A Comprehensive Investigation of Loading Variance Influence on Fuel Consumption and Gas Emissions in Mine Haulage Operation

Abstract

The data collected from haul truck payload management systems at various surface mines show that the payload variance is significant and must be considered in analyzing the mine productivity, diesel energy consumption, greenhouse gas emissions and associated costs. The aim of this study is to determine the energy and cost saving opportunities for truck haulage operations associated with the payload variance in surface mines. The results indicate that there is a non-linear relationship between the payload variance and the fuel consumption, greenhouse gas emissions and associated costs. A correlation model, which is independent of haul road conditions, has been developed between the payload variance and the cost saving using the data from an Australian surface coal mine. The results of analysis for this particular mine show that a significant saving of fuel and greenhouse gas emissions costs is possible if the Standard Deviation of payload is reduced from the maximum to minimum value.

Full text

A comprehensive investigation of loading variance influence on fuel
consumption and gas emissions in mine haulage operation
Soofastaei A.
⇑
, Aminossadati S.M., Kizil M.S., Knights P.
School of Mechanical and Mining Engineering, University of Queensland, Brisbane 4072, Australia
article info
Article history:
Received 16 July 2015
Received in revised form 9 October 2015
Accepted 28 February 2016
Available online 19 September 2016
Keywords:
Energy consumption
Haul truck
Surface mine
Greenhouse gas emissions
Cost
abstract
The data collected from haul truck payload management systems at various surface mines show that the
payload variance is significant and must be considered in analysing the mine productivity, diesel energy
consumption, greenhouse gas emissions and associated costs. The aim of this study is to determine the
energy and cost saving opportunities for truck haulage operations associated with the payload variance
in surface mines. The results indicate that there is a non-linear relationship between the payload variance
and the fuel consumption, greenhouse gas emissions and associated costs. A correlation model, which is
independent of haul road conditions, has been developed between the payload variance and the cost
saving using the data from an Australian surface coal mine. The results of analysis for this particular mine
show that a significant saving of fuel and greenhouse gas emissions costs is possible if the standard devi-
ation of payload is reduced from the maximum to minimum value.
Ó2016 Published by Elsevier B.V. on behalf of China University of Mining & Technology.
1. Introduction
Mining industry consumes a large amount of energy in various
operations such as exploration, extraction, transportation and pro-
cessing [1]. A considerable amount of this energy can be saved by
better managing the operations [2–5]. The mining method and
equipment used mainly determine the type of energy source in
any mining operation [6]. In surface mining operations, haul trucks
use diesel as the source of energy [7–10]. Haul trucks are generally
used in combination with other equipment such as excavators,
diggers and loaders, according to the production capacity and site
layout. Haul trucks use a great amount of fuel in surface mining
operation; hence, mining industry is encouraged to conduct a
number of research projects on the energy efficiency of haul trucks
[11–13].
There are many kinds of factors that affect the rate of fuel con-
sumption for haul trucks such as payload, truck velocity, haul road
condition, road design, traffic layout, fuel quality, weather condi-
tion and driver skill [14–18]. A review of the literature indicates
that the understanding of energy efficiency of a haul truck is not
limited to the analysis of vehicle-specific parameters; and mining
companies can often find greater energy saving opportunities by
expanding the analysis to include other effective factors such as
payload distribution and payload variance [17,19–21].
Loading process in truck and shovel operations is a stochastic
process [20]. An analysis of the haul truck payload data obtained
from a number of mine sites around the world shows that the pay-
load distribution can be estimated by a normal distribution func-
tion with a satisfactory error; and the variance associated with
haul truck payloads is typically large [19–21]. The payload variance
depends on a number of parameters such as the particle size distri-
bution, the swell factors, the material density, truck-shovel match-
ing, number of shovel passes and the bucket fill factor [19,20,22].
Many attempts have been made to reduce the payload variance
by using the latest developed technologies such as truck on-
board payload measurement system, direct connection between
this system and the shovel control system and on-line fleet moni-
toring system [19,20].
