Framework for stochastic modeling of dragline energy efficiency
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Transcript of Framework for stochastic modeling of dragline energy efficiency
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FRAMEWORK FOR STOCHASTIC
MODELING OF DRAGLINE ENERGY
EFFICIENCY
Maryam Abdi OskoueiDr. Kwame Awuah-offei
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Introduction
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Operators’ skills
Operating conditions
Equipment Efficiency
Energy consumption
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Objectives:• Study the effects of operators’ practice on
dragline energy efficiency– t-test
• Study the relation between operating parameters and energy efficiency to create a framework for stochastic model of dragline energy efficiency– Correlation analysis
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Dragline operation
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Hoist Bucket
Swing out
Dump material
Swing in (Return)
Spot Bucket
Fill Bucket
• A cycle of dragline operation
Ho
ist
Drag
Swing
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Data Acquisition
• A coal mine in Powder River basin, Wyoming• Dragline Bucyrus-Erie 1570w – 85 yd3 removes the
blasted overburden5
6- 1300 HP hoist motors4-1300 HP drag motors4- 1045 HP swing motors
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Monitoring system• AccuWeigh monitoring system
– Real time monitoring system• Records operating parameters in each cycle
– Adjusted to record energy consumption of three sets of motors
– Records 44 parameters in each cycle stores in database
– 34,327 cycles recorded in 33 days6
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Statistical analysis
• Useful parameters # Parameter1 Cycle time2 Swinging out time3 Swing in time4 Bucket loading time5 Dumping time6 Spotting time7 Angle of swinging out8 Bucket loading energy9 Dumping height10 Payload11 Drag energy12 Hoist energy13 Swing energy
Cycle time & cycle time components
Energy consumption
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Removing the outliers
• Outliers cause inaccurate inferences in the analysis– Number of bucket reloads
Bucket Reload Count Proportion (%)
1 33,493 97.56
2 738 2.15
3 or more 96 0.28
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Boxplot
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Middle quartile/ median
• Boxplot is used to identify the outliers Upper whisker
Upper quartile (q3)
Lower quartile (q1)
Lower whisker
Quartile group 1
Quartile group 2
Quartile group 3
Quartile group 425%
25%
25%
25%
++
+
+outlier
+
• Upper whisker = q3+1.5(q3-q1)
• Lower whisker = q1-1.5(q3-q1)
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Removing the outliers
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Operators• 13 operators operated during the 33 days• Working hours more than 32 hours or 2,200 cycles
Operator# of
cyclesTime
(hours)
Material weight
(tonnes)
Energy (kw-h)
Hourly production
(tonnes/hours)
A 3,897 56.9 496,18 44,850 8,719
B 3,611 54.6 450,22 43,894 8,243
C 3,350 49.6 427,23 39,827 8,613
D 3,058 45.6 383,55 36,879 8,404
E 2,211 32.8 277,55 23,395 8,46911
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Energy Efficiency• η – energy efficiency• P – payload• E – energy consumption
A B C D E9.5
10
10.5
11
11.5
12
12.5
13
En
erg
y E
ffic
ien
cy
(to
nn
es/K
w-h
)
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𝜂=𝑈𝑠𝑒𝑓𝑢𝑙𝑤𝑜𝑟𝑘𝐼𝑛𝑝𝑢𝑡𝑒𝑛𝑒𝑟𝑔𝑦
≅𝑃𝐸
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Normality
• To use the t-test data must follow normal distribution
• Kolmogrov-Smirnov test on energy efficiency of different operators rejects the hypothesis of normality
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Data transformation
• Right skewness• Log transformation
• The assumption of data follows normal distribution is valid:
Histogram plots Central limit theorem
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Results of the t-tests
Operators
Degree of
freedom
Pooled standard deviation
t-statistics CI p-value
D-B 6667 0.1026 2.1065 0.0004 0.0103 0.0352D-E 5267 0.1004 -19.6816 0.0606 -0.0497 <0.001D-A 6953 0.1028 -9.8622 -0.0294 0.0196 <0.001D-C 6406 0.1015 -4.0432 -0.0152 -0.0053 <0.001B-E 5820 0.1000 -22.3968 -0.0657 -0.0552 <0.001B-A 7506 0.1023 -12.6113 -0.0344 -0.0252 <0.001B-C 6959 0.1011 -6.4241 -0.0203 -0.0108 <0.001E-A 6106 0.1003 -11.4764 -0.0359 -0.0254 <0.001E-C 5559 0.0985 -16.6271 -0.0502 -0.0396 <0.001A-C 7245 0.1013 5.9610 0.0096 0.0189 <0.001
at 5% significance level all the t-tests reject the hypothesis of equal mean
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Pearson correlation
• Evaluate the relation between operating parameters and energy consumptions of swing, drag and hoist motors
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Swing Energy
Parameters PCC p-value
Swing angle 0.86 <0.0001
Cycle time 0.61 <0.0001
Swing out time 0.50 <0.0001
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Swing Energy
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PCC = 0.62 , p-value<0.0001
PCC = 0.57 , p-value<0.0001
PCC = 0.57 , p-value<0.0001
Parameters PCC p-value
Swing angle 0.86 <0.0001
Cycle time 0.61 <0.0001
Swing out time
0.50 <0.0001
• Evaluate the relation between these parameters to avoid confounding and duplicity in the model
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Swing Energy
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𝐸𝑆𝑤𝑖𝑛𝑔= 𝑓 𝑠 (𝜔𝑠 ) 𝑡𝑠
Swing power
𝑆𝑤𝑖𝑛𝑔𝑠𝑝𝑒𝑒𝑑(𝜔𝑠)=𝑆𝑤𝑖𝑛𝑔𝑎𝑛𝑔𝑙𝑒𝑆𝑤𝑖𝑛𝑔𝑡𝑖𝑚𝑒
PCC = 0.80, p-value<0.0001
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Hoist Energy
Parameters PCC p-value
Dump height 0.84 <0.0001
Dump time 0.36 <0.0001
Payload 0.34 <0.0001
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𝐸𝐻𝑜𝑖𝑠𝑡= 𝑓 h (h𝑑+𝑡𝑑𝑝+𝑃 ) 𝑡h
Hoist power
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Drag Energy
Parameters PCC p-value
Bucket loading energy 0.84 <0.0001
Loading time 0.36 <0.0001
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Drag Energy
Parameters PCC p-value
Bucket loading energy
0.84 <0.0001
Loading time 0.36 <0.0001
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PCC = 0.82 , p-value<0.0001
𝐸𝐷𝑟𝑎𝑔= 𝑓 𝐷 ( 𝑙𝑡 ) 𝑡𝑑
Drag power
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Framework of stochastic model
Drag power Swing powerHoist power
, ,t d t d h d dp h s s sE f l t f h t P t f t
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Drag power Swing powerHoist power
Energy efficiency, , ,t d t d h d dp h s s s
P P
E f l t f h t P t f t
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Question?
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