider if changes to current blast practices are likely to be
needed. In cases where changes are anticipated to be required,
this model will be used to explore the flow-on effects for the
main stakeholders involved. Due to the nature of blasting,
measures to minimise vibration levels seldom improve
blasting activities from the perspective of performance,
efficiency or cost. Whilst refinement of the initiation timing
can reduce vibration levels considerably, these improvements
soon plateau, making further design changes necessary. The
likely effect on production activities of the proposed changes
was also considered in order to gauge the implications of
these changes by reducing productivity and increasing costs.
Blast design
When using charge weight scaling laws to predict and
subsequently manage vibration levels, the only variable a
blast engineer can directly influence is what the MIC for the
designed blast will be. As MIC is also a main variable in the
commonly used charge weight scaling laws, it can be used
as the main driver for vibration constrained blast design. By
rearranging the charge weight scaling formula to the form
below and substituting the site constants determined earlier,
the highest MIC able to be used while staying within vibration
limits can be determined.
MIC =
e
K.D
2
1 o
PPV b
kg
Currently, Glendell uses 229 mm holes almost exclusively
which, when using ANFO, will not become an issue until
blasting comes within 350 m of infrastructure as blast holes
are seldom much more than 20 m.
(4)
When the limits on charge weights are calculated and
compared to current practices it becomes apparent that
staying under these limits will require changes to blast
designs in the future. In Figure 2, the maximum column
length (charge weight) for a range of blasthole diameters and
a 25 mm/s vibration limit is shown in relation to distance
from infrastructure. The calculations in this figure were made
for the case of 100 per cent ANFO.
Fig 2 - Maximum column length for three blasthole diameters.
All of the proposed blast changes to meet vibration limits are
likely to impact productivity at Glendell. In terms of excavation
units, there is often a reduction in dig rates associated with
decked blasts. This reduction is attributable to the uneven
distribution of explosive energy in the blast area, as well as the
lessened muck-pile heave and the associated ‘looseness’.
As the size of blasts is reduced, more blasts will be required
to maintain the same level of production. When blasts are
fired, equipment often has to be relocated so that it is outside
the blast exclusion zone. As tramming speeds are low,
these relocations can be time consuming, causing a loss of
productive time and an increase in downtime due to the extra
wear on undercarriage components. Additionally, smaller
blast sizes will result in more machine relocations and the
described loss to production.
Usually, dozer and grader work is required to make an
area suitable for drilling equipment to access and work on,
including the erection of windrows. With smaller blasts, these
tasks must be completed more frequently, increasing demand
on support equipment. This draws them away from other
tasks that more directly add value to the business, such as
haul road maintenance and coal preparation.
As blast sizes are reduced to deal with vibration, so too will
bench heights. A reduction in bench heights has scheduling
consequences, requiring a larger operational footprint with
more active benches. Previous long term planning is unlikely
to have taken these things into account and may need to be
modified to reflect the changed operating conditions.
economic
Implementing the blast designs required to manage vibration
levels at Glendell in the future, will come at an economic cost
to the operation. These costs will be direct, such as an increase
in initiation products, and indirect, such as lost production,
increased down time due to maintenance and greater
compliance costs.
As blast hole diameters are reduced, less volume is blasted
per metre of drilling, costing more in terms of consumables,
equipment and fixed costs such as labour.
In terms of blasting activities, the use of decked blasts
and smaller diameter holes will increase shot-firing labour
requirements and decrease the productivity of the existing
shot crew. Additionally, when decked blasts are used, there
is an increase in the amount of initiation equipment required,
as each deck is primed separately. This cost is exacerbated
when electronic detonators are used, which at $55.03 each,
plus $10.69 for a booster, increases the cost per hole by $65.72.
Further, to add a second deck, the initiating equipment
expense increased to $20 767 for an average blast before any
other costs are factored in.
In bulk mining operations such as Glendell, equipment
productivity is an important issue. The method by which
ground is blasted directly influences the excavation rate
achieved and is sensitive to the degree of fragmentation and
muck-pile heave. As Glendell operates three Hitachi EX5500
that are responsible for the majority of the waste moved on
site, the effect of reduced production rates was examined
for these machines. The consequence of a reduction in the
productivity of EX5500 is significant, as illustrated in Figure 3.
53
M k MckenzIe and d chalMerS
capable of being used in wet ground. However, even with this
design, the blast size needs to be restricted to approximately
40 000 bcm, otherwise reinforcement becomes an issue.
other
The site vibration model highlights the narrowing margins
for error that exist as mining approaches the infrastructure
items, presenting a challenge to maintaining a healthy
social licence. As such, ongoing attention should be directed
towards cultivating a positive relationship between Glendell
and its stakeholders.
Fig 3 - Hitachi EX5500 productivity losses.
These losses were calculated by dividing the foregone waste
movement by the product stripping ratio to determine the lost
coal production and then applying the current spot price for
thermal coal ex Newcastle. From the above, it is obvious that
a small drop in the productivity of an EX5500 will cost the
operation large sums of money, especially if the other two
excavators experience a drop in production rates.
While a drop in annual coal production due to lower
machine productivity is undesirable, the coal is not necessarily
sterilised, rather its value is simply diminished by being mined
at a later date. Conversely, if blasting is unable to occur, the
underlying coal is effectively lost. Due to the proximity of the
Great Northern Rail line to the ultimate pit limit, it is unlikely
that blasting could be used without exceeding the current
25 mm/s limit at the pits margins.
Based on an average cumulative coal thickness of 18.5 m and
pit width of 3.5 km, each metre that the final pit wall is moved
away from the rail line will sterilise 90 kt of coal, constituting
a $5.9 million loss at current spot prices.
Blasts with an average MIC cannot be used once within
400 m to 500 m of the rail line. Figure 4 shows the standoff
distance required to stay under the 25 mm/s threshold,
calculated using the constant values that were determined
using regression analysis. Also seen in Figure 4 is the lowest
standoff distance that can be achieved using viable blasting
practices for Glendell, though other operations such as Liddell
have managed to mine very close to the line. The blast design
used for the limiting case consists of a 10 m bench loaded in
two decks, separated by a gravel deck, with a bulk product
Fig 4 - Limiting distances for 25 mm/s.
54
Challenges to stakeholder relations are unlikely to be
confined to external parties such as ARTC and Camberwell
Village, the rationale is stakeholders such as crew members
and the CFMEU have a vested interest in maintaining
employment levels at the operation. These factors may lead to
conflict if the production rate is altered or mine life is reduced
due to the challenges presented by managing vibration.
The possibility of a loss of reserves also raises the issue
of resource utilisation, and reduced revenue to the state
government due to the foregone royalties that would have
been payable on the coal sterilised.
concluSIonS
The research project conducted highlighted that rule of thumb
values are not appropriate for vibration prediction at Glendell
Coal’s mining operation. Whilst rule of thumb values can be
used currently without ill-effect, future blasts will be within
the margin for error in managing vibration as the site matures.
While preparing and analysing the data, as well as in a
consideration of the results of the project, it became apparent
that the current system of record keeping for blasting activities
at Glendell is comprehensive as it captures useful information
in a manner that is conducive to subsequent study and
examination. The high standard of information retained
about blasting practices at the mine was a significant aid to
the completion of the research project and lends confidence
to derived results.
By analysing data from past blasts using methods such
as regression analysis, it was possible to exclude abnormal
blasts, leaving only those that are representative of typical
blasts at Glendell. This data was then grouped in several
ways to determine what the most effective and efficient way
of predicting vibration at this site would be.
Based on the analysis performed, grouping data based on
the lithology of the area being blasted produces the most
satisfactory result in the sense of statistically fit, broad
classifications where used. It is likely that this method could
be improved by more rigorously exploring the potential
correlations between rock-mass attributes and vibration
behaviour.
Having developed a vibration model, it was applied to
determine what changes to drill and blast practices are
required and when they will need to be implemented. Based
on order of magnitude style calculations, it was immediately
apparent that some degree of change is required. With the aid
of a vibration model tailored to the site conditions, accurate
predictions of the adaptations required could be made. Key
findings included the stand-off distances at which significant
changes are likely to be required, but perhaps the most
important finding is that the current blasting practices are not
suitable at distances closer than 500 m to most infrastructure
items. This is important considering the percentage of the
mining area within this zone. The changes are unlikely to
Prediction and Modelling of Blast Vibration and its Effects at Glendell Colliery
be significant until blasting reaches within 250 - 350 m of
infrastructure, at which point it is unlikely that 229 mm holes
can continue to be used.
Using highly constrained blasting practices, including 10 m
benches, decking, small diameter blast holes and reduced
overall blast size, blasting can come within 150 - 200 m of
infrastructure for most blast horizons. As changes are
made to blast designs and practice, mining costs will rise.
The increases will come from drill and blast, where the cost
of drilling, blasting products and labour will all increase,
and from the opportunity cost associated with the probable
reduction in equipment productivities, brought about by
smaller blasts and having to dig through decked blasts. With
a 150 - 200 m limit on the viability of blasting, it is quite likely
that areas of the current final pit shell will fall inside this
zone. The consequence of this is that if vibration limits remain
unchanged, it is quite possible that reserves will be sterilised
directly due to an inability to blast, or indirectly as the result
of diminished returns due to high costs.
Acknowledgements
The author acknowledges the support of Glendell Colliery,
specifically Mr Shaun Leary and Mr Anthony Davidson for
supporting the project and providing the data needed to
perform the investigation.
References
Blair, D P, 2004. Analysis and modelling of airblast and ground
Vibration, in Proceedings EXPLO, pp 1-10 (The Australasian
Davidson, A, 2012. Personal communication. Drill and Blast
Engineer, Glendell Coal, 14 February.
Gilbert, S, 2012. Personal communication, Drill and Blast Engineer,
Ravensworth Surface Operations. 18 January.
Humphreys, M, 2012. Personal communication, Consulting Mining
Engineer, MiPlan, 9 February.
Richards, A B and Moore, A J, 1995. Blast vibration control by
wavefront reinforcement techniques, in Proceedings EXPLO
’95, pp 323-328 (The Australasian Institute of Mining and
Metallurgy: Melbourne)
Richards, A B and Moore, A J, 1999. Predictive assessment of
surface blast vibration, in Proceedings EXPLO ‘99, pp 91-97 (The
Australasian Institute of Mining and Metallurgy: Melbourne).
55
56
Modelling Depletion of Queensland
Coal Resources using Historical
Production Trends
A Meikle1 and W Seib2
ABSTRACT
Coal is a finite energy resource. Therefore, by definition, that resource will inevitably be depleted.
The reporting of coal resources by the Queensland Government includes a projected resource
life value based on current production levels. This paper reviews the accuracy of government
reporting of resource life and explores more accurate means of modelling depletion profiles. The
modelling approaches considered historical production trends in order to forecast future coal
production. Future coal production would then dictate the depletion of the Queensland coal
Ultimate Recoverable Resource (URR), which was determined to consist of base and growth
resource groups. Using this process, low and high URR depletion profiles are provided in the
paper. In addition, using the same modelling process and past URR estimates, resource life
trends were investigated. However, as stated, modelling in this paper considers only historical
production trends. Therefore, a number of factors were determined to exist outside the scope of
this paper with the potential to impact on the depletion profile that will eventuate. Subsequently,
they were assessed in order to gauge the potential degree of influence and thus, the accuracy of
the modelling forecasts.
Introduction
Due to the finite nature of coal as an energy resource, it
will inevitably be depleted. Therefore, in the interest of the
Queensland coal industry the state and federal governments
provide annual coal resource life estimates. Such publications
include Australia’s Indicated Mineral Resources (Geoscience
Australia, 2012) and Queensland Exploration Scorecard
(Queensland Exploration Council, 2011). However, the method
in which the estimates are calculated in publications such as
these can be inaccurate and misleading. Therefore, this paper
reviews past approaches to depletion modelling of coal and
other finite energy resources, such as those by Hubbert (1956)
and Mohr et al (2011), with the aim of producing a new method
that will offer increased accuracy. All past studies indicate that
depletion will follow a bell-curve shape. Therefore, the desired
model will mirror this trend, using annual coal production
and coal resource data, inclusive of all coal types.
Using the proposed model, forecasts will be made regarding
peak coal and resource depletion. However, in order for the
forecasts to remain accurate a number of factors that influence
coal resource depletion will have to follow historical rates of
change. If this occurs, the accuracy of historical production
data will hold, as will depletion forecasts made using the
proposed model.
Queensland Coal Resources
Queensland has an extensive coal resource base (The
Queensland Coal Board), accounting for approximately 60 per
cent of Australia’s coal resources, which is the fifth largest in
the world (Geoscience Australia, 2012). A wide variety of
coal types are present in Queensland and they are exported
to a variety of international markets, which include highgrade metallurgical coking coal, blending coals, pulverised
coal injection coals, high and lower volatile thermal coals for
power generation and high and low volatile thermal coals for
industrial market (DME, 2007). However, all of Queensland’s
metallurgical coal is found in the Bowen Basin, whereas,
thermal coals are found throughout the state.
The distribution of Queensland’s coal-bearing basins is
shown in Figure 1, and the major basins include the:
••
••
••
••
••
Bowen
Surat
Galilee
Clarence-Moreton
Eromanga/Cooper.
In addition to the large resource size of both Queensland
and Australia, the region is positioned well as a coal exporter
and to handle increasing global demand for coal because of
(Productivity Commission, 1998):
••
••
••
••
••
••
abundant supplies of good quality coal
readily accessible coal deposits
coal deposits relatively close to shipping ports
established rail and port facilities
close proximity to major Asian coal markets
a proven reputation as a reliable supplier.
1. Mining Engineer Undergraduate, The University of Queensland, 25 Oakmont Street, Rothwell Qld 4022. Email: [email protected]
2. FAusIMM(CP), Casual Lecturer, School of Mechanical and Mining, The University of Queensland, 14 Crestview Street, Kenmore Qld 4069. Email: [email protected]
57
a MeIkle and w SeIB
logistic curves
The concept of modelling depletion rates and future
production of finite resources was first analysed by Hubbert
(1956). He theorised that production levels of a given finite
resource begin at zero, prior to production commencing, rise to
one or more maxima before returning to zero, when resource
exhaustion occurs. In following with this assumption, a bellshaped curve was suggested as an accurate model for the
production curve. Hubbert (1959) later adopted a generalised
logistic curve as the representation of production trends
(Mohr et al, 2011).
