UBC Theses and Dissertations

UBC Theses Logo

UBC Theses and Dissertations

The evaluation of haulage truck size effects on open pit mining Bozorgebrahimi, Enayagollah 2004

Your browser doesn't seem to have a PDF viewer, please download the PDF to view this item.

Item Metadata

Download

Media
831-ubc_2004-901513.pdf [ 16.38MB ]
Metadata
JSON: 831-1.0081125.json
JSON-LD: 831-1.0081125-ld.json
RDF/XML (Pretty): 831-1.0081125-rdf.xml
RDF/JSON: 831-1.0081125-rdf.json
Turtle: 831-1.0081125-turtle.txt
N-Triples: 831-1.0081125-rdf-ntriples.txt
Original Record: 831-1.0081125-source.json
Full Text
831-1.0081125-fulltext.txt
Citation
831-1.0081125.ris

Full Text

T H E EVALUATION OF HAULAGE TRUCK SIZE EFFECTS ON OPEN PIT MINING By ENAYATOLLAH BOZORGEBRAHIMI B.Sc. The University of Sh.B. Kerman, Iran, 1990 M.Sc. Amir-Kabir Tehran, Iran, 1993 A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE STUDIES DEPARTMENT OF MINING ENGINEERING We accept this thesis as confirming to the required standard THE UNIVERSITY OF BRITISH COLUMBIA March 2004 © Enayatollah Bozorgebrarurni, 2004 ABSTRACT This thesis investigates the effects of equipment size selection on the economics of open pit mining. The work presented, illustrates the importance of considering equipment selection in the wider context of the entire mine. A methodology is presented for evaluating the various variables that are affected by equipment size, Equipment Size Sensitive Variables, (ESSV) that will aid the industry in making more effective equipment selection decisions. Initially, the thesis explores, classifies and discusses ESSVs. The importance of these variables and identified interrelationships are highlighted through mathematical and discrete event simulation methods. The research uses a set of case studies to show that the influence of the ESSV extends beyond the central mine production to encompass the mill, environment and community. The work reveals that some ESSVs such as reliability, tire cost and productivity are related to the current equipment technology therefore their effects are comparable for different mines, while other ESSVs are related to the mine and deposit characteristics, therefore their effects vary from mine to mine. Through the synthesis and enhancement of existing work this thesis develops techniques for the economic evaluation of equipment size and shows that the use of larger equipment has significant cost effects for some mines on other areas of the operation such as the mill. The techniques developed for ESSV evaluation include integration of orebody modeling, mine design, mill performance prediction, equipment production and maintenance costs. As typical of many research products the results show the need and importance for further work to enhance the knowledge developed about ESSV and the effect of scale on the mining industry. II ACKNOWLEDGMENTS I am grateful to Highland Valley Copper Mine (HVC) and Fording Coal Company for their support providing data and comments for this work. The experience gained by spending time at the HVC mine and working with their personnel was important in the completion of this thesis. I would like to especially thank Peter Witt and Collin Murray at PfVC whose support was crucial to the successful completion of the research. I would also like to thank Professor Laeeque Daneshmend and Professor Garston Blackwell from Queen's University for their comments, support and time generously spent reviewing parts of the work. I would like to especially thank Professor Malcolm Scoble and Professor Mario Morin from the University of British Columbia for their input, advice and support. Special thanks go to my thesis supervisor Professor Robert A. Hall who continuously provided me the resources for research, and courage for work. His continual guidance, trust in my abilities, and support enabled me to experience significant growth during my time at the University of British Columbia. I would like to thank my classmate and colleague at UBC Ms. Carole Odell who kindly helped to edit the document. I would like to acknowledge the financial support I received from the Natural Sciences and Engineering Research Council of Canada, Highland Valley Copper Mine, and the Center for Environmental Research in Minerals, Metals, and Materials (CERM3) during the course of this research. Finally, I am deeply indebted to my wife Siranoush for her patient and unwavering support and to my children Afarin and Donna for their tolerance and kindness during my doctoral studies. Ill TABLE OF CONTENTS ABSTRACT II ACKNOWLEDGMENTS Ill TABLE OF CONTENTS IV TABLE OF FIGURES X LIST OF TABLES XIII GLOSSARY XV 1 INTRODUCTION 1 1.1 Current Status and Trends for Mining Equipment 1 1.1.1 New Technology for Mining Equipment 3 1.1.2 Limitation on Applying Economies of Scale in Mining Industry 3 1.2 Statement of Problem 4 1.3 Industrial Relevance 6 1.4 Objectives of the Thesis 7 1.5 Thesis organization 7 2 LITERATURE REVIEW... 9 2.1 Overview 9 2.2 Equipment Size Trend, Pros and Gons 10 2.3 Review of the Current Techniques to Select Mining Equipment 11 2.3.1 Loading Equipment Versus Haulage Equipment 14 2.3.2 The Selective Mining Unit and the Equipment Size 15 2.3.3 Current Techniques for Sizing Mining Equipment 15 2.4 Mine planning and design 17 2.4.1 Mine-Mill Integration Concept and Equipment Size Optimization 17 2.5 Economies of Scale and Long Range Average Cost Curves 18 2.5.1 Economies of Scale in the Mining Industry 19 2.5.2 Economies of Scale in Other Industries 21 rv 2.6 Simulation as a Tool for Solving Mining Problems 22 2.7 Summary 24 3 METHODOLOGY 25 3.1 Overview 25 3.2 Methodology for Direct ESSV 27 3.2.1 Capital and Operating Costs 27 3.2.2 Maintenance Costs 28 3.3 Methodology for Indirect ESSV 29 3.3.1 Lost Production Cost 30 3.3.2 Capital Interest Cost 31 3.3.3 Stripping Ratio and Overall Pit Slope 31 3.3.3.1 Analytical Approach for Stripping Ratio and Overall Pit Slope 32 3.3.3.2 Using Mining Engineering Software , 37 3.3.4 Dilution 37 3.3.4.1 Geometrical Analysis 37 3.3.4.2 Blast Hole Samples Analysis 39 3.3.5 Milling Cost 40 3.4 Simulation 41 4 EQUIPMENT SIZE SENSITIVE VARIABLES 42 4.1 Introduction 42 4.2 ESSV Classification 44 4.3 Direct Variables 45 4.3.1 Capital Cost. 46 4.3.2 Operating Costs 46 4.3.2.1 Maintenance 46 4.3.2.1.1 Parts - Materials 47 4.3.2.1.2 Labor 47 4.3.2.2 Operator Cost 48 4.3.2.3 Fuel 48 4.3.2.4 Tires 49 V 4.4 Indirect Variables 49 4.4.1 Downtime Cost 49 4.4.1.1 Lost Production Cost -50 4.4.1.2 Capital Interest Cost 51 4.4.2 Infrastructure 51 4.4.3 Complexity 52 4.4.4 Matching Factor 52 4.4.5 Haul Roads and Ground Bearing Pressure 53 4.4.6 Minimum Pit Bottom Area .54 4.4.7 Dilution and Selectivity 55 4.4.8 Flexibility and Versatility 55 4.4.9 Environment and Community 56 4.4.10 Milling Cost 56 4.5 Summary 57 5 ANALYSIS OF SOME DIRECT EQUIPMENT SIZE SENSITIVE VARIABLES.. 58 5.1 Total Production Cost - Manufacturer's Data Analysis 58 5.2 Case Study for Production Costs 59 5.3 Data Collection 59 5.4 Data Analysis and Assumptions 60 5.5 Results 60 5.5.1 Labor Cost 61 5.5.2 Spare Part and Material Cost 61 5.5.3 Infrastructure 62 5.5.4 Downtime Cost 62 5.5.4.1 Lost Production Cost ?62 5.5.4.2 Capital Interest Cost 63 5.5.5 Total Maintenance Cost 63 5.5.6 Total Costs 64 5.6 Discussion 65 5.7 Summary 68 VI 6 MINE DESIGN EQUIPMENT SIZE SENSITIVE VARIABLES 69 6.1 Dilution and Selectivity 69 6.1.1 Introduction 69 6.1.2 Geometry of Deposits and Dilution 70 6.1.2.1 Mining Block Model 70 6.1.2.2 Results 71 6.1.3 Bench Height and Dilution - Case Study 74 6.1.3.1 Introduction 74 6.1.3.2 Assumptions 75 6.1.3.3 Results 76 6.2 Stripping Ratio and Slope Design 77 6.2.1 Introduction 77 6.2.2 Example 77 6.2.3 Case Study 78 6.2.3.1 Results 79 6.3 Summary 80 7 EQUIPMENT SIZE EFFECTS ON MILLING COST 81 7.1 Introduction 81 7.2 Case Study Criteria and Assumptions 82 7.3 Limitations as a Result of Using Assumptions 83 7.4 Case Study 1 - Copper Deposit 84 7.4.1 Introduction 84 7.4.2 Results 84 7.5 Case Study 2 - Gold Deposit 93 7.5.1 Introduction 93 7.5.2 Results 93 7.6 Summary 100 8 SIMULATION 101 8.1 Introduction 101 VII 8.2 Objective 101 8.3 Data Collection 103 8.4 Data Analysis 103 8.5 Validation of Simulation Program 106 8.5.1 Validation Constraints 107 8.5.2 Results of the Model Validation 108 8.6 Case Study: Description of the Model 109 8.7 Assumptions 113 8.8 Simulation Results 113 8.9 Production 114 8.10 Lost Production Cost 115 8.11 Summary and Interpretation of the Results 116 8.12 Discussion and Overview 116 9 CONCLUSIONS 119 9.1 Direct Impacts 119 9.2 Indirect Impacts 120 10 FUTURE RESEARCH 122 10.1 Simulation 122 10.2 Mine Design and Equipment Selection 122 10.3 ESSVs Not Considered in This Work 123 10.4 Maturing Technology 123 11 REFERENCES 124 APPENDIX A- TRUCK SIZE POPULATION AROUND THE WORLD 133 APPENDIX B- COST ANALYSIS OF TWO DIFFERENT TRUCK SIZES 137 APPENDIX C - DETAILED INFORMATION ABOUT COPPER DEPOSIT 142 APPENDIX D - DETAILED INFORMATION ABOUT GOLD DEPOSIT 150 VIII APPENDIX E - STATISTICAL DISTRIBUTION FITTING, TREND TEST AND TEST FOR CORRELATION 158 TABLE OF FIGURES Figure 1-1 Evolution of mining trucks in the period 1950-2000 (After Mining Association of BC, 2003) 3 Figure 1-2 Typical open pit mining costs from Buhl (2000) 6 Figure 2-1 The equipment size selection environment 12 Figure 2-2 The mine equipment selection process (Singhal et al, 1986) 13 Figure 2-3 A typical long-run average cost (LRAC) curve 19 Figure 2-4 LRAC curves for different production systems (Runge, 1998) 20 Figure 2-5 LRAC of the US power generation industry (Christensen, 1976)...; 21 Figure 3-1 Production cost and ESSV 25 Figure 3-2 The equipment size effect on the overall pit slope 34 Figure 3-3 Geometry for ramp widening 35 Figure 3-4 Ramp extension impacts on overall pit slope .....37 Figure 3-5 A simple ore block ready to be mined 38 Figure 4-1 Equipment size considerations and equipment size sensitive variables 43 Figure 4-2 Equipment size sensitive variables 45 Figure 5-1 Total truck production cost ($/h) versus truck size...! 58 Figure 5-2 Truck production costs ($/tonne) versus truck size 59 Figure 5-3 Operating cost breakdown for CAT789 $/t 64 Figure 5-4 Operating cost breakdown for CAT793 $/t ; ....65 Figure 5-5 Operating cost for two truck size fleets 65 Figure 5-6 Projected results on the LRAC curves of haul trucks 66 X Figure 6-1 Sequence of equipment size impacts on open pit mining performance. 69 72 73 Figure 6-4 Dilution versus dip of orebody 73 74 Figure 6-6 A plan view of bench 2075, Mouteh gold mine - Iran 75 Figure 6-7 Variation in dilution for different levels within the mine 77 Figure 6-8 The effect of equipment size on the stripping ratio 78 Figure 6-9 Pit 1, 37-meter ramp width and Pit 2, 45.75-meter ramp width 79 Figure 7-1 The effect of mining equipment size on milling cost 81 Figure 7-2 Tonnage grade curve for copper deposit including recovery considerations. 88 Figure 7-3 A close-up view of the tonnage grade curves for copper deposit 88 Figure 7-4 Differences in copper content for 5-m blocks and 20-m blocks 89 Figure 7-5 Additional waste/low grade materials that have to be milled when 5-m blocks are mined instead of 20-m blocks for different cut-off grades for copper deposit. 90 Figure 7-6 Improvement in profit for small-scale mining 92 Figure 7-7 Gold content versus grade. Dotted curves include mill recovery 95 Figure 7-8 Additional waste/low grade materials that have to be milled when 5-m blocks are mined instead of 20-m blocks for different cut-off grades for gold deposit 97 Figure 7-9 Difference in gold production comparing 5-m block with 20-m block 98 Figure 7-10 Improvement in profit for gold deposit comparing 5-m block with 20-m block 100 Figure 8-1 Proposed integrated open pit mine simulator 102 X I Figure 8-2 Reliability versus time plot for two truck fleets 104 Figure 8-3 Haulage layout of the actual open pit mine used to validate the simulation program 107 Figure 8-4 Model used for verification of the simulation program 108 Figure 8-5 Frequency of production for validation model 109 Figure 8-6 Schematic of the open pit mine haulage system simulation model 110 Figure 8-7 Close up view of the parking area, maintenance shop and crusher Ill Figure 8-8 Flow chart of the discrete event simulation program 112 Figure 8-9 Distribution of production from three fleets during a one month period.. 115 XII LIST OF TABLES Table 3-1 Approximate haul road widths for various truck sizes 33 Table 4-1 Haul truck maintenance issues - staffing (Wohlgemuth, 2001) 47 Table 4-2 Haul truck maintenance issues - infrastructure (Wohlgemuth, 2001) 52 Table 5-1 Production and operating hours from January 2000 to December 2001 61 Table 5-2 Average maintenance labor cost 61 Table 5-3 Average part and material cost 61 Table 5-4 Truck downtime comparison 62 Table 5-5 Lost production cost 63 Table 5-6 Capital interest cost 63 Table 5-7 Total maintenance cost $/h and $/t 63 Table 5-8 Total cost CAT789 and CAT793 trucks 64 Table 6-1 Factors influencing dilution 70 Table 6-2 Dilution (°/o) in an ore block: thickness of ore body versus bench height when dip is considered to be constant 71 Table 6-3 Dilution (°/o) in an ore block: ore body dip versus bench heights when the ore thickness is considered to be constant 72 Table 6-4 Grades (g/t) of two parts of each drill hole in different benches 75 Table 6-5 Characteristics of dilution in mine benches 76 Table 6-6 Comparison of tonnage for two design with different ramp width 80 Table 7-1 Tonnage-grade for three different block sizes of the copper deposit 85 Table 7-2 Copper content and average grades of the 5-m and 20-m block models 87 Table 7-3 Total income and profit improvement for smaller block model project 91 XIII Table 7-4 Tonnages and average grades for different classes - gold deposit 94 Table 7-5 Tonnage to be milled and gold production 5-m and 20-m block models 96 Table 7-6 Total improvement in income for different cut-off grades - gold deposit 99 Table 8-1 Distributions of the mean time to repair 105 Table 8-2 Distributions of mean time between failures 105 Table 8-3 Load distributions for trucks 106 Table 8-4 Summary of 200 runs of the validation model 108 Table 8-5 Average maintenance time 113 Table 8-6 Breakdowns for two fleets 114 Table 8-7 Average production for three fleets 115 Table 8-8 Lost production costs for small trucks versus larger trucks 115 Table 8-9 Model to evaluate ESSV for trucks larger than 200 tonnes 118 XTV G L O S S A R Y CAES™ Caterpillar's Computer Aided Earth Moving System E.U. Effective Utilization ESSV Equipment Size Sensitive Variables Y(X) Mathematical expression of Equipment Size Sensitive Variables FSU Former Soviet Union GPS Global Positioning Systems LRAC Long-Run Average Cost MMS Modular Mining System MTBF Mean Time Between Failures MTTR Mean Time to Repair NPV Net Present Value SLM Suspended Load Measurement System SMU Selective (Smallest) Mining Unit VIMS™ Vital Information Management System y(h) Semi Variogram AC Average Cost APR Average Production Rate LCF Labor Cost Function TCPM Total Cost of Materials and Parts LCF Labor Cost Function AHWB Average Hourly Wages and Benefits TMH Total Manpower Hours OP Operating Hours MH Maintenance Hours SB Standby Hours R, Road Width Trw Truck Width Rvalue Residual Value Ton Short ton Tonne Long tonne 1 INTRODUCTION In response to increasing globalization and low product prices, the Canadian and global mining industry have become more competitive by applying economy of scale at their operations. These economies have mainly been achieved by increasing productivity via use of larger equipment. To some extent the approach has been successful. For example, reports from some mines indicate that a significant reduction in equipment costs has been associated with the employment of larger equipment (Baumann, 1999; Wusaty, 1996). In terms of productivity, the mining industry continues to adhere to a "bigger is better" mentality. This attitude is being implemented to such an extent that "bigger is better", is becoming a rule of thumb in mine equipment selection (Woof, 2003). The focus on increase size has affected almost all aspects of the mining operation from production to waste management, yet little attention has been given to the effects larger equipment have on areas such as ore dilution, maintenance, safety, milling and other aspects of the operation. Previous work has suggested that the indiscriminate selection of larger equipment may not always be advantageous (Djan-Sampson et al, 1998). This thesis aims to investigate the benefits and potential disadvantages of continuing the trend toward larger equipment in open pit mining by developing mathematical and simulation methods for analyzing the Equipment Size Sensitive Variables (ESSV). This chapter discuses the current status and trends in mine equipment size. It then proceeds to describe the research questions and objectives and its relevance to the mining industry. The chapter ends with an outline of the structure of the thesis as a whole. 1.1 Current Status and Trends for Mining Equipment The concept of "economy of scale" embraces three main elements: a) the size of the firm, b) the size of manufacturing plant, and c) the size of the machine (Adams, 1986). For the mining industry these elements are actualized as: a) mergers between mining companies, 1 b) large-scale mining operations, and c) the utilization of increasingly larger equipment. In open pit mines, particularly, economy of scale has been achieved mainly by increasing productivity through the use of larger equipment. As a result, over the past five decades, mining equipment has steadily increased in size and complexity. For instance, the 360-tonne trucks of today are about 10 times the size of the 35-tonne trucks of the 1950's and there has been an increase of approximately 50% in truck payload every decade (Krause, 2001). This trend resulted in the introduction of a new generation of ultra large trucks (greater than 240 tonne) during the last decade of the 20th century. Figure 1-1 shows the evolution of mining trucks during the last 50 years (after Mining Association of B.C., 2003). In addition, Appendix A provides the results of a recent survey on worldwide haulage truck population, which demonstrates the distribution of large haul trucks (larger than 90 tonnes) by country, commodity and manufacturer. In addition to haul trucks, other examples of increasing mining equipment size are: - 125-tonne capacity cable shovels - 5000t/h gyratory crushers - SAG mills of 13 m diameter 2 Mining Truck Evolution 450 c 400 H 350 > 300 I 250 « 200 w 150 0) co 50 0 HI 360J r 240 r '0 1 9 0 * ' 1 2 0 ^ - n 6 5 j f 3 5 5 0 . 