Capital Investment Appraisal for Advanced Mining - cIRcle

198 Pages·2009·23.46 MB·English

Capital Investment Appraisal for
Advanced Mining Technology:
Case Studies in GPS and Information Based
Surface Mining Technology
By
Sean Dessureault
B. Eng., McGill University, 1997
A thesis submitted in partial fulfillment of
the requirements for the degree of
Master of Applied Science
The Faculty of Graduate Studies
Department of Mining and Mineral Process Engineering
University of British Columbia
We accept this thesis as conforming
to the referred standard
The University of British Columbia
February 1999
©Sean David Dessureault, 1999
Author: Sean Dessureault Supervisor: Dr. Malcolm J. Scoble
B.Eng., McGill University, 1997 M.M.P.E. Department Head
M.A.Sc. Candidate Co-Supervisor: Dr. Scott Dunbar
University of British Columbia Industrial Supervisor: Dr. M. G. Lipsett
Mining and Mineral Process Engineering Industrial Supervisor: Mr. Paul Richard
[email protected]
In presenting this thesis in partial fulfilment of the requirements for an advanced
degree at the University of British Columbia, I agree that the Library shall make it
freely available for reference and study. I further agree that permission for extensive
copying of this thesis for scholarly purposes may be granted by the head of my
department or by his or her representatives. It is understood that copying or
publication of this thesis for financial gain shall not be allowed without my written
permission.
Department of M f m^ <wl AWraA f,„,„cr f ^ ^ e r . '^
The University of British Columbia
Vancouver, Canada
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Justification Techniques for Mining Automation ii
1 Abstract
Canadian, American and Australian mining industries are currently faced with the
requirement to streamline their operations in light of new economic realities. Automation has
been seen as a key step toward the survival and competitiveness of these industries.
Unfortunately, technology providers seldom have the expertise or resources to properly
transfer technology from the development stage to the productive stage. Mining companies,
the potential customers of technology providers are similarly inexperienced at appraising and
implementing these new technologies. The manufacturing industry is well experienced in
appraising (referred most commonly in manufacturing as justifying) and implementing new
technology and has developed numerous capital investment appraisal (CIA) methods that can
more accurately appraise new technology. These techniques offer the potential to be adapted
to suit the mining industry.
The primary objective of this thesis is to investigate the usefulness of various decision
making tools in the CIA of advanced technologies for the mining industry. This research
discusses the current technological situation in the mining industry to show the requirement
for new capital appraisal techniques. The current evaluation methods are analyzed and their
weaknesses are identified. The classification schemes and CIA methods derived from the
manufacturing industry are adapted to mining. Analytical examples are provided in terms of
hypothetical situations and two case studies at an open pit copper mine in British Columbia
are described. The first case study reveals the limitations of current CIA methods when
applied to advanced technology and applies alternative CIA techniques whose applicability is
rated by the decision-maker at the mine as being "very useful". The second case study uses
object based simulation as a CIA tool for a blending project by estimating parameters within
the project that were previously only subjective opinion.