The logistic curve has since been widely used in subsequent
studies when modelling fossil fuel depletion such as coal
(Höök et al, 2010; Patzek and Croft, 2010). The logistic equation
is defined as (Mohr et al, 2011):
QL ^ t h = QT c
1
1
m
e`-rL `t - t pjj + 1
where:
QL(t)
is the cumulative production for the logistic curve
QT
is the Ultimately Recoverable Resources (URR)
tp
is the peak year of production
rL
is the rate constant.
ModellIng approacheS
A recent study by Mohr et al (2011), compared four
modelling approaches, including logistics curves, to project
Australian coal production. The study considered a standard
or conservative URR estimate and a high or optimistic
estimate of URR when conducting modelling. The results
indicated depletion of Queensland coal resources occurring
in approximately 2135 and 2150, for the standard and high
URR scenarios, respectively.
government resource life
gompertz curves
Fig 1 - The distribution of Queensland’s coal basins (Mutton, 2003).
The reporting of national or state coal resource endowment
by the government often includes a resource life estimate. The
resource life is calculated by dividing the reported resource
endowment by the current annual production level at the
time of the report. While this figure provides an interesting
point of reference, it is highly inaccurate and often misleading
(Bartlett, 2006).
The main reason for the inaccuracy of the figures is the
assumption of constant production and zero resource growth.
In reality this would mean constant levels of production until
the year of resource depletion, at which time production
would abruptly cease. This represents a highly unlikely
scenario for the coal industry or the depletion of any resource
in general. The government method of calculating resource
life is shown in Figure 2.
The Gompertz curve has also been used in past studies
to model fossil fuel depletion (Fitzpatrick, Hitchon and
McGregor, 1973; Moore, 1966). The general shape of the
Gompertz curve is similar to the logistic curve, in that they
are both bell-shaped. However, the Gompertz curve is not
constrained to being symmetrical. The Gompertz equation is
defined as (Mohr et al, 2011):
r t - t pj
c ` G`
m
QG ^ t h = QT e - e
(2)
where:
QG(t)
is the cumulative production for the Gompertz
Curve
QT
is the URR; tp is the peak year of production
rG
is the rate constant
The depletion rate of the remaining recoverable resources is
defined as (Mohr et al, 2011):
ddG ^ t h =
P^ t h
QT - Q^ t h
(3)
where:
Fig 2 - Government resource life representation.
58
dδG(t)
is the depletion rate for the Gompertz curve at time t
QT
is the URR; Q(t) is the URR at time t
P(t)
is the annual production at time t
In a study by Mohr et al (2011), Gompertz curves were used
to project Australian coal production. The study considered
a standard or conservative URR estimate and a high or
optimistic estimate of URR when conducting modelling. The
Modelling Depletion of Queensland Coal Resources using Historical Production Trends
results indicated depletion of Queensland coal resources
beyond 2200 in both scenarios, standard and high URR.
Supply-demand models
The supply-demand model investigated was designed by
Mohr and Evans (2009). The model consisted of a defined
number of idealised mines, each consisting of a mine life,
L, and production rate, P. Depending on the production
requirements at the time, mines would either begin or
cease production for the given time period. The time of first
production was defined as T0, the production rate constant,
r, dictated the rate at which new mines began production.
As with previous modelling methods, a standard and high
URR estimate was used, and it influenced the number of
mines allocated to a given region. Using this approach, two
constraints were used:
1. the static case – no interaction between supply and
demand would occur
2. the dynamic case – both supply and demand influenced
each other.
Modelling of the supply-demand scenario hypothetically
began in 1880, with an initial demand of 1 Mt that would
increase at 4.8 per cent per annum. The production rate
constants for each state and coal type were obtained from
historical production data (Mohr et al, 2011).
Depletion of Queensland coal resources was found to
occur approximately in 2125, for both URR scenarios using
the dynamic scenario. This would represent a resource life of
approximately 113 years.
Peak coal
Peak coal, or the point where coal production reaches a
maximum, is key when predicting resource depletion. Bardi
and Lavacchi (2009) theorised that in the initial stages of
extraction of an abundant and cheap resource, economic
growth and increasing investments into extraction/
exploration will result. As the more easily accessed and
cheaply extracted resources become depleted, extraction
costs begin to climb due to increased overburden depths
and reduced resource quality. In time, investments are
outweighed by rising extraction costs; growth slows and
eventual production enters a depletion-driven decline.
The three modelling approaches used in Mohr et al (2011)
all indicate similar years when peak coal will be reached:
2048 - 2052, for the lower URR and 2056 - 2057 for the higher
URR. Excluding the Gompertz curve model, the production
rate experienced at peak coal is also relatively similar 453 489 Mt/a for the lower URR and 564 - 644 Mt/a.
Modelling Conclusions
From the three modelling approaches investigated, it is clear
that the proposed model should produce a depletion profile
that is a bell-shaped curve. Furthermore, the results of the
study conducted by Mohr et al (2011) can be used as a rough
guide to the desired outcomes of the proposed modelling
scenario. Therefore, peak coal should occur approximately in
the period 2048 - 2057, with a magnitude of 564 - 644 Mt/a.
Additionally, resource depletion should approximately occur
between 2125 - 2150, depending on the determination of URR
used in the proposed scenario.
Modelling Methodology
Resource and production data
In order to produce an accurate model of Queensland coal
resource deletion, accurate historical data must be sourced.
In addition, in order to achieve maximum accuracy, where
possible, a single data source was used to collect data, so
that no disparity in data occurred due to differing reporting
standards.
Raw coal production data for Queensland was sourced
from the Australian Bureau of Agricultural and Resource
Economics and Sciences (ABARES) for 1950 - 2010. The
60-year period was deemed sufficient to indicate any
production trends for coal production in Queensland and
was, therefore, suitable for the purpose of the project.
For the purposes of the proposed model, the coal resources
were separated into two categories: base and growth
resources. The base resource group consisted of Measured
and Indicated JORC Resources. Whereas, the growth category
of resources comprised Inferred JORC Resources and coal
exploration targets. However, due to the reduction in resource
size that occurs when a resource is upgraded to a JORC
category of higher geologic certainty, it is assumed that only
20 - 40 per cent of the resource will be maintained through the
upgrading process. The 20 to 40 per cent range was decided
upon following review of resource statements for companies
such as New Hope Coal and BHP Billiton. Whilst the resource
statements did not indicate a definitive figure, the range was
considered a best judgment of the trends present.
Queensland coal resources for the base resource group
could not be obtained from a single data source. Therefore,
the Queensland Coal Board Industry Review was used for
the 1980 - 2003 period. Whereas the Geoscience Australia
publication, Australia’s Identified Mineral Resources, was
used for the later 2004 - 2011 period. Whilst the two data
sources used different reporting standards, they were found
to correlate strongly with one another, and were, therefore,
used interchangeably.
Resources in the growth resource category were only
collected for 2011, and were sourced from the most recent
resource statements of approximately 40 coal mining and
exploration companies.
Symmetric Gaussian modelling
From the review of past depletion modelling techniques it
was determined that coal resource depletion would follow
a general bell-curve shape. Gaussian curves were deemed to
produce a suitable depletion profile. Furthermore, Gaussian
curves could be simply manipulated, which would aid the
modelling process.
A modelling approach of this nature will aim to accurately
project annual production until which point that the
cumulative production is equal to the proposed URR. At
this point in time, the Queensland coal resource will be
declared as depleted. The depletion profile will, therefore, be
directly related to annual production figures, thus making the
assumption that coal resources will be mined in full.
The symmetric form of the Gaussian function is its most
basic form; it is symmetrical about its central peak and is
defined by the equation (Guo, 2011):
y = a.e
- ^ x - bh
2c 2
2
(4)
where the following variables define:
a: curve amplitude
b: location of the curve peak on the x axis
c: curve width.
For the purpose of this report and the modelling process,
the height of the curve will indicate the magnitude of peak
59
a MeIkle and w SeIB
coal, ie the variable a. Variable b will dictate when peak coal
occurs and variable c will relate to the annual growth in coal
production.
Using the historical coal production data for Queensland,
the Gaussian curve can be manipulated in order to best
match the historical trend. Due to the symmetric nature of the
curve, the growth profile of the curve, dictated by historical
data, will be mirrored to indicate the projected decline in
production post-peak coal. The area under the projected
curve, or sum of annual coal production, will correspond to
the URR for Queensland coal. Therefore, changes in peak coal
are expected to be the most evident result for different URR
scenarios.
The different URR scenarios will be produced by
modelling each combination of the full base resource with
either 20 per cent or 40 per cent of each of the Inferred
JORC Resources and exploration targets, resulting in seven
hypothetical scenarios. From the seven modelled scenarios,
the scenario using 20 per cent of both Inferred JORC Resources
and exploration targets will represent the theorised low URR
depletion scenario. Whereas the scenario using 40 per cent of
both growth resource categories will represent the theorised
high URR depletion scenario. The low and high URR scenarios
will aim to encompass the true Queensland coal depletion
scenario within their bounds.
asymmetric gaussian modelling
Whilst symmetric modelling techniques have been used in
the past to make depletion projections for finite resources, a
distinct problem exists within the modelling approach. The
problem stems from the ability of the modelling technique
to adapt to a larger and expanding URR scenario. A larger
URR simply results in a peak coal of greater magnitude using
the symmetric modelling approach and minimal increases in
resource life; this result is expected to be a poor representation
of reality. Therefore, an asymmetric modelling approach was
also tested. An asymmetric Gaussian curve will rectify this
problem. The asymmetric Gaussian curve is defined by the
equation (Kato, Omachi and Aso, 2011):
1 c 2 x-b 1 1
z
y = y0 + a ec 2 ` d j - d mc 2 + 2 erf c 2 mm
d
(5)
historical resource life comparison
This historical resource life comparison component of this
project aimed to review resource life trends present in
government resource life calculations. Those trends were then
compared with those made by modelling past Queensland
coal resource inventories. This was done by using the
asymmetric Gaussian model to model the base resources,
ie Measured and Indicated JORC Resources, present in
1980, 1995 and 2010. Ideally, growth resources would have
been accounted for in each of these years and used when
modelling. However, Inferred JORC Resources were rarely
reported quantitatively prior to 2003 and were instead only
given a qualitative measure. In addition, whilst exploration
targets were reported for all time periods, locating mining
and exploration companies operating at that time and their
current exploration targets was deemed to be too difficult
for the time frame allowed for this project. Therefore, using
only base resources was deemed to be the most viable option
as it would still indicate any trends that exist in resource life
projections using the asymmetric Gaussian model.
Furthermore, 1980 and 1995 have been chosen as the periods
to be tested for the comparison process. This will indicate any
trends on a 15-year basis. In addition to using the asymmetric
Gaussian model for comparison, two different techniques
were used:
1. Technique 1: use the full range of historical production
data (1950 - 2010) combined with the individual URR for
each period of time to be tested.
2. Technique 2: use only the historical production data to
date for each period of time to be tested, combined with
the individual URR for each period of time to be tested.
The individual results for each technique were then
compared to see if any differences eventuated, and the more
accurate technique was selected.
depletIon ModellIng reSultS
where:
z = x-b - c
c
d
lower range will be defined by the peak coal projection made
by the symmetric Gaussian curve for the base growth scenario.
Three peak coal values will then be selected for testing within
the indicated range. Three URR scenarios will also be tested,
corresponding to the base, low and high URR scenarios tested
using the symmetric Gaussian modelling approach.
(6)
and the following variables define:
a: curve amplitude
b: location of the curve peak on the x-axis
c: curve width
resource and production data
Using the sources identified earlier in this paper, the annual
coal production and resource data shown in Figure 3 was
produced.
From Figure 3, annual coal production can be seen to
steadily increase from 1950 to 2010, as expected. Resource
d: modification factor or skewness.
The key difference between the two Gaussian curves is
the addition of the modification factor. The modification
factor decreases the rate of reduction in annual production,
therefore, extending the resource life. Therefore, when using
the asymmetric Gaussian curve, the curve is fit to historical
production data in the same manner as before, except a larger
URR can be accounted for by increasing the modification
factor, thus extending the projected resource life.
However, for the asymmetric Gaussian curve the magnitude
of peak coal must be defined. This value will be determined
by reviewing the symmetric Gaussian curve data to discern an
expected time frame for the occurrence of peak coal. A simple
polynomial fit of historical production data will then be used
to project an upper range for the magnitude of peak coal. A
60
Fig 3 - Queensland coal resources and production.
ModellIng depletIon of queenSland coal reSourceS uSIng hIStorIcal productIon trendS
data appears to indicate that peak levels have been reached.
However, this is most likely in response to changes in the
JORC Code, resulting in the downgrade of resources due to
the requirement of increased geological certainty. Therefore,
the increase in resource size from 2008 onwards can be
expected to continue in coming years.
Futhermore, Table 1 shows the Resource and production
data for 2011, which was used for the various modelling
approaches and scenarios tested.
TABLE 1
Coal Resource and production data for 2011.
Cumulative production (Mt)
4742
Resource (Measured and Indicated) (Mt)
37 156
Resource Inferred (Mt)
60 187
Exploration targets (Mt)
62 417
Fig 4 - Symmetric modelling of base, low and high ultimate recoverable
resource scenarios.
TABLE 3
Symmetric modelling projected depletion bounds.
Symmetric modelling
The symmetric Gaussian modelling approach was successful
in modelling each of the seven URR scenarios proposed for
testing. Table 2 shows the individual modelling results that
were produced.
From Table 2 the impact of increasing URR size is evident.
By almost doubling the URR from scenario one to seven
the projected peak coal level increased from 558 Mt/a to
892 Mt/a. Whereas the resource life only increases by 32 years
from scenario one to seven. Therefore, the initial assumption
that the symmetric model would have difficulty accurately
reflecting an increase in URR, was proved correct.