1940 1950 1960 1970 1980 1990 2000 2010 Year 'igure 1-1 Evolution of mining trucks in the period 1950-2000 (After Mining Association of BC, 2003) 1.1.1 New Technology for Mining Equipment The increasing size of mining equipment has occurred in parallel with the addition of new technologies that have brought considerable changes to the mining industry. Examples of these new technologies are: Dispatching and Global Positioning Systems (GPS) for fleet management, Caterpillar's Computer Aided Earthmoving System (CAES™) for earthmoving project control, Vital Information Management System (VIMS™) for real-time machine monitoring for maintenance and production management. 1.1.2 Limitation on Applying Economies of Scale in Mining Industry These technologies enable engineers in operations management to monitor and optimize equipment scheduling using accurate real time data and have led to considerable improvements in operations performance. 3 Even though there are significant advantages from employing economies of scale, not all mines have been able to implement this concept. Some reasons for this failure are: - Equipment size selection is determined by several variables including the size of the deposit. The size of the deposit, in combination with productivity, determines both the final value of the project and the mine life. Therefore in order to justify the purchase of larger equipment, the deposit must be of a large enough size, - The size of operations is constrained by the amount of capital available for investment. Where insufficient capital can be raised, companies may apply a sequential (phased) mining system, thus frustrating the purchase of larger equipment. For these reasons the application of larger equipment in the future is limited to large-scale operations where capital is not overly constrained. The requirement for very large operations may impede the development of the next generation of larger equipment, as the demand may not be sufficient to justify the investment for manufacturers. "The key question for truck suppliers might not be whether the technology can be developed, but whether there are enough big mines that can use a 1,000-tonne truck" (Gilewicz, 2001). 1.2 Statement of Problem There is now a broad spectrum of equipment sizes available for mine production. For example, the Caterpillar Company provides 13 different sizes of haulage trucks and Komatsu provides 9. There is, however, limited understanding of how equipment size affects various aspects of the mining operations. This means that the cost benefit of the next generation of larger equipment is not clear. The RAND Institute (2001) has questioned "whether the size of haul trucks and excavators has reached a feasibility threshold where the economies of scale have peaked?" as one indicator of this uncertainty. Uncertainty has also been evident in other works such as those presented in the following conferences: "Is Bigger Better" in Edmonton Alberta in June 2001 and "Haulage 2002" in Tucson Arizona in May 2002. User's uncertainty appears to be impacting sales as indicated 4 by a depression in the larger haul truck market (larger than 140-ton) during 2000 and 2001 (Gilewicz, 2001). KMC MINING, one of the leading mining contractors in North America, has also expressed concerns regarding high capital cost of larger equipment (Klemke, 2001). In addition, there are indications that difficulties have accompanied the introduction of larger equipment into mines. These include complexity, dilution, lost production and reduced flexibility (Djan-Sampson and Daneshmend, 1998; Dunbar et al., 1999). These difficulties may limit the overall benefit of implementing larger equipment, however, data quantifying these difficulties are scarce. Another area, which has not been well researched, is the impact of larger equipment on other parts of the mine such as the mill. This lack of knowledge raises the question whether the gains at the mine operation from employing larger equipment, may be counterbalanced by negative impacts on other parts of the operation. Potential impacts on wider aspects of the mining operation suggest that the whole process of the mining operation must be considered as a single unit. A final issue impacting equipment selection is that there is no reliable method for determining the optimum equipment size for a mine. The open pit optimization process, which determines the ultimate pit limit, applies assumptions concerning economic conditions (costs and prices), possible excavation geometries, pit slopes, selectivity, production rates, etc. However, the pit optimization process is done prior to final equipment selection, which in turn affects all of these design input parameters (Lizotte, 1988). Equipment selection and pit optimization are thus strongly interrelated, which suggests that equipment selection should take a more important role in the mine planning and design process. Given the aforementioned, the key issues for the mining industry with regard to larger equipment are: 5 The uncertainty regarding the economic benefits of utilizing even larger equipment, The lack of a robust tool for equipment size selection, A general lack of understanding about the effects of equipment size on the entire process. Clearly these uncertainties have the potential to significantly impact mine economics. 1.3 Industrial Relevance "The mining and the mineral industry is an enormous and vital contributor to the Canadian economy. About 3.7 percent of the national gross domestic product (GDP) is contributed by the mining and mineral processing industries" (Minister of Public Works and Government Services, 2001). For many other countries worldwide the contribution to GDP is much higher. Mining equipment capital and operating cost represent a significant portion, about 50% of the mining cost at an average mine (Blackwell, 2000) although, for some mines the portion may be even more. For example, data for a set of open pit gold mines shows that 65% of the mining costs are related to loading and hauling activities (Buhl, 2000). Figure 1-2 shows the cost breakdown for the various activities of the mining operations shown in this study. Typical Open Pit Mining Costs 0.6 0.5 0.46 0.2 0.4 0.3 0.1 0.09 0.11 3.09 •ife I i 0.19 0.06 Engineering Drilling Blasting Loading Hauling Roads & Dumps Figure 1-2 Typical open pit mining costs from Buhl (2000) Since mining equipment costs represent a significant portion of the investment and operating costs, even a small improvement in efficiency resulting from improved equipment selection can significantly affect the viability of a mine. The mining industry thus has considerable economic incentives in improved understanding of equipment size selection costs and benefits. 1.4 Objectives of the Thesis Due to uncertainties concerning the future trend in equipment size and the absence of reliable data concerning the impacts and benefits of equipment selection decisions described in this chapter, there is a compelling need to investigate the details of how equipment size impacts a mine's return on investment. It is this need that this thesis aims to address. More concretely the objectives of this research have been to understand the effects of equipment size in surface mining and to develop a technique for the economic evaluation of size effects. The research includes: Equipment Size Sensitive Variable (ESSV) identification ESSV quantification Integration of equipment size selection and mine planning Demonstration of the potential use of simulation for equipment size selection These activities are then synthesized to provide data, tools and analysis to support rational equipment size selection in the future. 1.5 Thesis organization This thesis consists of 11 chapters and 5 appendixes. Brief descriptions of these sections follow: Chapter 1 states the problem, and discusses the current status and trends for mining equipment. Chapter 2 contains reviews of previous work in the area and related literature including equipment size selection, modeling and simulation. 7 Chapter 3 discusses the methodology of the thesis for two different types of equipment size sensitive variables (ESSV), direct and indirect. Chapter 4 identifies and discusses ESSV. Chapter 5 discusses and quantifies direct variables and long-range average cost curves. Chapter 6 discusses and quantifies the effect of equipment size on mine design parameters such as ore dilution, selectivity, overall pit slope and stripping ratio. This is explained in detail using an example and two case studies. Chapter 7 discusses and quantifies the effect of equipment size on milling cost using case studies. In these case studies two different ore-bodies are analyzed to quantify the effect of equipment size on milling cost. Chapter 8 presents a discrete event simulation program as a potential tool for modeling ESSV. Chapter 9 contains the conclusions Chapter 10 discusses future research opportunities. 8 2 LITERATURE REVIEW 2.1 Overview In addition to exploiting economies of scale through utilizing larger equipment, the mining industry has spent a significant amount of time and money developing and implementing new technologies to be used in tandem with larger equipment. The industry has also invested in the development of strategies to reduce costs or improve productivity in areas such as: blasting, drilling, equipment performance, mine-mill integration, planning and design. To assess the current state of the industry, a literature review has been carried out focusing on the following areas: • Equipment size selection • Modeling and simulation • Data analysis • Mine planning and design • Economies of Scale • Selective Mining Unit (SMU) • Mine-mill integration The review begins with a description of the literature examining the impacts and benefits of the trend to larger equipment. It proceeds with an examination of current methodologies for equipment selection both identifying key parameters and discussing the relationships between loading and haulage equipment. This section continues with a section reviewing the interdependence between the Selective Mining Unit (SMU) and equipment sizing. The section ends with a review of the two main techniques currently in use for sizing equipment. In the next section a conceptual model for integrating mine equipment criteria into the mine design process is described. Section 2.4 considers the implications of mine-mill integration on equipment selection. The literature review then expands its focus to a higher level and discusses research on economies of scale in other industries and the 9 mining industry. A final section addresses the state of knowledge regarding simulation and its application to the mining industry. 2.2 Equipment Size Trend, Pros and Cons Current research indicates that the trend in equipment selection for the surface mining industry is the use of larger but fewer machines. Many researchers have discussed the advantages and benefits of the economies of scale in the mining industry. Several researchers have indicated that an increase in productivity has reduced total cost through the implementation of larger equipment in big mines. (Krause, 2001; Gilewicz, 2001; Richards, 1999; Dunbar et al, 1999; Baumann, 1999; Van Wieren, 1999; Djan-Sampson, 1998; Lewis, 1998; Powers, 1996) However, some of the authors have expressed concerns about diseconomies of scale; questioning whether increasing scale represents a rational strategy for the mining industry (Djan-Sampson et al., 1998; Dunbar et al., 1999). For example Djan-Sampson and Daneshmend (1998) conclude that the trend of mining equipment size has not been wholly advantageous. Their concerns focus on the increase in maintenance costs associated with larger equipment. Sullivan's research highlights haul road geometry, selectivity, infrastructure, and the mine personnel as issues, which have the potential to limit the economies produced by larger equipment (Sullivan, 1990). The effects of equipment size on performance, as demonstrated through factors such as flexibility, maintenance, productivity, cost effectiveness, dispatching and reliability have also been discussed in resent research: (Lewis, 1998; Hall et al., 2000; Dunbar et al., 1999; Richards, 1999; Baumann, 1999; Djan-Sampson et al., 1998). Dunbar's research suggests that the dependence on fewer but larger pieces of equipment tends to reduce a mining system's inherent flexibility (Dunbar et al 1999). Research in the areas of geostatistics and grade control also points to links between economies of scale and concerns about operation and production rates (Dimitrakopoulos, 2003; Blackwell, 2000). 10 2.3 Review of the Current Techniques to Select Mining Equipment The basic goal of equipment size selection is to satisfy production rate requirements while minimizing the mining cost. Driving parameters for equipment size selection include deposit specifications, design criteria and mine economics. Figure 2-1 shows the interactions between equipment size and other mining variables schematically. Generally the starting point and arguably the most important consideration in mine design is the production rate. Production rate is generally determined by considering the reserve, the market, the company's production strategy and the expected payback period. Mining equipment is sized based on the daily production rate and potential operational conditions such as utilization, availabilities and the mine layout. In turn, equipment size influences the mining cost, which eventually affects the optimized pit limits, as the design passes through successive iterations. Lizotte (1988) suggests that the equipment selection process is mainly needed to satisfy the production requirements. He points out that the equipment selection process consists of: a) Firstly choosing the types of equipment, b) Secondly sizing the equipment, c) And finally determining the number of units required meeting a selected production rate. He also indicates that proper matching of equipment is also critical to the process. 11 Figure 2-1 The equipment size selection environment Srajer et al. (1989) studied the equipment selection process in western Canada. Based on this work, most mining companies leave the selection of haulage equipment to truck manufacturers and evaluate their proposals on the basis of the best available deal. The personal experience and knowledge of the line of equipment offered, is often paramount in the truck selection process. Srajer suggests that the selection practice variances are tied to "a) the production requirements, b) mining plan, c) life of the mine and d) projected usage of the hauling equipment." Haidar and Naoum (1999) have used genetic algorithms to solve an optimization model for equipment selection. The objective function of their model is minimizing the total cost of the equipment. In their model, two sets of variables are introduced a) independent variables such as the numbers of each model and the number of hours the equipment operates over its operating life and b) dependent variables such as the production rate of the equipment, and the expected equipment life in hours. Under this model total production rate and the mine life are the key constraints in determining equipment selected. The model determines the number of each piece of equipment required and the additional time (hours) of operation required for the equipment over its operating life to minimize the total cost. 12 Further research concerning equipment selection was carried out by Singhal (1986). Figure 2-2, summarizes the factors involved in the equipment selection process according to this research. Once again production requirements are determined to be a key factor in equipment selection although Singhal also emphasizes site and geological conditions that are important in the equipment selection process, such as ground water condition, abrasiveness and thickness, dip and distribution of the reserve. He also identifies that loading equipment must be considered prior to haulage equipment in the equipment selection process. TERMS OF REFERENCE: Production Requirements, Capital Investment Guidelines, In-sl tu Characteristics VS Product Quali ty. * SOIL/SITE INVESTIGATION: Loose or Bank, Size and Weight, Abrasiveness, Ground Water Conditions, Geotechnical S t a b i l i t y . * ~~~ GEOLOGICAL ENVIRONMENT: Thickness, Dip, Distr ibution of Ore Reserves, F r i a b i l i t y , Physlco-Chemlcal Ore Characteristics, Selective Mining. f ~ PRODUCTION CAPACITY: Loading Capacity, Hauling Capacity, Crushing and Processing Capacity. * OPERATING CONDITIONS: Al t i tude , Temperature, Dust, Humidity. Terrain, Slope, Drainage, Manpower A v a i l a b i l i t y , A v a i l a b i l i t y of Services, Si te Location and Infrastructure. f Z Z MATERIAL PREPARATION: Str ipping, Ripping, D r i l l i n g , Blas t ing. LOADING EQUIPMENT: Performance, ManoeuverabUHy, Productivity, A v a i l a b i l i t y , Se lec t iv i ty , Adaptabil i ty. HAULING EQUIPMENT: Performance, ManoeuverabUHy, Hauling Capacity, Performance on Grade, Haul Road Conditions and Distance, F l e x i b i l i t y . i CRUSHING/PROCESSING PLANT: Blending Capacity, F l e x i b i l i t y , Crushing Capacity, Processing Adaptabili ty and Capacity. COST ANALYSIS: Capi ta l , Fixed and Operating Costs, Material Preparation, Maintenance, Anci l la ry Equipment, Unit Operation Costs. V ' ~ MINIMUM COST PER TON Figure 2-2 The mine equipment selection process (Singhal et al, 1986) 13 Overall then, there seems to be a consensus among researchers that production rate is the key variable in equipment selection. However, geological and site conditions are also considered to be important. In addition, Singhal suggests that equipment selection needs to commence with the sizing of loading equipment. The relationship between loading and haulage equipment selection is discussed in the following section. 