The payload variance not only affects the production rate and
fuel consumption, but it is also an important parameter in the anal-
ysis of gas emissions and cost. Many research studies have already
been conducted on the measurement of the haul truck gas emis-
sions in the mining industry [23–27]. In addition, several numbers
of economic models have been presented to predict the cost of die-
sel and gas emissions [28].
In this paper, the effects of payload variance on fuel consump-
tion for a mostly used haul truck in Australia surface coal mines
(CAT 793D) are investigated. A model is presented to estimate
the effect of payload variance on the gas emissions and the total
cost associated with fuel consumption and gas emissions. The
corresponding energy saving opportunities to the reduction of
payload variance is also investigated.
http://dx.doi.org/10.1016/j.ijmst.2016.09.006
2095-2686/Ó2016 Published by Elsevier B.V. on behalf of China University of Mining & Technology.
⇑
Corresponding author. Tel.: +61 7 33658232.
E-mail address: a.soofastaei@uq.edu.au (A. Soofastaei).
International Journal of Mining Science and Technology 26 (2016) 995–1001
Contents lists available at ScienceDirect
International Journal of Mining Science and Technology
journal homepage: www.elsevier.com/locate/ijmst

2. Theoretical analysis
2.1. Haul truck payload variance
Loading performance depends on different factors such as
bench geology, blast design, muckpile fragmentation, operators’
efficiency, weather conditions, utilisation for trucks and shovels,
mine planning and mine equipment selection [19,20]. In addition,
for loading a truck in an effective manner, the shovel operator must
also strive to load the truck with an optimal payload. The optimal
payload can be defined in different ways, but it is always designed
so that the haul truck will carry the greatest amount of material
with lowest payload variance [15]. The payload variance can be
illustrated by carrying different amount of ore or overburden by
same trucks in each cycle. The range of payload variance can be
defined based on the capacity and power of truck. The payload
variance in a surface mine fleet can influence productivity greatly
due to truck bunching phenomena in large surface mines [19].
The increasing of payload variance decreases the accuracy of main-
tenance program. This is because the rate of equipment wear and
tear is not predictable when the mine fleet faces with a large pay-
load variance. Minimising the variation of particle size distribution,
swell factors, material density and fill factor can decrease the pay-
load variance but it must be noted that some of the mentioned
parameters are not controllable. Hence, the pertinent methods to
minimise the payload variance are real-time truck and shovel pay-
load measurement, better fragmentation through optimised blast-
ing and improvement of truck-shovel matching.
2.2. Haul truck fuel consumption
The fuel consumption for haul trucks is determined based on
the following parameters (see Fig. 1):
The Gross Vehicle Weight (GVW), which is the sum of the
weight of an empty truck and the payload.
The Haul Truck Velocity (V).
The Total Resistance (TR), which is equal to the sum of Rolling
Resistance (RR) and the Grade Resistance (GR) when the truck
is moving against the grade of the haul road.
The Rimpull Force (RF), which is the force available between the
tyre and the ground to propel the truck.
Caterpillar trucks are the most popular vehicles amongst all dif-
ferent brands of trucks used in Australian mining industry. Based
on the power and capacity of haul truck and mine productivity,
CAT 793D was selected for the analysis presented in this study.
The specification of selected truck is presented in Table 1.
Fig. 2 presents the Rimpull-Speed-Grade ability curve extracted
from the manufacturer’s catalogue for CAT 793D.
The rate of haul truck fuel consumption can be calculated by the
following equation [24].
FC ¼0:3ðLF PÞð1Þ
where LF is the ratio of average payload to the maximum load in an
operating cycle. The percentage of LF in different condition is pre-
sented in Table 2 [24] and Pis the truck power (kW).
For the best performance of the truck operation, Pis determined
by:
P¼1
3:6ðRF V
max
Þð2Þ
where RF is the force available between the tyre and the ground to
propel the truck. It is related to the torque (T) that the truck is cap-
able of exerting at the point of contact between its tyres and the
road and the truck wheel radius (R).