Figure 4 graphically depicts the depletion profiles of
scenarios 1, 3 and 7, corresponding to the base, low and high
URR scenarios. For all scenarios a high degree of accuracy
was present in representing the historical production data,
reflected by an average R2 value of 0.99 for all tested scenarios.
Ultimate recoverable resource (Mt)
56 430 - 75 966
Resource life (y)
136 - 153
Peak coal (Mt/y)
709 - 892
Peak coal (y)
2054 - 2063
as the inaccuracy of the symmetric model would be more
prevalent for the increased URR used in each case.
Initially, literature was consulted, and the Department of
Infrastructure and Planning (2010) indicated that annual coal
production in Queensland would double by 2030. This would
mean annual production levels of roughly 500 Mt for the state
by this time.
Modelling conducted using the symmetric Gaussian
approach also indicated that peak coal would occur
Therefore, from the symmetric modelling results, the
bounds of the low and high URR scenarios indicate that the
true depletion scenario for Queensland will reside between the
bounds shown in Table 3. These bounds are also represented
in Figure 4, between the low and high URR depletion profiles.
asymmetric modelling
Prior to conducting any asymmetric Gaussian modelling,
suitable peak coal levels had to be determined. In the case of
the base URR scenario, peak coal levels of 500, 550 and 600 Mt
were selected due to their proximity to the initial peak coal
prediction made by the symmetric. However, for the low and
high URR scenarios, a different approach had to be taken
Fig 5 - Peak coal projection for asymmetric low and high ultimate recoverable
resource scenarios.
TABLE 2
Symmetric modelling results.
inferred resources
(%)
Explore target (%)
Ultimate recoverable
resource (Mt)
Res life (y)
Peak coal (Mt)
Peak coal (y)
Case 1
0
0
41 902
121
558
2047
Case 2
20
0
53 925
131
700
2052
Case 3
20
20
56 430
136
709
2054
Case 4
20
40
58 930
139
732
2056
Case 5
40
0
65 969
145
798
2059
Case 6
40
20
70 960
149
846
2061
Case 7
40
40
75 966
153
892
2063
61
a MeIkle and w SeIB
approximately between 2050 and 2060. Using this information
historical data was then plotted, as shown in Figure 5, with
high and low level projections. The high-level production
projection consisted of a constant growth scenario and the low
scenario was produced using the same bell-curve modelling
approach used to produce the symmetric modelling scenarios.
The peak coal range was determined to exist within the
bounds of the low and high production projections. Therefore,
a conservative range of 650 - 750 Mt was selected to be tested
using the asymmetric Gaussian modelling approach. This
would mean calculating projected scenarios for peak coal
levels of 650, 700 and 750 Mt of annual coal production.
Using the projected peak coal estimates for each of the three
URR scenarios, projections were made. Once again, high
R2 values were produced. However, using the asymmetric
modelling approach, the resource life of each scenario was
increased, compared to the symmetric approach, especially
in the case of lower projected peak coal levels. Therefore, as
expected, lowering the peak coal level for a given scenario
increases the projected resource life. The modelling results at
this stage are shown in Table 4.
Increased resource life for similar URR scenarios with lower
peak coal levels is a trend the symmetric coal model was
unable to produce. Also, the asymmetric model, as with the
symmetric approach, produces a consistent estimate of peak
coal. This result is supported by the qualitative reasoning that
annual coal production will not be dictated by the size of the
URR, but rather factors such as coal price and global supply/
demand.
In order to produce a final upper and lower projection of the
expected depletion profile for Queensland coal resources, an
appropriate peak coal level had to be selected from within the
tested 650 - 750 Mt range.
Furthermore, to choose a level for peak coal, the symmetric
modelling results were reviewed. Due to the method in which
the symmetric model is fit to a URR scenario by adjusting the
magnitude of peak coal, the suitable peak coal level should
reside below that calculated by the symmetric model for
a similar URR. As a result, the low URR scenario was used
to make the comparison. The symmetric model indicated a
projected peak coal magnitude of 709 Mt of coal; therefore,
the 650 Mt peak coal level should be used for the asymmetric
model as the scenario for comparison.
Using the 650 Mt peak coal level for both the low and high
URR scenarios, the projections in Table 5, were produced.
From Figure 6, the low and high URR scenarios indicate
the theorised bounds for which the actual depletion profile of
Queensland coal resources will occur. Therefore, a resource
life of between 186 and 314 years is projected with peak coal
occurring in 2050 - 2053, with a magnitude of 650 Mt. The
TABLE 5
Asymmetric modelling low and high ultimate recoverable resource scenarios,
with specified peak coal level.
Unit
Low ultimate
recoverable
resource
High ultimate
recoverable resource
Resource
Mt
56 447
75 975
Resource depletion
y
2198
2326
Resource life
y
186
314
Peak coal
Mt
650
650
Peak coal
y
2050
2053
Fig 6 - Asymmetric modelling low and high ultimate recoverable resource
scenario.
proximity of the true scenario to either of the bounds should
be related to the actual URR of Queensland coal that will be
dictated by the proportion of growth resources, ie Inferred
JORC Resources and exploration targets, which are upgraded.
Modelling technique comparison
For the purpose of this project, two modelling techniques were
used to forecast the depletion of Queensland coal resources:
symmetric and asymmetric Gaussian curves. As a point of
comparison, the low and high URR scenarios were used for
each of the modelling techniques. The projected data for the
two comparison scenarios is shown in Figure 7.
The most substantial difference in the way the two
modelling methods are calculated is the way a larger URR
is accounted for by each model. The symmetric modelling
technique increases the peak coal level in order to account for
the larger URR; whereas the peak coal level is fixed for the
asymmetric modelling approach and instead a modification
factor extends the resource life by decreasing the rate of
production decline post peak coal. This trend is evident in
Figure 7. In fact, a 35 per cent increase in URR from the low to
high scenario results in a 69 per cent increase in resource life
for the asymmetric model, whereas only a 13 per cent increase
TABLE 4
Asymmetric modelling low and high ultimate recoverable resource scenarios.
Ultimate recoverable resource
Low ultimate recoverable resource
High ultimate recoverable resource
56 447
75 975
Mt
0.99
0.99
0.99
0.99
0.99
0.99
Peak coal
Mt
650
700
750
650
700
750
Peak coal
y
2050
2051
2052
2053
2054
2057
Resource depletion
y
2198
2170
2143
2326
2291
2257
Resource life
y
186
158
131
314
279
245
R
2
62
ModellIng depletIon of queenSland coal reSourceS uSIng hIStorIcal productIon trendS
at about 40 - 55 for the past four decades. Continuing the trend
of 40 - 55 operating coal mines in Queensland, in order to reach
the projected 650 Mt of annual production at peak coal for the
asymmetric scenario, annual production of 11.8 - 16.3 Mt/
mine is projected. This projection is supported by current
coal projects, with expected annual coal production in excess
of 30 Mt/a such as Carmichael, Alpha, Galilee and Kevin’s
Corner (Department of Natural Resources and Mines, 2012).
historical resource life comparison
Using the annual production and coal resource data collected
as part of this paper, resource life estimates can be made in
the same method as shown in government reports. These
estimates are shown graphically in Figure 9.
Fig 7 - Comparison of symmetric and asymmetric modelling results.
occurs for the symmetric modelling technique. However, the
symmetric model figure is affected by the increase in peak
coal that occurs from the low to high URR scenario.
Due to the greater ability of the asymmetric model to adapt
to a larger URR by extending the resource life of the scenario, it
is considered the more accurate of the two models. Therefore,
all final projection figures will be based on the depletion
bounds produced by the low and high URR scenarios using
the asymmetric Gaussian model. This translates to a resource
life of 186 - 314 years depending on the URR that eventuates
for Queensland coal. Peak coal is projected to occur between
2050 and 2053, with a magnitude of 650 Mt.
projected annual production per mine
As mentioned in the previous section, modelling indicates a
resource life of 186 - 314 years, with a peak coal of 650 Mt
occurring in 2050 - 2053. However, in order to reach the
projected peak coal level for 2050 - 2053, the current average
annual production levels per operating coal mine will have to
increase dramatically. Geoscience Australia (2012) indicates
that 57 coal mines were operating in Queensland in order to
produce 257.44 Mt of coal for that year (Australian Bureau
of Agricultural and Resource Economics, 2010). This equates
to an average annual production per mine of approximately
4.5 Mt/mine. From data collected from Geoscience Australia
(2004 - 2011) and the Queensland Coal Board (1950 - 2003),
trends in the number of operating coal mines and annual
production per mine has been shown graphically in Figure 8.
Fig 9 - Government resource life projections.
The resource life trend indicated from the government
resource life figures indicates that annual production is
increasing at a greater rate than resource expansion and,
therefore, resource life has steadily declined. However, in
recent years, the decline has plateaued and resource life for
Queensland coal resources has remained between 130 and
160 years. This trend can be linked to record coal prices for
the 2008 - 2010 period, resulting in increased exploration
expenditure.
Using the first comparison technique, the trends shown in
Figure 10 indicate that resource life and both the magnitude
and expected date of peak coal has increased from the 1980 to
2010 scenario. This is the opposite of the trend indicated by
the government resource life calculation method and further
emphasises its misleading nature.
From Figure 8, a clear trend of increasing annual production
per mine can be observed. This trend is expected to continue,
at least until the time of projected peak coal. The number of
operating coal mines in Queensland appears to have plateaued
Fig 10 - Historical resource life comparison - Technique 1.
Fig 8 - Annual production per mine site.
Using the second comparison technique, the indicated
trends shown in Figure 11 are quite different, in comparison to
the first. The resource life for the three periods of comparison
is relatively constant (146 - 151 years), with only five years
variation. Although peak coal for each time period varies in
a similar way to the previous technique, the reason for the
relatively constant resource life is production trends indicated
at each period of time. When the production data is stacked
63
a MeIkle and w SeIB
sufficient demand will exist and that the global coal market
does not become over-supplied.
one consistent trend is shown, which is represented by the
1950 - 2010 production data. However, when the production
data time frame is shortened for the other periods, the
projected rate of increase in annual production decreases. This
effectively lengthens the resource life for the 1980 and 1995
scenarios to mirror that of the 2010 scenario, by decreasing the
rate in which the URR is depleted.
Of the two historical resource life comparison methods, the
second method is best representative of depletion projections
that would have been made at each period, ie 1980 or 1995.
This conclusion stems from the fact that production data for
the full 1950 - 2010 period would not have been known in
either 1980 or 1995.
ModellIng lIMItatIonS
Introduction
With any modelling process, there will invariably be
limitations involved. In the case of the modelling approach
taken in this project, the limitations stem from assumptions
made, and factors of influence not included, within the project
scope.
Two main assumptions were made in order to produce the
modelling results of this project. The assumptions were:
1. historical production will indicate future production
trends
2. Queensland coal resources and annual production data
are directly related, ie coal resources are mined in full.
Whilst past studies indicate that historical data can be a
good indication of future trends when modelling, future
changes in the coal industry have the ability to dramatically
alter the true depletion profile of Queensland coal resources.
Furthermore, the scope of this project considers solely
historical production data when projecting depletion profiles
for Queensland coal resources. Therefore, all other factors
that influence coal production and coal resource size have the
ability to reduce the accuracy of the projections made. Some
of these factors include:
•
•
•
•
•
coal supply
coal demand
coal price
government policy
coal-related infrastructure.
coal supply and demand
For the purpose of this project, only coal produced in
Queensland was included within the scope. However, the
greater coal supply market includes other coal-producing
states in Australia as well as other nations. By only including
coal supplied by Queensland, the assumption is made that
64
350
Annual Coal Exports (Mt)
Fig 11 - Historical resource life comparison - Technique 2.
Future coal exports are the factor that will affect most on
the forecasts made as part of this project. Therefore, export
trends for the past decade were assessed in Figure 12, for the
countries of high export quantities or areas of projected coal
export growth, as nominated by the Queensland Department
of Infrastructure and Planning (DIP, 2010). The DIP indicated
that Indonesia, Kazakhstan and Mongolia would all
experience considerable export growth in the coming decade
as coal export infrastructure is developed and expanded
(DIP, 2010). Figure 12 shows that substantial export growth
has occurred in both Australia and Indonesia during the past
decade.
300
250
200
150
100
50
0
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Year
Australia
USA
Mongolia
Indonesia
South Africa
Russia
Kazakhstan
Fig 12 - Global coal export trends, 2000 - 2010.
Using Indonesia as an export growth example, it appears
approximately a decade is required for a nation to transition
from small-cap coal exporter (<50 Mt/a) to one of the global
leading coal exports, exporting in excess of 200 Mt/a. If this
assumption proves to be true, Kazakh and Mongolia exports,
combined with continued growth in Indonesian exports, have
the potential to over-supply the global coal market. In this
event, the forecasts made as part of this project will most likely
prove to be inaccurate, with the peak coal event occurring
prior to the 2050 - 2053 forecast. However, the likelihood of
over-supply occurring in the global coal market can be best
assessed by also investigating global coal demand.
As with coal supply, coal demand was outside of the scope of
modelling conducted as part of this project. However, global
coal demand will ultimately play a large role in dictating
annual production in Queensland. If the global demand for
Queensland coal does not exist, then the resource will simply
not be extracted. Therefore, expected global coal demand
should be assessed in order to determine possible impacts on
depletion forecasts made.
Figure 13 indicates total coal imports for China and India
to reach approximately 280 Mt by 2025. If the Chinese and
Indian import levels continue to 2050 - 2053, as with Japanese
demand, approximately 70 per cent of annual coal production
will be exported to these nations during that period.
It would then be reasonable to assume that domestic coal
demand, combined with the imports of other nations, will
account for the remaining annual coal production that occurs
at the peak coal level devoted to coal export. Therefore,
provided there are no drastic global events affecting
international coal supply and demand, the projections should
be relatively accurate regarding resource depletion and
annual production.