2.3.1 Loading Equipment Versus Haulage Equipment The key size selection criterion for loading and haulage equipment is not the same (Singhal, 1986). While the size of the loading equipment is important for selective and clean mining, the performance of the entire haulage system is determined by the availability of the loading equipment. The loading equipment size selection criteria can be listed as: a. Selectivity, b. Productivity, c. Availability, and d. Flexibility. On the one hand, haulage equipment size directly influences the mine layout and design and, on the other hand, the mine layout must be matched to the loading machines. The haulage equipment size selection criteria can be listed as: a. Performance, b. Flexibility, c. Haul road conditions, d. Distance, and e. Hauling capacity. The acquisition cost for loading equipment is significantly higher than the acquisition cost for haulage equipment. However, in terms of operation costs typically the cost of haulage is more than twice the cost of loading (Blackwell 1999; Buhl 2000). In this 14 thesis the most attention has been paid to the haulage equipment, because of its higher overall contribution to costs. 2.3.2 The Selective Mining Unit and the Equipment Size In their work Lizotte (1986) and Pelley et al. (1983) examine the relationship between equipment size and mine geometry. Equipment size influences the economic value of the rock to be mined because it has a direct influence on mine geometry. On the other hand, the selective mining unit (SMU) is the smallest mineable mining block that maximizes the pit value and has great influence on mine geometry determination. The run of mine grade, particularly in metal mines, is sensitive to the SMU and consequently to the equipment size. In a mine with an erratic spatial ore distribution, such as most gold deposits, the SMU has considerable impact on the final pit value and should be determined with care. In well-distributed ore deposits, such as large porphyry copper deposits, it is possible to increase the size of the SMU without influencing the selectivity. Obviously, maximizing the size of the mining block in waste rocks is not as constrained as in ore blocks. Bench height is an example of a practical dimension for the SMU. In this regard, Lizotte (1986) suggests that, "Higher bench heights will normally result in lower operating costs, explained by economies in drilling and equipment relocation time savings." 2.3.3 Current Techniques for Sizing Mining Equipment At present, there is not a direct and deterministic way to size mining equipment. Typically mining equipment is sized as follows: 1. Calculate the loading equipment size necessary to satisfy the production rate. 2. Calculate the haulage truck size to match the loading equipment. As an example of the methods for sizing mining equipment, equation [2-1] computes the dipper capacity (volume) of a shovel (Atkinson, 1992). 15 B = Q /[(60 / tc) x S x A x O x (fillability I swellfacto r)] [2-1] Where: B is dipper capacity, Q_is production required per shovel, tc is shovel cycle time, S is swing factor, A is mechanical availability, O is job operational factor. One of the pitfalls of equation [2-1] is that not all of the values of the variables are known and assumptions have to be made. The quality of these assumptions can significantly impact the final result. Another pitfall is that the equation does not consider the effect of equipment size on other areas of the mine operations. In an attempt to estimate costs for preliminary feasibility studies, O'Hara et al (1992) provide a set of formulae, which can be used to determine truck size. They suggest that truck size is a function of daily production rate (equations [2-2] and [2-3]). 5 = 0.145x7;° 0.4 [2-2] S, =9.0x5 i.i [2-3] where: S is the optimum shovel size in cubic yards of dipper size T is the daily tonnage of ore and waste 16 S, is the size of truck in tons Neither Atkinson nor O'Hara methods provide a means for considering size effects on other aspects of the mine operation. 2.4 Mine planning and design Lizotte (1988) discusses the relationship between open pit mine design and equipment selection. He demonstrated that the equipment selected has great bearing on the technical and economic parameters necessary for open pit design, thus affecting outcomes. To formalize the interdependencies between equipment selected and open pit design parameters, he suggests the following characterizations for mining equipment: Assign specific numerical operating ranges to existing equipment Define the equipment performance as a numerical function of the working site conditions Derive cost formulae, which relate mining costs to equipment type, size and number of units. Then he suggests using these characterizations in open pit mine design programs in order to achieve improved results. 2.4.1 Mine-Mill Integration Concept and Equipment Size Optimization Most mining operations have long operated as two separate organizations: the mine and the mill (Dance, 2001). In many cases this creates difficulties such as reduction of efficiencies and increased overall costs. For example, Klein et al. (2003) and Eloranta (1999) advise that optimization of a localized component of any system can result in a negative impact on the performance of the complete system. The premise is that in order to achieve optimum effectiveness and efficiency from a system, the complete system must be considered. In this regard, Michaud et al. (1997) concluded that the concept of optimum 17 level of fragmentation in surface mining operations, i.e. the degree of fragmentation, which corresponds to the lowest overall associated costs of drilling, blasting, loading, haulage and crushing, was developed thirty years ago. They investigated the benefits of new technology for fragmentation monitoring as an example of mine-mill integration. Dance (2001) reports that Highland Valley Copper (HVC) benefited from adapting the concept of mine-mill integration. He reports a 15% increase in throughput of the mill without any extra cost by changing blasting patterns. Eloranta (1999) also studied the concept of mine-mill integration by comparing the drilling/blasting costs versus crushing/grinding costs. He lists cases where mines have succeeded in improving overall cost efficiency by increasing their powder-factor. From the literature it appears that significant work has gone into blast optimization towards mine-mill integration. However, other areas such as the impacts of larger material handling equipment's impact on the mine-mill integration objective have not been well addressed. 2.5 Economies of Scale and Long Range Average Cost Curves There is a consensus on the definition of economies of scale. Getzen (1997) states, "economies of scale is a status which in, the cost of producing a good or service decreases as the number of goods or services produced increases". Economies of scale occur when mass-production of a commodity results in lower average costs for each unit of production. Economies of scale occur within a company (internal) or within an industry (external). Average cost of production begins to fall because of improvements in the following areas: technical, managerial, financial, marketing, commercial, research and development. In contrast, when a company has become too large and inefficient, diseconomies of scale appear. The long-run average cost (LRAC) curve depicts the per-unit cost of producing goods or service over time. Figure 2-3 shows a typical LRAC curve (biz/ed, 2003). The curve shows three distinct zones. The first zone is the period when there may be economies of scale, which reduce the cost per unit. In the second part of the curve the cost per unit 18 remains the same, no matter how much the quantity of production is increased. Eventually, the curve shows that the cost per unit may begin to rise as diseconomies of scale appear. Economies V of scale Constant Costs Diseconomies of scale Scale Figure 2-3 A typical long-run average cost (LRAC) curve 2.5.1 Economies of Scale in the Mining Industry As was mentioned in Chapter 1 the mining industry has practiced economies of scale in different ways. A common approach has been increasing the productivity via employing larger equipment. Runge (1998) compares different loading and hauling systems in open pit mines using their LRAC curves. Figure 2-4 shows that diseconomies of scale emerge with increasing annual production rate for each equipment type. This figure shows that large rope shovels in combination with large trucks have the least production cost among other production systems. The dashed line in Figure 2-4 (by the author) connects the inflection points of each production system's curve. It shows that savings on costs are generally slowing down as the curve is leveling for larger equipment. This line shows that the current systems of production in open pit mines may be approaching diseconomies of scale. 19 Loading Equipment Cost Per Cubic Meter I $1.50 Small Front-End Loader, 77-t Trucks (Two Fleets) Large Front-End Loader, 136-t Trucks Hydraulic Excavator, 190-t Trucks Large Rope Shovel, 218-t Trucks I ! I I I I I I 1 2 3 4 5 6 7 8 9 10 11 Annual Production (million m3) Figure 2-4 LRAC curves for different production systems (Runge, 1998) At the operations level many mines have demonstrated the advantages of large-scale operation not only by employing larger equipment but also by utilizing creativity in large-scale mining techniques. For example, the Iron Ore Company of Canada attempted to reduce costs through implementing large-scale blasts up to 4,000,000t (El-Alfy and Atkinson, 1993). This resulted in considerable cost reductions associated with: 1) The concentration of drilling operations with fewer drill rig moves and prolonged access for maintenance, hole charging, etc.; 2) Fewer shovel moves; 3) Easier and better supervision; 4) Reduced consumption of explosives per ton of broken ore; and 5) Better fragmentation. The current typical blast size in this mine is about 500,000 tonne (IOCC, 2003). However, this example demonstrates that the potential for internal economies of scale in the industry is not limited to using large shovels and trucks. 20 2.5.2 Economies of Scale in Other Industries Many different and diverse industries have benefited from economies of scale. For example, the transportation and agriculture industries have produced significant decreases in terms of cost per ton for their services and products through the application of economies of scale (McCann, 1999). While some industries are enjoying benefits of economies of scale, others have reported no significant economies or diseconomies of scale. For example, Christensen (1976) shows the presence of constant returns with respect to scale for U.S. Electric Power Generation. Figure 2-5 shows the average cost curve for power generation in the United States. The curve shows a wide flat region with no significant economies or diseconomies of scale. Arrows in the curve mark this part. Christensen (1976) concluded that "the identification of this range provides an important tool for analyzing proposals to restructure the electric power industry at that moment". Work of this type in the power generation led the electricity industry to change its focus from a "bigger is better" approach to the design of optimally sized facilities. 6 6 1 5 6 4 5 8 6 3 0 0 6 1 4 3 X * 5 9 6 5 • o o o 5 . 8 2 8 . 5 6 7 0 . o 5 . 5 1 3 . LU o 5 . 3 5 5 < tx u 5 . 1 9 8 < 5 . 0 4 0 4 . 8 8 3 4 . 7 2 5 T 1 1 i . i • i t | i 0 5 10 15 2 0 2 5 3 0 3 5 4 0 O U T P U T ( B I L L I O N 4 5 5 0 KWH) 5 5 6 0 6 5 7 0 7 5 1 5 7 | 21 | 12 1 7 | 4 | 5 1 4 | 1 I 1 1 1 I I I I I SIZE DISTRIBUTION OF FIRMS Figure 2-5 LRAC of the US power generation industry (Christensen, 1976) 21 Alamaro (1994) suggests that, "after their inception many industries start with big machines. However, after a few decades, when the technical knowledge becomes diffused, and the product or service offered by the big machine becomes a necessity of life, smaller-scale machines replace or supplement their predecessors, even though initially they may be less economical". He listed a large number of industries that have had a shift from centralized big machine processes to smaller systems. These industrial shifts include: Personal computers vs. mainframe computers Fax vs. telex Co-generation and small independent power generation systems vs. large centralized power plants Mini steel mill vs. large steel plant VCRs vs. movie theaters Automobile vs. railroad 2.6 Simulation as a Tool for Solving Mining Problems Simulation allows evaluation of the uncertainties involved in a project and enhances understanding of the risk. Another main purpose of computer simulation is to duplicate the operations of real life systems or processes. The advantage of such simulation is that operational scenarios can be tested and evaluated without the need or use of actual experimentation (Schafrik, 2001). Despite its advantages, researchers must bear in mind that the results of the simulation are very sensitive to the accuracy of the inputs and the quality of the model. An inappropriate description of risk may be costly or result in a lost opportunity (Coombes, 2000). Therefore creating a good simulation model requires extra caution and validation. Uncertainty and risk have always been associated with the mining industry. Major sources of uncertainty include reserve calculation, ground condition, commodity prices and equipment performance. Mining engineers have used simulation methods to solve problems since the early ages of computers. The first experiments with simulation dates back to 1961 when Rist (1961) used the Monte Carlo technique to optimize the number of 22 trains in an underground mine. Sturgul (1999) surveyed early simulations of mining applications. In his book, he presents detailed historical data about solving mining problems using simulation. Simulation is now being used as a tool to solve problems in different areas of the mining industry such as hauling systems, mine design and mineral processing. Many researchers and companies have used simulation to optimize haulage system performance and fleet size. For example, Panagiotou and Michalakopoulos (1996) introduced STARPAC2 software as a tool for shovel-truck operation analysis. They developed the program in order to assist mining engineers in optimal equipment selection, sizing and scheduling. Schafrik (2001) introduced WebConSim, a web-based computer simulation tool for mining engineers, to plan the optimum mining sequence for different equipment layout and mine geometries. Another example is the work of Erdem et al (1996), who developed a series of computer simulation models as part of an expert system for selecting dragline and stripping method in surface coal mines. As well, Fytas (1983) developed an interactive computer simulation model of open pit haulage systems. A very good reference for mine system simulation is the work that has been done by Sturgul (1996). In his work, he referenced and listed every paper ever published on the subject of Mine System Simulation from 1961 to 1995. As well Sturgul has published numerous papers dealing with mine simulation for example (Sturgul, 1998), (Sturgul, 19994), and (Sturgul, 1989). In addition he recently published a book on mine simulation (1999) using GPSS/H (a simulation programming language). Another example is the work presented by Dimitrakopoulos (2003), who developed methodologies for scheduling mining blocks in order to maximize the profit using simulation. Ellis (1996) describes the typical facilities and operation planning questions that can be studied using simulation models and the problems or shortcuts that occur when simulation is not used. 23 McTurk et al (1996) used simulation models to assist Syncrude Canada Ltd. in the selection and sizing of key elements of their integrated operation of mining and Hydrotransport equipment. The models helped Syncrude to establish shovel and truck fleet sizes, examine queue times at shovels and crushers, select crusher rate, determine hopper and intermediate bin sizes, and to enable system designers to focus on bottlenecks. 2.7 Summary This chapter has reviewed the current knowledge and research on mining equipment selection and related issues. The review shows that despite extensive work on mining equipment little attention has been paid to developing techniques to evaluate the impacts of scale rationally. In addition existing techniques for optimization of equipment size focus exclusively on optimizing within the mine itself. There is not enough knowledge about the effects of equipment size on different areas of the mining industry and thus the real benefits and costs of larger equipment are unknown. The literature review also highlights the rather empirical basis for equipment selection in the mining industry. Rather than following a rational technical procedure, equipment selection relies on manufacturer's recommendations and miners' experience. The review therefore points to a compelling need to more rigorously evaluate the effects of equipment size on efficiency of mining operations and to develop a technique to optimize equipment size selection. These two elements form the core of the research undertaken in this thesis. The following chapter describes the methodological approach used to address the research objectives. 