RF ¼T
Rð3Þ
In this paper, the fuel consumption by haul trucks has been sim-
ulated based on the above mentioned formulas.
Payload
Total resistance
(TR)
Rolling resistance (RR)
Truck velocity (V)
Rimpull force (RF)
Gradient
Gross vehicle weight (GVW)
Fig. 1. Haul road and truck key parameters.
Table 1
CAT 793D haul truck specifications [28].
Specification Value
Engine Engine model CAT 3516B HD
Gross power (kW) 1801
Net power (kW) 1743
Weights-approximate Gross weight (tonnes) 384
Nominal payload (tonnes) 240
Body capacity Struck (m
3
)96
Heaped (m
3
) 129
Standard arrangement gross weight (t)
100 200 300 400 500 600
100
80
60
40
20
0
02010 4030 50 60 70
Speed (km/h)
5%
10 %
15 %
20 %
25 %
30 %
LE
1st Gear
1st Gear
2nd Gear
3rd Gear
4th Gear
5th Gear
6th Gear
Rimpull (t)
Total resistance
Typical field
empty weight
Gross machine
operating weight
Fig. 2. Rimpull-Speed-Grade ability curve for truck CAT 793D [28].
Table 2
Load Factors (LF) for different conditions [22].
Operating
conditions
LF (%) Conditions
Low 20–30 Continuous operation at an average GVW less
than recommended, no overloading
Medium 30–40 Continuous operation at an average GVW
recommended, minimal overloading
High 40–50 Continuous operation at or above the
maximum recommended GVW
996 A. Soofastaei et al. / International Journal of Mining Science and Technology 26 (2016) 995–1001

2.3. Greenhouse gas emissions
Diesel engines emit both Greenhouse Gases (GHG
S
) and
Non-Greenhouse Gases (NGHG
S
) into the environment [28]. Total
greenhouse gas emissions are calculated according to the Global
Warming Potential (GWP) and expressed in CO
2
equivalent or
CO
2
-e[23,24]. The following equation can be used to determine
the haul truck diesel engine GHG
S
emissions [23,29].
GHG
emissions
¼ðCO
2
-eÞ¼FC EF ð4Þ
where FC is the quantity of fuel consumed (kL) and EF is the emis-
sion factor. EF for haul truck diesel engines is 2.7 t CO
2
-e/kL [30–32].
2.4. Cost of greenhouse gas estimation and fuel consumption
2.4.1. Cost of greenhouse gas emissions
There are many empirical models for the cost estimation of
greenhouse gas emissions, based on the US potential CO
2
legisla-
tion [27]. For this research project, the US Energy Information
Administration (EIA) model, which is known as a conservative
model, is selected. This model assumes different allowance prices
per year or in other words a CO
2
penalty under various scenarios:
Core Case scenario (CC
S
), High Cost scenario (HC
S
), No International
Offsets scenario (NIO
S
), Limited Alternatives scenario (LA
S
) and
NIOs/LAs [23].
Table 3 presents a prediction of cost GHG
S
emissions for differ-
ence years (from 2015 to 2050) based on the mentioned scenarios
[27].
In this study, the latest scenario which is a combination of
(NIO
S
) and (LA
S
) scenarios has been used to calculate the GHGs cost.
This scenario states that the key low emissions technologies,
nuclear, Carbon dioxide Capture and Storage (CCS) and renewables
will be developed in a timeframe consistent with emissions reduc-
tion requirements without encountering major obstacles where
the use of international offsets is severely limited by cost or
regulation.
2.4.2. Cost of fuel consumption
The cost of fuel depends on many economic and international
policy parameters. There are several numbers of models which
can be used to estimate the future diesel price [33]. The EIA model
can be used in this area as well. A graph showing the forecast of
diesel price estimated from this mode is shown in Fig. 3.