Modelling Depletion of Queensland Coal Resources using Historical Production Trends
Coal price and exploration spending
The price of coal influences a variety of factors affecting the
depletion forecasts made in this project. Coal price influences
profitability margins for extraction and overall company
revenue. Historical coal price data is shown in Figure 14;
exploration spending is also shown.
200
Coal Imports (Mt)
180
160
140
120
Coal-related infrastructure
The final projected depletion scenario indicates a peak coal of
650 Mt to occur between 2050 and 2053. While the operating
coal mines during the 2050 - 2053 period may have the
capability to extract the required 650 Mt of coal to reach the
peak coal projection, there will be no economic motivation to
do so if the required infrastructure to export and sell the coal
does not exist.
In order to meet the projected peak coal level, sufficient
coal systems will have sufficient capacity. A coal system is
comprised of (DIP, 2010):
100
80
60
40
20
0
2009 2011 2013 2015 2017 2019 2021 2023 2025 2027 2029
Year
China
India
Japan
Fig 13 - International coal imports.
700
350
600
300
500
250
400
200
300
150
200
100
100
50
0
•• mines and associated coal preparation plants, coal stock
piles and load-out facilities
•• regional road, power and water infrastructure
•• a rail network and rail-haul providers that deliver product
coal to domestic users and export ports
•• one or more coal export terminals
•• a deep-water export coal port, and supporting
infrastructure and services
•• bulk carriers to transport the coal overseas.
The Queensland coal systems are divided into four areas,
and are shown in Figure 15. The four subsystems are the:
1. Newlands system
2. Goonyella system
3. Blackwater-Moura system
4. Western system.
In the case of Queensland, the main limiting factors of the
coal system are rail or port facilities. Therefore, by assessing
current and planned rail and port facilities, the potential
Coal Price ($/t)
Figure 14 indicates a strong correlation between coal price
and exploration spending. Exploration spending leads to
exploration drilling, which invariably results in increases
in overall resource size for Queensland coal. Therefore, the
conclusion can be made that coal price will play a large part in
the determination of the final URR scenario that eventuates.
Coal price is expected to increase with demand and as the
resource becomes depleted. Whilst it is difficult to predict
the coal price required to produce the projected URR and
annual production scenarios, trends indicate that coal price
will continue to increase for the near future. Therefore, the
accuracy of the projections made should be maintained.
Exploration Spending
(million $)
government policy, namely royalties and taxes, will not
overly affect the accuracy of the depletion projections made
as part of this project.
0
1980
1985
1990
1995
2000
2005
2010
Year
Exploration Spending
Coking Coal Price
Thermal Coal Price
Fig 14 - Historical coal price and exploration spending.
Government policy
The state and federal government has the ability to impact coal
industry revenue in the form of royalties and taxes. The size
of the government royalties and taxes impacts on the overall
economic viability of coal extraction and, thus, resource size
and annual production levels are impacted. However, whilst
both governments increase royalties and taxes with time it is
in their best interest to keep the tax at a level that does not
impact annual coal production or exploration spending in
an overly negative manner. If it does, the revenue generated
for either government will be diminished, which would be
an undesirable scenario. Therefore, it can be concluded that
Fig 15 - Queensland coal systems (DIP, 2010).
65
ability of Queensland coal infrastructure to export 550 Mt of
coal during the peak coal scenario can be assessed (assuming
an 85 per cent export rate).
Current port expansion plans and current projects under
construction indicate that Queensland will have sufficient
port facilities to meet the projected export requirements
throughout the depletion forecast. However, rail limitations
could prove to be an issue. The main capacity limitation will
exist where the proposed Surat Basin Railway links with the
Blackwater-Moura rail system. However, the Surat Basin
Railway will be a key component of any future coal projects
in the Surat Basin (DIP, 2010).
Additionally, increased capacity will be required of all
aspects of the Queensland coal system when coal mining
begins in Galilee Basin and at a later point, the Eromanga
Basin. Currently, there are no operating mines in these areas,
and this is largely due to the costs involved in establishing
sufficient coal transport infrastructure for the two basins.
In order to handle the additional coal supplied annually by
mines produced in the Galilee and Eromanga Basins, a standalone rail link will have to be created to one of the existing
port facilities.
Provided planned expansion projects progress as planned
and a stand-alone rail link is established for the Galilee and
Eromanga Basins, coal production can feasibly progress as
forecasted.
Conclusions
Having conducted two separate modelling techniques,
using symmetric and asymmetric Gaussian curves, the final
and most accurate forecast was determined to be that using
the asymmetric curve for the low and high URR scenarios.
Using this modelling approach it was found that the true
Queensland coal depletion scenario would consist of the
following characteristics:
••
••
••
••
••
URR – 56 447 - 75 975 Mt
Resource depletion – year 2198 - 2326
Resource life – 186 - 314 years
Peak coal – 650 Mt/a
Peak coal – year 2050 - 2053.
Using the 650 Mt peak coal projection and historical data
on the number of operating mines, it was concluded that
40 - 55 mines would be operating at the time of peak coal, with
average annual production between 11.8 - 16.3 Mt/mine.
Additionally, historical resource life estimates were
assessed. It was found that using the government method
of calculating coal resource life, 130 - 160 years has been
projected for the past five years. Prior to this period, a steady
trend of decreasing resource life was evident. In comparison,
resource life projections were made using the asymmetric
Gaussian curve modelling technique. Projections were made
for 1980, 1995 and 2010. The modelling indicated that resource
life varied between 146 - 151 years for the three time periods
tested. However, the productions trend for each period
indicated that peak coal would increase by 100 Mt for each
period following the 350 Mt/a projection for 1980.
The modelling techniques used in this project indicated that
projections correlated strongly with historical production
data. It was determined that a number of factors existed
outside the scope of the project with the ability to influence
the true depletion scenario. These factors included:
•• coal supply and demand
•• coal price
66
•• government policy
•• coal infrastructure systems.
Review of the identified factors indicated that whilst they
could affect the true depletion scenario, accuracy of the
projections should be maintained.
References
Australian Bureau of Agricultural and Resource Economics, 2010.
Australian commodity statistics 2010 [online]. Available from:
<http://www.adl.brs.gov.au> [Accessed: 7 May 2012]
Bardi, V and Lavacchi, A, 2009. A simple interpretation of Hubbert’s
model of resource exploitation, Energies 2009, 2:646-661.
Bartlett, A, 2006. A depletion protocol for non-renewable natural
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151-164.
Department of Natural Resources and Mines, 2012. Queensland’s
coal – Advanced mines and projects [online]. Available from:
<http://mines.industry.qld.gov.au/assets/coal-pdf/new_coal_
min_adv_proj.pdf> [Accessed: 20 September 2012].
DIP, 2010. Coal Plan 2030 – Laying the foundations of a future
[online], Queensland Government. Available from: <www.deedi.
qld.gov.au> [Accessed: 7 May 2012].
DME, 2007. Department of Mines and Energy. Queensland’s worldclass coals [online]. Available from: < http://mines.industry.qld.
gov.au/ assets/coal-pdf/wcc_nov_07_1.pdf> [Accessed: 10 May
2012].
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<http://www.ga.gov.au/cedda/publications/1201> [Accessed:
20 September 2012].
Guo, H, 2011. A simple algorithm for fitting a Gaussian function,
IEEE Signal Processing Magazine, 89:134-137.
Höök, M, Zittel, W, Schindler, J and Aleklett, K, 2010. Global coal
production outlooks based on a logistic model, Fuel, 89:3546-3558.
Hubbert, M, 1956. Nuclear energy and the fossil fuels, Drilling and
Production Practice, 205: 7-25.
Kato, T, Omachi, S and Aso, H, 2011. Asymmetric Gaussian and its
applications to pattern recognition, PhD thesis (unpublished),
Graduate School of Engineering, Tohoku University, Sendai-shi.
Mohr, S, Höök, M, Mudd, G and Evans, G, 2011. Projection of longterm paths for Australian coal production – Comparisons of four
models, International Journal of Coal Geology, 86(4):329.
Mohr, S and Evans, G, 2009. Forecasting coal production until 2100,
Fuel, 88:2059-2067.
Moore, C L, 1966. Projections of US petroleum supply to 1980, Office
of Oil and Gas, Washington DC, p 47.
Mutton, A J, 2003. Queensland Coals 14th Edition: Physical and
Chemical Properties, Colliery and Company Information, Queensland
Department of Natural Resources and Mines.
Patzek, T and Croft, G, 2010. A global coal production forecast with
multi-Hubbert cycle analysis, Energy, 35:3109-3122.
Productivity Commission, 1998. The Australian Black Coal Industry
[online]. Available from: <http://www.pc.gov.au> [Accessed: 7
May 2012].
Queensland Exploration Council, 2011. Queensland Exploration
Scorecard, Queensland Resources Council.
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annual reports/reviews from 1980 to 1997 [online]. Available
from: <http://www.nrm.qld.gov.au/mines> [Accessed: 4 May
2012].
Management of Trailing Cables on
Electrically Powered Load-Haul-Dump
Units
W Paterson1 and P Knights2
ABSTRACT
Electric load-haul-dump units (eLHDs) using trailing cables are more increasingly being used
in underground hard rock mining operations in place of conventional diesel load-haul-dumps
(LHDs). This is due to electric LHD’s desirable operational characteristics and lower operating
costs. Unlike diesel LHDs, electric LHDs do not release diesel particulates, have lower heat and
noise emissions and have cheaper energy requirements. This in turn reduces requirements on
the installed capacity of the mines’ ventilation system. A number of mine design restrictions that
impact on the operational application of electric LHDs were identified as part the research project.
Electric LHDs were found to be most suited to block and panel caving operations, due to their
centralised long life extraction levels. The extraction level layouts identified as most suited for
the implementation of electric LHDs were the offset herringbone and herringbone layouts. Other
mine design considerations identified as part of this study were: trailing cable length, automation
barrier systems, gate end bays, electrical requirements, type of trailing cable and roadway design.
Data pertaining to electric LHD downtime was sourced from Northparkes Mines. The average
availability, utilisation and effective utilisation of the electric LHDs was significantly different
from previously projected values. The mean time between failure and the mean time to repair
were also determined as part of the analysis. The key causes of maintenance downtime were
identified as part of the study. Scheduled maintenance was the largest contributor, followed by
drive and gearbox faults and then trailing cable and electric faults.
INTRODUCTION
LOAD-HAUL-DUMPS
Electric LHDs using trailing cables are becoming more
common-place in the mining industry and have many
benefits over traditional diesel LHDs. However there is a
lack of information relating to the practical application of
eLHDs. This project summarises information relating to
the safe operation and reliability of trailing cables. Data
provided by Northparkes Mine (NPM) is used to assess the
impact maintenance of eLHDs using trailing cables has on
their availability and performance. The data provided allows
for the determination of the primary causes of unplanned
maintenance.
Significant advancements were made in underground
metalliferous mining in the 1960s. One of the more
significant advancements made was the introduction of new
underground mining machinery such as LHDs (Matikainen,
1991). LHDs are a mid-sized mining vehicle that are used
in underground metalliferous operations to load, haul and
dump material (Ward, 2009). The introduction of LHDs in the
1960s resulted in substantial changes to mine planning and
production rates. A number of problems were encountered
with the LHDs after they were initially introduced; these
related to their lower than expected availability and significant
amount of maintenance required. Most of these problems
were overcome with better understanding of the machines
and how to effectively and properly manage them. Since the
1960s there have been a number of advancements relating to
LHDs with a focus on developing electrically powered and
automated LHDs (Matikainen, 1991).
When assessing the application of eLHDs in a mining
environment, consideration must be given to the impacts
their use will have on the mines’ design. One of the key
design issues associated with the application of eLHDs is
their limited movement due to the length of the trailing
cable. This movement restriction will in turn affect the layout
of the extraction level and the manner in which the ore is
extracted. This information is summarised allowing NPM
and industry to easily assess what design changes would
need to be implemented or improved for the use of eLHDs
using trailing cables. This project is part of Mining Education
Australia’s (MEAs) electric mine initiative and can be used by
the industry to help assess the benefits of using eLHDs.
Most LHDs are powered by either diesel or electricity through
the use of trailing cables. Each of these methods has their
own specific advantages and disadvantages. Diesel powered
LHDs have many desirable operating characteristics. These
include high productivity, high equipment utilisation, high
mobility and operational flexibility (Weiss et al, 1981; Ross,
2008). Problems associated with using diesel LHDs include:
1. SAusIMM, School of Mechanical and Mining Engineering, The University of Queensland, Brisbane Qld. Email: [email protected]
2. MAusIMM, Head of Division of Mining Engineering, School of Mechanical and Mining Engineering, The University of Queensland, Brisbane Qld. Email: [email protected]
67
higher noise levels, increasing fuel price, exhaust emissions
and increased heat emissions. One of the consequences
associated with the use of diesel LHDs, is the need to increase
and improve ventilation due to the presence of diesel exhaust
emissions. Additional ventilation requirements would result
in increased ventilation costs (Grigg, 1985; Brannon et al,
2008).
repositioned to the gate end bay at the opposite end of the
extraction drive in order to bog the remaining drawpoints.
LHDs would also be unable to reverse into the opposite drive
without running over the trailing cable and risking damage
(deWolfe, 2009).
Electrically powered LHDs have a number of associated
advantages and disadvantages. The advantages include:
minimal exhaust emissions, reduced heat emissions,
cheaper energy requirements and reduced noise levels.
Due to the reduced heat and exhaust emissions, a reduced
ventilation capacity can be installed, thus reducing associated
ventilation costs (Abrahamsson and Johansson, 2008; Grigg,
1985). Disadvantages associated with the application of
eLHDs include: reduced flexibility, limited haul range,
limited mobility, additional development and their limited
application to specific mining methods and extraction level
layouts (Grigg, 1985; Weiss et al, 1981).
The continuous trough layout is characterised by its extensive
longitudinal undercut trough drift which is developed and
blasted prior to the production drifts and drawpoints. The
design of the layout results in no minor apices, meaning that
there is no lateral restraint to the major apex. The continuous
trough layout has been used and considered by a number of
mines, however, no recorded cases could be found of eLHDs
being utilised for this layout. The layout can be modified to
allow for the use of eLHDs, by orientating the drawpoints
towards the incoming LHD. Other infrastructure would have
to be installed to cater for the use of eLHDs (Brown, 2003).