24 3 METHODOLOGY 3.1 Overview To accomplish the objective of the research, it is proposed to compare different equipment sizes in terms of their life cycle costs. Production cost is assumed to be a complex function of the Equipment Size Sensitive Variables (ESSV), discussed in Chapter 4). (Figure 3-1) depicts how final production cost is influenced by several ESSVs. i Ypo} Figure 3-1 Production cost and ESSV Equation [3-1] introduces an approach to formulate the relationship between the variables associated with mining cost. These variables are categorized as Y(X) (ESSV) which are related to the equipment size (X). The initial step in comparing different equipment 25 sizes in terms of their life cycle costs is the quantification of ESSVs. This quantifying requires the determination of the relationship between equipment size (X) and each related cost Y(X). Having quantified each of these ESSV then the overall production cost Z(Yl,X) for each equipment size can be determined through summation. Preparing a set of equations that formulates the relation between equipment size and costs/benefits then provides a means to select the appropriate equipment size. Z(YnX) = F [ £ (Yi(X))]3 [3-1] 1 = 1 Where: Z(Yt,X) is the production cost function Xis the equipment size Yt(X) is the cost function of each Equipment Size Sensitive Variable (ESSV) n is the number of equipment size sensitive variables The first step in utilizing this equation is to identify ESSVs, which will be carried out in chapter 4. The next step is to quantify the cost function Yt(X) for each ESSV. Unfortunately, in many cases the form of Yt(X) is not known. One of the difficulties in determining Y(X) is that ESSV have both direct and indirect effects. This aspect of ESSV was accounted for by separating variables into direct and indirect variables and considering the direct and indirect effects separately. The following is an outline of the methodology used in an attempt to develop an understanding of the Yt(X)\. a) Identify and discuss the equipment size sensitive variables, b) Perform data collection, such as the Mean Time Between Failure (MTBF) and Mean Time to Repair (MTTR), delays, standbys, production data, and costs, 26 c) Model the relationship between equipment size and their effects on the mining operation and economics, d) Perform cost analysis, e) Perform data analysis using statistical methods, f) Quantify the equipment size sensitive variables for both direct and indirect variables where possible, g) Compare equipment size effects for possible mine design scenarios and interpret the results. 3.2 Methodology for Direct ESSV 3.2.1 Capital and Operating Costs The following describes the method used to calculate the capital and operating cost effects of equipment size. The method selected utilizes manufacturer's data to calculate this ESSV. Equation 3-2 shows the formula used for this calculation. Pr™>=J77^3 [3-2] APR Where: prcost is the average productions cost for different trucks sizes in $/t AC is the average hourly ownership and operating costs in S/h APR is the average hourly production rate in t/h In using this formula the following considerations, assumptions and data sources were used: a. Operating environments are considered for three different conditions: moderate, average and severe. 27 b. Hourly owning and operating costs for different truck sizes and for different operating conditions are extracted from manufacturer's tables. c. Average hourly productions for different truck sizes and for different operating conditions are extracted from manufacturer's tables. d. Dividing average hourly cost by average hourly production the production cost for different operating conditions and different truck sizes are computed. 3.2.2 Maintenance Costs The following method is used to calculate and evaluate the effect of equipment size on maintenance: a. Operating data for a period of two years from a large open pit mine employing two different sizes of trucks were collected. Data included both maintenance and operating information. b. The manpower time spent for maintenance and the amount of parts used for each truck were collected (h). c. The average hourly wages and benefits were calculated ($/h). d. The cost of parts used was collected ($). e. Total operating hours for each fleet was calculated (h). f. The average hourly production for each fleet was calculated (t). g. The average maintenance expenses, in terms of parts and labor, were calculated ($/t). In order to quantify the effect of equipment size on the cost of parts, the average cost of each item of materials and parts used for different equipment can be compared. In order to calculate this factor, the total cost of materials and parts consumed and used for a given period of operating time is calculated. This is then prorated by the total operating hours or tonnages (Equation [3-3]). 28 TCPM TP(h) [3-3] Where: PCF is the Part Cost TCPM is the total cost of materials and parts $ TP(h) is the total amount of production or total hours of operations, t or h In order to quantify the effect of equipment size on labor cost, the average person-hour cost spent to maintain different units of equipment is calculated. In order to calculate this parameter, the average hourly wages and benefits for labor are multiplied by the total manpower hours spent to maintain the equipment for a given period of time. This value is then prorated to the total operating hours or tonnages (Equation [34]). LCF = AHWB x TMH [ 34] Where: LCF is the labor cost function AHWB is the average hourly wages and benefits for labor TMH is the total manpower hours spent to maintain the equipment The same approach is used to calculate the tire costs, fuel costs and operator costs in terms of $/t for each fleet size. 3.3 Methodology for Indirect ESSV Five indirect ESSV were modeled. These are lost production cost, capital interest cost, stripping ratio and overall pit slope impacts, dilution and milling costs. The methodology for each ESSV is outlined in sections 3.3.1 to 3.3.5. 29 3.3.1 Lost Production Cost Lost production cost can be quantified based on the effective utilization of equipment. In reality and in the case of a breakdown, the cost of recompense is somewhat greater than the normal production cost. Factor/in equation [3-5] represents this additional loss. If this factor is considered to be 1, it means that a machine (substitute system) is available to produce the equivalent amount of the lost product, with the same cost at the moment of breakdown, so that the mine continues without lost production. LP™, = & - EU)x prrate x prcost x / [3-5] OP EU = — [3-6] OP + MH + SB w. here: EU is the effective utilization, LPC0St is the lost production cost $, Prrate *s t n e production rate in ton/h, prcost is the production cost in $/ton / is a factor greater than or equal to 1 representing the fact that the cost of recompense is usually greater than the normal production cost, OP is the operating hour h, MH is the Maintenance hour h, SB is the standby hours h. 30 For the situation where no substitution system is available to replace the broken-down equipment, the mine goes behind the schedule and loses (l - EU) x prrate amount of production. For this situation the lost production can be quantified using equation [3-7]. ^cos, = (1 - EU)x prrate x AOPTice [3-7] where: AO^ice is the average ore price in the same period $/t 3.3.2 Capital Interest Cost Effective utilization is also used to quantify the capital interest cost ESSV. Equation [3-8] provides a method to quantify this cost. CICost=Rvaluexix(l-EU) [3-8] where: CICosl is the capital interest cost in $/hour of operation, Rvalue I S t n e r e s i d u a l value of the fleet in dollars, i is the interest rate. 3.3.3 Stripping Ratio and Overall Pit Slope Two methods are examined in determining the costs associated with changes in stripping ratio and pit slope. The first one is an analytical approach that is suitable for a quick assessment. This is useful on pre-feasibility studies and for field work. The second one is based on a more detailed analysis through mine engineering software. 31 3.3.3.1 Analytical Approach for Stripping Ratio and Overall Pit Slope Safety, traffic speed, and truck width are among the parameters of concern in ramp width determination. The general rule of thumb for ramp width considering 2-lane traffic is shown in equation [3-9] (Couzens, 1979): *w^4x7>w [3-9] Where: Rw is the road width, Trw is truck width Adding an additional lane for uphill traffic will speed up the traffic flow. Accordingly for large mines, 3-lane traffic is now more common than two-lane traffic. The general rule of thumb for 3-lane traffic is (Couzens, 1979): * ^ 5 x 7 > w [3-10] Table 3-1 shows the ramp width for trucks based on data obtained from Caterpillar's Performance Handbook (1999). The last column in Table 3-1 shows the additional ramp width for every sequential increase in truck size. For example, it shows that employing 320-tonne trucks instead of 230-tonne trucks causes an 8.7m increase in ramp width for 3-lane traffic and a 7m increase for 2-lane traffic. 32 Table 3-1 Approximate haul road widths for various truck sizes Truck Size Approx. Width 2-way road Difference 3-way road Difference (Tonne) (m) width (m) (m) width (m) 3-way (m) 37 5.01 20.04 0 25.05 0 40 5.01 20.04 0 25.05 0 53 5.08 20.32 0.28 25.4 0.35 63 5.21 20.84 0.52 26.05 0.65 96 6.1 24.4 3.56 30.5 4.45 153 6.64 26.56 2.16 33.2 2.7 196 7.67 30.68 4.12 38.35 5.15 232 7.41 29.64 -1.04 37.05 -1.3 326 9.15 36.6 6.96 45.75 8.7 Using larger trucks forces the mine to push back pit walls in order to accommodate wider ramps. This causes a change in the stripping ratio and the overall pit slope as shown in Figure 3-2. The amount of additional slope due to the use of larger trucks is calculated from the formula: _, Axxsin(a) Aa = tan [ ^- ] y [3-11] 33 Overall s l o p e^i— i Slope change due tc oequipment size Ramp extension due lo equipment size 3=t Overall slope \ •ngle / " s i Figure 3-2 The equipment size effect on the overall pit slope Ramps may be in the shape of spiral, zigzag or a combination of both. Where the ramp passes through a section of the mine more than once, equation [3-12] should be used. See Figure 6-8: . _! ^ Axxsm(a) Aa = tan [(> —)] i y [3-12] The value of n in equation [3-12] is given by: n=INJ[^-j] [3.13] where: 34 n is the number of times that the ramp cuts (crosses) the wall. gis the ramp gradient in percent / is the length of pit wall that the ramp occupies Ax is the amount of road width that should be widened to utilize larger trucks in meters, Aa is the amount of overall pit slope increased by implementing larger trucks in degrees, a is the overall pit slope before implementing larger trucks in degrees, y is the pit depth in meters. Figure 3-3 shows the extension for a haul ramp in an open pit mine due to employing larger trucks. Knowing the amount of the ramp width extension, (that can be obtained from tables like Table 3-1) and knowing the depth of the mine (dh) and length of the ramp, it is possible to calculate the amount of extra material that has to be removed due to employing larger trucks. Equation [3-14] can be used to calculate this: 35 YlgY [3-14] 0 0 where: W is the amount of extra material that has to be moved due to ramp width y is the average specific weight of the materials that have to be mined in t/m3, Ax is the amount of the ramp extension in m, dh is an element of pit depth in m, dl is an element of the length of the ramp in m, g is the ramp's gradient in percent, Y is the maximum depth of pit in m. Integrating equation [3-14] results in: Figure 3-4 illustrates the same haul ramp expansion for different pit slopes. The total area of triangles for different cases is the same. This can be proved by simple geometry. The base and height of each triangle are the same so that their areas are the same. This means that the amount of materials that should be mined due to the ramp extension is independent of the overall pit slope. expansion in m" W = [(0.5xyx Ax)xY2 -s- g] [3-15] 36 Ramp extension Equal area for expansion Pit depth J. Figure 3-4 Ramp extension impacts on overall pit slope 3.3.3.2 Using Mining Engineering Software A porphyry copper deposit was selected to investigate the effect of equipment size on stripping ratio and overall pit slope. Two different open pits were designed, based on two different sizes of trucks that require two different ramp widths. The amount of waste, ore and the average grade for each case was computed. SURPAC™ software was used to perform the mine design and volume calculation. The results of this modeling can be found in Chapter 6. 3.3.4 Dilution Two different approaches were used, to investigate dilution in an open pit mine. The first approach is based on a geometrical analysis of the deposit and the second one is based on samples taken from blast holes. 3.3.4.1 Geometrical Analysis A simple geometrical model of a deposit is illustrated in Figure 3-5. This simple model is used to analyze deposit-related parameters affecting ore dilution. The deposit related parameters analyzed are ore thickness and dip of the orebody. Bench height is 37 another parameter that is studied using this model. By changing each parameter individually while the other parameters are kept constant the sensitivity of dilution to each parameter is calculated from: D = [———]xl00 [3-16] (W + O) Where: D is dilution by percent, W is tonnes of waste, O is tonnes of ore For simplicity, in this model, it is assumed that the thickness and dip of the orebody is uniform for the entire deposit. 38 3.3.4.2 Blast Hole Samples Analysis Another method used to evaluate the amount of dilution in an open pit mine is through blast hole samples analysis. In most open pits, there is more than one sample taken from each blast-hole making measurement of dilution relatively straightforward. Using mining engineering software, it is possible to separate samples that belong to specific levels within a bench. Comparing the average grade and grade distribution of different levels within a bench, determines the magnitude of dilution in open pit mines. To do this the following procedure is used: 1- Determine the number (n) of samples taken from each blast hole 2- Check if sub-drilling is omitted from the process of sampling if it is not fix the last sample 3- Divide each bench into (») sub levels 4- Classify samples based on their altitude . 5- Compare grades; the average for the whole sub level, for the hanging-wall and for the foot-wall 6- Interpret the results This method was applied to a case study in this thesis. For the case study each blast-hole log consists of an upper and a lower part (n=2), whose samples were carefully separated and compared using mining engineering software. Based on the difference between the two samples of each blast hole, the drill-holes in the ore zone are classified into two different groups. The ore grade difference between the two samples in the first group is less than 1.0 g/t and in the second group the difference is more than 1.0 g/t. It is assumed that dilution occurs for the second group where the chance to separate ores with differences more than lg/t is missed. The percentages of these occurrences are then calculated for each bench in order to estimate dilution. 39 3.3.5 Milling Cost In order to investigate the effect of mining equipment size on milling cost, two types of deposits were selected as the focus for study. Firstly, a gold deposit was selected representing an erratic, non-homogenous ore deposit type; and secondly, a porphyry copper deposit that represents a uniform and homogenous type ore deposit was selected. The diamond drill hole databases for these two deposits were collected and based on these databases, block models sizes were created using different block sizes. Using these block models the tonnage grade curves were produced. The tonnage-grade curves were modified using published recovery curves in order to compute the initial mill product for each block size. Final production was calculated for different block sizes and cut-off grades. The methodology is outlined below in a step-by-step format that was used for both deposits. SURPAC™ software was used for geostatistics, block modeling, and volume calculations. 1- Data preparation: since samples were in different lengths, the drill holes were composited to 5-meter intervals. 2- Using this composite database, sample files were extracted as inputs for the next process, geostatistic analysis. 3- Variogram models were created for each sample file. 4- Block models were created for each case with block dimensions of 5 m. 5- Using kriging and variogram models, the grades for each block were then estimated. 6- Then by resizing the dimensions of each block, the model was changed twice, once to 10 meter and again to 20 meters. At the end of this step there were 6 separate block models 3 for each deposit with block sizes of 5, 10, and 20 meters. 7- Reports were extracted showing tonnages versus different grades. 8- Using these reports, the tonnage grade curves for each block size are calculated. 9- Tonnage grade curves are then modified using published recovery curves 10- Feeds of the mill for each block size with different cut-off grades are computed. 11- Final production for each block size with different cut-off grades is calculated. 12- Total net benefits for various cut-off grades for each block size are calculated. 40 13- Outputs of the 5-meter block model are compared with outputs of the 20-meter block model. 3.4 Simulation The uncertainties involved in quantifying ESSV for any given scenario makes simulation a natural tool for their evaluation. For this research two different simulation-programming languages were evaluated. They are GPSS/H™ (Wolverine software 2003) and AutoMod™ (Autosim, 2003). Although both programs have shown strong capabilities for this work, AutoMod was selected as the primary language for the research. To prepare validated input for the model, equipment size data were collected from the field. The following methodology was adapted for the simulation part of the research. 1. Data collection; including maintenance, production and financial data for two equipment sizes 2. Data analysis including a. Data validation to identify errors. b. Statistical distribution fit test using Weibul""™ (Reliasoft, 2003) 3. Creating an open pit haulage system model using real data 4. Writing a simulation program using AutoMod 5. Program validation using real operating data 6. Using the program to evaluate selected ESSV 41 4 EQUIPMENT SIZE SENSITIVE VARIABLES In order to evaluate the effect of equipment size on open pit mining the first step is to identify the variables that are influenced by size. In this thesis, Equipment Size Sensitive Variables (ESSV) is a term that refers to the variables that are influenced by equipment size either directly or indirectly. This chapter discusses the characteristics and significance of the principal ESSV. 4.1 Introduction In order to make appropriate equipment selection decisions, in addition to the deposit characteristics, the operating scenario must be considered. The operating scenario comprises two sets of variables, which between them encompass the entire mine planning scenarios and the operating environment: 1. Deposit characteristics are those parameters inherent to the mine design. These are not of a nature that can be changed. For example, the ore grade, rock mass, geometry of the deposit and topography of the site are unalterable characteristics of the orebody. 