3. Results and discussion
3.1. Haul truck payload variance
The payload variance can be shown by variance of Standard
Deviation (
r
). The standard deviation measures the amount of
variation from the average. A low standard deviation indicates that
the data points tend to be very close to the mean; a high standard
deviation indicates that the data points are spread out over a large
range of values. Fig. 4 illustrates the different kinds of normal pay-
load distribution (the best estimation function for payload distri-
bution [20]) based on the difference
r
for CAT 793D.
This illustration shows that by reduction of
r
, the range of GVW
variation is reduced as well. Based on the CAT 793D technical spec-
ifications the range of GVW variation is between 165 tonnes
(empty truck) and 385 tonnes (maximum payload). Hence, the
maximum
r
for this truck can be defined as 30; that is because,
for higher
r
, the minimum GVW is less than the weight of empty
truck and the maximum GVW is more than the maximum capacity
of truck.
3.2. Haul truck fuel consumption
3.2.1. Rimpull analysis
The Rimpull-Speed-Grade ability curve for CAT 793D truck (see
Fig. 2) is used to determine the Rimpull (R) and the Maximum
Truck Velocity (V
max
) of the truck based on the values of GVW
(in the range of 165–385 tonnes) and TR (in the range of 1–30%).
In this study DataThief
Ò
5.6 and Curve Expert Professional V.2.1
were used to find an equation for Ras a function of TR and GVW.
R¼0:183GVWð0:006 þ0:053 TRÞð5Þ
3.2.2. Maximum truck velocity
The data for maximum truck velocity curve are collected by
DataThief
Ò
software and the best correlation between Rand V
max
has been defined by applying a non-linear regression method
Table 3
Different kinds of scenarios to estimate the cost of greenhouse gas ($/tonne CO
2
-e)[24].
Scenarios 2015 2020 2030 2040 2050
Core Case scenario (CCs) 20.91 29.88 61.01 124.57 254.37
High Cost scenario (HCs) 26.60 38.01 77.61 158.48 323.60
No International Offsets scenario (NIOs) 31.03 41.53 84.81 173.17 353.60
Limited Alternatives scenario (LAs) 48.83 44.34 90.54 184.87 377.50
No Intl. Offsets/Lim. Alt scenario (LAs/NIOs) 53.53 76.50 156.20 318.95 395.28
1.2
1.1
1.0
0.9
0.8
0.7
Nov-10Jul-09 Apr-12 Aug-13 Dec-14 May-16
April
2015
0.99 $/L
Date
Price ($/L)
Fig. 3. Forecast of diesel price [30].
80
70
60
50
40
30
20
10
0
150 175 200 225 250 275 300 325 350 375 400
Gross vehicle weight (t)
Gross vehicle weight frequency
5
σ
=
10
σ
=
15
σ
=
20
σ
=
25
σ
=
30
σ
=
Fig. 4. Normal payload distribution for difference standard deviations (
r
) (CAT
793D).
A. Soofastaei et al. / International Journal of Mining Science and Technology 26 (2016) 995–1001 997

(Curve Expert Professional Software V.2.1). The following equation
presents this correlation.
V
max
¼abexpðcR
d
Þð6Þ
where a= 53.867, b= 54.906, c= 37.979 and d=1.309.
3.2.3. Fuel consumption
Fig. 5 illustrates the variation of V
max
and FC with GVW for six
values of TR. The results generally show that for all values of total
resistance, the V
max
decreases and the FC increases as the GVW
increases. It must be noted that the rate of fuel consumption is
calculated based on the best performance of the truck recom-
mended by the manufacturer, which are for the maximum truck
velocity and the corresponding Rimpull.
3.3. Effects of payload variance on fuel consumption
The effect of payload variance on haul truck fuel consumption
in different haul road conditions is illustrated in Fig. 6.
In this figure, TR has been changed from 5% to 30% and
r
is
varied between 0 and 30. It is noted that, to have a better under-
standing, a Fuel Consumption Index (FC
Index
) has been defined. This
index presents the quantity of fuel used by a haul truck to move
one tonne of mine material (Ore or Overburden) in an hour.