METHODS SUITABLE FOR THE APPLICATION
of eLOAD-HAUL-DUMPS
The El Teniente layout was developed and named after the
El Teniente copper mine in Chile. Drawpoint cross-cuts are
developed in straight lines at a 60˚angle to the major apices
and production drifts. The minor apex of the El Teniente
layout is shorter and more stable than other designs; this is
in part due to the rectangular shape of the columns (deWolfe,
2009; Esterhuizen and Laubscher, 1992).
Diesel LHDs are widely used in metalliferous mining
operations and can be used in a variety of circumstances
and mining methods. This contrasts with eLHDs, which
are limited to the few mining methods that have long-life
centralised extraction levels. The limited application of
eLHDs is associated with the necessary infrastructure and the
limited range of operations caused by the length of the trailing
cables. These limitations mean that eLHDs are most suited
to block caving operations and mining methods based on
similar principles. Mining methods that could utilise eLHDs
are: panel caving, inclined drawpoint caving and front caving
(Brown, 2003). Block caving and panel caving are the most
common of these methods and are discussed in more detail.
EXTRACTION LEVEL LAYOUT
Introduction
The design and performance of the extraction level in block
or panel caving operations have significant impacts on the
operational efficiency and cost effectiveness of the operation
as a whole (Brown, 2003). Several horizontal LHD layouts are
utilised in block caving operations; however of these only four
are significantly different from each other. The four extraction
layouts to be considered are: Henderson, Continuous Trough,
El Teniente and Offset Herringbone. The application and
feasibility of using eLHDs varies for each of the layouts (Brown,
2003).
Henderson (Z design)
The Henderson layout was developed at Henderson mine,
USA. The layout has since been phased out of Henderson’s
operations and there are no other known cases of this layout
being used. The Henderson layout consists of opposite
drawpoint drifts that are in line with each other but inclined
to the production drift. Drawbells and drawpoints at right
angles to the production drifts (Brown, 2003).
The application of eLHDs has not been trialled for the
Henderson layout. A number of modifications would need to
be made to allow for their use. The modified layout would
require power sources to be located at opposite ends of
the extraction drives, as only half of the drawpoints would
be orientated towards an incoming LHD. Once half of the
drawpoints had been bogged the LHD would need to be
68
Continuous trough (trench layout)
El Teniente (parallelogram)
Electric LHDs using trailing cables were trialled at El
Teniente in the 1980s, this resulted in unsatisfactory outcomes.
However electric LHD technology has changed significantly
since the 1980s and more is understood about the mine design
requirements for their application (Anon, 2010). To allow
for the use of electric LHDs a power feed would be required
at each end of the panel. The LHD would be required to
reposition to the different feeds more frequently, which
would cause operational delays. Similar to the Henderson
design, the LHDs would not be able to reverse into the
opposite drawpoint without running over the trailing cable
and potentially damaging it (deWolfe, 2009).
Offset herringbone
The offset herringbone layout is a modification of the
herringbone layout. Due to the improved geotechnical and
operational aspects associated with the offset herringbone
layout, the original herringbone layout has all but been
phased out. The offset herringbone layout has become the
most commonly used layout for new block and panel cave
operations. The layout is characterised by drawpoints which
are offset and at right angles to the major apex (Esterhuizen
and Laubscher, 1992). The offset herringbone layout was used
as the extraction level layout for the block caving operations
at NPM (Brown, 2003).
Electric LHDs using trailing cables are most suited in their
application to the offset herringbone layout. An advantage of
the offset herringbone layout is that the LHD will always face
the same direction. This is particularly important when the
LHD trams to a permanent crusher or ore pass. As a result a
power feed only needs to be installed at one end of the panel;
this means that there will not be operational delays from
repositioning the LHD. The Northparkes E26 Lift 2 offset
herringbone extraction level layout can be seen in Figure 1.
This layout was designed with the intention of using eLHDs
with trailing cables, and a number of modifications have been
made to accommodate them including permanent gate end
bays (deWolfe, 2009).
ManageMent of traIlIng caBleS on electrIcally powered load-haul-duMp unItS
first extraction level design for Lift 2 E26 did not include any
dedicated GEBs. Instead, the LHDs were to be parked in the
extraction drives and plug in points were to be installed on the
sidewall opposite the drive entrance. This design would have
prevented any vehicular movement along the perimeter drive
whilst LHDs were operating. The design was later changed
to one which had permanent GEBs allowing for improved
operations (Rio Tinto, 2002; Ross, 2008).
electrical
The electrical requirements of the mine should be calculated
and the appropriate infrastructure should be installed to
allow for the operation of eLHDs. The designed electrical
system must meet certain predetermined criteria.
Hartman (1992) defines the ten key criteria for a designed
electrical system:
Fig 1 - Northparkes E26 Lift 2 (deWolfe, 2009).
deSIgn conSIderatIonS for electrIc
load-haul-duMpS
trailing cable length
One of the key factors that must be considered when designing
an extraction level layout for eLHDs is the required length of
the trailing cable. If the cable is too short the LHD will not be
able to function for its designed task, however if the cable is
too long the LHD will become less efficient as it is carrying
unnecessary weight and any future cable replacements
will be more costly. NPM encountered this problem when
transitioning from Lift 1 to Lift 2 of the E26 block cave. The
eLHDs used for Lift 1 had a total trailing cable length of
280 m, this fell short of the 330 m length required to operate in
Lift 2. A program was put in place to refurbish the cable reels
and associated motors allowing them to operate in Lift 2 (Rio
Tinto, 2006; Ross, 2008).
1. safety to personnel and property
2. reliability of operation
3. simplicity
4. maintainability
5. adequate interrupting ability
6. current-limiting capacity
7. selective-system operation
8. voltage regulation
9. potential for expansion
10. initial capital cost.
Sandvik (2011) outlines a number of considerations that
need to be taken into account when designing an electrical
system for eLHDs:
•
•
•
•
Barrier systems
NPM is in the process of trialling the automation of its eLHD
fleet. As part of this process a barrier system using a light
curtain is installed. If one of the light curtains is breached, the
automated eLHDs shut down and cannot be reactivated until
the cause or the breach is investigated and they are manually
reset. Barrier systems were originally installed close to the
floor, however this resulted in unforseen operational delays.
During everyday operation of eLHDs, trailing cables are
occasionally subject to recoiling. The recoil from the trailing
cables was enough to set off the safety barriers, shutting down
all automated production for a period of time. Modifications
were made to the height of the barrier systems from the
floor reducing the downtime associated with trailing cable
recoil (Rio Tinto, 2011). However this problem is ongoing
and still results in LHD downtime. To avoid downtime from
trailing cable recoil further research should be conducted
into improving the design of the automation barrier system
however, this exceeds the scope of this project.
gate end bays
Gate end bays (GEBs) are designed to allow for the parking
of eLHDs while providing space for the Gate End Panels
(GEPs) or ‘plug in’ points. GEBs are not required for the
operation of eLHDs using trailing cables, but allow for
improved operations that reduce the potential for damage,
to trailing cables and their associated infrastructure. NPMs
•
•
•
electric LHDs typically demand 1.5 to 1.8 times the
nominal power while mucking
the power demand on the supplying network will be
moderately high
the transformer size should be dimensioned to the
expanded power requirements
±10 per cent tap changer should be equipped to the
supplying transformer to regulate source voltage and to
compensate for voltage and torque drops
it is not advisable to connect any other loads other than
LHDs to the same substation
advisable not to supply all of the LHDs from the same
substation, and
recommended limit of four LHDs per substation.
trailing cable
Modern electrical cables used in underground mining
operations are constructed using a complex layering system.
Cables may comprise of a number of layers including a
conductor, conductor shield, electrical insulation (dielectric),
insulation shield, auxiliary shield, primary shield, dielectric
field, insulation and an outer cover (Thue, 1999). Many
manufactures recommend specific trailing cables for their
electric LHDs. Sandvik (2011) recommends:
•
•
•
•
a four core rubber trailing cable
pilot wireless trailing cables
pilot wireless earth continuity monitoring
vector-plus connectors and receptacles.
roadway design
The typical operation of eLHDs poses many hazards that
can result in damage to the trailing cable. One of the hazards
which trailing cables are most commonly exposed is the risk
of the trailing cable becoming stuck or damaged by debris in
the extraction drive. Other hazards include the cable being
69
damaged or suffering abrasion from a rough roadway surface.
These hazards were taken into consideration during the design
of extraction levels of the block caves at Northparkes. Once
the extraction level had been developed, a concrete roadway
was constructed for the extraction level. The concreting of the
roadways resulted in less damage and abrasion to the cables,
reduced machine maintenance, improved water drainage and
an increased tyre life (Rio Tinto, 2006).
The block cave concrete roadway designs were modified
to improve upon the previous design. The E48 Feasibility
Study Report (2006) outlined a number of learnings from the
previous roadway designs, as follows:
•• the use of 80 MPa concrete is important for abrasion
resistance
•• the practice of installing dowel bars for join bonding
between the road panels successfully inhibits vertical
movement
•• the use of steel reinforcement in the extraction drives and
no steel reinforcement in the draw point slabs is suitable
•• the blow-off of the floor to solid rock is time and material
consuming
•• the road panel thickness previously used is sufficient
•• efforts need to be made to further inhibit cracking
•• the design with the sharp corner on the drawpoint panel
is insufficient
•• the implementation of a QA-system as used in E26 Lift
two effectively ensures the production of high quality
roadways
•• kerb construction lowers maintenance costs significantly
•• water runoff, coming mainly from the dust suppression, is
very limited making drains redundant.
The extraction level of lift one E48 required 4862 m of
concrete roadway to be laid at a rate of 20 m a day (Rio Tinto,
2006):
•• The concrete laid in the drawpoints is subject to significant
wear due to the mining operations. This was taken into
consideration as part of NPM’s roadway design of the
drawpoints which only used in situ cast concrete slabs
without reinforcement; this differed from the typical
roadway design which used reinforcement. The primary
reason for this design alteration was that the drawpoint
roadway could be easily repaired and did not contain any
steel reinforcement which could pose a safety hazard to
the operation of the eLHDs.
OPERATIONAL CHARACTERISTICS of LOADHAUL-DUMPS
Overview of data set
Data was provided by NPM pertaining to breakdowns,
scheduled maintenance and machine downtime for their
LHD fleet. The data set contains information from the 1st
August 2011 to the 17th September 2012, a period of 414
days. The data provided contained information relating to the
operating characteristics of 12 eLHDs and two diesel LHDs.
Six of the eLHDs had insufficient data or were not scheduled
for production over the period that the data was recorded
and were excluded from the analysis. The data recorded for
the remaining six eLHDs was extensive with an average of
1660 separate incidents recorded per LHD over the period
of operation. Table 1 summarises information relating to the
provided data set while Table 2 contains information relating
to the data sets of the LHDs analysed.
70
TABLE 1
Summary of the data set provided by Northparkes Mine.
Parameter
Value
Shift period
12 hours
Period over which data recorded (dates)
1/08/11 to 17/09/12
Period over which data recorded (days)
414
Total number of recorded incidents
14 218
Number of eload-haul-dump units
12
Number of eLHDs with adequate data sets
6
Average number of recorded incidents (eLHD)
1660
Number of diesel load-haul-dump units
2
Number of diesel LHDs with adequate
data sets
2
Average number of recorded incidents
(diesel LHD)
1415
TABLE 2
Summary of the data from the load-haul-dump units analysed.
Load-hauldump type/no
LHD No
Period (days)
No of incidents
Electric
LHD8
414
1746
Electric
LHD9
414
1740
Electric
LHD10
414
1712
Electric
LHD11
414
1701
Electric
LHD12
414
1739
Electric
LHD14
414
1331
Diesel
LHD7
414
1446
Diesel
LHD13
414
1384
Previous studies
Before the commencement of Lift 1 E48 block cave the
availability and utilisation of the eLHDs had to be determined.
NPM operates a fleet of six Tamrock 1400E Electric LHDs and
one Tamrock Diesel 1400D LHD in their current production
operations. Rio Tinto’s feasibility study of Lift 1 E48 (2006)
determined the availability and utilisation the eLHDs and
the diesel LHD to be 82 per cent and 92 per cent respectively.
Table 3 provides a breakdown of the various losses associated
with the availability and utilisation. Service time was
determined to be the largest loss associated with eLHD
availability; this was closely followed by contingency for
major component failures and break-downs, including cable
and tyre damage.
Shift change was found to be the only loss associated with
the utilisation, most of the time lost is associated with travel
to and from the shift changeover. The E48 feasibility report
(2006) made a number of recommendations to improve the
utilisation of the LHDs, these include:
•• hot seat changeovers
•• utilising the automation of the eLHDs to remotely control
them from the surface, meaning that the operator does not
need to waste time traveling down to the level.
Electric load-haul-dump-units
The operating characteristics of the eLHDs were calculated
using information provided by NPM. Table 4 summarises
Management of Trailing Cables on Electrically Powered Load-Haul-Dump Units
TABLE 3
Availability and utilisation values for load-haul-dump units (Modified from
Rio Tinto, 2006).
Availability
Value
Losses
82%
Service time (6%)
Breakdowns, including
cable and tyre damage
(4%)
Contingency for major
component failures (5%)
Utilisation
Effective utilisation
92%
Shift change (8%)
75.44%
the values calculated for all of the eLHDs. The calculated
availability of the eLHDs was similar for all machines. The
availability of the eLHDs ranges between 85.91 per cent for
LHD11 and 92.93 per cent for LHD14. The average availability
of the eLHDs was found to be 88.29 per cent, this value is
higher than the 82 per cent availability predicted in the E48
feasibility study. The utilisation was also determined for the
eLHDs to range between 75.13 per cent and 79.07 per cent,
with an average value of 77.18 per cent.
