research and innovation in the copper sector -...

Research and Innovation in the Copper Sector

Presentation to the Copper to the World – Delivering South Australia’s Copper Strategy June 2017

Overview

• Synergy between innovation and research

• Mining - large scale underground

• Processing

• Integrated Operations

University of Adelaide 2

Synergy between Innovation and Research

• “creative and systematic work undertaken in order to increase the stock of knowledge and to devise new applications of available knowledge”


• “deliberate application of information, imagination and initiative in deriving greater or different value from resources, and includes all processes by which new ideas are generated and translating into useful products”

Innovation

Research OECD

Collaboration between Innovation and Research


Mining Companies

Technology Suppliers (METS and Start-ups)

ResearchProviders

Research Outcomes & Technology Transfer

• Challenges• Specifications• Implementation

• Fast Win; Fast Fail• Impacts Assessment

• Stock of knowledge• Qualified people

Government

Mining–Accelerated Development

–Fragmentation

–Underground Automation

–High Productivity Mining• Finer fragmentation

• Increased haulage and drawpoint rate

• Further exploits benefits of automation

–Grade Engineering®

–Pre-concentration


Impact of Innovation on Mining- Accelerate development – Earlier access to ore- Increase Return on Investment

University of Adelaide

6

Year Advance rateM per day

1980 15

2001 7.2

2011 3.8

2016 2.6

Target Advance Rates

• Decrease in Advance rate in spite of large and sophisticated equipment

• Focus not on the production system as a whole

DecreaseInvestment

AccelerateRevenue

IncreaseNPV

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Years

RapidDevelopment

NormalDevelopment

Impact of Innovation on Mining- Accelerate development – Earlier access to ore- Increase Return on Investment


TemporaryCanopy

• Reduce Development Cycle time from >16 hours to <10.5 hours• Reduce face cycle ~ 8 hrs• Reduce muck cycle ~2 hours by rapid transport of rock

Mining – Fragmentation affects Productivity from Draw Points, Mucking and Hauling and Downstream


1. Fragmentation – depending on joint spacing and stress, the

need for preconditioning & large draw points

2. Dilution –Dilution and mud rushes between large rocks

3. Caveability - Lifts needed to prevent deviation of cave

4. Recovery – Depending on dip, footwall may be left

Cave TrackerBeacons and detectors

Sublevel CavingSteep dip and continuing to depthHigh development costs

Block Caving

Mining – Fragmentation affects Productivity from Draw Points, Mucking and Downstream


Draw Point Productivity t/m2 per day • Reducing Powder Factor to reduce mine costs• Less Jointed and Harder Ore• Concept that SAG milling needs coarse particles• Mucking rate reduced• Hanging due to coarse particles

Courtesy Fidel Baez

Hart, S, et al (2001) Optimisation of the Cadia Hill SAG Mill Circuit. Proc. Int. Conf. Autogenous and Semi-autogenous Grinding Technology, vol 1, pp.11-30.

Mining – Fragmentation affects Productivity from Draw Points, Mucking and Hauling


Continuous MiningAutonomous Continuing Mining

• Autonomous CMS implemented by optical sensing and control to maximise ore drawing plus achieve a uniform load on Panzer

• 22% increase in d50 rock size decreased productivity by 43% for non-autonomous

• Autonomous increased production by 30% for both coarse and fine rock distribution

• Conditions which increased interactions in flowing zones gave less rock hang ups and increased drawpoint productivityCastro, R., et al. (2015). "Automation fundamentals of continuous mining system."

International Journal of Mining, Reclamation and Environment 29(5): 419-432.