Fig. 6 demonstrates that, there is a non-linear relationship between
r
and FC
Index
for all haul road total resistance. Moreover, the FC
Index
rises with increasing TR.
3.4. Effects of payload variance on greenhouse gas emissions
The variation of CO
2
-ewith
r
for CAT 793D is presented by
CO
2
-e
Index
in Table 4. The CO
2
-e
Index
presents the amount of green-
house gas emissions generated by truck to haul one tonne ore or
overburden in an hour.
Based on the tabulated results, it is obvious that there is a non-
linear relationship between CO
2
-e
Index
and the standard deviation
for each haul road total resistance. The minimum greenhouse gas
is emitted for the minimum total resistance (TR = 5%) when the
standard deviation has been zero (
r
= 0) and the maximum pollu-
tion is generated for the maximum total resistance and standard
deviation (TR = 30% and
r
= 30).
3.5. Effects of payload variance on cost
3.5.1. Cost of greenhouse gas emissions
All scenarios that can be used to predict the cost of greenhouse
gas emissions estimate that this cost is in the range of $20.91–
53.53 in 2015 (Table 3). In this project, the maximum cost of
CO
2
-eemissions ($53.53 per tonne) was considered.
3.5.2. Cost of fuel consumption
Fig. 3 illustrates that there is a vast difference in the price of
diesel between 2010 and 2015 but it is estimated that the price
of this type of fuel will be approximately $1 per litter in 2015 for
industrial use. Hence, in this project the price of fuel for haul trucks
in surface mines is assumed $0.99 per litter in 2015.
3.5.3. Total cost
The calculated FC
Index
, the cost of fuel consumed by haul truck
for each
r
(Fuel Cost
Index
), the greenhouse gas emitted by haul truck
to move one tonne of mine material in an hour (CO
2
-e
Index
), the cost
of greenhouse gas emissions (CO
2
-e Cost
Index
) and Total Cost
Index
for
CAT 793D with TR = 5% in 2015 are tabulated in Table 5.
In this haul road condition, there is a direct relationship
between increasing the payload variance and Total Cost
Index
. The
Total Cost
Index
presents the total cost of fuel consumed and CO
2
-e
emitted to haul one tonne mine material by truck in an hour. In
this case, the Total Cost
Index
can be vary between $0.42 and $1.10/
(htonne) for different values of standard deviation (
r
= 0–30).
3.5.4. Saving opportunities
The variation of total cost of fuel consumption and greenhouse
gas emissions can be used for saving opportunities. Using a truck
100 150 200 250 300 350 400 450
GVW (t)
V
max
FC
250
200
150
100
FC (L/h)
25
20
15
10
5
0
35
30
45
40
50
TR :5%
TR :10%
TR :15%
TR :20%
TR :25%
TR :30%
TR :30%
TR :25%
TR :20%
TR :15%
TR :10%
TR :5%
V
max
(km/h)
Fig. 5. Variation of V
max
and FC with GVW for different TR.
1.2
1.1
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0 5 10 15 20 25 30
Standard deviation ( )
σ
TR:30%
TR:25%
TR:20%
TR:15%
TR:10%
TR:5%
FCIndex (L/(h·tonne))
Fig. 6. Variation of FC
Index
with standard deviation (
r
) (CAT 793D).
Table 4
Variance of CO
2
-e
Index
(kg/htonne) with
r
(CAT 793D).
