EEO: Opportunities
Dr Marcin Ziemski
September 2011
1
Challenges for the Mining Industry
Declining ore grades
Average Ore Grades Over Time
8
Copper Grade (%)
Lead Grade (%)
7
Increasing complexity
Nickel Grade (%)
6
Gold Grade (g/t)
Increased world
demand
Ore Grade
5
4
•
•
3
2
•
1
0
1970
1975
1980
1985
1990
Year
1995
2000
2005
2010
•
•
Social Expectations
Environmental
Legislation
Energy
Cost/Availability
Water Limitations
Carbon Taxes
•Driving a significant increase in energy consumption
•Mining Multi-Factored Productivity fell by 24.3% between 2001 - 2007*
*Australian Government Productivity Commission (2008)
2
Increasing Energy Consumption
Growth in mining energy consumption has
been particularly strong since 2001/02
• 9.1 per cent a year
Research Report 08.15 December 2008. www.Abare.gov.au
3
Reversing the Trend
Extend approach from:
Doing the same things more (energy) efficiently
Alumina 3-6%
Process changes*
Alumina 1115%
Iron/Steel 89%
Technology changes*
Iron/Steel
~15%
*Energetics
To:
Doing things that are more energy efficient
15% 30%+?
4
‘Standard’ Energy Mitigation Approach
Doing the same things more efficiently
Typically energy consumption/costs reduction limited to ~10%
•
Standard methodology to reduce energy demand in mining operations
•
Step-by-step methodology includes:
–
Pumping systems
–
Motor selection/application/operating regimes
–
Power factor correction considerations
–
Energy and waste heat recycling
–
Alternative energy source options
–
Automation and control
–
Energy Contracts
Pumping
Motors
Energy Mitigation
Standard Methodology
Energy
recycling
Alternative
Energy
5
‘Standard’ Energy Mitigation Approach
Doing the same things more efficiently
Case study 1 – Haulage to Kurri Kurri Smelter, Hunter Valley
•
•
Monitoring of fuel usage
–
Engine upgrades
–
Revised maintenance regimes
Driver benchmarking and re-training
Haulage fuel consumption reduced by ~13%
6
‘Standard’ Energy Mitigation Approach
Doing the same things more efficiently
Case study 2 – Xstrata North Queensland
•
•
Conversion of waste heat to steam at Xstrata MIM copper smelter (2009)
Reduced consumption of natural gas by 1.15 PJ
•
•
Underground Pelton Wheel hydro power generator at the Xstrata MIM
copper operations (2009)
Reduced consumption of natural gas by 37,500 GJ
•
•
Solar hot water, thickener and cooling pump upgrades, boiler replacement
Reduced consumption of natural gas by another 2,000 GJ
7
‘Standard’ Energy Mitigation Approach
Doing the same things more efficiently
Case study 3 – Downer EDI Mining Commodore Mine
• Haul truck analysis, benchmarking and operations
• Improved haulage energy intensity by 18% between 2005-2010
Case study 4 – Rio Tinto Coal Blair Athol mine
• Revised operation/control of Coal Handling and Prep Plant (CHPP)
• Improved CHPP energy intensity by more than 10% (~2007)
8
Extending Energy Mitigation Approach
Doing things that are more energy efficient
•
Future circuits
–
–
Effective use of (alternative) transport and fragmentation technologies
In-circuit sorting/separation: Multiple waste removal points
• Integrating optimisation of the whole extraction cycle
– Optimising blasting, comminution and further processing in combination
– Modelling the end-to-end mine site operations
•
•
•
•
Insights and new opportunities
Understanding up/down-stream effects
Optimising one stage may not improve overall operation!
Extend to off-site processes???
9
Future/flexible circuits
98% of milled material is barren (precious and base metals)
•
Replace mills with alternatives where applicable (eg HPGR)
•
Remove waste at every opportunity before mill
– XRT, colorimetric, density, magnetic, electrostatic sorting
(others?)
•
Identify new waste removal opportunities
– Dependent on deposit properties – improve orebody knowledge
– Eg Blast/primary crush fragmentation size bias of grade
Many of these technologies are being trialled NOW
10
Selective Blasting
0.25
Ore
Low
Grade
0.2
0.15
Blast Energy Distribution
0.1
0.05
0
0
5
10
15
20
25
30
P50 (x10 mm)
Size
Upgrading metal content through selective higher fragmentation of high grade material:
•
Increase the metal content of the ore material (grade up ~20%++)
•
Reduce waste to be processed (30%+ reduction of waste to mill)
•
Blasting energy is more cost-effective than crushing and produces low GHG emissions
Expected 20%++ energy reduction per ton blasted
11
Selective Blasting: Matching Energy Distribution to Metal Content
Standard
blast
Selective
blast
12
Integrated (systems) analysis and optimisation
Blending
Mine
Block
Info
Reporting
Drill & Blast
Simulator
Reporting
Load & Haul
Model
Flotation
Simulator
Reporting
Full Reporting of:
Energy, Water, Emissions
and Costs..
