The document presents a method to identify the key parameters that cause differences in energy efficiency between operators of heavy equipment. The method involves collecting operational data from multiple operators over many cycles. Pairwise comparisons are made between operators, and linear regression is used to determine which parameters are most correlated with differences in energy efficiency. The method is demonstrated on data from a dragline excavator from a surface coal mine. Analysis found that dump height, drag distances, and spot time were the parameters most responsible for efficiency differences between operators. The probabilistic approach provides insights into best practices for improving operator performance.

1. A METHOD TO IDENTIFY THE KEY
CAUSES OF DIFFERENCES IN
ENERGY EFFICIENCY OF
OPERATORS
Maryam Abdi Oskouei
Dr. Kwame Awuah-Offei
1

2. Energy Efficiency in Coal Mining
•
Energy consumption in coal mines is estimated as
142 billion kWh per year (DOE, 2007)
• About 49% of this energy can be saved by improving
energy efficiency and implementing best practices
(Bonskowski et al. 2006)
• Dragline, as one of the main energy consumers in
surface coal mines, consumes about 15-30% of total
mine energy (Orica Mining Services 2010)
2

3. Energy Efficiency of Dragline
Operation
Operator
practice
Operating
condition
Energy Efficiency
Mine design
& planning
Equipment
characteristics
3

4. Responsible Parameters
• Which parameter is causing the difference
between energy efficiency of operators?
• Why identify responsible parameters?
4

5. The Flowchart
START
Correlation
analysis
i=1
Select ith
pair
Use linear regression
of differences to find
significant
parameters *
Repeat
n times
i= i+1
Yes
No
5

6. Pearson Correlation
• Evaluate the relation between relevant
parameters and energy efficiency
 X ,Y 
cov( X , Y )
 X Y
• Test the hypothesis of no correlation
under significant level of α
6

7. Structure of the Data (opr_i)
Energy
efficiency
Correlated parameters
cycle 1
ηi1
pari11
pari12
. . .
pari1v
cycle 2
ηi2
pari21
pari22
. . .
pari2v
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
ηic
paric1
paric2
cycle ci
. . .
paricv
7

8. Issues with Missing Data
Opr j
Cycle 1
Xi1
Xj1
Cycle 2
Xi2
Xj2
.
.
.
.
.
.
Cycle cj
Xi cj
Xj cj
.
• Proposed method is based on
pair-wise comparison of
operators
Opr i
.
Cycle k
Xi k
.
.
Cycle ci
Xi ci
• Select equal number of cycles
• Repeat the process n times to
reduce the effect of random
sampling error
Select cj
cycles at
random
8

9. Difference matrix (opr_i - opr_j)
Δη
Δpar
cycle 1
ηi1 – ηj1
pari11 – parj11
pari12 – parj12
.
.
.
pari1v – parj1v
cycle 2
ηi2 – ηj2
pari21 – parj21
pari22 – parj22
.
.
.
pari2v – parj2v
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
ηic – ηjc
paric1 – parjc1
paric2 – parjc2
.
.
.
paricv – parjcv
cycle c
Dependent
variables
Independent variables
9

10. Linear Regression Analysis
par1i
Operator i
par2i
ηi
.
.
.
parni
par1j
Operator j
par2j
.
.
.
parnj
ηj
• Test the significance of coefficients
(ki) to identify responsible
parameters (significance level of α)
• A parameter is a responsible
parameter, if in (1-α)100 % of runs it
is recognized as a responsible
parameter.
10

11. Case Study
• Dragline Bucyrus-Erie 1570w – 85 yd3
removes the blasted overburden
• Real time monitoring system by Drives &
Controls Services, Inc.
– Adjusted to record energy consumption of three sets
of motors in the free slots of the database
– 34,327 cycles recorded in one month, 13 operators
11

12. ‹#›

13. ‹#›

14. The Flowchart for the given data
START
Correlation
analysis
nop = 5 operators
n = 30 runs for each pair
95% significance level
Create 10 pairs
of operators
i=1
Select ith
pair
Use linear regression
of differences to find
significant
parameters *
Repeat
30 times
i= i+1
No
Yes
i <=10
14

15. Correlated Parameters
PC
P-value
-0.6560
(<0.001)
#
Parameter
8
Cycle time
Hoist energy
-0.5857
(<0.001)
9
Swing energy
-0.2724
(<0.001)
3
Drag distance (vertical)
-0.5089
<0.001
10
Swing in time
-0.3362
(<0.001)
4
Drag energy
-0.4569
<0.001
11
Spot time
-0.1725
<0.001
5
Drag distance (horizontal)
-0.4807
<0.001
12
Angle swing out
-0.1556
(<0.001)
6
Load bucket time
-0.4548
<0.001
13
Swing out time
0.0123
(0.0913)
7
Dump time
-0.3050
<0.001
14
Payload
0.2429
(<0.001)
#
Parameter
1
Dump height
2
PC
P-value
-0.3755
(<0.001)

16. Results of Linear Regression
Analysis (30 runs)
Correlated parameters
D,B D,E D,A D,C B,E B,A B,C E,A E,C A,C
Dump height
30
30
30
30
30
30
30
30
30
30
Drag distance (vertical)
30
30
30
30
30
30
30
30
30
30
Drag distance (horizontal)
30
30
30
30
30
30
30
30
30
30
Load bucket time
4
8
20
5
22
30
Dump time
30
8
30
30
30
30
Cycle time
3
number times
30 26 of 30 0
of non-zero
30
8
regression30 30
coefficient 2
16
6
2
26
0
10
5
27
Swing in time
3
16
28
10
16
29
30
3
6
7
Spot time
30
30
30
30
30
30
30
21
30
30
Angle swing out
14
30
4
8
18
30
30
22
30
2
Payload
14
3
15
12
2
13
1
6
8
1630

17. Responsible at significance level
95%
Correlated parameters
D,B D,E D,A D,C B,E B,A B,C E,A E,C A,C
total
Dump height
1
1
1
1
1
1
1
1
1
1
10
Drag distance (vertical)
1
1
1
1
1
1
1
1
1
1
10
Drag distance (horizontal)
1
1
1
1
1
1
1
1
1
1
10
Load bucket time
0
1
0
1
0
0
0
0
0
1
9
Dump time
1
1
0
1
1
0
1
1
1
1
8
Cycle time
0
0
0
0
0
0
0
0
0
0
4
Swing in time
0
0
1
0
0
1
1
0
0
0
4
Spot time
1
1
1
1
1
1
1
0
1
1
2
Angle swing out
0
1
0
0
0
1
1
0
1
0
1
Payload
0
0
0
0
0
0
0
0
0
1
0
17

18. Responsible Parameters
total
probability
Dump height
10
100%
Drag distance (vertical)
10
100%
Drag distance (horizontal)
10
100%
Spot time
9
90%
Dump time
8
80%
Load bucket time
4
40%
Angle swing out
4
40%
Swing in time
2
20%
Payload
1
10%
Cycle time
0
0%
Correlated parameters
18

19. Discussion
• There is only a 40%
probability for dig time (load
bucket time) to be a
responsible parameter
• High probability for
engagement and
disengagement position of
bucket to affect dragline
performance
Probability of being a responsible
parameter
In this case study:
100%
80%
60%
40%
20%
0%
• Payload and cycle time have
a low chance of being a
responsible parameter
19

20. Conclusion
• A guideline for improving
operator performance can be
established using the results
• The method suggested for
identifying responsible
parameter is a valid and robust
approach and can be used for
other mines and draglines
20

21. Question?
21