r
TR =5% TR = 10% TR = 15% TR = 20% TR = 25% TR = 30%
0 0.64 0.80 1.02 1.21 1.37 1.58
5 0.84 1.00 1.22 1.40 1.57 1.78
10 1.06 1.22 1.44 1.63 1.79 2.01
15 1.31 1.47 1.69 1.88 2.04 2.26
20 1.59 1.76 1.97 2.16 2.32 2.54
25 1.91 2.07 2.29 2.48 2.64 2.86
30 2.27 2.43 2.65 2.84 3.00 3.22
998 A. Soofastaei et al. / International Journal of Mining Science and Technology 26 (2016) 995–1001

on-board payload measurement system, developing a direct con-
nection between the truck payload measurement system and the
shovel, improvement of truck-shovel matching or developing an
on-line fleet monitoring can be used to reduce the payload vari-
ance. Fig. 7 illustrates the correlation between the Standard Devi-
ation Reduction (
D
r
) and the Saving
Index
. The Saving
Index
presents
the amount of saving cost with reducing diesel consumption and
greenhouse gas emissions for hauling one tonne mine material
(ore or overburden) in one hour. This graph is independent of haul
road condition (RR and GR) and presents the quantity of saving for
different kinds of
D
r
.
Finding the best correlation between the
D
r
, and the Saving
Index
can be very important in calculation of the effect of payload vari-
ance on production cost. Hence, the following equation has been
developed to estimate the Saving
Index
for different road conditions
and values of the
D
r
.
Sa
v
ing
Index
¼0:01ð
Dr
Þ
1:25
ð7Þ
Eq. (6) presents the correlation between Saving
Index
and
D
r
.
4. Case study
The effect of payload variance on haul truck fuel consumption
and GHG
S
emissions is an important matter in real mine sites. In
this project, a large surface mine in Australia has been investigated
to determine the effect of payload variance on energy used, GHG
S
emitted by haul trucks and the cost of them to find saving
opportunities.
Fig. 8 shows a schematic diagram of the surface parameters
used to model haul truck fleet requirements. The mine parameters
used for this case study are presented in Table 6.
Fleet requirements are calculated using Talpac
TM
software. The
average of TR in this case is 15%. Therefore, FC
Index
and CO
2
-e
Index
can be measured by using Fig. 6 and Table 4, respectively. The total
cost is calculated based on the cost of fuel consumption and CO
2
-e
emissions in 2015 that is illustrated in Fig. 3 and Table 5, respec-
tively. The price of fuel and CO
2
-eis assumed constant during the
years of operation. The results of calculation are presented in Table 7.
The results show that in this case by reducing one unit of pay-
load variance, $0.02/(htonne) is salvable. The case study mine is
under 8 h of operation in each shift and there is one shift in each
day. This mine has 360 working days at year. The calculation shows
that, maximum 35% of total fuel and CO
2
-ecost is salvable by
reducing
r
from 30 to zero. This amount of saving is equal to mil-
lion $7.33 annually.
5. Conclusions
This paper aimed to develop a model to find saving opportuni-
ties based on the reduction of payload variance in surface mines.
There is a significant payload variance in loading process in surface
mines. This variance needs to be considered in analysing the mine
productivity, diesel energy consumption, greenhouse gas emis-
sions and associated costs. This paper investigated the effects of
Table 5
Calculated indexes for CAT793D with TR = 15% in 2015 (sample).
r
FC
Index
L/(htonne) Fuel Cost
Index
$/(htonne) CO
2
-e
Index
kg/(htonne) CO
2
-e Cost
Index
$/(htonne) Total Cost
Index
$/(htonne)
0 0.38 0.37 1.02 0.05 0.42
5 0.45 0.44 1.22 0.07 0.51
10 0.53 0.52 1.44 0.08 0.60
15 0.63 0.61 1.69 0.09 0.70
20 0.73 0.72 1.97 0.11 0.83
25 0.85 0.83 2.29 0.12 0.95
30 0.98 0.96 2.65 0.14 1.10
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
5 1015202530
Standard deviation reduction ( )
σ
Δ
Saving
Index
($/(h·tonne))
Fig. 7. Correlation between
D
r
and Saving
Index
.
150 m
200 m
250 m
300 m
10%
Crusher
Fig. 8. Schematic of open pit used to model fleet requirements.
Table 6
Mine parameters of case study.
