improving truck-shovel matching-nel kizil and knights
TRANSCRIPT
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Improving Truck-Shovel Matching
Dr Mehmet Kizil
Prof Peter Knights
Stephen Nel
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Overview
Shovel-Truck mining system
Standard time definitions
Key Performance Indicators
Case study
Fleet Optimsation
Conclusions
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Aim and objectives
Aim: to optimise a truck and shovel mining system
Objectives: to analyse the data sample;
to assess the current fleet performance levels;
to identify means for improving performance
and reducing downtime; and
to increase the efficiency or effectiveness of the
fleet.
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Truck-Shovel Mining System
Costs 50-60% of the total Mining cost
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Standard Time Definition
Total Calendar Time
(CT)
Scheduled Time
(ST)
Working Time
(WT)
Available Time
(AT)
Operating
Time
(OT)
OperationalDelays
(OD)
Maintenance Time
(MT)
PlannedMaintenance
(PM)
BreakdownMaintenance
(BM)
Idle Time
(IT)
UnscheduledTime
(UT)
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Key Performance Indicators
Productivity: Production (tonnes
or BCM) per operating hour (h)
Utilisation: The percentage ofavailable time that is consumed
by operating time (OT/AT)
Availability: The proportion ofscheduled working time for
which the equipment is available
(ST/CT)
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Factors Influencing Truck-Shovel
Productivity
Several factors can affect the productivity of atruck and shovel fleet, including:
Truck-shovel match and allocation;
Shift operating schedules;
Haul road design and ramp gradients;
Haul road rolling resistance; and
Mix truck sizes in the fleet (Runge Mining, 1993).
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Data and Methodology
Sample period: 01 Nov 200831 Jan 2009
Sample size: 90 Scheduled
working days, 180 shifts
Limitations: Seasonal effects,
FMS outages and machine
module malfunctions
FMS: Real-time monitoring andrecording but no truck
assignment
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Case Study Truck and Shovel
Fleet
Code Machine Model Capacity
EX01 Hydraulic Excavator O&K RH170 22 m3
EX02 Hydraulic Excavator Liebherr R996 34 m3
EX03 Hydraulic Excavator O&K RH120 16.5 m3
EX04 Hydraulic Excavator Liebherr R996 34 m3
SH01 Electric Rope Shovel Marion 301M 35 m3
SH02 Hydraulic Shovel Liebherr R996 34 m3
TR01-07 Rear Dump Truck Komatsu 830E 147 m3
TR08-20 Rear Dump Truck Terex MT4400 144 m3
TR21-23 Rear Dump Truck Cat 785C 78 m3
TR24-26 Rear Dump Truck Cat 789C 105 m3
TR27 Rear Dump Truck Cat 785 78 m3
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Operating Delay Summary for the
Excavator Fleet
0
50
100
150
200
250
300
350
ShiftChange
WaitonTruck Crib
NoOperator
Standby-
NoWorkAvailable
Relocate
Safety
Refueling
Standby-
RepairsComplete
WaitOtherEquipment
WaitDozer-
Operationa
l
AccidentDamage
WaitBlast-
Operationa
l
WorkInstructions
Housekeeping
TotalTime(h)
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Operating Delay Summary for the
Terex MT4400 Truck Fleet
0
200
400
600
800
1,000
1,200
1,400
1,600
1,800
Crib
WaitLoadingUnit
Standby-
NoWorkAvailable
QueueatLoader
NoOperator
ShiftChange
Standby-RepairsComplete
AccidentDamage
WaitOtherEquipment
Safety
Refueling
WaitBlast-Operationa
l
QueueatDump
TravelTo/FromWorkshop
WorkInstructions
TotalT
ime(h)
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Shovel Wait Time
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Shovel Truck Limited
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Cost Implications of Truck-
Shovel Wait
Fleet Sample PeriodCost
Projected AnnualCost
Wait on Shovel
Caterpillar 785 $128,428 $513,712
Caterpillar 789C $231,104 $924,417
Komatsu 830E $122,273 $489,094Terex MT4400 $422,172 $1,688,691
Subtotal $903,977 $3,615,914
Wait on Truck
Excavator $291,807 $1,167,229
Shovel $77,604 $310,419Subtotal $369,411 $1,477,648
Total $1,273,389 $5,093,562
Assumptions: Shovel= $600/h; Exc= $800/h; Truck= $400/h
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Truck Shovel Assignment
ShovelTruck Fleet
785 789C 830E MT4400
R996 (EX) 3 4 6 6R996 (SH) 3 4 6 6O&K RH120 6 9 12 12O&K R170 5 7 10 10Marion 301M 2 3 4 4
Shovel MF SH Cycle(s)
TR Cycle(s)
TruckNo.
R996 (EX) 1 113 861 7
R996 (SH) 1 186 892 4
O&K RH120 1 175 666 3
O&K RH170 1 177 995 5
Marion 301M 1 108 706 6
Fleet Pass Count Summary
Optimal Truck Number based on MF
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Truck Allocation for Liebherr
R996 on Pre-strip Material
0
5
10
15
20
25
30
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0
TruckNumber
Haul Distance (km)
Caterpillar 785 Caterpillar 789C Komatsu 830E Terex MT4400
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Recommendations
Switch from a semi-automatic to automaticdispatch system;
Reduce truck and shovel wait time through
mix fleet simulation in TALPAC or CAT FPC;
Manage crib break and shift change-over
delays;
Minimise shutdown delays by conducting
opportunity maintenance; and
Conduct a time and motion study to qualify
the FMS data.
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Conclusions
Truck-shovel operations typically have highoperating costs;
Maximise productivity, availability and
utilisation to achieve operational efficienciesand lower cost per tonne;
Optimisation closes the gap between current
and capacity production levels; and One challenge is Truck-Shovel
interdependency.