The values calculated for the utilisation of the eLHDs were
found to be similar to each other with no outlying values. The
effective utilisation was found to range between 65.80 per cent
for LHD8 and 72 per cent for LHD14. The average effective
utilisation for the eLHDs was calculated to be 68.13 per
cent this value is significantly lower than the 75.44 per cent
predicted in the E48 feasibility study. The discrepancy in these
two values is due to an unrealistically high utilisation being
used in the feasibility study which only takes into account
shift change and does not consider either other operating
delays or no scheduled production.
As part of the their feasibility study for the E48 block cave
Rio Tinto completed a productivity simulation as part of this
simulation they used a MTBF of 120 operational hours and
a MTTR of 251 minutes. The MTBF for the eLHDs ranged
between 18.26 hours for LHD8 and 31.42 hours for LHD11.
The average MTBF was found to be 21.95 hours, this value
is significantly lower than the one used in the productivity
simulation. The variation between the two MTBF values
can be partially attributed to the difficulties of operating an
automated eLHD fleet which at the time of compiling the E48
feasibility report had not been trialed at other sites. The MTTR
was found to range between 136 minutes for LHD11 and 188
minutes for LHD12. The average MTTR for the eLHDs was
found to be 167 minutes this value is significantly smaller than
the 251 minute value used in the E48 feasibility study. The
lower MTTR value means repairs to the eLHDs are taking a
significantly shorter period of time than previously predicted,
this could be due to a number of things, including:
•• improved competency of workforce (both operators and
service personal)
•• improved maintenance procedures
•• increased frequency of minor repairs
•• reduced frequency of major repairs.
Diesel load-haul-dump-units
The two diesel LHDs in operation at NPM were analysed as a
comparison point for the eLHDs. These LHDs are used in both
production and development operations and are exposed
to different operating conditions than their electric counter
parts. The smaller sample size of diesel LHDs means that the
results from the analysis are not as representative as that of
the eLHD analysis. Table 5 shows the calculated operating
characteristics of the diesel LHDs.
The availability of the diesel LHDs was found to range
between 89.30 per cent for LHD7 and 94.57 per cent for LHD13,
with an average availability of 91.94 per cent. The average
availability of the diesel LHDs was higher than the previously
predicted value of 82 per cent in the E48 feasibility study.
The increased availability of the LHDs may be attributed to
any number of operational or environmental factors. The
utilisation was determined to range between 65.75 per cent
and 68.01 per cent, with an average value of 66.88 per cent.
The effective utilisation of the diesel LHDs were found to
range between 60.73 per cent for LHD7 and 62.18 per cent for
LHD13, with an average effective utilisation of 61.46 per cent.
The values calculated for the effective utilisation of the diesel
LHDs were found to be significantly lower than the previously
indicated in the E48 feasibility study. The main reason for the
large discrepancy between that of the feasibility study and the
calculated utilisation can be attributed to unrealistic estimates
being used for the feasibility study.
Both the MTBF and MTTR were calculated for the diesel
LHDs. The MTBF for the diesel LHDs ranged between
27.50 hours and 38.02 hours with an average MTBF of
32.07 hours. The MTBF values for both diesel LHDs differ
significantly and a representative MTBF value cannot be
determined due to the small sample size. The calculated
MTBF values are significantly smaller than the ones used in
the E48 feasibility studies productivity simulation. Possible
reasons for the large discrepancy between the calculated and
the predicted MTBF values include:
TABLE 4
Calculated operating characteristics of Northparkes Mine’s eload-haul-dump units.
Load-haul-dump
unit no
Availability (%)
Utilisation (%)
(%)
MTBF (hours)
MTTR (minutes)
LHD8
87.52
75.18
65.80
18.26
169
LHD9
89.29
75.13
67.08
19.24
180
LHD10
86.01
78.28
67.32
20.78
164
LHD11
85.91
79.07
67.93
31.42
136
LHD12
88.07
77.95
68.65
20.15
188
LHD14
92.93
77.47
72.00
21.83
166
Average
88.29
77.18
68.13
21.95
167
71
TABLE 5
Operating characteristics of diesel load-haul-dump units at Northparkes Mine.
Load-haul-dump units
no
Availability (%)
Utilisation (%)
(%)
MTBF (hours)
MTTR (minutes)
LHD7
89.30
65.75
60.73
27.50
226
LHD13
94.57
68.01
62.18
38.02
142
Average
91.94
66.88
61.46
32.76
184
•• unrealistic MTBF values used for the productivity
simulation
•• changes to the environmental or operational conditions
•• untrained or unskilled workforce (both operational and
service personal)
•• ageing LHD fleet.
The calculated MTTR values range between 142 minutes for
LHD13 and 226 minutes for LHD7, with an average MTTR of
184 minutes. There is a large variation between the two LHDs
MTTR values; this means making a meaningful representative
value impossible. Both the calculated MTTR values are
smaller than the ones predicted in the E48 feasibility study.
Electric and diesel load-haul-dump-unit
comparison
A comparison, of the electric and diesel LHDs operating at
NPM, was made to ascertain which of these LHDs has more
desirable operating characteristics. Due to the smaller sample
size of diesel LHDs their operating characteristics are not as
conclusive as the eLHDs.
Both electric and diesel LHDs were found to have similar
availabilities, however the diesel LHDs were found to have
an availability that was 3.65 per cent higher than the eLHDs.
There was a greater variance between the two types of LHD
for the utilisation. The eLHDs were found to have an average
utilisation that is 10.3 per cent higher than that of the diesel
LHDs. The larger variation in utilisation can be in part
attributed to the diesel LHDs operating under more varied
and challenging conditions than the eLHDs. The variation
TABLE 6
Classification system used by Northparkes Mine to categorise various
types of maintenance.
Category
Description
Planned maintenance
Scheduled maintenance
Communications fault
Power failure and not defined
Drive/gearbox fault
Foreign object/contamination, incorrect installation,
mechanical overload, temperature and not defined
Leak
Foreign object/contamination, incorrect installation
and not defined
Low flow
Hydraulic oil flour
Low pressure
Tyre failure
Roller fault
Foreign object/contamination, fines/carry back and
not defined
Sensor/proxy fault
Human error, incorrect installation, not defined,
power failure and sensor error
Structural fault
Bent, collapsed, cracked and not defined
Trailing cable/electrical fault
Electrical fault, electrical overload, foreign object/
contamination, human error, incorrect installation,
not defined, power failure, temperature, PLC fault
and trailing cable fault
72
between the two types of LHD for the utilisation may also be
attributed to a preference to use eLHDs over diesel LHDs due
to their lower operating costs.
The average MTBF variation between the types of LHD was
10.81 hours, with diesel LHDs having an average MTBF value
of 32.76 hours and eLHDs having an average MTBF value of
21.95 hours. This is a considerable variation and is indicative
of diesel LHDs having a lower failure frequency than eLHDs.
There is also considerable variation between the MTTR values
for both types of LHD, with electric and diesel LHDs having a
MTTR value of 167 and 184 minutes respectively. This means
that it takes on average 17 minutes less to repair an eLHD
than it does a diesel LHD. Although this variation may be due
to a number of things, there are two likely causes:
1. failures that occur in diesel LHDs are more labour
intensive
2. eLHDs have an increased number of minor failures when
compared to diesel LHDs.
DISTRIBUTION OF MAINTENANCE DOWNTIME
Introduction
It is important for a mining operation to understand the
main causes for machine maintenance so appropriate plans
and procedures can be introduced so as to improve machine
availability. NPM classifies maintenance preformed on LHDs
into ten distinct categories, which are outlined in Table 6.
Electric load-haul-dump-units
Time lost due to maintenance for the eLHDs was quantified
into the ten distinct categories used by NPM. Planned
maintenance was found to be the largest contributor to
downtime for all of the eLHDs analysed. Planned maintenance
ranged between 36.96 per cent and 49.49 per cent. Faults
associated with the drive/gearbox were the second largest
contributor of maintenance time for all of the eLHDs with the
exception of LHD14. These ranged between 14.15 per cent and
27.20 per cent. LHD14 had trailing cable and electrical faults
as its second largest contributor to maintenance downtime.
The availability and utilisation of LHD14 was also higher than
all of the other eLHDs assessed. There may be a number of
reasons for this, however the mostly likely reasons are that
LHD14 is newer than the other eLHDs or it has been recently
refurbished and therefore does not have as many problems
with its drive/gearbox.
The third largest contributor to downtime for all of the
eLHDs, excluding LHD14 was trailing cable/electrical faults.
The trailing cable/electrical faults had a range between
13.04 per cent and 21.06 per cent. Structural faults were
the fourth most time consuming repairs, this was followed
by communication faults, leaks and sensor/proxy faults
which all accounted for approximately the same amount of
downtime. The downtime associated with low flow and roller
faults was insignificant. Low pressure only accounted for a
small amount of downtime on LHDs 8 and 9.
ManageMent of traIlIng caBleS on electrIcally powered load-haul-duMp unItS
LHDs accounts for 47 per cent of all maintenance downtime
whereas it only accounts for 42 per cent of downtime in
eLHDs. The variation of planned maintenance is most likely
due diesel LHDs requiring scheduled maintenance every
125 hours compared to eLHDs which only require it every
500 hours (Rio Tinto, 2006).
Dive/gearbox faults accounted for 31 per cent of all of
the maintenance downtime for diesel LHDs this value
is significantly larger than the 22 per cent for eLHDs.
Maintenance downtime from trailing cables/electrical faults
accounted for six per cent of total maintenance time for diesel
LHDs whereas it accounted for 15 per cent for eLHDs. This
large variation between the two types of LHD, for both drive/
gearbox faults and trailing cable/electrical faults is probably
due to diesel LHDs not using trailing cables resulting in a
redistribution of the maintenance downtime.
Fig 2 - Graph of the average classified eLHD maintenance downtime.
Communications and sensor/proxy faults contributed
marginally less to downtime for diesel LHDs than for the
eLHDs. The variation between these values may be due to
the smaller sample size of the diesel LHDs. Structural faults,
leaks, low pressure, low flow and roller faults account for a
similar proportion of maintenance downtime for both electric
and diesel LHDs.
concluSIonS
Fig 3 - Graph of the average classified diesel LHD maintenance downtime.
diesel load-haul-dump units
The percentage of total repair time required for each of the
ten distinct maintenance categories used at NPM for diesel
LHDs was calculated. The largest contributor to downtime
for diesel LHDs was planned maintenance, which ranged
between 38.85 per cent and 63.34 per cent of total downtime.
The main cause of downtime for LHD7 was drive/gearbox
faults which accounted for more downtime than planned
maintenance however the difference between the two
accounted for less than one precent of lost time. The second
largest contributor to downtime was the faults associated
with the dive/gearbox. Trailing cable/electrical faults
and structural faults were the next largest contributors to
downtime, followed by leaks. Minimal amounts of downtime
were associated with low flow, low pressure, roller faults and
sensor/proxy faults.
comparison of electric and diesel load-hauldump units
A comparison of diesel and eLHD maintenance downtime
was completed using the average downtime associated for
each classification of maintenance. Figure 2 and Figure 3
show the distribution of maintenance downtime for electric
and diesel LHDs respectively.
The maintenance downtime distribution of the two types of
LHD varies significantly. Planned maintenance for the diesel
This project determined that eLHDs are suited to block caving
operations and other mining methods which are based on the
same fundamental principles. The offset herringbone and
herringbone extraction level layouts were found to be most
accommodating to the application of eLHDs. A number
of mine design restrictions that impact on the operational
application of eLHDs were identified. These included; trailing
cable length, automation barrier systems, gate end bays,
electrical requirements, type of trailing cable and roadway
design. The project identified the need for further research,
into ways in which the design of the automation barrier
systems, could be modified so as to avoid interference from
trailing cable recoil.
The average availability of eLHDs and diesel LHDs
operating at NPM was determined to be 88.29 per cent and
91.94 per cent respectively. The utilisation of eLHDs was
found to be 77.18 per cent. This value was larger than the
66.88 per cent determined for diesel LHDs. This variation in
utilisation may be attributed to the diesel LHDs operating
under more varied conditions. Electric LHDs were found to
have a significantly lower MTBF value than diesel LHDs. The
MTTR for eLHDs was found to be 17 minutes less than diesel
LHDs. This difference was attributed to eLHDs requiring less
complex maintenance than diesel LHDs.
Planned maintenance was found to be main cause of electric
and diesel LHD downtime. Drive and gearbox faults were
determined to be the second largest contributor to LHD
downtime. Electrical and trailing cable faults accounted
for 15 per cent of total eLHD maintenance downtime. This
value varied greatly from the 6 per cent recorded for diesel
LHDs. A larger sample size of diesel LHDs is required if a
comprehensive analysis between diesel and eLHDs is to
occur.
acknowledgeMentS
This research project would not have been possible without
the support and assistance of several people and parties. The
73
authors would like to thank Rio Tinto Northparkes Mine
for providing data for this project, in particular Brendon
Burrows and Trent Burke for their time and assistance with
the collection of data. The authors would also like to thank
Markus Chang and Matthew Elston for their assistance in
proofreading and the discussion of technical concepts.
Matikainen, R, 1991. How to increase LHD productivity in hard
rock mining, in Proceedings International Conference: Reliability,
Production and Control in Coal Mines, pp 85-88 (The Australasian
Institute of Mining and Metallurgy: Wollongong).
REFERENCES
Rio Tinto Pty Ltd, 2002. Northparkes mines Lift 2 north extension
feasibility study, Rio Tinto report E26L2NPFS.
Abrahamsson, L and Johansson, J, 2008. Work culture and gender
issues in a changing technical context – examples from LKAB
iron ore mine in Kiruna, in Proceedings 5th International Conference
and Exhibition on Mass Mining, pp 1129-1138 (Lulea University of
Technology, Division of Mining and Geotechnical Engineering:
Lulea).
Anon, 2010. El Teniente goes deeper, International Mining, October,
38:10-15.
Brannon, C, Casten, T, Hewitt, S and Kurniawan, C, 2008. Design
and development update of the Grasberg block cave mine, in
Proceedings 5th International Conference and Exhibition on Mass
Mining, pp 433-442, Lulea University of Technology, Sweden.