Masters of Applied Science Thesis. Sean Dessureault
iii
Table of Contents
2 Table of Contents
1 ABSTRACT II
2 TABLE OF CONTENTS Ill
3 TABLE OF FIGURES VII
4 TABLE OF EQUATIONS VIII
5 LIST OF TABLES IX
6 ACKNOWLEDGEMENTS XI
7 LIST OF ACRONYMS XII
1 INTRODUCTION 1
1.1 OBJECTIVES 2
1.2 CONTRIBUTIONS 2
1.3 METHODOLOGY 3
1.4 THESIS STRUCTURE 3
2 DEFINITIONS & REASONING 5
2.1 DEFINITIONS 5
2.2 INADEQUACIES 6
2.3 REASONS FOR THE INADEQUACY OF TRADITIONAL JUSTIFICATION SCHEMES 6
3 CLASSIFICATION OF TECHNOLOGIES AND CIA METHODS 9
3.1 CLASSIFICATION SCHEME 9
3.2 THREE JUSTIFICATION CATEGORIES 11
3.2.1 Levell 13
3.2.1.1 Level 1: Manufacturing - Stand Alone Systems 13
3.2.1.2 Level 1 - Mining - Production Units 14
3.2.1.3 Level 1 - Economic CIA methods 14
3.2.1.3.1 Simulation over blanket hurdle rates for New Technology 15
3.2.1.3.2 Acceptable level of Risk & Management's personal attitude towards risk 15
3.2.1.3.3 Financial Analysis Paramount 16
3.2.1.3.4 Conclusion to Financial CIA methods 18
3.2.2 Level 2 18
3.2.2.1 Level 2: Manufacturing - Cells 18
3.2.2.2 Analysis of Mining as a Muck Factory 19
3.2.2.2.1 Exploration 19
3.2.2.2.2 Production 20
3.2.2.2.3 Discussion of the Analysis 21
3.2.3 Level 2 Mining - Integrated process 21
3.2.4 Level 2 CIA methods 22
3.2.5 Level 3 22
3.2.5.1 Level 3: Manufacturing - Linked Islands 23
3.2.5.2 Level 3: Mining - Total Mine Integration 23
3.2.5.3 Level 3 CIA methods 24
3.2.5.3.1 Analysis of risk and uncertainty 25
3.2.5.3.2 Management's attitude towards risk 25
3.2.5.3.3 Risk identification 26
3.2.5.3.4 Risk application 26
3.2.6 Level 4 : 28
3.2.6.1 Level 4: Manufacturing - Full integration 28
Masters of Applied Science Thesis. Sean Dessureault
Table of Contents iv
3.2.6.2 Level 4: Mining - Blue-Sky R&D 28
3.2.7 Classification Conclusion 29
4 ANALYTICAL EXAMPLES 31
4.1 LEVEL 1 CIA METHODS - COMMON ECONOMIC 31
4.1.1 Payback 31
4.1.1.1 Analytical Example of the Payback Method 31
4.1.1.2 Analysis of the Validity of the Payback Method 32
4.1.1.3 Reasoning for Selecting the Payback method 33
4.1.2 Discounted Cash Flow Techniques (DCF) 34
4.1.2.1 Net Present Value 34
4.1.2.1.1 Numerical Example of the NPV method 35
4.1.2.1.2 Validity of the NPV method 35
4.1.2.1.3 Reasoning for selecting the NPV method 36
4.1.2.2 Internal Rate of Return (IRR) 36
4.1.2.2.1 Numerical Example Illustrating the IRR method 36
4.1.2.2.2 Validity of the IRR method 37
4.1.3 Risk Considerations 37
4.2 LEVEL 2 JUSTIFICATION SCHEMES - PORTFOLIO & SCORING MODELS 38
4.2.1 Programming models 38
4.2.1.1 Integer Programming 38
4.2.1.2 Goal Programming 40
4.2.2 Scoring Models 42
4.2.2.1 Weighted Average Method 42
4.2.2.2 The Analytical Hierarchy Process (AHP) 43
4.2.3 Option Pricing 48
4.2.3.1 Black-Scholes 49