2. The mining operating scenario characteristics are those aspects, which an engineer can influence, such as the daily production rate, schedule, and mine geometry. Size sensitive variables may be subdivided into those that are direct and those that are indirect in nature. Direct variables have a clear and quantifiable relationship to equipment size, whilst indirect variables are affected in a less tangible manner. Figure 4-1 shows the variables/parameters that are considered in the process of the equipment size 42 selection at the top and the variables that can be influenced by^  equipment size at the bottom. Deposit Characteristics Operating Scenarios A- Deposit Characteristics: 1- Reserves 2- Geometry 3- Topography 4- Groundwater 5- Hardness and abrasivrty 6- Grade distribution 7- Swell factor 8- Diggability 9- Ground bearing pressure 10- Rock mass quality, structure B- Mine Planning Needs: 1- Daily production rate 2- Bench geometry 3- Mine layout 4- Scheduling 5- Matching factors C- Operating Environment: 1- Personnel 2- Weather 3- Shifts per day 4- Management 5- Maintenance 6- Training Equipment Size Sensitive Variables I • 1 A- Direct Impact: B- Indirect Impact: 1- Productivity 1- Selectivity and dilution 2- Capital cost (Basic price, Commission, 2- Environmental management Shipment, Insurance, etc) 3- Safety 3- Operating cost (Tires, Fuels, 4- Milling efficiency - cost Maintenance,...) 5- Mine longevity 4- Flexibility and versatility 6- Community 6- Infrastructure: Haul roads, etc 7- Minimum working area 8- Risk management (lost production) 9- Efficiency 10- Utilization 11- Waste dump management 12- Matching factor: drills, loaders, haulers Figure 4-1 Equipment size' considerations and equipment size sensitive variables 43 4.2 ESSV Classification Figure 4-2 shows the range of what are considered to be equipment size sensitive variables. The mining variables, displayed in this figure form the basis of the discussion presented in this chapter. As mentioned in the introduction, ESSV can be viewed from two perspectives as direct and indirect variables. Direct variables include those variables, which are currently considered to be segments of the final production cost for the equipment and are well recognized and methodologies exist for quantifying them. These variables occur both as capital costs and as operating costs. Examples of this type of variable are the acquisition cost of the equipment, tire cost, and operator cost. Indirect variables occur either in the form of additional costs in other areas of the production process which result from the equipment selection decision or in the form of lost income/saving opportunities. An example of indirect variables would be a higher energy consumption at the mill due to coarser fragmentation, which occur as a consequence of employing larger equipment at the mine. Indirect costs are usually hidden and are not easily quantified. 44 [Equ\ pment Size Sensitive Variables (Environment and Community) { Mining ") (Milling ) ( Equipment Variables ) C Mine Planning Var iab les ) /Operating Cost) (Capital Cost) \ sr.—~— — ~—:—z \ r_ ? y v—r_ y y*{Haul Roads and Ground Bearing Pressure) {^Downtime Cost) ^Maintenance (^Operator) .( Parts ) { Labour ) (Complexity, Versatility and Flexibility) (Minimum Pit Bottom Area) ^Selectivity and Dilution) ^(Matching Factor) ^Stripping Ratio) (Direct Effects) (indirect Effects) (jWlixed Effects^ Figure 4-2 Equipment size sensitive variables In the following section direct variables will be described in more detail. A section discussing indirect variables follows in section 4.4. Section 4.4 also includes a brief treatment of environment and community variables related to equipment selection as well as a discussion of the impacts of equipment selection on mill variables. 4.3 Direct Variables Following from figure 4-2, the direct variables, which will be analyzed in more detail in this section are capital cost, operating cost, maintenance, parts-materials, labor, operator cost, fuel, and tire cost. 45 4.3.1 Capital Cost Unit capital cost is the total purchase cost for a piece of equipment divided by its total production over its productive life. In the example of a haulage truck, purchase costs include initial cost, shipment, assembly, and commissions. It is difficult to estimate the exact capital cost for the equipment prior to the end of its useful life because: 1- The exact amount of production of the equipment is unknown before its end of life. This is because of uncertainty associated with equipment life, and performance. 2- The purchase costs for the equipment vary from operation to operation. Cost of shipment, and commission account for the majority of uncertainty. 3- The salvage price of the equipment is unknown. Despite these uncertainties it is possible to make approximate assumptions and to derive useful estimates of capital costs. 4.3.2 Operating Costs Operating costs contribute a significant proportion of total costs for mining equipment. For instance, operating costs contribute approximately 2/3 of the total cost for trucks (Blackwell, 1999). For an open pit mine, the major haulage equipment operating costs include: maintenance costs, operator, tires, fuel and downtime costs. Each of these variables is discussed in more detail in the following section. 4.3.2.1 Maintenance Maintenance contributes a large portion of the total unit cost. In Canadian and Australian open pit mines, maintenance costs accounts for approximately 45% of the total operating cost (Campbell, 1998). Maintenance costs can, in turn, be subdivided into two major components: parts-material cost, and labor cost. Downtime cost and infrastructure cost are other components of the maintenance cost. However, because other types of 46 downtime and infrastructure costs unrelated to maintenance exist and because they are considered indirect variables downtime and infrastructure costs are discussed in separate sections (4.4.1 and 4.4.2). 4.3.2.1.1 Parts - Materials Larger equipment have more components, making them more complicated than equivalent smaller equipment. For example, to deliver more power, some new trucks are equipped with two engines. Most large equipment is equipped with diagnostic tools to compensate for the added complexity and these new monitoring and control systems have added numerous components. Where different equipment sizes are used at the same mining operation further complexity is introduced into maintenance in terms of an increased diversity in number and size of components. Clearly this may pose complications to both maintenance and part warehousing. Two major cost issues that may relate to this aspect of size and maintenance are: • Personnel- the number of people required to disassemble, move and reassemble components • Maintainability- the ease of repairing a machine and hence the time require for maintenance operations 4.3.2.1.2 Labor The number of technicians required to maintain a truck is a function of its size. This is defined as the technician ratio. Table 4-1 shows the technician ratio for different sizes of truck as determined by one oil sand mining company in Canada (Wohlgemuth, 2001). This shows that for this mine larger trucks require more maintenance labor. Table 4-1 Haul truck maintenance issues - staffing (Wohlgemuth, 2001) Truck size 240 ton 320 ton 360+ ton Technician ratio 1.5 1.5 1.75-2 47 Adaptability of personnel can be another problem for mines using new technologies, including the introduction of larger trucks into a mine. An employee's career spans about 30 years, while the rate of change in technology standards related to the high technology applications applied to trucks is currently less than 5 years. The question is how much training time is needed to develop proficiency with a machine and its technology? Switching technology consumes time, money and resources. In the period of transition, job efficiency is expected to drop. Another area, which is difficult to quantify is personnel acceptance and adaptability to new technology. Overall observation indicates that mine personnel, particularly senior staff, find the adoption of new technology, challenging. 4.3.2.2 Operator Cost Since larger fleets employ fewer units, in terms of operator cost, there is a saving associated with the implementation of larger equipment. For most open pit mine operations, the operator cost is 20% to 30% of the total truck haulage cost (Blackwell, 1999). However due to wage differences in different parts of the world, operator cost must be viewed separately for each individual project. There is also no simple relationship between salary level and operator cost because operations efficiency is another key variable impacting overall operator cost when it is viewed as a cost per tonne mined. 4.3.2.3 Fuel Fuel consumption of equipment depends on a wide range of variables. For example, the fuel consumption rate of haulage trucks is a function of these variables: load, road condition, maintenance, operator, climate condition, altitude and type of truck. It is not easy to quantify the real fuel consumption rate for different truck sizes, however the manufacturers data can be referenced to have an approximate estimation. Another uncertainty in quantifying fuel cost of different equipment size originates from the fact that there is no specific price for the fuel. Even in a certain region different mines may have different contract, with their fuel supplier. 48 4.3.2.4 Tires Tire cost has a considerable impact on the overall operating cost. For example, total tire cost for a haulage truck is equal to its capital cost over its operating life (Barton, 2001). Some related considerations are as follows: Tire longevity has been found to have a strong correlation to the temperature at which the tire operates. To prevent high temperature, tires are constrained to travel within speed limits, for instance, less than 32 k/h for 80 series of Michelin tires (Michelin Data Book). This makes it difficult to be flexible in terms of reducing truck cycle times. Consequently larger tires reduce flexibility, negating a primary benefit of rubber wheeled equipment. To cool tires sometimes it is required to install tire-cooling facilities that add additional costs to the system. Large tires need special tools for handling, which must be added to the operating cost. Speed constraints, the potential need for tire-cooling facilities and the additional expenses associated with tools for large tires are all considered direct variables impacted by equipment size. 4.4 Indirect Variables This section discusses the indirect ESSV shown in figure 4-2. The main variables discussed are: downtime cost, infrastructure, complexity, matching factor, haul roads and ground bearing pressure, minimum pit bottom area, dilution and selectivity, flexibility and versatility, environment and community, and finally the milling cost. 4.4.1 Downtime Cost Downtime is the interval during which a functional piece of equipment is not productive. In an open pit downtime can be subdivided into two main categories: 49 • Maintenance downtime. This may occur as regular preventative maintenance or as breakdowns, • Operational downtime. This includes delays and standbys Due to downtime, the mine loses a) the production for the period that the mine is down as well as b) the interest on the capital invested in the machine. As the equipment size grows, the amount of lost production from downtime becomes greater. Downtime is related to lost productivity according to the following formula: Lp=Pu*Td [4-1] where: L p is the lost production in tonnes, Pu is the productivity in tonnes per hour, Td is the total downtime of unit in hours. Larger equipment has a higher productivity (PJ. If increasing capacity of a unit is not accompanied by an improvement in its availability, equation [4-1] shows that lost production increases. Hence, according to equation [4-1], because larger trucks have more capacity, lost production can be a more serious problem for them than for smaller trucks. 4.4.1.1 Lost Production Cost The magnitude of lost production depends on the amount of downtime on one hand and the capacity of the equipment on the other hand. Lost production is susceptible to the quality of management practices, work efficiency, and to the type and size of the equipment employed. In a haulage system, delays may also occur due to breakdowns in other systems such as crushers. 50 To investigate the effect of truck size on lost production, it is very important to separate delays and standbys that are caused by only trucks rather than delays and standbys caused by other systems. In terms of maintenance, there are always breakdowns that are impossible to predict. This unpredictability leads to difficulties in responding immediately. As a result, they often cause production losses. Unpredictable breakdowns are usually related to the quality of the machine, maintenance, and spare parts. To reduce the impact of these types of failures, the common approach is to ensure that extra equipment capacity is available. Another common approach is to employ temporary contractors to make up the work of the failed equipment. 4.4.1.2 Capital Interest Cost Time spent for scheduled maintenance may vary for different equipment sizes. The length of scheduled maintenance (Preventative Maintenance) is a characteristic of machine design and may be improved by interaction with the manufacturer. This may result in improved maintainability, accessibility and person-portability. The total length of downtime is also a function of maintenance management, as it has been shown to have the ability to change the overall time to repair. The maintenance manager is able to manage tasks in an attempt to decrease the overall downtime. In addition to maintenance, due to operational problems and management, there are a variety of delays and standbys during which there is no production activities. Capital interest cost is the revenue lost by having capital dollars tied up in a machine that is not producing. 4.4.2 Infrastructure Infrastructure is needed to keep maintenance and operation at an acceptable level of quality. Shop bays, washing bays, cranes, and special tools represent such infrastructure for maintenance. Bridges, overpasses and roads represent infrastructure made for operations. Table 4-2 shows how some of these items may change with equipment size. Quantifying this ESSV requires details about the capital investment required for each size of equipment. 51 Table 4-2 Haul truck maintenance issues - infrastructure (Wohlgemuth, 2001) Truck size 240 ton 320/360 ton Crane height 40 feet 55 feet Crane capacity 20 ton 30 ton Body removal 50 ton , 65 ton Door width 8 meters 12 meters Shop bays Space for 2 trucks Space for 1 truck 4.4.3 Complexity As previously discussed, larger equipment usually has a greater number of components, which tends to result in more complicated machines. Theoretically, this increased component count will result in lower reliability. The implementation of innovative technologies for monitoring and control is increased for larger trucks. As a result, a mechanic's training may be inadequate to service the larger and more complex equipment. These issues may pose serious maintenance challenges and result in even longer downtimes (Hall, 2000). Generally speaking, maintenance, repair and operation of larger equipment requires a higher level of skill for personnel and specialized tools. Operators and maintainers need to be better educated and require more training. An additional aspect associated with complexity and larger equipment is that although these machines are equipped with the newest technology, their availability has not improved. This is shown in the report of an oil-sands company, which finds that the availability for larger trucks is on overall approximately 80%, which is the same as that observed for smaller trucks (Wohlgemuth, 2001). 4.4.4 Matching Factor Open pit mining equipment operate interdependently, thus any change in one part of the production chain directly impacts the other parts. As the size of trucks increase, the 52 truck/shovel matching issue begins to become a more serious issue (Chiasson, 2001). Matching problems for truck and shovel include: • Shovels must increase in size to match the new larger trucks. For an operating mine, switching to a higher level of production, there are numerous restrictions especially the mine geometry which impose constraints on new equipment. Shovels are more sensitive to these restrictions and therefore this issue has a potentially greater impact on mine economics because shovels are more expensive and have a much longer life. • Frequently changing to larger mining equipment results in a mixed fleet of trucks. Different truck sizes impair the ability to load all the trucks with their optimal payload especially when they are being loaded by a one-size shovel fleet. This creates problems such as out of range loading and improper load distribution in the truck boxes. Matching is not considered to be a major problem for projects in the planning stage when it is possible to match loading machines with the appropriate haulage machines, however it becomes more of a problem when changing equipment during the mine life. Matching can also be an issue between blasting and other areas of mine operation. There are some restrictions for blasting in darkness, which may restrict equipment scheduling and production rate. The magnitude of blasts also must be matched with the production rate and can adversely affect the slope stability of the pit as described in an example in section 2.5.1. 