Comminution
Simulator
Reporting
Reporting Summary
13
Integrated (systems) analysis and optimisation
PF=1.5 TPH=830
Power=30.8kWh/t
PF=1 TPH=810
Power=32.1kWh/t
1.0 2.9
1.0 2.8
Result: Throughput Increase of 25% and Energy Reduction of 15%
16.8
18.1
D&B
D&B
L&H
L&H
Comminution
Comminution
Flotation
Flotation
78.1
PF=1 TPH=810
Power=33.4kWh/t
79.3
PF=1.5 TPH=990
Power=29.5kWh/t
PF=1 TPH=965
Power=31.1kWh/t
1.1 3.0
1.0 2.9
0.9 2.7
26.2
D&B
24.2
D&B
L&H
L&H
Comminution
Comminution
69.9
D&B
21.4
Flotation
L&H
Flotation
71.7
PF=1 TPH=810
Power=32.1kWh/t
1.0 2.8
Comminution
18.1
D&B
75.0
L&H
Flotation
Comminution
PF=2 TPH=1010
Power=28.4kWh/t
Flotation
78.1
PF=1 TPH=965
Power=31.1kWh/t
1.0 2.9
3.0 2.8
26.2
D&B
L&H
Comminution
Flotation
69.9
D&B
22
PF=1 TPH=810
Power=32.1kWh/t
L&H
PF=1.5 TPH=830
Power=30.8kWh/t
1.0 2.8
1.0 2.9
16.8
18.1
Comminution
D&B
D&B
L&H
L&H
Comminution
Comminution
Flotation
Flotation
78.1
72
PF = Blast Powder Factor
TPH = Mill Throughput (t/h)
Flotation
79.3
PF=1 TPH=965
Power=31.1kWh/t
PF=1.5 TPH=990
Power=29.5kWh/t
1.0 2.9
1.1 3.0
26.2
D&B
24.2
D&B
L&H
L&H
Comminution
69.9
Flotation
Comminution
71.7
Flotation
14
Ultra-High Intensity Blasting
•
Concept: Use ultra-high intensity blasts (PF >>3) to
reduce required comminution energy
•
Preliminary analysis: significant energy reduction and large throughput gains
Feasible blast designs
already prepared with
PF=~4
250
63
60
60.0
58
54
220
48
50.0
Energy and CO2
SMI is partnering with
world’s leading
blasting companies
64
200
42
134
30.0
20.0
150
161
35
40.0
91
92
98
100
108
26
118
100
50
10.0
Ultra high energy blast
(PF ~4) trials expected
by mid 2012
0.0
Relative TPH
70.0
Comm energy
kWh/t
Blast energy
kWh/t
Total CO2 kg/t
0
0.7
0.9
1.2
1.6
2.1
3.0
4.5
5.7
7.2
Relative TPH %
Powder Factor kg/m3
15
Efficiency of New Projects?
Minerals & Energy – Major Development Projects - April 2009 (ABARE.gov.au).
16
Energy efficiency Opportunities Summary
Project
Description
Potential Opportunity
Flexible circuits: Equipment and
Highly dependent on material
properties
design for multiple waste removal
points to minimise processing of barren
material
Integrated optimisation analysis
Energy mapping, operational
planning and design for better
energy performance
Selective blasting: Early removal of
~20%+ energy reduction per
ton blasted
waste through targeted blast
fragmentation
energy well beyond typical regimes to
reduce comminution energy
250
63
60
58
54
220
48
50.0
Energy and CO2
Ultra-high intensity blasting: Blast
64
60.0
200
42
134
30.0
20.0
150
161
35
40.0
91
92
98
100
108
26
118
100
50
10.0
0.0
Relative TPH
70.0
Comm energy
kWh/t
Blast energy
kWh/t
Total CO2 kg /t
~15% energy reduction with
~30% increase in
comminution throughput
0
0.7
0.9
1.2
1.6
2.1
3.0
4.5
5.7
7.2
Relative TPH %
Powder Factor kg/m3
17
EEO: Indicators
Dr Marcin Ziemski
September 2011
18
Selecting useful energy performance Indicators
•
Accept that KPI’s will not always improve
– Minimising reduction in good KPI is better than improving a misleading KPI
– Prepare management for a dose of reality!
•
Leverage the available data, capture new data
– Collate, analyse, compare, report, repeat
– Allocate dedicated energy personnel
•
Key Energy Indicator: Variability
– Short term (1min/10min/1hr intervals)
Effect of plant instability
– Medium term (shift/weekly/FIFO schedule/monthly)
– Long term (seasonal/annual)
Effect of operators/teams
Effect of externalities
– Start up/ Shut down procedure energy profiles
Often overlooked
19
Summary: energy performance indicators
Compare the right KPIs
Average Ore Grades Over Time
8
Copper Grade (%)
Embrace weakening KPI values
Lead Grade (%)
7
Nickel Grade (%)
6
Gold Grade (g/t)
Ore Grade
5
4
3
2
1
0
1970
Start using kWh per product (as well)
1975
1980
1985
1990
1995
2000
2005
2010
Year
Dec--04
700
Jan-05
Look carefully at variability
600
Feb-05
500
Mar-05
400
Apr-05
May-05
300
Jun-05
200
Jul-05
100
Aug-05
Sep-05
0
Residential Electricity Usage (KW) Dec-04 to Nov-05
Oct-05
Nov-05
Allocate dedicated energy management personnel
20
Thank you
Questions?
Dr Marcin Ziemski
September 2011
21