4.2.3.2 Binomial Options Theory 50
4.2.3.3 Azzone's option-based model for Manufacturing systems 52
4.3 LEVEL 3 JUSTIFICATION SCHEMES - RISK & ANALYTIC METHODS 55
4.3.1 Value Analysis 56
4.3.2 Decision Analysis 56
4.3.2.1 Payoff Table 58
4.3.2.2 Expected Value 59
4.3.2.3 Decision Trees 59
4.3.2.4 Expected Value of Perfect Information (EVPI) 61
4.3.2.5 Multistage Decisions 62
4.3.2.6 Baye's Theorem 63
4.3.2.7 Sensitivity Analysis 67
4.3.2.8 Utility Theory 67
4.3.2.9 Mining Companies' attitudes toward Risk 72
4.3.2.10 Discussion of Decision Analysis & Other methods 72
4.3.2.10.1 Laplace approach 73
4.3.2.10.2 Maximax 73
4.3.2.10.3 Maximin 74
4.3.2.10.4 Minimax Regret 74
4.3.2.11 Conclusion to Decision Analysis 75
4.3.3 Risk Analysis 75
4.3.3.1 Simulation as a Justification Tool 75
4.3.3.2 History of Simulation 76
5 BACKGROUND TO PRACTICAL EXAMPLES 77
5.1 CHRONOLOGY OF RESEARCH 77
5.2 HIGHLAND VALLEY COPPER MINE: BACKGROUND INFORMATION 78
5.2.1 Location 7#
5.2.2 Production Statistics 79
5.2.3 Maintenance SO
5.2.4 Environment 80
5.2.5 Human Resources SO
Masters of Applied Science Thesis. Sean Dessureault
Table of Contents v
5.2.6 Ownership & Hierarchy 80
5.3 CASE STUDY 1: AQUILA DRILL TECHNOLOGY & FRAGMENTATION OPTIMIZATION 81
5.3.1 AQUILA technology 82
5.3.2 Blasting Methodology Pre-Technological Improvement 85
5.3.3 Chronology of Technology A cquisition 87
5.3.4 Changes Anticipated 87
5.3.4.1 Anticipated Analytical benefits 87
5.3.4.2 Anticipated Financial benefits 88
5.3.5 Reasoning Behind Level 2 Classification 88
5.4 CASE STUDY 2: CAES TECHNOLOGY & ORE BLENDING 89
5.4.1 CAES Technology 89
5.4.2 Chronology 92
5.4.3 Current System 93
5.4.4 Optimization of Ore Blending 98
5.4.5 Associated Technologies 101
5.4.6 Financial Reasoning for CAES/Blending project 103
5.4.7 Blending Summary 104
6 PRACTICAL EXAMPLES OF T HE JUSTIFICATION SCHEMES 106
6.1 CASE STUDY 1: AQUILA DRILL TECHNOLOGY & FRAGMENTATION OPTIMIZATION 106
6.1.1 Current Blasting Methodology 106
6.1.1.1 Preliminary Surveying 106
6.1.1.2 Blast Design 106
6.1.1.3 Plan Execution 107
6.1.2 Qualitative Benefits 107
6.1.2.1 Predicted Qualitative Benefits 108
6.1.2.2 Unpredicted Benefits 109
6.1.2.2.1 Potential Projects: 109
6.1.2.2.2 Unpredicted Qualitative benefits 110
6.1.3 Financial Benefits Ul
6.1.4 Costs & Financial Assessment 113
6.1.5 Analysis of Results 113
6.1.5.1 Original Justification Circumstances 114
6.1.5.2 Financial Assessment 114
6.1.5.2.1 Taking into account the long-term effects 114
6.1.5.2.2 Assumptions about the Future 115
6.1.5.2.3 Benefits Ignored 117
6.1.5.2.4 Management Control 119
6.1.6 Selection of Level 2 Justification Scheme(s) 119
6.1.7 Application ofAHP at HVC 120
6.2 CASE STUDY 2: CAES TECHNOLOGY & ORE BLENDING 121