Underground Automation


• Real-time tracking of equipment

• Equipment and production data

• Draw control important to Sublevel Caving

• Production over shift change and during blasting

• Less damage to equipment

Mining – High Productivity Methods


High capital cost requiring thousands of metres of tunnel

Several blasting cycles required to establish drawbell &

undercut, damaging surrounding rock mass

Each blasting event requires remedial ground support

Slow cave establishment

Undercut drill horizon

• Untried concept but technology for method exists today• Rock mass above cave is conditioned after production blast• Post conditioning holes initiated after production holes• Wireless initiators required for this method• Blast wave reflections off free face• Post conditioning increases cave establishment rate, thus NPV

Post Conditioning using Wireless Initiation

MPa8

MPa4

MPa20

MPa30

MPa8

MPa4

MPa20

MPa30

• Wave reflects as a tensile wave with higher magnitude• Reflected tensile wave superimposed on incoming tensile wave• Superposition has magnitude higher than tensile strength

Conventional Sub-level Caving

Grade Engineering®

• Reject below cut off grade material in

stages of rock handling

• Amenability to upgrading

determined in core analysis and

distributed across resource

• Accentuate upgrading through

selective fragmentation (e.g.,

differential blasting)

• Grade Engineering® value optimised

with re-scheduling to exploit new

block model attributes constrained

by operational parameters

• Sensing and sorting depends on

mineralisation – poly-minerals and

dispersement in fragmentation


Ore Zones amenable to Grade Engineering® through Differential

Blasting

Mary Kathleen Uranium – Mary K – Radiometric Ore Sorter for U3O8

Scintillometer – Gamma Radiation plus Optical Detector for Particle Size

Stream U3O8

Grade (ppm)

U3O8

Recovery(%)

Mass Recovery(%)

Feed 1300 100 100

Reject 120 6 55

Pre-concentrate 2700 94 45

• Jaw Crush to -150 mm• +40 mm to Ore Sorting• -40 mm and Pre-concentrate

report to Fine Crushing• -40 mm higher grade

Pre-concentration

Stream Pb Zn Mass Recovery (%)

Feed 5 7 100

Reject 0.7 0.9 32

Pre-concentrate 7 10 68

Recovery (%) 95 96 -

• 14 mm top size• Feed Preparation – 1.7 mm• Throughput >1000 tph• -1.7 mm higher grade

Mount Isa Mines– Lead/Zinc – Heavy Medium Plant


Processing– Fine crushing and grinding to liberation size

• High Pressure Grinding Rolls

• Stirred mills with graded charge

• On-line Screen Efficiency

• Mill slurry and media volumetric load sensors

• Hydrocyclone sensors

– Flotation

• Integrated froth sensors for mass pull control

• Liberation sensor to maximise mill throughput

• Coarse particle recovery

– Leaching (Tank, Heap, Dump)

• Robust sensing and optimisation

• Bioleaching

– Product Handling

. Thickener 3d slurry density sensing


Integrated Operations

– Digital provides the tools

– Exploit automation by linking data pools

– Augment machine data with resource data

– Rapid feedback to mine operations

– Rapid feedforward to downstream

– Overcome resource variability which gives rise to variable downstream performance

– Exploit variability, or make products closer to specifications


Resource Heterogeneity On-Belt Feed Analysis

Arena, T., & McTiernan, J. (2011). METPLANT 2011 - Metallurgical Plant Design and Operating Strategies.

Feed Variability

17

Drill and geophysical

sensors

Resource Knowledge

Belt Sensors

• Rapid resource knowledge

updating (reconciliation)

• Accurate interpolation of ore

attributes

• Enable rapid decisions on ore

destinations to optimise value

• Enable rapid decisions in

downstream processing

• Enable rapid feedback to mine

operations

• Ore tracking from resource to

mill feed

• Sensor response interpreted in

terms of resource knowledge

• Relative contributions to ore

feeds quantified rapidly

Downstream Decisions

MiningDecisions

Integrated Operations


LHD and conveyor sensors

Conclusions– Optimisation opportunities exist in mining &

processing

• Fragmentation is key

– Integrated operations seek to link and optimise overall resource value

• Integration by exploiting automation

– Opportunities for research, technology transfer and impact through collaboration


research and innovation in the copper sector -...

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