4.4.5 Haul Roads and Ground Bearing Pressure Roads are one of the most important elements of infrastructure in open pit mines. The road width, for larger trucks is significantly wider than the road width for smaller trucks. In comparison with other mining methods such as strip mining, the economics of an open pit mine are more sensitive to the width of haul roads as any changes in the road width directly influence the overall pit slope. For deep open pit mines, this can dramatically 53 change the stripping ratio. Depending on the location of the haul roads (internal or external), construction of ramps results in losing ore or adding waste to the production or a combination of both. A specific method for calculating the amount of extra waste removed due to employing larger trucks was developed in chapter 3. The gross vehicle weight of some trucks now exceeds 550 tonnes (Caterpillar, 1999). Because of this weight a higher standard (consolidated) road is required. In weak ground conditions, such as those found at high-grade oil sands or during rainy season and other environments prone to soft road conditions a truck's performance dramatically decreases. This phenomenon is due to the increased rolling resistance in soft ground, which results in higher fuel consumption and increased maintenance costs. It has been reported by an oil sand company that during the summer when the ground is very soft, they have to load to about 70% of target load to prevent getting stuck (Wohlgemuth, 2001). Construction of standard roads in a mining environment is not easy or cheap. One of the more challenging elements of haulage route planning is the construction of loading areas whose positions are constantly changing and where there is always fresh ground exposed. Another critical part of a haulage route is the dumping areas where the freshly dumped waste rocks are too loose to support high ground pressure. 4.4.6 Minimum Pit Bottom Area The equipment size and its maneuverability determine the minimum area on which operations can be done efficiently. The area required not only affects the final stripping ratio but also limits the opportunities to open new benches, which may create difficulties in the blending process of ore grades. For pit expansions regardless of the method used for ore access, the equipment dimensions control the minimum width of the working area. For deep open pit mines, employing large trucks may result in a higher stripping ratio, as they require more space to maneuver in. 54 4.4.7 Dilution and Selectivity Improved selectivity is one of the main advantages of surface mining methods over underground mining methods. Utilizing larger equipment forces the mine layout to use larger mining blocks, which consequently weakens this capability by producing blocks characterized by a mixture of ore and waste/low grade. In fact this equates to transferring a part of the mining cost to the processing plant by feeding more waste to the mill. In chapters 6 and 1 this ESSV is discussed in detail using two case studies. 4.4.8 Flexibility and Versatility For a specific level of production, the number of equipment operating in a fleet decreases as the unit size increases. In order to be flexible and in order to avoid the risk of losing production due to breakdowns, it is not wise to decrease the number of unites in the fleet to lower than a certain threshold. For example, employing only one large shovel instead of several smaller shovels may cause fluctuations in production and also may waste other resources such as trucks and crushers due to delays. Having several shovels ready at the pit, assures that production is at the target level. In addition access to several blocks simultaneously which can be achieved by a more numerous fleet eases the blending and grade control tasks as well. Therefore in order to increase flexibility, it is necessary to have excess equipment capacity. As the size of equipment increases, the cost of having spare units increases dramatically. "The dependence on fewer but larger pieces of equipment in striving to exploit economies of scale for improved competitiveness also tends to reduce a mining system's inherent flexibility" (Dunbar, 1999). Krause (2001) in terms of flexibility raises a prudent question: Is going from a fleet of eight small trucks to four large trucks, the same as going from a fleet of 120 trucks to 60 trucks? Obviously the answer is no as clearly the mine going from eight trucks to four is at more risk in terms of the lost production associated with a truck failure. 55 4.4.9 Environment and Community The most important environmental issue for larger equipment relates to the additional waste processed by the mill due to the mining of larger blocks, which as described in section 4.4.7 contain higher proportion of waste. In addition to the impacts to the mill, waste management becomes more costly and complicated, as more land must be acquired to contain additional waste dumps and tailing dams, requiring more capital investment. In addition, if mill tailings or waste rocks are in any way toxic, an increase in their volume will require additional expenditures for environmental protection either for containment or for treatment. The additional reclamation costs mandated by larger tailings and/or waste dumps must be taken in account too. The trend over the last decades has been for mining companies to minimize costs through implementing economies of scale. It is believed that larger equipment can result in shorter mine life. This conflicts with local communities who are more likely to have an interest in sustainability, which tends to be correlated with longer mine life (Odell et al 2003). Environment and community are two important equipment size sensitive variables that are considered beyond the scope of this thesis. 4.4.10 Milling Cost Since the milling cost is usually several times more than the mining cost, it is very important to quantify the effect of equipment size on the mill before making any equipment size decision. The impact of equipment size on the processing plant costs is associated with two main factors: 1) The unit cost of a processing plant is sensitive to the size of ore feeding the mill. Although the size of rocks mined in the pit are not directly related to the size of mining equipment. The higher mass production associated with larger trucks 56 and shovels necessitate the use of bigger drill bits, which in turn results in coarser fragmentation. 2) Increased dilution is a potential impact of using larger equipment. Increased dilution leads to the milling of lower grade diluted ore, which results in greater processing cost. The head grade, mining block size and milling cost is the subject of a separate chapter, where this issue is discussed in some detail through the use of two case studies. 4.5 Summary This chapter has identified the main equipment size. sensitive. variables. It has shown that equipment size has a large range of impacts on the mining industry ranging from mining operations to the mill, environment and community. It has been shown that these variables are interrelated, which makes the investigation of their influences more complicated. 57 5 ANALYSIS OF SOME DIRECT EQUIPMENT SIZE SENSITIVE VARIABLES 5.1 Total Production Cost - Manufacturer's Data Analysis Figure 5-1 shows ownership and operating cost ($/h) for different classes of trucks based on manufacturer's data. The operating environment impacts the total equipment cost. In this graph, costs are classified according to three different service categories namely moderate, average and severe. "Moderate" represents normal circumstances in terms of operation, environment and management, and "Severe" represents difficult operating conditions. Note that this data does not include the cost of the operator. Operator cost is a local parameter and must be studied separately. For example, operator cost in North America is 5 times greater than those costs related to South America. Owning & Operating Cost versus Size of Truck 125 175 225 275 Truck size (tonnes) 325 • Moderate Average • Severe 375 Figure 5-1 Total truck production cost ($/h) versus truck size Figure 5-2 shows the average owning and operating costs ($/t) for different sizes of truck. These graphs are produced by dividing hourly costs of trucks (Figure 5-1) by hourly production rates, assuming that the fill factor and cycle time for all types of trucks are the 58 same. These data suggests that economies of scale exist up to 150 tonne trucks and that after no economies of scale or diseconomies of scale begin to occur. To expand upon this field data were recorded in case studies for comparison. Total production cost versus truck size ($/t) 1.80 g 1.60 1.40 g 1.20 jj 1.00 .2 0.80 1 0.60 g 0.40 £ 0.20 0.00 0 50 100 150 200 250 300 350 Truck size (tonnes) • Moderate • Average —A—Severe Figure 5-2 Truck production costs ($/tonne) versus truck size 5.2 Case Study for Production Costs Field data from a large open pit mine that utilizes two sizes of haulage trucks were compiled. This case study examines the effect of truck size on direct costs such as operating cost, with particular emphasis on maintenance cost. The two types of trucks compared are: a fleet of CAT789 trucks with 165 metric tonne payload and a fleet of CAT793 trucks with 218 metric tonne payload. The CAT793 trucks were introduced in the operation in early 2000 and gradually replaced some of the older smaller trucks. 5.3 Data Collection Data were collected for the period of January 2000 to December 2001 from different sources at the mine, including the Vital Information Management System (VIMS™), Modular Mining System (MMS), J.D.Edward, Suspended Load Measurement Module (SLM), and standard operating reports. The data collection process at the mine is made 5 9 easier, by the existence of a computer network system shared by different mine groups, such as Maintenance, and the Engineering office. This enables access to, use and storage of almost all data types. 5.4 Data Analysis and Assumptions Due to some inconsistencies in the data, some assumptions/adjustments had to be made: • It is assumed that both fleets have worked evenly in ore and waste. A survey on a period of one month of trucks' cycle time has shown that the average cycle time for small trucks is 24.6 min and for larger trucks is 24.0 min. • In the cost analysis, the warranty items were neglected because they were determined not to be significant. • In order to maintain confidentiality, values related to the smaller trucks are set to 100% and the larger trucks values are compared to this. • The operating hours on the larger fleet (CAT793C) at the beginning of the period were 110,000 hours. For the smaller fleet, there was no information available, but it is certain that the time on this type of trucks was larger. However, because of the lack of actual data, the time on the smaller trucks is assumed to be the same as for the larger ones. For the residual value calculation of the capital cost, the average hours on both fleets were assumed to be the same. • In this case study "tonne" as well as "$/t, t/h" are in metric units and "ton" is an Imperial unit. • ESSV refers to Equipment Size Sensitive Variables. 5.5 Results From the total operating hours and the total production of each truck type, the average production rate for this period is calculated. The production rate (t/h) achieved by 60 the larger trucks was 7.7% higher than for the smaller trucks. On equal hauls a 20% to 25% higher production rate can be expected from the larger Cat 793 trucks. This data is shown in Table 5-1. Table 5-1 Production and operating hours from January 2000 to December 2001 Truck Year 2000 Production Hours tonnes t/h Year 2001 Production hours tonnes t/h Average production t/h CAT789 100% 100% 100% 100% 100% 100% 100% CAT793 24.4% 23.9% 97.8% 94.9% 108.9% 114.7% 107.7% 5.5.1 Labor Cost The total manpower spent on trucks was extracted from the J.D. Edwards database. Compared to the smaller trucks, the average maintenance labor cost for the larger trucks was 97.8% in terms of $/h or 90.9% in terms of $/t (Table 5-2). Table 5-2 Average maintenance labor cost Truck Costs $ Operating hours Production tonnes Labor cost 2000 2001 Total $/h $/t CAT789 100% 100% 100% 100% 100% 100% 100% CAT793 27.8% 76.8% 49.3% 50.4% 54.2% 97.8% 90.9% 5.5.2 Spare Part and Material Cost Compared with the smaller trucks, the average part and material costs for the larger trucks was 7% greater in terms of $/h or 0.6% less in terms of $/t (Table 5-3). Table 5-3 Average part and material cost Truck Parts costs $ Operating hours Production tonnes Average part costs 2000 2001 Total $/h $/t CAT789 100% 100% 100% 100% 100% 100% 100% CAT793 24.30/0 75.3% 53.9% 50.4% 54.2% 107.0% 99.4% 61 5.5.3 Infrastructure For this mine all of the facilities used for the smaller trucks are also suitable for the larger fleet. One auxiliary piece of equipment was replaced due to the addition of the larger trucks. This was a tire changer that had been in service for 15 years and as such needed to be replaced anyway. Since the cost of the tire changer that could be attributed to the larger trucks is small the decision was made to not include it in the overall analysis. 5.5.4 Downtime Cost Table 5-4 shows the downtime and operating hours for the two fleets during 2000 and 2001. These include all the preventative maintenance hours and breakdowns. Larger trucks are the most new trucks in the mine, this explains why larger trucks have less downtime. Downtime cost include two parts a) lost production cost and b) capital interest cost. These two parts are calculated separately in next two sections Table 5-4 Truck downtime comparison Truck 2000 2001 Total Operating hours Downtime hours Operating hours Downtime hours Operating hours Downtime hours CAT789 120,229 48,955 70,155 19,689 190,384 48,955 CAT793 29,366 6,812 66,554 12,174 95,920 18,986 Total 149,595 55,767 136,709 31,863 286,304 67,941 5.5.4.1 Lost Production Cost Using equation [3-5] (section 3.3.1) the Lost Production costs for the two fleets were calculated. Compared to the smaller trucks, the lost production cost for the larger trucks was 1.7% lower in terms of $/h. 62 Table 5-5 Lost production cost Truck Reliability Average Production t/h Cost $/t Lost production cost $/h CAT789 100% 100% 100% 100% CAT793 100.5% 107.66% 110.14% 98.28% 5.5.4.2 Capital Interest Cost Table 5-6 shows the capital interest cost for the two fleets. As shown the larger trucks represent a 19% higher capital interest cost for every operating hour and 12% higher capital interest cost for every tonne of product. Table 5-6 Capital interest cost Truck Downtime hours Capital cost $/h Residual value $ Interest Rate Capital interest cost $ $/h $/t CAT789 100% 100% 100% 10% 100% 100% CAT793 38.8% 131.6% 119.4% 10% 119.7% 111.9% 5.5.5 Total Maintenance Cost Table 5-7 shows the total maintenance cost calculated for the CAT789 and CAT793 fleets at the mine. The total maintenance cost for the larger trucks (CAT793) was 4.3% higher than for the smaller trucks for every hour of operation, however, it cost 3.1% less in terms of tonne of product. Table 5-7 Total maintenance cost $/h and $/t Truck Labor Part Lost production Capital interest Total maintenance cost $/h $/t CAT789 100% 100% 100% 100% 100% 100% CAT793 97.8% 107.0% 98.3% 119.7% 104.3% 96.9% 63 5.5.6 Total Costs Table 5-7 shows that the maintenance cost of the larger trucks is slightly less than for the smaller trucks. However, the operating cost and total cost indicate that some advantages lie in using the smaller trucks. Table 5-8 shows the results of this study in terms of total costs. The total cost of the larger truck (CAT793) fleet was 10% higher than for the smaller truck (CAT789) fleet. This is in part due to the fact that major overhauls scheduled for the CAT789's were not done during the period of study since it was planned to phase them out of operation. Figure 5-3 to Figure 5-5 illustrate the results graphically. Table 5-8 Total cost CAT789 and CAT793 trucks Operator Maintenance Operating Total Capital cost cost Fuel cost Tire cost cost cost cost Truck $/t $/t $/t $/t $/t S/t $/t CAT789 100% 100% 100% 100% 100% 1000/0 100% CAT793 122.2% 92.9% 107.70/0 148.8% 97.0% 106.6% 109.70/0 Driver cost Figure 5-3 Operating cost breakdown for CAT789 S/t 64 0.350 — 0.300 * 0.250 g 0.200 ° 0.150 0.100 0.050 0.000 Maintenance 29% Tire cost 18% Driver cost 18% Fuel cost 35% Figure 5-4 Operating cost breakdown for CAT793 $/t 3.5 cents/ton Capital cost Driver cost Fuel cost Tire cost Maintenance Operating cost Total cost I Smaller fleet • Larger fleet Figure 5-5 Operating cost for two truck size fleets As Figure 5-5 indicates while driver cost and maintenance were less for the larger fleet, capital, fuel, tire and operating costs were all higher. 5.6 Discussion Figure 5-6 shows the long-range average cost curves for haul trucks that have been discussed earlier in section 5.1. The case study results have also been plotted on this graph. As can be seen, the results are comparable to the cost range obtained from manufacturer's data. This mine is one of the most efficient mining operations in the world operating in 65 conditions that do not present major obstacles for equipment, explaining why the results are lower than the moderate cost range. Total production cost versus truck size ($/t) 50 100 150 200 Truck Size 250 300 350 -•— Moderate Average Severe Figure 5-6 Projected results on the LRAC curves of haul trucks It should be stressed that one important assumption made in this study has been that both fleets have worked equally in ore and waste hauls. It is clear, however, that the type of haul has an influence over the fleet productivities. Mines may choose to employ their larger or smaller trucks preferentially to different haul types. Cycles, for example, may require less time because the destination is a dump where there is little waiting time before dumping. On the other hand a fleet may be preferentially employed on steeper, generally shorter, ore hauls. Another factor may be the reliability of crushers that may increase the fixed time component of a haul cycle. Truck dispatch systems that may be in use at the mine also have the potential to impact haul times. According to the manufacturer's Performance Handbook (Caterpillar, 1999) the nominal payload ratio for these two types of trucks is 1.32. This study, however, has showed that their productivity ratio is 1.08, which is much lower than expected. This indicates that either the smaller fleet is producing beyond expectation, which is unlikely, or 66 that the larger fleet is producing below expectation, or possibly both. Finally, the distinction between payload ratio and productivity ratio must be considered. Since productivity ratio includes utilization, cycle times, etc, it is very dependant on the mine operational decisions. The maintenance cost of the larger truck fleet is almost identical to that of the smaller truck fleet. In terms of maintenance cost, improving the production rate of larger trucks may result in more significant benefits to the mine in using CAT793. The maintenance labor cost of the larger truck fleet is shown in Table 5-2 as being slightly less than for the smaller truck fleet. One reason for this is that the larger trucks, being newer, experienced less lengthy downtimes. Despite the fact that the smaller trucks represent an aging fleet and have more parts replaced, the total parts cost for the larger truck fleet was greater. However, on a cost per tonne basis costs they are equal. This reflects the fact that the spare parts of these larger trucks are comparatively more expensive, but that their increased production mitigates this effect. Since the CAT793 is larger, its hourly maintenance cost, as might be expected, is higher than for the CAT789. However, due to the greater production achieved by the CAT793 fleet, the production cost associated with maintenance is slightly lower than that of the CAT789's. Figure 5-5 shows that the total operating cost of the larger trucks is about 3.5<t/t more than for the smaller trucks. This is mainly because the larger trucks production rate was not as high as was projected. For the larger trucks, the tire and fuel costs were the two major operating cost components responsible for the higher operating cost. In terms of maintenance and operator costs, as shown in Figure 5-3 to Figure 5-5 and as discussed earlier, there is a small 67 saving associated with acquiring larger trucks. These savings, however, are not great enough to offset the increased costs associated with tire and fuel consumption. Figure 5-5 shows the operating and total costs for two fleets, reflecting the conclusion that the larger trucks represented greater costs. Appendix B provides more data and explains details about the methods used for calculations in this case study. 