# A method to identify the key causes of differences in energy efficiency of operators

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Technology

Published on February 28, 2014

Author: MaryamAbdi

Source: slideshare.net

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

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

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

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

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

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

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

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

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

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

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

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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

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)

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

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

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

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

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

Question? 21

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