Parameter Value Description
Operating hours per year
(h/year)
4799
Pit depth (m) 300
Total ore and wast (Mt) 2500 Haulage requirement
Haulage routs 4 150, 200, 250 and 300 m
Ramps 2
Length of the longest ramp
(km)
3
Horizontal haulage distance
(m)
60 In-pit
120 Ex-pit
Width of haul road (m) 35
Truck down ramp speed (km) 30 Limited due to safety
considerations
Grade Resistance (GR) (%) 10
Rolling Resistance (RR) (%) 5
Shovels 3 On level 1 (150 m)
4 On level 2 (200 m)
2 On level 3 (250 m)
2 On level 4 (300 m)
A. Soofastaei et al. / International Journal of Mining Science and Technology 26 (2016) 995–1001 999

payload variance on diesel energy consumption, greenhouse gas
emissions and their associated cost in surface mining operations.
This study examined CAT 793D model truck, which is one of the
mostly used haul trucks in surface mining operations. Based on
the technical specifications of this truck, the variation range of pay-
load was assumed to be between 0% and 30%. All data in Rimpull-
Speed-Grade ability curve for examined truck was digitalised by
DataThief
Ò
software. The correlations and equations to calculate
the maximum truck velocity and fuel consumption were defined.
To investigate the effects of payload variance on fuel consumption,
greenhouse gas emissions and associated costs, main indexes were
presented. The associated cost of greenhouse gas emissions and
cost of diesel consumption were determined based on models pre-
sented by US EIA. The results showed that the fuel consumption,
rate of greenhouse gas emissions and their costs non-linearly
increase as the payload variance rises for all haul road conditions.
The correlation between the payload variance and cost saving was
developed. This correlation is independent of haul road condition
and presents the cost saving for different kinds of payload variance
reduction. Presented model was utilised in a real mine site in
Australia as a case study. The results of this project indicated that
there is a great cost saving opportunity by decreasing the payload
variance in surface mines that used truck and shovel method for
mining operation. This can be achieved by using a truck on-
board payload measurement system and on-line fleet monitoring.
Acknowledgments
The authors would like to acknowledge CRC Mining and the
University of Queensland for their financial support for this study.
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Table 7
Case study results.
Parameter Value Description
Max (
r
= 30) Min (
r
=0)
FC
Index
(L/(htonne)) 0.98 0.38 Fig. 5
CO
2
-e
Index
(kg/(htonne)) 2.65 1.02 Table 4
Cost
Index
($/(htonne)) 1.10 0.42 Fig. 6 and Table 5
Truck fuel consumption (empty) (L/h) 175 Average the rate of fuel consumption for
empty truck CAT 793D [32]
Truck greenhouse gas emission (empty) (kg/h) 682
Truck cost of fuel and greenhouse gas (empty) ($/h) 209
Average truck payload (t) 142
Fleet size (Truck) 15
Total production per year (Mt/year) 19
Truck availability (%) 80
Loader availability (%) 85
Queue time at loader (min/cycle) 3.05
Spot time at loader (min/cycle) 0.95
Average loading time (min/cycle) 2.06
Travel time (hauling) (min/cycle) 16.13
Travel time (returning) (min/cycle) 6.03
Spot time at dump (min/cycle) 0.76
Average dump time (min/cycle) 1.02
Average cycle time (min) 30.00
Average No. of bucket passes 3
Rate of fuel consumption (fleet) (L/h) 3774.9 2429.7
Rate of greenhouse gas emission (fleet) (kg/h) 11795.4 8124.6
Rate of cost (fleet) ($/h) 4349.1 2821.5
Total fuel consumption annually (ML/year) 18.12 11.66
Total greenhouse gas emission annually (10
6
kg/year) 56.61 38.99
Total cost of fuel consumption and greenhouse gas
emission annually (10
6
$/year)
20.87 13.54
Saving cost percentage (%) 35
Total saveable cost (10
6
$/year) 7.33
1000 A. Soofastaei et al. / International Journal of Mining Science and Technology 26 (2016) 995–1001

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