6.2.1 Weakness in Current Justification of Ore Blending project. 121
6.2.2 Simulation Description 123
6.2.2.1 Ore Generating Module 123
6.2.2.2 Alternating truck allocations module 124
6.2.2.3 Blending on Crusher Apron Feeder Module 125
6.2.2.4 Optimization of Feed onto Storage Piles Module 126
6.2.3 Simulation Results 129
6.2.4 Validity of the results 129
6.2 A. 1 Lack of real-time optimization algorithm 129
6.2.4.2 Lack of other blending control mechanisms 130
6.2.4.3 On-site characteristics not fully modeled 130
6.2.4.4 Milling Characteristics 130
6.2.4.5 Ore Variability 130
6.2.4.6 Limitations of the Hardware 131
6.2.5 Application of Simulation Results 132
6.2.6 Conclusion and Recommendations for Case Study 2 134
7 CONCLUSIONS A ND RECOMMENDATIONS 136
Masters of Applied Science Thesis. Sean Dessureault
Table of Contents vi
7.1 SUMMARY OF THE INVESTIGATION 136
7.2 SIGNIFICANT CONTRIBUTIONS MADE BY THIS THESIS INCLUDE: 137
7.3 CONCLUSIONS 137
7.3.1 Thesis Stimulus 137
7.3.2 Review of the CIA methods 137
7.3.3 Review of the Main Conclusion of the Practical Applications 138
7.3.4 Validity of the Approach 139
lA RECOMMENDATIONS 139
8 REFERENCES 141
9 LIST OF APPENDICES 148
9.1 APPENDIX 1: INTEGER PROGRAMMING 149
9.2 APPENDIX 2: GOAL PROGRAMMING 152
9.3 APPENDIX 3: ORIGINAL AFE 155
9.4 APPENDIX 4: CAES AFE 158
9.5 APPENDIX 6: SIMULATION RESULTS 184
Masters of Applied Science Thesis. Sean Dessureault
Table of Contents vii
3 Table of Figures
FIGURE 3-1: ADVANCED MANUFACTURING TECHNOLOGY CONTINUUM 11
FIGURE 3-2: ADVANCED MINING TECHNOLOGY CONTINUUM 11
FIGURE 3-3: CIA METHODS 12
FIGURE 3-4: JUSTIFICATION APPROACHES FOR ADVANCED MANUFACTURING SYSTEMS, AFTER MEREDITH
AND HILL 13
FIGURE 3-5: CHOOSING THE APPROPRIATE CIA METHOD 29
FIGURE 4-1: OPTIMAL SOLUTION FOR MINECOMP EXAMPLE 40
FIGURE 4-2: OPTIMAL SOLUTION FOR THE OPMC EXAMPLE 41
FIGURE 4-3: PAIRWISE COMPARISON MATRIX SOLUTION FOR 3X3 EXAMPLE 45
FIGURE 4-4: PAIRWISE COMPARISON MATRIX FOR OBJECTIVES FOR IOM EXAMPLE 46
FIGURE 4-5: NORMALIZED PAIRWISE COMPARATIVE MATRIX 46
FIGURE 4-6: PAIRWISE COMPARISON MATRIX OF OPTIONS FOR FRAGEMENTATION FOR IOM EXAMPLE 48
FIGURE 4-7: FINAL SOLUTION FOR IOM EXAMPLE 48
FIGURE 4-8: BINOMIAL FLUCTUATIONS 50
FIGURE 4-9: OPTION PAYOFF 51
FIGURE 4-10: NORMAL CURVE DISTRIBUTION P(CF) VS CF 54
FIGURE 4-11: DECISION ANALYSIS PROCESS 57
FIGURE 4-12: DECISION TREE 60
FIGURE 4-13: DECISION TREE EXAMPLE 61
FIGURE 4-14: MULTI-STAGE DECISION TREE 63
FIGURE 4-15: DECISION TREE FOR UNMC EXAMPLE 66
FIGURE 4-16: SENSITIVITY ANALYSIS DECISION TREE 67
FIGURE 4-17:UTILITY CURVE FOR UNMC MANAGER 69
FIGURE 4-18: UTILITY CURVES FOR VARIOUS RISK PREFERENCES 70
FIGURE 4-19: DECISION TREE USING UTILITY VALUES 71
