Presented at Society for Mining, Metallurgy & Exploration (SME) 2012 Annual Meeting. This talk covered research done with funding from Illinois Clean Coal Institute (ICCI).

1. Improving Truck-Shovel
Energy Efficiency through
Discrete Event Modeling
Kwame Awuah-Offei
Bismark Osei
Hooman Askari-Nasab
1

2. Outline
• Background
• DES of Truck-Shovel
Energy Efficiency
• Case study
• Conclusions
2

3. Background
• US mining industry consumes approx. 365
billion kWh of energy/yr.
• US DOE estimates that energy consumption
can be reduced by 49% by using current best
practice and with more research.
• This translates into nearly $3.7 billion of
potential savings at 5.3¢/kWh of energy.

4. Background
• Current energy-saving
strategies in mining
tend to involve
technology
improvements (e.g.
improving engine
performance).
• However, there is evidence that operator
practices and mine operating conditions
significantly affect the energy consumption.

5. DES of Truck-Shovel Energy
Efficiency
1. Problem formulation
2. Solution
methodology
3. System specification
4. Modeling
5. Verification &
validation
6. Experimentation & analysis
7. Documentation, reporting & dissemination
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6. Problem Formulation
• Strip mine in IL
• Annual production of
600,000 tons of coal
• Average stripping ratio
of 17:1 (yd3/ton).
• Objective • Constraints:
– To evaluate production – Don’t sacrifice
strategies that will productivity
improve the energy
– New capital
efficiency of the truck-
expenditures should be
shovel overburden
a last resort
removal system
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7. Solution Methodology
• Discrete event simulation chosen as
the solution approach
• Arena®, based on SIMAN, used in this
study
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8. System Specification
• Fragmented overburden is removed by carry dozers with
truck-shovel removal for the last ~15 ft.
• One Hitachi EX1900 hydraulic shovel (14.4 yd3 dipper) and
two CAT® 785C (150-ton), rigid frame, haul trucks
• The mine also owned two CAT® 777 (100-ton) trucks, which
are used on long hauls
• Typical haul length is ~4,000 ft (3,960 ft surveyed) at designed
grade of 10%
• The mine runs two 11-hour shifts per day
• The shovel and trucks had on-board data logging systems that
were used to collect data.
• Shovel cycle times were obtained using time and motion
studies. 8

9. Input Data
Process time (mins) Distribution Expression
Dumping time Lognormal LOGN(0.0349, 0.0156)
Return time Lognormal LOGN(0.173, 0.0969)
Loading time Gamma GAMM(0.0464, 3.05)
Spotting time Lognormal LOGN(0.155, 0.109)
Process Distribution Expression
Payload (tons) Normal NORM(139, 10.8)
Empty travel time (mins) Normal NORM(2.3, 0.471)
Loaded travel time (mins) Beta 2.26 + 1.66 × BETA(3.3, 4.06)
Dumping time (mins) Erlang ERLA(0.458, 2)
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10. Modeling
• Entities = operators
• Transporters = trucks
• Resources = shovel
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11. Modeling
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12. Modeling
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13. Verification & Validation
• Verified with
Actual Simulated Error
animation etc. Mean Half-
width
• 100 replications for Production [tons] 15,887 16,590 57 4%
each scenario Number of loads 114 120 0.4 5%
Total fuel 488.87 502.60 1.54 3%
consumption
• Model validation [gals]
based on VIMS truck Average fuel 4.24 4.27 0.01 1%
consumption per
data cycle [gals]
Overall fuel 17.81 18.51 0.03 4%
• Model prediction of efficiency
[tons/gal]
shift utilization used as
estimate of engine load
factor for a shift 13

14. Experimentation & Analysis
• The model was used to
evaluate two scenarios after
discussions with
management
• Scenario 1: Additional CAT
777 trucks
– Payload of 777 truck
described with 94 tons mean
• Scenario 2: Using EX2500 shovel instead of EX1900
– EX2500’s dipper is 20.4 yd3 and was assumed to load 777
and 785 in 4 and 5 passes, respectively.
– Same shovel cycle times and truck payloads assumed (fill
14
factors remain the same)

15. Scenario 1 Results
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16. Scenario 1 Results
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17. Scenario 2 Results
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18. Scenario 2 Results
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19. Conclusions
• A valid discrete event simulation model of truck-shovel
operations to evaluate energy efficiency has been built and
validated using Arena®
• The results show that using a larger excavator increases the
fuel efficiency of the operation while optimizing truck-shovel
matching does not
• Using a larger shovel, without adding additional trucks, will
lead to under-utilization of the shovel
• It is recommended that the mine add one CAT® 777 truck to
the two 785 trucks with the existing Hitachi EX1900 shovel –
this is expected to increase the production/shift by 4,400 tons
with approx. the same fuel efficiency.
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20. Q&A