5.7 Summary With analysis of the manufacturer's data and a case study, this chapter has demonstrated many of the difficulties associated with larger haul trucks. The manufacturer's data analysis shows the presence of a long flat range in hauls trucks' LRAC curves. This illustrates the presence of a constant returns to scale for trucks larger than 200 tonne capacity. The case study confirmed that larger trucks in some cases are more expensive. 68 6 MINE DESIGN EQUIPMENT SIZE SENSITIVE VARIABLES 6.1 Dilution and Selectivity This section compares theoretical models and case study data to determine the impacts of increasing bench height, which is required when larger equipment is used. 6.1.1 Introduction There is a strong relation between equipment size and mine geometry. As equipment size increases key dimensions of the geometry, like bench height, need to be modified. As previously described these changes may result in less control of dilution. Figure 6-1 shows the consequences of employing larger equipment in terms of open pit mining performance. Less Revenue, More Cost Figure 6-1 Sequence of equipment size impacts on open pit mining performance 69 Dilution simply changes a valuable ore to waste rock or lessens its value by lowering its grade. For example, consider a drill hole consisting of two samples 0.1 g/t and 1.8 g/t over equal intersected lengths. The average grade value for this drill hole length is 0.95 g/t. This ore, at a mine with 1 g/t cut-off grade, never has the chance to go to the mill. Because of dilution, mines throw away valuable ore in waste dumps and spend monies to treat low-grade ore at the mill. Therefore, the efforts to reduce mining costs by implementing larger equipment may increase milling cost. Whether the reduced mining costs offset the increased milling costs, is a fact that needs to be determined. In open pit mines factors that can affect the degree of dilution can be regarded as deposit-related or mine-related. Table 6-1 shows these factors. Deposit-related factors are the ore distribution model, dip and thickness. These are inherent to the deposit and are constraints for the run of mine grade optimization. The mine-related factors are the mining method, the mine geometry, the mining direction, the equipment size and the skill of the operators. In order to reduce dilution the second group of factors must be controlled to contend with deposit-related factors. In this chapter, these two groups of factors are examined. Table 6-1 Factors influencing dilution Deposit-Related Mine-Related Grade distribution model Mining Method Ore thickness Mine Geometry Ore slope Mining Direction Equipment Size Skill of Operators 6.1.2 Geometry of Deposits and Dilution 6.1.2.1 Mining Block Model As explained in chapter 3 (section 3.3.4.1.), a simple geometrical model of a mining block has been used to calculate the amount of dilution for various conditions of geology and operating environment. This section investigates the relationship between mine 70 geometry, geometry of the deposit and the dilution as a means to evaluate equipment size effects. 6.1.2.2 Results Using the model in section 3.3.4.1 the thickness, the dip and the height of the block (in fact the height of the bench) are each changed while holding the other parameters constant. For each case, the amount of ore and waste are calculated. Using equation [3-16], dilution is calculated for each case and the results are presented in Table 6-2 and Table 6-3. Table 6-2 Dilution (%) in an ore block: thickness of ore body versus bench height when dip is considered to be constant. Thickness (m) Dip (d) Bench Height (m) 10 7.5 5 2.5 Dilution % Dilution % Dilution % Dilution % 2 45 77.95 72.62 63.87 46.92 4 45 63.87 57.00 46.92 30.65 6 45 54.10 46.92 37.08 22.76 8 45 46.92 39.86 30.65 18.10 10 45 41.42 34.65 26.12 15.02 12 45 37.08 30.65 22.76 12.84 14 45 33.56 27.47 20.16 11.21 16 45 30.65 24.89 18.10 9.95 18 45 28.20 22.76 16.42 8.94 20 45 26.12 20.96 15.02 8.12 71 Table 6-3 Dilution (%) in an ore block: ore body dip versus bench heights when the ore thickness is considered to be constant Width (m) Bench Height (m) Dip (d) 10 7.5 5 2.5 Dilution % Dilution % Dilution % Dilution % 10 0 0.00 0.00 0.00 0.00 10 10 49.62 42.48 32.99 19.76 10 20 48.45 41.34 31.97 19.02 10 30 46.41 39.38 30.22 17.80 10 40 43.38 36.49 27.69 16.07 10 50 39.13 32.53 24.32 13.84 10 60 33.33 27.27 20.00 11.11 10 70 25.49 20.41 14.60 7.88 10 80 14.80 11.52 7.99 4.16 10 90 0.00 0.00 0.00 0.00 Figure 6-2, figure 6-3 and figure 6-4 are based on Table 6-2 and Table 6-3. They depict the relationship between the amount of dilution and elements of the ore geometry dimension. Figure 6-2 shows that for a particular orebody where other ore geometry parameters are constant, increasing the bench height results in an increase in dilution. 45 40 35 | 30 1 25 a 20 15 10 2 4 6 8 10 Bench Height (m) Figure 6-2 Dilution versus bench height 72 Figure 6-3 illustrates the effect of ore thickness on dilution. The figure shows that where the other ore geometry parameters such as dip of the orebody and bench height are the same, dilution increases with thinner orebodies. 0 5 10 15 20 Ore Thickness (m) Figure 6-3 Dilution versus ore thickness Finally Figure 6-4 shows that for steep orebodies the amount of dilution is less than for orebodies with shallower dip where the bench height and ore thickness are constant. 10 o -I , , , 0 20 40 60 80 Dip of Orebody (degree) Figure 6-4 Dilution versus dip of orebody Figure 6-5 shows a more realistic model incorporating these relationships to a simplified geometrical model. Figure 6-5 shows how reducing the bench height in an open pit mine results in eliminating waste from the ore block and consequently increasing the 73 run of mine grade. This figure is a representation of the situation of the actual deposit introduced in the case study that follows. Figure 6-5 The effect of reducing bench height on dilution 6.1.3 Bench Height and Dilution - Case Study 6.1.3.1 Introduction Field data from a small open pit gold mine have been used in this case study. In this mine, ore and wastes were mined from 5-meter high benches. The mineralization occurs inside the fractured zone of a fault. The average run of mine grade is 2.2 g/ton and the ore slope varies from 50 to 35 degrees. As Figure 6-6 shows, the grades are not well distributed in the plan view. Using 72mm bits, blast holes are drilled on a 2.5 by 2.7 m pattern. Two samples were taken from every blast hole (each from a separate rod). After analyzing them using fire assays, the average grade of two samples were assigned to the related blast hole. Data from twelve consecutive benches, which had already been mined, are considered for this study. 74 Figure 6-6 A plan view of bench 2075, Mouteh gold mine - Iran. 6.1.3.2 Assumptions Because of topography or other problems, some of the holes in each bench have a depth less than 5 meters. These holes are omitted. Drill holes with a grade less than 0.5 g/t are considered as waste rock and thus only drill holes with grade values above 0.5 g/t are considered as ore and were selected for this study. Table 64 shows samples taken from different benches of the mine. From this table it can be seen that at this mine there is significant ore dilution due to mixing different grades. Table 6-4 Grades (g/t) of two parts of each drill hole in different benches Bench 2070 Bench 2075 Bench 2080 Bench 2085 Bench 2090 Upper half Lower half Upper half Lower half Upper half Lower half Upper half Lower half Upper half Lower half 1.7 0.5 4.6 0.9 7.4 1.6 5.7 1.2 4.0 0.8 1.1 0.3 3.4 0.6 3.0 0.6 0.5 3.0 1.6 0.4 1.9 0.6 2.3 0.3 0.8 4.1 8.1 1.6 0.3 2.5 3.7 1.2 1.3 0.1 0.5 5.2 2.7 0.4 0.1 2.4 3.0 0.5 0.8 0.0 7.3 1.4 4.5 1.3 5.6 26.8 2.6 0.5 0.6 0.0 6.5 2.0 7.7 1.0 38.5 4.5 0.6 1.8 1.4 0.4 1.4 0.2 3.0 0.6 20.2 2.5 3.4 0.8 1.3 0.5 2.4 0.4 2.3 0.4 8.9 1.2 0.9 1.3 2.9 0.7 1.4 5.4 0.8 9.0 5.1 1.3 0.6 1.8 4.6 1.3 6.8 0.4 1.1 3.7 7.5 0.6 4.7 0.7 0.7 1.7 11.4 0.6 1.7 10.6 0.6 2.6 1.4 3.9 0.1 1.7 2.5 0.5 0.6 3.5 8.0 1.1 1.7 8.1 2.0 0.4 7.1 16.1 1.0 4.5 5.1 1.5 6.0 1.5 1.1 3.1 3.7 13.0 1.6 5.2 10.2 0.6 4.5 17.5 10.7 1.1 2.0 9.8 1.0 3.9 3.0 0.5 3.8 9.4 0.3 2.2 0.3 7.4 0.6 3.7 4.6 0.9 1.8 4.7 0.2 1.8 6.5 2.0 0.3 5.0 0.6 5.5 75 6.1.3.3 Results Table 6-5 shows the number of blast holes in each bench and their characteristics. It is assumed that dilution occurred where two samples with a difference of more that 1 g/ton are mixed. The differences between the upper and the lower part of a drill hole is quite significant for this mine. In some cases the difference is as high as 30 g/t. Table 6-5 Characteristics of dilution in mine benches Benches 2115 2110 2100 2080 2070 2065 Total Drill holes 366 329 297 269 333 336 The number of holes with the average more than 0.5 gram/tonne 153 156 206 204 280 234 The number of holes with differences between two parts more than 1 gram/tonne 48 59 63 111 122 103 The percentage of holes with differences between two parts more than 1 gram/tonne 31.4% 37.8% 30.6% 54.4% 43.6% 44.0% A detailed study of the bench plans showed a logical distribution of grades. In the footwall zone, the grades in the lower half of the drill hole are less the than upper half, while in the hanging wall the opposite relationship is seen. An analysis of the data showed that on average, more than 40% of the drill holes consisted of two parts with differences more than 1 g/t. This data suggests that the potential for serious dilution problems exists at this mine. Figure 6-7 shows the average amount of dilution in different benches of the mine. Dilution in the bottom levels (benches 2080, 2070,and 2065) is on average 48%, this is more than dilution in the upper levels (benches 2115, 2110,and 2100), which is on average 33%. The main deposit characteristic variation between these two groups of benches is dip of the ore zone, which is greater on the top levels resulting in lower dilution. Here again the field data corroborate the theoretical model described. 76 Dilution in Different levels of the mine 60.0% 50.0% c 40.0% 1 30.0% 5 20.0% 10.0% 0.0% 2060 48% Dilution «> • • 33% Dilution 2070 2080 2090 Benches 2100 2110 2120 Figure 6-7 Variation in dilution for different levels within the mine 6.2 Stripping Ratio and Slope Design 6.2.1 Introduction In an open pit mine, roads are essential to the operations. Any changes in the design of the road systems impacts on the mine geometry and may change the overall pit slope. As trucks become larger in capacity they also increase in width. This eventually results in a wider ramp. For example, 300-tonne trucks are 3 meters wider than 100-tonne trucks and require a 15 m wider ramp for 3-way haul roads. As previously described construction of wider ramps in an open pit may change the overall pit slope and stripping ratio. In this section, the impact of larger haulage trucks on the stripping ratio and the overall pit slope are investigated. 6.2.2 Example According to equation [3-11] (section 3.3.3.1) and Table 3-1, for a pit with a depth of 100 meters, switching from a 230-tonne fleet to 320-tonne fleet, which includes an 8.7 meter ramp extension, decreases the overall pit slope by 3.5 degrees. For a ramp with an 8% gradient and a 2.65 rock specific gravity this ramp extension results in mining about 1.5 77 million tonnes of extra waste. Figure 6-8 shows the impact of ramp extension due to implementing larger equipment on the overall pit slope and stripping ratio. These are not the only concerns about haul ramp width expansion although they are the only ones considered in this study. Not considered is that for a mine in operation, shifting from a fleet of small equipment to a fleet of larger ones, some road restrictions such as bridges, overpasses, power lines and pipelines, can pose serious obstacles. Figure 6-8 The effect of equipment size on the stripping ratio 6.2.3 Case Study For this case study, field data from a porphyry copper deposit are used. To quantify the effects of equipment size on overall pit slope and stripping ratio a method was introduced in Chapter 3 (section 3.3.3.1.). This method is useful in the field for preliminary studies when detailed information is not available. However, in addition to this method the current commercial mining design software can be used as they have the capability to perform more accurate volume and slope calculation. For this study, SURPAC™ software was used to calculate volumes and slopes. Two identical open pits were designed using this software, considering two different scenarios. In the first scenario a fleet of 220-tonne trucks was selected. This type of truck requires a ramp width of 37 m for 3-lane traffic. For 78 the second scenario it is assumed that the mine utilizes a fleet of 320-tonne trucks, which requires a 46 m ramp width for 3-lane traffic. The amount of waste and ore for the two scenarios are calculated separately. The shapes of these two open pit mines are shown in Figure 6-9. Figure 6-9 Pit 1, 37-meter ramp width and Pit 2, 45.75-meter ramp width 6.2.3.1 Results Table 6-6 shows the results of this analysis. It shows that a significant increase in waste removal equal to 36.9 million tonnes is associated with the larger trucks. Results also show that in terms of average ore grade, a slight reduction is associated with the larger truck mine design. This is because of the additional waste and low-grade ore mined due to the ramp extension. 79 Table 6-6 Comparison of tonnage for two design with different ramp width PIT2- 45.75m ramp PIT 1-3 7m ramp Difference Tonnes Grade (%) Tonnes Grade (%) Tonnes Waste 1,979,970,844 1,943,085,094 36,885,750 Ore 247,548,750 0.6027 247,178,906 0.6030 369,844 W/O 8.00 7.86 0.14 6.3 Summary Based on the work presented in this chapter it can be said that the shape and geometry of orebodies create great influence on the amount of dilution. Dilution can also be seen as a consequence of implementing larger sizes of equipment. Data is presented to illustrate these influences. The geometry of the mine also has significant influence on the amount of dilution. Specifically, the bench height dramatically changes the average grade of materials being mined. Enlarging the mining block (through increasing the bench height) causes the mining selectivity to decrease. This ultimately results in lower head grades to the mill. Because the geometry of the orebody is an inherent characteristic of the deposit, in order to reduce dilution, the mine geometry should be designed to match those characteristics. This chapter also investigated the effect of equipment size on stripping ratio and overall pit slope. This work showed that due to ramp width requirements, larger equipment may change the overall pit slope and stripping ratio. In equipment size selection the cost effects of this change must be taken in account. Therefore, this work suggests that in the process of equipment size selection, several mine design scenarios (in terms of ramp width) must be reviewed. This will help designers understand the cost effects of larger equipment better before making their selection decision. 80 7 EQUIPMENT SIZE EFFECTS O N MILLING COST 7.1 Introduction Mining equipment size influences the mill operation mainly through its influence on the mine geometry. As mining equipment grows in size, in order to accommodate the larger equipment, the mine's geometry changes as well. In this regard, the two most important effects are changes in bench height, and changes in borehole spacing. The issue of fragmentation and blast scale has been evaluated by others such as Eloranta, 1999 and will not be considered in this work. Different bench heights result in different mining blocks with different values of ore grades. This may change the overall head grade of the mill, resulting in a change in recovery. Figure 7-1 shows the effects of equipment size on mill performance. Based on the aforementioned and Figure 7-1 the impact of implementing larger mining equipment on the mill can be summarized as: 1- Higher comminution cost 2- Unwanted feed in the form of waste or low-grade materials 3- Decrease in overall recovery The Effect of Mining Equipment Size on Milling Cost Due to less fragmentation at the mine, the size reduction at the mill will be more 1 r Fragmentation Head grade < Due to lack of selectivity additional waste/low grade ore will be milled Decrease in Recovery Figure 7-1 The effect of mining equipment size on milling cost 81 7.2 Case Study Criteria and Assumptions In this chapter two different types of deposits were selected to investigate the effects of equipment size (mining block size) on milling cost. They are a copper porphyry deposit, and a tabular form gold deposit. This chapter's main goal is to show the methodology of the work and to demonstrate the sensitivity of the deposits to the equipment size. Obviously, it is not possible to do this investigation to the level of a feasibility or pre-feasibility in this thesis. Consequently the following criteria have been used. 1- Databases for both deposits were obtained from Canadian sources and are real data. 2- The specific weight of rocks for both deposits is assumed to be 2.63 t/m3 3- Recovery for copper in the porphyry deposit is assumed to follow equation [7-1] based on the work presented by Winckers (2003). Rc =2.6315 xln(g c) + 85.581 [7-1] Where: Rc is the % recovery of the copper gc is the % head grade of the mill 4- Recovery for gold is assumed to follow equation [7-2] based on the work presented by Winckers (2003). Rg = 6.2702 x \n(gg) + 75.375 [7-2] Where: Rg is the % recovery of the gold gg is the head grade of the mill in g/t 82 5- Milling cost for copper ore is assumed to be $3.5 CDN/tonne based on a Canadian source. 