FIGURE 5-1: HVC LOCATION 79
FIGURE 5-2: HVC MANAGEMENT HIERARCHY 81
FIGURE 5-3:49R DRILL RIG SHOWING GPS RECEIVERS 82
FIGURE 5-4: SCREEN SHOT - OPERATOR'S STATUS SCREEN 83
FIGURE 5-5: UPLOADING AND DOWNLOADING DRILL DATA IN THE DRILL CAB (LEFT) AND IN THE MINE
ENGINEERING OFFICE (RIGHT) 84
FIGURE 5-6: DRILL NAVIGATION SCREEN 85
FIGURE 5-7: FRONT-END LOADER OPERATOR'S VIEW OF THE CAES SCREEN 90
FIGURE 5-8: GRINDING AT HVC 93
FIGURE 5-9: CONVEYOR BELT FLOWSHEET, HVC, JUNE, 1998 95
FIGURE 5-10: COARSE ORE STORAGE PILES '. 96
FIGURE 5-11: SURGE PILE 97
FIGURE 5-12: MATERIAL FLOW 97
FIGURE 5-13: HARDNESS CONTOURS 98
FIGURE 5-14: MONITORING SCREEN OF MUCK FLOW FROM PIT TO PILE, HVC 99
FIGURE 5-15: GYRATORY, SEMI-MOBILE, IN-PIT CRUSHER AT HVC 99
FIGURE 5-16: SURGE PILE DIAGRAM 100
FIGURE 5-17: SURGE PILE BLENDING ALGORITHM 100
FIGURE 5-18: A) WHIPFRAG IMAGE CAPTURE BP WHIPFRAG DELINEATION 102
FIGURE 5-19: WHIPFRAG RESULTS 102
FIGURE 5-20: ACTUAL VS. PREDICTED THROUGHPUT OVER TIME 103
FIGURE 5-21: RECOVERY VS. TIME 104
FIGURE 6-1: METERS DRILLED PER DRILL OVER THE TIME SCALE 116
FIGURE 6-2: ORE GENERATING MODULE, EXTEND™ SIMULATION 124
FIGURE 6-3: ALTERNATING TRUCK ALLOCATION, EXTEND™ SIMULATION 124
FIGURE 6-4: BLENDING ON CRUSHER APRON, EXTEND™ SIMULATION 125
FIGURE 6-5: DISTRIBUTIONS OF X, Y AND Z, FOR PILES 1,2 AND 3 126
FIGURE 6-6: DECISION AND CALCULATION MODULE FOR IDEAL BLEND 128
Masters of Applied Science Thesis. Sean Dessureault
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FIGURE 6-7 : AVERAGE DEVIATION FOR RUNS FOR PILE 1 129
FIGURE 6-8: DECISION TREE FOR BLENDING PROJECT 133
FIGURE 6-9: UTILITY CURVE FOR BLENDING PROJECT 133
FIGURE 6-10: DECISION TREE CONSIDERING UTILITY 134
FIGURE 9-1: OPTIMAL SOLUTION FOR MINECOMP EXAMPLE 150
FIGURE 9-2: SOLVER WINDOW FOR MINECOMP EXAMPLE 151
FIGURE 9-3: OPTIMAL SOLUTION FOR THE OPMC EXAMPLE 153
FIGURE 9-4: SOLVER WINDOW FOR OPMC EXAMPLE 154
FIGURE 9-5: PLANNED vs. ACTUAL EXPENDITURE 166
FIGURE 9-6: RESULTS FROM AHP EVALUATION 171
4 Table of Equations
EQUATION 4-1: NET PRESENT VALUE 35
EQUATION 4-2: BENEFIT/COST RATIO 36
EQUATION 4-3: INTERNAL RATE OF RETURN 36
EQUATION 4-4: PAIRWISE COMPARISON MATRIX FOR 3 OBJECTIVES 44
EQUATION 4-5: BLACK-SCHOLES OPTION PRICE 49
EQUATION 4-6: VALUE OF FLEXIBILITY 53
EQUATION 4-7: VALUE OF FLEXIBILITY ASSUMING A NORMAL DISTRIBUTION 54
EQUATION 4-8: EXPECTED VALUE 59
EQUATION 4-9: EXPECTED VALUE OF DECISION 60
EQUATION 4-10: EQUATION SHOWING BAYESIAN PRINCIPLE 65
EQUATION 4-11: UTITLITY FUNCTION 71
EQUATION 4-12: LAPLACE DECISION 73
EQUATION4-13: MAXIMAX 74
EQUATION 4-14: MAXIMIN 74
EQUATION 4-15: MINIMAX REGRET 74
EQUATION 6-1: EXTRACTION OF X,Y AND Z FIGURES FROM IBF 127
EQUATION 6-2: PILE HARDNESS EQUATIONS 127