Hartman, H, 1992. SME Mining Engineering Handbook, 2161 p (Society
for Mining, Metallurgy, and Exploration: Littleton).
Rio Tinto Pty Ltd, 2006. E48 project feasibility study October 2006,
Rio Tinto report E48PFS.
Rio Tinto Pty Ltd, 2011. Automation at Northparkes [online].
Available from: <http://www.dpi.nsw.gov.au/__data/assets/
pdf_file/0004/353479/Automation-@-Northparkes-Rio-Tinto.
pdf> [Accessed: 1 May 2012].
Ross, I, 2008. Northparkes E26 Lift 2 block cave – A case study, in
Proceedings 5th International Conference and Exhibition on Mass
Mining, pp 25-34 (Lulea University of Technology, Division of
Mining and Geotechnical Engineering: Lulea).
Sandvik Mining and Construction, 2011. Sandvik electric LHDs,
Northparkes.
Brown, E, 2003. Block Caving Geomechanics, 511 p (Julius Kruttschnitt
Mineral Research Centre: Brisbane).
Thue, W, 1999. Electrical Power Cable Engineering, 417 p (Marcel
Dekker: New York).
deWolfe, C, 2009. Drawpoint layouts for block caves, Rio Tinto report
12733-2.
Ward, B, 2009. Load haul dump (LHD) automation [online],
Commonwealth Scientific and Industrial Research Organisation.
Available from: <http://research.ict.csiro.au/research/labs/
autonomous-systems/field-robotics/mining-robotics/loadhaul-dump-lhd-automation> [Accessed: 1 May 2012].
Esterhuizen, G and Laubscher, D, 1992. A comparative evaluation
of production-level layouts for block caving, in Proceedings 2nd
International Conference and Exhibition on Mass Mining, pp 63-70
(University of Pretoria, Department of Mining Engineering:
Pretoria).
Grigg, F, 1985. Future developments in underground mobile
equipment, in Proceedings First International Federation of Automatic
Control Symposium, pp 165-174 (University of Queensland,
Department of Mining Engineering: Brisbane).
74
Weiss, P, Fettweis, G, Moschitz, I, Olsacher, A and Riedler, H, 1981.
Relevant factors for development and draw control of block
caving, in Proceedings Design and Operation of Caving and Sublevel
Stoping Mines (American Institute of Mining, Metallurgical, and
Petroleum Engineers: Denver).
The Effect of Joint Properties on a
Discontinuous Rock Mass
M Sherpa1 and P Hagan2
Abstract
When considering the strength of a rock mass having joint sets, the dominant parameters are
joint orientation, joint frequency and joint strength. Under experimental testing conditions, these
parameters strongly influence rock mass strength, elastic properties and the mode of failure.
A review of the literature identified a lack of information concerning the influence of joint
frequency on the properties of a rock mass. This study aimed to assess the effects of joint frequency
on rock mass properties, particularly in a confined stress state. A total of 24 uniaxial and 87 tri-axial
compressive strength tests were undertaken using intact and discontinuous, jointed sandstone core
specimens, the latter having sawn-cut joints orientated across the longitudinal axis of the core.
Based on the results, several conclusions have been made concerning the Anisotropic Effect
factor, the Joint Factor and the mode of rock failure. First, the Aniostropic Effect factor can be
adjusted for joint frequency. Second, the joint strength parameter of the Joint Factor can also be
modified and finally, a single shear failure plain was observed for intact and jointed rock specimens
under biaxial confinement.
These conclusions provide an insight into the effects of joint frequency in a discontinuous rock
mass.
Introduction
Rock masses often contain discontinuities having varying
length, frequency and orientation. Discontinuities are
considered as planes of weakness, that can control and
influence the strength, deformation and failure behaviour of a
rock mass (Brady and Brown, 2004).
In the laboratory, a discontinuous rock mass can be modelled
through induced joints in intact rock samples. Laboratoryscale testing can provide an insight into the influence of
joint properties on a discontinuous rock mass and hence in
understanding rock mass behaviour.
Background
The Joint Factor method as proposed by Ramamurthy and
Arora (1994) provides a method of estimating the unconfined
compressive strength ratio (σcr) of a discontinuous rock
mass. This was based on a series of confined and unconfined
strength tests using plaster and sandstone cores that contained
induced joints of different configurations.
The Joint Factor (Jf) is defined in terms of joint frequency
(Jn) expressed in units of fractures per metre (f/m), joint
orientation (n), and joint strength (r) as shown in Equation 1
(Ramamurthy and Arora, 1994).
Jf =
Jn
n×r
(1)
The Joint Factor represents a reduction in rock mass quality.
A modification was proposed to the Joint Factor by Jade and
Sitharam (2003) who found it could be used to estimate the
compressive strength of a rock mass as shown in Equation 2.
J
f m
c
vcr = 0.039 + 0.893e - 160.99 (2)
The Joint Strength parameter (r), is defined as:
r= x vn
(3)
where τ and σn are the shear and normal stresses acting along
a joint plane. In their original proposal, Ramamurthy and
Arora (1994) recommended direct shear testing to obtain
joint strength parameter values. If direct shear testing is not
possible then they provided suggested values for the joint
strength parameter which for clean joints varied with the
compressive strength of the intact rock as shown in Table 1.
An earlier study by Byerlee (1978) found that for joints
containing no infill material, when a high normal stress is
applied, the joint strength parameter is independent of rock
type and UCS. Byerlee (1978) suggested that the joint shear
stress, t, is dependent on the applied joint normal stress, σn, as
shown in Equation 4.
x = 0.85 × v n (4)
Equation 4, known as Byerlee’s Law, is valid over the normal
stress range of 5 to 200 MPa for clean joints only (Paterson and
Wong, 2005).
1. GAusIMM, The University of New South Wales, Sydney. Email: [email protected]
2. FAusIMM, The University of New South Wales, Sydney. Email: [email protected]
75
M Sherpa and p hagan
TABLE 1
Suggested values for the Joint Strength parameter, for various strength
rock samples (after Ramamurthy and Arora, 1994).
Uniaxial compressive
strength of intact
rock (MPa)
Joint strength
parameter, r
2.5
0.30
5
0.45
15
0.60
25
0.70
45
0.80
65
0.90
100
1.0
Remarks
Fine-grained micaceous to
coarse-grained
Singh, Rao and Ramamurthy (2002) stated that the joint
strength parameter can be estimated at ‘sufficiently low
normal stress.’ When subjected to compressive strength
testing, a discontinuous rock mass and its joints are quite
often subjected to high normal stresses as is the case when
joints are orientated at or near to perpendicular to the axial
loading direction.
As joint orientation rotates from the perpendicular to
parallel to the axis of loading, the rock mass properties are
significantly affected. In an unconfined stress state, the rock
mass strength can be predicted by a single plane of weakness
theory, which was first proposed by Jaeger (1960). The effect
of joint orientation on a discontinuous rock mass is further
complicated by the addition of confining pressure. Einstein and
Hirschfeld (1974) and Jade and Sitharam (2003) suggested that,
at high confining pressures, strength is independent of joints
and failure often occurs across joints, through the intact rock.
Fig 1 - Effect of confinement on a discontinuous rock mass containing a single
induced joint at varying orientation angles, β (Ramamurthy and Arora, 1994).
The addition of confining pressure can suppress the influence
of joints in the rock mass. This can be seen in Figure 1 where
Ramamurthy and Arora (1994) showed how the effect of joint
orientation on the ratio of maximum to minimum principle
stresses decreases as confining pressure is increased.
To quantify the effect of joint orientation on a discontinuous
rock mass under a confining pressure, Ghazvinian, Hadei and
Madani (2008) proposed the Anisotropic Effect factor (Ae)
such that:
Ae =
via - v ja
via
(5)
Fig 2 - Effect of joint orientation on the principle stress ratio
(Ghazvinian, Hadei and Madani, 2008).
where σia and σja are the intact rock and jointed rock mass
compressive strengths at some confining pressure a. The
Anisotropic Effect factor can be used in conjunction with the
Joint Factor method to predict confined compressive strength
ratios. They based their findings on tests using plaster core
specimens having an intact strength of 8.7 MPa and two
orthogonal joints at varying joint orientations.
Their results are similar to those of Ramamurthy and Arora
(1994) in Figure 1, although the results of Ghazvinian, Hadei
and Madani (2008) in Figure 2 are symmetrical at about 45°, as
a result of the two orthogonal joints.
The Anisotropic Effect factor is greatest for joint orientations
which produce minimum strength. As the confining pressure
is increased, the Anisotropic Effect factor is reduced for all
joint orientations as can be seen in Figure 3.
To date, the Anisotropic Effect factor has only accounted
for joint orientation. It could be expected that joint frequency
would similarly influence rock mass strength.
76
Fig 3 - Variation in the Anisotropic Effect factor (Ae) with joint orientation (after
Ghazvinian, Hadei and Madani, 2008).
the effect of joInt propertIeS on a dIScontInuouS rock MaSS
eXperIMental work
research aims
The objective of the study was to assess whether the spacing
of discontinuities in a rock or the joint frequency alters the
Anisotropic Effect factor in a jointed rock mass. To achieve
this, an experimental testing regime was undertaken,
consisting of unconfined and confined compressive strength
tests using intact rock and discontinuous rock mass samples.
Tests were undertaken using a Hawkesbury Sandstone
sample. Joints were sawn across the diameter of the diamond
cored rock specimens having a diameter of 41 mm with
length of 102 mm. These cuts were orientated perpendicular
to the axial loading direction as shown in Figure 4. The
joints were free of any infill. Due to the fine grained nature
of the sandstone, the diameter was considered sufficient for
compressive strength testing.
Fig 5 - Effect of loading platens that are stiffer than the test material
(Tang and Hudson, 2010).
Compressive strength testing
The testing apparatus recorded axial force and axial
displacement, allowing for calculations of axial stress-strain
values. Estimations of peak strength, linear elastic modulus
and normal stiffness were made from the stress-strain curves.
The joint frequencies tested were 10, 20, 40 and 60 f/m. The
discontinuous rock mass samples contained one, two, four
and six sawn cut joints respectively for these joint frequencies.
Fig 4 - Examples of sandstone specimens containing induced joints or
discontinuities.
Methodology
The confining pressures (σ3) selected for testing are
presented in Table 2. Originally, the confining pressures
selected were much higher, in the range of 5 to 50 per cent
of the intact rock strength, σci, as used by Ghazvinian, Hadei
and Madani (2008) in their testing. Preliminary testing at the
five per cent level resulted in a three-fold increase in rock
strength. It was assumed that the effects of confining pressure
on joint frequency would be observed at lower strengths, thus
the confining pressures were lowered to the values presented
in Table 2.
TABLE 2
Range of confining pressures (σ3) used in the test program.
Experimental considerations
The rock strength tests were undertaken using an MTS815
model stiff testing machine. A tri-axial cell provided
confinement to the rock specimens during testing.
A constant deformation rate of 0.003 mm/s was used in
all tests. This rate achieved peak strengths within five to ten
minutes of loading.
Differences in elastic properties between rock and the steel
loading platens used in testing were expected to influence
results. Brady and Brown (2004) stated that the applied axial
stress can become non-principle when the test material and
loading platens are of different stiffnesses.
This issue and its effects are described by Tang and Hudson
(2010) who stated that pre-peak and peak stress-strain
behaviour is independent of loading platen properties and
are only found to influence post-peak stress-strain and failure
behaviour. Being made of steel, the platens were much stiffer
than the sandstone. The effect that this difference in stiffness
would have on the stress distribution can be seen in Figure 5.
With the sample/platen interface at the top and bottom of
the rock specimen, compressive failure cones are observed.
A tensile failure plane is also evident in the middle of the
sample (Tang and Hudson, 2010).
Tests were replicated three times to account for variability
in the rock sample and test method.
Levels of confining pressure
σ3 (as per
cent of σci)
0.5
1.5
2.5
4
6
σ3 (MPa)
0.1
0.25
0.5
0.8
1.2
reSultS
A summary of the results of the uniaxial and triaxial tests at
increasing levels of joint frequency and of confining pressure
is shown in Table 3.
In the unconfined state, it was found that the peak stress
reduced with joint frequency as shown in Figure 6. This is
consistent with the results of Hagan et al (2012). Similarly,
elastic modulus also decreased with joint frequency.
Considering the combined effects of the discontinuities and
confinement, it was found that the presence of discontinuities
in the rock reduced the peak strength; it was more sensitive
at the highest joint frequency of 60 f/m as shown in Figure 8.
Whereas at the lower discontinuity frequencies of 10 and
20 f/m, there was little difference between the peak strength
values at the various levels of confinement attained compared
to the intact specimens. A similar variation was observed with
Elastic Modulus as shown in Figure 9.
77
TABLE 3
Test results.
Joint
frequency
(f/m)
0 f/m
10 f/m
20 f/m
40 f/m
60 f/m
Confining
pressure
(MPa)
Peak stress
(MPa)
Elastic
modulus
(gPa)
Normal
stiffness
(MPa)
0.00
20.6
3.8
931
0.10
25.8
4.6
683
0.25
38.3
4.7
504
0.50
50.6
6.4
563
0.80
65.5
7.3
518
0.00
21.3
5.0
986
0.10
24.1
4.9
464
0.25
37.5
6.4
404
0.50
56.9
7.7
419
0.80
69.6
8.5
562
0.00
18.8
4.0
549
0.10
25.6
4.8
581
0.25
37.7
5.8
452
0.50
54.3
6.6
457
0.80
71.5
7.6
363
0.00
15.7
2.4
410
0.25
31.2
3.3
328
0.50
47.2
4.3
404
0.80
59.8
4.8
412
0.00
13.5
2.0
312
0.10
21.0
2.5
308
0.25
28.9
2.9
346
0.50
43.1
3.9
333
0.80
53.7
4.2
295
Fig 7 - Variation in the peak strength of intact specimens with
confinement pressure.
Fig 8 - Variation in peak strength with confinement pressure for varying levels
of discontinuity frequency.
pronounced change in the elastic modulus, however, which
decreased with confinement pressure and joint frequency.
