Improving Truck-Shovel Energy Efficiency

through Discrete Event Modeling

Kwame Awuah-OffeiBismark Osei

Hooman Askari-Nasab

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Outline

• Background• DES of Truck-Shovel

Energy Efficiency• Case study• Conclusions

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.

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.

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DES of Truck-Shovel Energy Efficiency

1. Problem formulation

2. Solution methodology

3. System specification

4. Modeling5. Verification &

validation6. Experimentation & analysis7. Documentation, reporting &

dissemination

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Problem Formulation• Strip mine in IL • Annual production

of 600,000 tons of coal

• Average stripping ratio of 17:1 (yd3/ton).

• Objective– To evaluate

production strategies that will improve the energy efficiency of the truck-shovel overburden removal system

• Constraints:– Don’t sacrifice

productivity– New capital

expenditures should be a last resort

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Solution Methodology

• Discrete event simulation chosen as the solution approach

• Arena®, based on SIMAN, used in this study

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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.

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Input DataProcess 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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Modeling

• Entities = operators• Transporters = trucks• Resources = shovel

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Modeling

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Modeling

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Verification & Validation

• Verified with animation etc.

• 100 replications for each scenario

• Model validation based on VIMS truck data

• Model prediction of shift utilization used as estimate of engine load factor for a shift

Actual Simulated Error Mean Half-

width

Production [tons]

15,887

16,590

57 4%

Number of loads

114 120 0.4 5%

Total fuel consumption [gals]

488.87

502.60

1.54 3%

Average fuel consumption per cycle [gals]

4.24 4.27 0.01 1%

Overall fuel efficiency [tons/gal]

17.81 18.51 0.03 4%

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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 factors remain the same)

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Scenario 1 Results

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Scenario 1 Results

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Scenario 2 Results

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Scenario 2 Results

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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.

Q&A