EQUATION 6-3: AVERAGE ORE HARDNESS FOR STORAGE PILEX 127
Masters of Applied Science Thesis. Sean Dessureault
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5 List of Tables
TABLE 3-1: MANUFACTURING/MINING COMPARISONS 30
TABLE 4-1: ECONOMIC VARIABLES FOR PAYBACK EXAMPLE 32
TABLE 4-2: PAYBACK EXAMPLE RESULTS 32
TABLE 4-3: PAYBACK EXAMPLE RESULTS USING DISCOUNT FACTOR 33
TABLE 4-4: DATA TO BE USED PW NPV EXAMPLE 35
TABLE 4-5: NPV EXAMPLE 35
TABLE 4-6: IRR EXAMPLE DATA 37
TABLE 4-7: IRR EXAMPLE RESULTS 37
TABLE 4-8: DATA FOR MINECOMP EXAMPLE 39
TABLE 4-9: RESTRICTIONS FOR MINECOMP EXAMPLE 39
TABLE 4-10: GOALS FOR OPMC EXAMPLE 41
TABLE 4-11: IMPROVEMENT METHODS FOR OPMC EXAMPLE 41
TABLE 4-12: WEIGHTED SCORES FOR UMC EXAMPLE 43
TABLE 4-13: RESULTS OF WEIGHTED AVERAGE METHOD FOR UMC EXAMPLE 43
TABLE 4-14: INTERPRETATION OF VALUES IN PAIR WISE COMPARISON MATRIX 44
TABLE 4-15: OBJECTIVES AND SOLUTION OPTIONS FOR IOM EXAMPLE 45
TABLE 4-16:RANDOM INDICES FOR CONSISTENCY CHECK 47
TABLE 4-17: BPNOMIAL PRICE LATTICE FOR GOLD OVER A 10 YEAR PERIOD 51
TABLE 4-18: VALUE OF GOLD PRODUCTION (SMILLION) USING GOLD PRICE FLUCTUATIONS AND MINE MODEL
51
TABLE 4-19: VALUE OF GOLD PRODUCTION WITH DESIGN MODIFICATION 52
TABLE 4-20:PAYOFF TABLE STRUCTURE 58
TABLE 4-21: PAYOFF EXAMPLE RESULTS 58
TABLE 4-22: EXPECTED VALUE EXAMPLE RESULTS 59
TABLE 4-23: DATA REQUIRED FOR EVPI CALCULATION 61
TABLE 4-24: FINANCIAL DATA FOR UNMC EXAMPLE 62
TABLE 4-25: HISTORICAL DATA 64
TABLE 4-26: PRIOR PROBABILITIES 65
TABLE 4-27: POSTERIOR PROBABILITIES 66
TABLE 4-28:DECISION ANALYSIS EXAMPLE - MANAGER'S RESPONSES 69
TABLE 4-29: PERCENTAGES IN THE CALCULATION OF R 72
TABLE 4-30: MPNIMAX REGRET SOLUTION 74
TABLE 5-1: PERCENT OWNERSHIP OF HVC 80
TABLE 5-2: MATERIAL FLOW 97
TABLE 5-3: BLENDPNG SUMMARY 101
TABLE 6-1: QUALITATIVE BENEFITS PROMISED rN THE AFE VS. ACTUAL OUTCOME 108
TABLE 6-2: FINANCIAL BENEFITS - RESULTS 112
TABLE 6-3: FINAL FMANCIAL OUTCOME 113
TABLE 6-4: DESIGN COSTS 118
TABLE 6-5: DATA REQUIRED FOR AHP APPLICATION 120
TABLE 6-6: RESULTS FROM THE AHP EVALUATION 120
TABLE 6-7: FINAL SCORE FOR HVC-AHP EXAMPLE 120
TABLE 6-8: SIMULATION DATA ENTRY TABLE 123
TABLE 6-9: IDEAL BLENDS 126
TABLE 6-10: EXAMPLE OF POSSIBLE ERRORS GIVEN 5 HARDNESS INCREMENTS 130
TABLE 6-11: DIVERGENCE FROM IDEAL BLEND AT EXTREME FEED HARDNESS VALUES 131
TABLE 6-12: NEGLECTED STORAGE PILES 132
TABLE 6-13: DECISION TREE PROBABILITIES FOR BLENDING PROJECT 132
TABLE 6-14: DECISION TREE NUMERICAL VALUES FOR BLENDING PROJECT 133
TABLE 9-1: DATA FOR MPNECOMP EXAMPLE 149
TABLE 9-2: RESTRICTIONS FOR MINECOMP EXAMPLE 149
TABLE 9-3: GOALS FOR OPMC EXAMPLE 152
TABLE 9-4: IMPROVEMENT METHODS FOR OPMC EXAMPLE 152
Masters of Applied Science Thesis. Sean Dessureault