Elastic properties
For the intact samples, the load fell sharply after achieving the
peak load to the residual strength level. By contrast, in those
test specimens containing discontinuities there was a more
gradual reduction in load as can be seen in Figure 10.
The discontinuous samples underwent significantly more
deformation which, as suggested by Einstein et al (1970), is
due to closing of the induced joints.
Failure observations
Fig 6 - Variation in peak stress and elastic modulus joint frequency in
unconfined tests.
When considering the effects of confinement on the
intact test specimens, strength was found to increase with
confinement pressure, σ3, as shown in Figure 7.
Initial observations showed no significant difference in the
levels of peak strength between the intact and discontinuous
samples at joint frequencies of 10 and 20 f/m over the range
of confining pressures tested. There was, though, a more
78
When testing specimens in an unconfined state, a buckling
tensile failure at the midpoint of the specimen was often
observed as seen in Figure 11 as well as vertical splitting. This
is consistent with the type of failure predicted by Tang and
Hudson (2010) due to differences in the elastic properties of
the steel loading platens and the rock material, resulting in
creating a tensile plane, as seen in Figure 5.
Furthermore, compressive cones were observed at the top
and bottom of the test specimens, also described by Tang and
Hudson (2010), resulting in minimal fracturing within these
areas with all tested specimens. An example of the effect of the
compressive cone can be seen in Figure 12. In this example,
the failure zone is circumferential around the top and bottom
edges of the test specimen.
the effect of joInt propertIeS on a dIScontInuouS rock MaSS
were subjected to confinement, the middle section underwent
significantly more radial deformation compared with the
intact and the more widely spaced, lower joint frequency
specimens. The cause of the increase in radial deformation
is a result of top and bottom sample segments acting as
intermediate loading platens and concentrating the applied
stress within the middle segment.
Fig 9 - Variation in the Elastic Modulus with confinement pressure for varying
levels of discontinuity frequency.
Fig 12 - Failure around sample end.
Shear failure proposal
For a sample to result in a single shear failure, confinement was
required. As a result of an increase in the joint frequency, the
confining pressure required for a single shear also increased.
A linear relationship has been identified between confining
pressure and joint frequency for a single shear failure, and
is presented in Figure 13. The relationship is presented in
Equation 6.
Fig 10 - Stress-strain curves of intact and discontinuous (40 f/m) samples.
This relationship provides an indication of when the failure
of a discontinuous rock mass under confinement stops being
influenced by joint frequency.
Equation 6 has been developed from discontinuous rock
mass samples containing induced joints perpendicular to the
major loading direction. Considering the major effect joint
orientation has on rock mass strength and failure mode, it
would be expected that this relationship would not hold for
joints sets orientated at other angles.
v 3 = 0.0006 # J f + 0.0043
(6)
where σ3 is expressed as a percentage of the intact rock
compressive strength (per cent σci).
dIScuSSIon
anisotropic effect factor
Fig 11 - Example of buckling failure observed in unconfined tests.
In the confined state, however, and particularly at the higher
levels of confinement, the dominant failure mode resulted in a
single shear failure plain.
For tests with a joint frequency greater than 20 f/m, it
was observed that failure would always occur through the
middle section of the test specimen. When the specimens
Based on the earlier observations by Hagan et al (2012), it
was expected that the presence of joints in the discontinuous
samples would result in a reduction in peak strength when
compared to the intact rock. However, this was not observed
in testing specimens having widely spaced discontinuities of
10 and 20 f/m as can be seen in Figure 14, where there was
little significant difference in the peak strength between the
intact and jointed rock specimens over the range of confining
pressures studied.
The findings of peak strength behaviour between the intact
and discontinuous samples have a significant effect when
comparing the experimental results with the Anisotropic
Effect factor, as presented in Figure 15.
79
Fig 13 - Required confining pressure for shear failure with an increase in joint
frequency.
Fig 14 - Comparison of the peak strength values between the intact and low
frequency jointed (10 and 20 f/m) specimens.
For the 10 and 20 f/m specimens there was a poor correlation
between joint frequency and the Anisotropic Effect factor. At
higher joint frequencies of 40 and 60 f/m, a reduction in the
Anisotropic Effect factor was observed as confining pressure
was increased. This behaviour is similar to the observations
for joint orientation by Ghazvinian, Hadei and Madani (2008).
It can be concluded that at low joint frequencies when the
rock mass quality is still high, there is little difference in
peak strength between an intact rock and discontinuous rock
mass. However, as the rock mass quality decreased through
an increase in joint frequency, the Anisotropic Effect tends to
become more dominant.
Fig 15 - Experimental results analysing the Anisotropic Effect for joint
frequency.
Fig 16 - Experimental results and Joint Factor method predictions.
For joints orientated perpendicular or near perpendicular to
σ1, Byerlee’s Law should be used in the Joint Factor method.
concluSIonS
Compressive strength testing of intact rock and discontinuous
rock mass sandstone core samples was undertaken. The
discontinuous samples contained induced joints, sawn cut at
varying spacing to replicate the effects of joint frequency.
joint strength parameter modification
A shear failure relationship was proposed. This
relationship, a function of the applied confining pressure and
joint frequency, can be used to identify the influence of joint
properties on the failure mode of a rock mass sample.
The Joint Factor method has been compared with the
experimental results. Initial Joint Factor predictions were
made using the values recommended by Ramamurth and
Arora (1994), that is, 0.98 for joint orientation and 0.65 for
the joint strength parameter. These resulted in a significant
underestimation compared with the experimental results, as
seen in Figure 14.
It was found that, at the low joint frequencies tested of 10
and 20 f/m, there was little significant differences in strength
between the intact and jointed rock specimens. Consequently
at these joint frequencies, there was no correlation with the
Anisotropic Effect factor. As joint spaced decreased and joint
frequency increased, the impact of the Anisotropic Effect
factor was similar to that previously seen for joint orientation.
By substituting the joint strength parameter value of 0.65
with Byerlee’s (1978) recommendation of 0.85, this reduced
the underestimation also shown in Figure 16.
If direct shear testing is unavailable to estimate a joint
strength parameter, care should be taken when using
recommended values provided by Ramamurthy and Arora
(1994).
80
A modification of the Joint Factor method has been proposed.
It was found that for clean joints orientated perpendicular to
the major loading direction, the joint strength parameter is
dependent upon the applied axial stress and not on the intact
rock UCS. Modifying the joint strength parameter reduced the
error in the empirical predictions of the Joint Factor method
compared with the experimental results.
The Effect of Joint Properties on a Discontinuous Rock Mass
References
Brady, B H G and Brown, E T, 2004. Rock Mechanics for Underground
Mining, third edition (Kluwer Academic Publishers: Netherlands).
Byerlee, J, 1978. Friction of rocks, Pure Applied Geophysics, 116:615-626.
Einstein, H H and Hirschfeld, R C, 1974. Model studies on mechanics
of jointed rock, Journal of the Soil Mechanics and Foundations
Division, 99(3):229-248.
Einstein, H H, Nelson, R A, Bruhn, R W and Hirschfeld, R C, 1970.
Model studies of jointed rock behaviour, in Proceedings 11th US
Rock Mechanics Symposium, California (American Rock Mechanics
Association).
Ghazvinian, A, Hadei, M R and Madani, B, 2008. Behaviour of
mechanical anisotropic specimens under triaxial testing, in
Proceedings 42nd US Rock Mechanics Symposium, San Francisco
(American Rock Mechanics Association).
Hagan, P, Zhang, L, Mitra, R and Kodama, J, 2012. The effect of
discontinuities in uniaxial compressive strength testing of rock,
in Proceedings 7th Asian Rock Mechanics Symposium, Seoul, Korea.
Jade, S and Sitharam T G, 2003. Characterisation of strength and
deformation of jointed rock mass based on statistical analysis,
International Journal of Geomechanics, 3:43-54.
Jaeger, J C, 1960. Shear failure of anisotropic rocks, Geological
Magazine, 97:65-78.
Paterson, M S and Wong, T, 2005. Experimental Rock Deformation: The
Brittle Field, second edition (Springer: Netherlands).
Ramamurthy, T and Arora, V K, 1994. Strength predictions for jointed
rocks in confined and unconfined states, International Journal of
Rock Mechanics and Mining Sciences and Geomechanics Abstracts,
31(1):9-22.
Singh, M, Rao, K S and Ramamurthy, T, 2002. Strength and
deformation behaviour of a jointed rock mass, Rock Mechanics and
Rock Engineering, 35(1):45-64.
Tang, C and Hudson, J A, 2010. Rock Failure Mechanisms: Explained and
Illustrated (CRC Press/Balkema: Netherlands).
81
82
APPeNDIX 1 – MeA STUDeNT CoNFeReNCe PRoGRAMMe
oCToBeR 2012
2012 MEA Student Research Conference Curtin University (WASM), 12 Oct 2012, Kalgoorlie WA Programme (12 Oct 2012, WASM, Hobson Lecture Theatre):
08:45 – 09:00 09:00 – 10:20 09:00 – 09:20 09:20 – 09:40 09:40 – 10:00 10:00 – 10:20 10:20 – 10:40 10:40 – 12:00 10:40 – 11:00 11:00 – 11:20 11:20 – 11:40 11:40 – 12:40 12:40 – 14:00 12:40 – 13:00 13:00 – 13:20 13:20 – 13:40 13:40 – 13:50 13:50 – 14:00 Welcome & Opening of the Symposium (A. Jarosz & P. Hagan) Session 1 Vanessa Collins (UQ): Maximising production rates at Brockman 4 by Minimising Truck Delays at the Crusher Tim Graham (UNSW): Strategic Project Risk Management for an Emerging Miner Dermott Sundquist, Vaughn Golding, Davood Jafari, Brajesh Rajopadhyaya (UA): Multi-‐Objective Optimisation of Mining-‐Metallurgical Systems Weilin Wang (WASM): ANSYS for Stress Analysis of Underground Structures Morning Tea in Rm.703-‐211 Session 2 Seung Hyeon Lee (WASM): Comparing Neural Networks with JKSimblast Prediction Model in Purpose of Optimal Ground Vibration Induced by Blasting McLeod Mackenzie (UNSW): Prediction and Modelling of Blact Vibration and its Effects at Glendell Colliery Casey Costello (UQ): Grizzly Modifications at Ridgeway Deeps Block Cave Gold Mine Lunch in Rm.703-‐211 Session 3 Brenton Goves (UQ): Continuous Surface Miner Operations at Fortescue Paul Carmichael (UNSW): An Investigation into Semi-‐
intact Rock Mass Representation for Physical Modelling of Block Caving Mechanics Zones Vadim Strukov (WASM): A Comparative Study of Truck Cycle Time Prediction Methods Afternoon Tee in Rm.703-‐211 & Meeting of Prize Committee Announcements of Prizes & Closing of the Conference The order of presentations was determined by a ballot drawn on 10/10/2012 at WASM (Present: A.Jarosz, A.Halim, B.Suwardi, J. Hackney)
vi
2 Appendix 2 – Presenters in MEA Student Research
Conferences
Year/Venue
University
Student presenter(s)
Paper title
UoA
Dermott Sundquist and Davood
UNSW
Paul Carmichael
An investigation into semi-intact rock mass representation for physical modelling of
block caving mechanics zones
McLeod McKenzie
Prediction and modelling of blast vibration and its effects at Glendell Colliery
Multi-objective optimisation of mining-metallurgical systems
Jafari
Tim Graham (third prize)
2012 WASM
UQ
Vanessa Collins
Maximising production rates at Brockman 4 by minimising truck delays at the crusher
Casey Costello (first prize)
Brenton Goves (second prize)
WASM (Curtin)
2011 UQ
2010 UoA
Strategic project risk management for an emerging miner
Seung Hyeon Lee
Grizzly modifications at Ridgeway Deeps block cave gold mine
Continuous surface miner operations at Fortescue
Comparing neural networks with JKSimblast prediction model in purpose of optimal
ground vibration induced by blasting
Vadim Strukov
A comparative study of truck cycle time prediction methods
Weilin Wang
ANSYS for stress analysis of underground structures
UoA
James Boffo
Acoustic emission monitoring of impregnated diamond drilling for deep exploration
UNSW
Owen Riddy (first prize)
UQ
Brendan Murphy
WASM (Curtin)
Colin Thomson
UoA
Adam Schwartzkopff and
Developing a truck allocation model for Bengalla Mine
Development of an underground coal scheduling and simulation program
Stoping analysis of Golden Grove under high stress conditions
Comparisons between three-dimensional yield criteria for fractured and intact rock
Daniel Hardea
2009 UNSW
UNSW
James Tibbett
Failure criteria of three major rock types affecting Ridgeway Deeps
UQ
William Hartley (first prize)
WASM (Curtin)
Nathan Colli
Analysis of the principles for sound waste dump design and placement
UNSW
Kali Dempster
Quantifying time-dependent rock behaviour at Ridgeways Deeps block cave operation
UQ
Michael Baque
Optimisation of coal scheduling at Dawson Mine
WASM (Curtin)
Kieran Rich (first prize)
vii
An investigation into final landform criteria required for a safe, stable, sustainable and
non-polluting landform in the Bowen Basin
Paste fill for stope stability at the Challenger Gold Mine
Author Index
C
Carmichael, P
K
1
Karakus, M
37
45
Chalmers, D
51
Kerai, M
Chanda, E
19
Kizil, M S
7, 25
Collins, V
7
Knights, P
13, 67
Costello, C
13
G
M
McKenzie, M K
51
Golding, V
19
Chalmers, D
51
Goves, B
25
Meikle, A
57
Graham, T K C
31
Paterson, W
H
Hagan, P
75
Halim, A
45
Hebblewhite, B
Holland, A
37
R
Rajopadhyaya, B
19
S
Saydam, S
Seib, W
37
J
Jafari, D
67
1
I
Iles, M
P
31
25, 57
Sherpa, M
75
Sundquist, D
19
19
83
84

Mining Education Australia Research Projects Review

Transcription

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