6- Copper price is assumed to be $2300 CDN/tonne based on the market prices presented daily in Commodity Charts (2003). 7- Milling cost for gold ore is assumed to be $25 CDN/tonne, (Sutter Gold Project, 2003). 8- Gold price is assumed to be $16.20 CDN/gram based on the market prices presented daily in Commodity Charts (2003). 9- Overall gold production cost including mining and milling costs for the Canadian mining industry is assumed to be $8.2 CDN/gram. This is based on the report published by Natural Resources Canada (Miron, 2002). 10- Total net profit for copper production is calculated to be $450 CDN/tonne of copper based on average production cost of $1.64 CDN/kg and average copper price of $2300 CDN/tonne (Commodity Charts, 2003; Copper: Technology and Competitiveness, 1988) 11- Total net profit for gold production is calculated to be $8 CDN/gram, based on $16.20 CDN/gram of gold price and average gold production cost $8.2 CDN/gram (Commodity Charts, 2003; and Miron, 2002). 12- Truck sizes that are matched to 5-m bench height are usually 35 t to 60 t. 13- Truck sizes that are matched to bench heights bigger than 15-m are usually greater than 240 t. 14- The exchange rate is considered to be 1.35 CDN$/US$ 7.3 Limitations as a Result of Using Assumptions The objective of doing these case studies is to demonstrate the effects of equipment size on final production and to demonstrate the methodology for evaluating different equipment sizes. Although the results presented will be valid for the given conditions stated above, they will not provide a definitive answer as to which size is best for which type of deposit. 83 What the studies are expected to reveal is the importance of doing this type of work in evaluating equipment selection scenarios. 7.4 Case Study 1 - Copper Deposit 7.4.1 Introduction A large porphyry copper orebody of relatively even grade distribution has been selected in order to investigate the effect of mining equipment size on milling cost in porphyry deposits. The diamond drill hole database was collected and used to perform geostatistical analysis. Based on this database three different block models were created using different block sizes. The minimum block size dimensions assigned to each model are 5m, 10m and 20 meters. Appendix C provides detailed information about this deposit and related results, including complete tonnage-grade reports for the block models. 7.4.2 Results Table 7-1 gives the tonnages and average grades of the models for different grade intervals. Column 1 shows the grade interval (classes of the grades) in percent used to classify tonnages. Columns 2, 4, and 6 are the tonnages of the orebody in each class. Columns 3, 5, and 7 are the average grades for each interval. For example, this table shows that there are 1,108,216 tonnes of ore between 1.0% to 1.1% for the 5-m block model. The average grade for this class is 1.047%. The results show that smaller block sizes have a wider range of grades compared with larger block sizes. For example, the 5-m block model shows grades up to 2.33% whereas the 20-m block model shows grades up to 1.85%. 84 Table 7-1 Tonnage-grade for three different block sizes of the copper deposit Average Average Average Classes Grade % Grade % Grade % of Cu % Tonnes 5-m 5-m Tonnes 10-m 10-m Tonnes 20-m 20-m 0.0-0.1 6,020,805,414 0.004 6,012,051,130 0.004 6,004,332,080 0.004 0.1-0.2 540,064,254 0.148 554,714,340 0.148 569,679,040 0.147 0.2-0.3 352,797,405 0.245 355,428,720 0.245 355,449,760 0.246 0.3-0.4 186,629,731 0.344 184,223,610 0.344 184,962,640 0.343 0.4-0.5 89',852,306 0.444 87,555,330 0.444 85,674,880 0.445 0.5-0.6 49,359,183 0.545 48,094,810 0.546 47,718,720 0.544 0.6-0.7 26,639,599 0.643 26,347,340 0.644 23,017,760 0.645 0.7-0.8 12,447,790 0.742 11,503,620 0.745 10,477,920 0.742 0.8-0.9 5,306,354 0.848 4,847,090 0.846 4,607,760 0.849 0.9-1.0 2,858,153 0.941 2,288,100 0.939 1,746,320 0.94 1.0-1.1 1,108,216 1.047 957,320 1.046 862,640 1.04 1.1-1.2 759,413 1.144 836,340 1.146 631,200 1.145 1.2-1.3 688,074 1.248 497,070 1.249 273,520 1.239 1.3-1.4 416,198 1.345 286,670 1.351 273,520 1.338 1.4-1.5 165,033 1.457 370,830 1.448 315,600 1.451 1.5-1.6 189,689 1.551 131,500 1.546 168,320 1.536 1.6-1.7 84,818 1.64 136,760 1.629 63,120 1.66 1.7-1.8 40,436 1.748 26,300 1.749 63,120 1.753 1.8-1.9 103,556 1.845 7,890 1.865 42,080 1.852 1.9-2.0 5,260 1.936 0 0 0 0 2.0-2.1 0 0 55,230 2.091 0 0 2.1-2.2 0 0 0 0 0 0 2.2-2.3 0 0 0 0 0 0 2.3-2.4 39,121 2.325 0 0 0 0 For different cut off grades the cumulative tonnage and cumulative average grades are calculated for the three different block models. The copper content of the block models are also calculated using the recovery equation (equation 7-1). Table 7-2 shows the cumulative tonnage, average grades, and copper production of the 5-m and 20-m block models for different cut-off grades. In this table column 1 shows the cut-off grades in percent. Columns 3 and 5 are tonnages of the materials that are expected to be milled over the mine life for the 5-m and the 20-m block models respectively. Columns 2 and 6 are the cumulative average grades for different cut-off grades. Columns 4 and 7 show the copper 85 produced over the mine life in tonnes. They are calculated by multiplying average grades (columns 2 and 6) with tonnages (columns 3 and 5). Figure 7-2 shows the tonnage-grade curves for the three block models. The horizontal axis is the cut-off grade and the vertical axis is the actual copper content. Tonnages of copper (Y axis) are computed by multiplying the average grades of each category with the correspondent tonnages of ore then multiplying by the recovery factor obtained from equation [7-1] using the average grade. These numbers are then summed to calculate the cumulative values for grades and tonnages for different cut-off grades. Figure 7-3 shows a close up look at the same graph. As can be seen for any cut-off grade, the 5-m block model results in more copper production compared to the larger size block models. For example based on this graph for a cut-off grade of 0.3% there are 85,000 tonnes more copper in the 5-m block model than for the 20-m block model. 86 Table 7-2 Copper content and average grades of the 5-m and 20-m block models Cut off Cumulative Cumulative grade Grade % Cumulative Cumulative Cumulative Grade % Cumulative % 5-m Tonnage 5-m Cu 5-m Tonnage 20-m 20-m Cu 20-m 0 0.05 7,290,360,003 2,935,797 7,290,360,000 0.05 2,889,707 0.1 0.26 1,269,554,589 2,764,682 1,286,027,920 0.26 2,719,061 0.2 0.35 729,490,335 2,120,823 716,348,880 0.34 2,044,633 0.3 0.45 376,692,930 1,413,092 360,899,120 0.44 1,328,577 0.4 0.55 190,063,199 881,685 175,936,480 0.54 803,497 0.5 0.65 100,210,893 548,788 90,261,600 0.64 485,340 0.6 0.75 50,851,710 322,865 42,542,880 0.74 267,339 0.71 0.86 24,212,111 178,262 19,525,120 0.85 141,995 0.8 0.99 11,764,321 99,943 9,047,200 0.98 76,069 0.9 1.11 6,457,967 61,628 4,439,440 1.12 42,758 1 1.25 3,599,814 38,654 2,693,120 1.24 28,737 1.1 1.33 2,491,598 28,710 1,830,480 1.33 21,050 1.2 1.42 1,732,185 21,244 1,199,280 1.43 14,839 1.3 1.53 1,044,111 13,845 925,760 1.49 11,919 1.4 1.65 627,913 9,011 652,240 1.55 8,759 1.5 1.72 462,880 6,929 336,640 1.64 4,795 1.6 1.84 273,191 4,377 168,320 1.74 2,554 1.7 1.93 188,373 3,169 105,200 1.79 1,643 1.8 1.98 147,937 2,553 42,080 1.85 680 1.9 2.28 44,381 888 0 0.00 0 2 2.33 39,121 799 0 0.00 0 2.1 2.33 39,121 799 0 0.00 0 2.2 2.33 39,121 799 0 0.00 0 2.3 2.33 39,121 799 0 0.00 0 87 Tonnage Grade Curves for Copper Deposit- Recovery Included 3,500,000 3,000,000 8 2,500,000 c O 2,000,000 I-® 1,500,000 Q. 2 1,000,000 500,000 0.000 0.500 1.000 1.500 2.000 Cut off Grades % 2.500 Figure 7-2 Tonnage grade curve for copper deposit including recovery considerations 0) Q. a o o Tonnage Grade Curves for Copper Deposit- Recovery Included 1,800,000 1,600,000 1,400,000 1,200,000 1,000,000 800,000 600,000 400,000 200,000 0 0.400 0.500 0.600 0.700 0.800 0.900 1.000 Grades % Figure 7-3 A close-up view of the tonnage grade curves for copper deposit 8 8 Figure 7-4 shows the difference between actual copper content for the 5-m block model and the 20-m block model. This is calculated by subtracting column 4 from column 7 in Table 7-2. The maximum difference of copper content for the two models occurs at a cut-off grade of 0.3%. At this cut-off grade using larger blocks (20-m instead of 5-m) results in a loss of 85,000 tonnes of copper. 90,000 80,000 £ o CM E ~ 70,000 re c c o Qi Q. a o o o o c CD I 60,000 50,000 40,000 30,000 20,000 10,000 0 0 0.5 1 1.5 Cut off grade % 2.5 Figure 7-4 Differences in copper content for 5-m blocks and 20-m blocks Figure 7-5 shows the difference between amounts of materials that have to be milled for the 5-m blocks and the 20-m blocks versus different cut-off grades. This difference is calculated by subtracting column 3 from column 5 in Table 7-2. The graph shows that at a cut-off grade of 0.3% 16,000,000 tonnes of extra materials have to be sent to the mill over the entire mine life if the smaller block model is chosen to be mined. The average grade of this extra material is also 0.01% higher than the average grade for the large block size. The graph shows that for cut-off grades less than 1.3% in this case there is additional material 89 that has to be sent to the mill associated with the 5-m block model. This has a cost effect that will be computed later. 1 8 , 0 0 0 , 0 0 0 I 1 6 , 0 0 0 , 0 0 0 c ~ 1 4 , 0 0 0 , 0 0 0 1 1 2 , 0 0 0 , 0 0 0 *•> £ 1 0 , 0 0 0 , 0 0 0 2 8 , 0 0 0 , 0 0 0 | 6 , 0 0 0 , 0 0 0 _ i » 4 , 0 0 0 , 0 0 0 M | 2 , 0 0 0 , 0 0 0 0 0 0 . 5 1 1 . 5 2 2 .5 Cut off grade % Figure 7-5 Additional waste/low grade materials that have to be milled when 5-m blocks are mined instead of 20-m blocks for different cut-off grades for copper deposit. In Table 7-2 columns 3 and 5 shows feed tonnages for the mill at different cut-off grades. It is assumed that the milling cost for this deposit is 3.5 S/t. Based on this information the milling cost for each block model is calculated. Table 7-2 also provides the total copper produced in each case. It is assumed that $ 450 net benefit is associated with the sale of every tonne of copper. Therefore, the total profit for each case can be calculated. Columns 2 and 3 in Table 7-3 show the total profit for the 5-m and 20-m block models. Column 3 is calculated by multiplying the total copper production by $ 450. Column 2 is calculated in the same way, except that the milling cost of the extra materials is also considered in the calculation. Column 4 shows the difference between column 3 and 90 column 4 in millions of dollars. Column 5 shows the percentage improvement in overall benefit for the 5-m blocks compared to the 20-m blocks. Based on this table there is not any improvement in overall benefit for smaller blocks up to 0.7% cut-off grade while for higher cut-off grades smaller blocks show some improvements in overall benefit. Figure 7-6 shows the improvement in the profit if small-scale blocks are employed instead of large-scale blocks. This happens for cut-offs greater than 0.6% copper. In the current situation of technology the cut-off grade for a typical porphyry copper deposit in open pit mines is less than 0.7%, which according to this data favors implementing larger mining blocks, which in turn supports implementing larger equipment. Table 7-3 Total income and profit improvement for smaller block model project Cut off grade % Total net benefit $ Difference of benefits million $ Improvements % 5-m blocks 20-m blocks 0.2 906,116,003 918,076,199 -11.960 -1.30% 0.3 579,659,530 597,036,624 -17.377 -2.91% 0.4 346,902,142 361,238,952 -14.337 -3.97% 0.5 211,934,031 218,245,747 -6.312 -2.89% 0.6 116,105,339 120,226,128 -4.121 -3.43% 0.7 63,759,077 63,857,719 -0.099 -0.15% 0.8 35,435,817 34,210,671 1.225 3.58% 0.9 20,651,562 19,230,957 1.421 7.39% 1 14,212,599 12,926,260 1.286 9.95% 1.1 10,600,415 9,469,515 1.131 11.94% 1.2 7,691,370 6,676,140 1.015 15.21% 1.3 5,814,129 5,362,972 0.451 8.41% 1.4 4,138,958 3,941,297 0.198 5.02% 1.5 2,675,547 2,157,756 0.518 24.00% 1.6 1,602,331 1,149,073 0.453 39.45% 1.7 1,134,583 739,289 0.395 53.470/o 1.8 778,359 305,815 0.473 154.520/Q 91 Improvement in Profit When Employing 5m blocks instead of 20m blocks £ 100.00% a. c o E a> > o i_ a. E 80.00% 60.00% 40.00% 4 20.00% 0.00% 0.4 0.6 0.8 1 1.2 Cut off Grade % 1.4 1.6 1.8 Figure 7-6 Improvement in profit for small-scale mining 9 2 7.5 Case Study 2 - Gold Deposit 7.5.1 Introduction A large gold orebody has been selected to investigate the effect of mining equipment size on milling cost in non-homogenous deposits. The diamond drill hole database was collected and used to perform geostatistical analysis. Using this database three different block models were created using different block sizes. The dimensions of the minimum block size assigned to each model are 5m, 10m and 20 meters. Appendix D provides more detailed information about this deposit. 7.5.2 Results Table 7-4 shows the tonnages and average grades for each grade interval. In this table column 1 shows the grade intervals in g/t that were used to classify tonnages within each deposit. Columns 2, 4, and 6 are the tonnages of the orebody in each grade interval. Columns 3, 5, and 7 are the average grades for those grade intervals. For example, there are 67,723 tonnes of ore in the 5-m block model between 1.0 to 1.5 g/t of gold. The average grade for this grade interval is 1.25 g/t. The smaller block size shows a wider range of grades compared with the larger block size. For example, the 5-m block model shows grades up to 17.6 g/t whereas the 20-m block model shows grades up to 10.5 g/t. 93 Table 74 Tonnages and average grades for different classes - gold deposit Classes of Aug/t Aug/t Aug/t AUg/t Tonnage 5-m 5-m Tonnage 10-m 10-m Tonnage 20-m 20-m 0.0-0.1 5,010,667,453 0.0035 5,012,027,820 0.0036 5,013,558,480 0.0037 0.1-0.2 35,796,601 0.1338 34,744,930 0.1343 33,979,600 0.134 0.2-0.3 8,323,621 0.2392 8,242,420 0.2395 7,805,840 0.2393 0.3-0.4 3,172,766 0.3437 3,150,740 0.3443 2,966,640 0.3492 0.4-0.5 1,800,564 0.4436 1,664,790 0.4451 1,683,200 0.4466 0.5-0.6 907,021 0.5458 791,630 0.5452 694,320 0.5391 0.6-0.7 466,496 0.642 370,830 0.6453 252,480 0.6494 0.7-0.8 155,828 0.7437 149,910 0.7424 63,120 0.7673 0.8-0.9 53,915 0.8447 78,900 0.8386 42,080 0.8782 0.9-1.0 30,245 0.9476 34,190 0.9623 21,040 0.9361 1.0-1.5 67,723 1.2518 131,500 1.2356 189,360 1.2366 1.5-2.0 80,544 1.7687 99,940 1.7378 210,400 1.8274 2.0-2.5 95,009 2.2583 134,130 2.2492 147,280 2.1422 2.5-3.0 109,803 2.7611 131,500 2.7356 126,240 2.6897 3.0-3.5 88,105 3.2727 92,050 3.2128 105,200 3.2933 3.54.0 84,160 3.7478 84,160 3.7853 126,240 3.6624 4.04.5 92,050 4.2576 126,240 4.2625 189,360 4.1768 4.5-5.0 95,009 4.7456 99,940 4.7148 189,360 4.8323 5.0-5.5 93,036 5.2524 89,420 5.227 0 0 5.5-6.0 93,694 5.745 92,050 5.7351 42,080 5.6123 6.0-6.5 88,763 6.2289 60,490 6.2856 84,160 6.2635 6.5-7.0 75,941 6.7452 84,160 6.7444 84,160 6.723 7.0-7.5 59,833 7.2451 55,230 7.2752 21,040 7.3799 7.5-8.0 67,065 7.7394 52,600 7.7165 63,120 7.7111 8.0-8.5 42,738 8.2442 44,710 8.2823 21,040 8.0188 8.5-9.0 35,505 8.7411 39,450 8.7715 21,040 8.8517 9.0-9.5 27,615 9.2437 10,520 9.2156 21,040 9.0352 9.5-10.0 23,341 9.7421 21,040 9.7714 21,040 9.5637 10.0-10.5 16,766 10.219 23,670 10.196 0 0 10.5-11.0 11,178 10.723 7,890 10.706 21,040 10.521 11.0-11.5 7,233 11.255 5,260 11.273 0 0 11.5-12.0 5,589 11.751 0 0 0 0 12.0-12.5 4,274 12.133 2,630 12.149 0 0 12.5-13.0 2,959 12.722 5,260 12.648 0 0 13.0-13.5 2,630 13.19 0 0 0 0 13.5-14.0 1,644 13.843 0 0 0 0 14.0-14.5 1,315 14.2 0 0 0 0 15.0-15.5 329 15.084 0 0 0 0 17.0-17.5 1,315 17.118 0 0 0 0 17.5-18.0 329 17.646 0 0 0 0 Grand Total 5,062,750,000 0.0067 5,062,750,000 0.0067 5,062,750,000 0.0067 Figure 7-7 shows tonnage-grade curves for the three different block sizes used for this deposit. The dotted curves show the final production of the gold calculated using the head grade-recovery equation (equation 7-2). The figure shows that implementing larger blocks result in less gold production. It also shows that, at higher cut-off grades this effect is more significant. 14,000,000 12,000,000 2 10,000,000 o CD 8,000,000 j§ 6,000,000 4,000,000 2,000,000 4 6 8 10 Cut-off grades g/t 12 14 5m 10m 20 - - - 5m-R - - - 10m- R - - - 20m - R Figure 7-7 Gold content versus grade. Dotted curves include mill recovery For different cut off grades the cumulative tonnage and cumulative average grades are calculated for the three different block models. The gold production of the block models is also calculated using equation 7-2. Table 7-5 shows the cumulative tonnage sent to the mill, the gold production in grams and the average grade for 5-m and 20-m block models. In this table column 1 shows the cut-off grades in g/t. Columns 2 and 3 are tonnages of the materials that are expected to be milled over the mine life for the 5-m and the 20-m block models respectively. Columns 4 and 5 are the cumulative average grades 9 5 mined at different cut-off grades. Columns 6 and 7 are the gold content of the different block models. They are calculated by multiplying average grades (columns 4 and 5) by tonnages (columns 2 and 3) and then multiplying by the recovery factor obtained from equation [7-2] using the average grade. Table 7-5 shows that as block size decreases the total gold content of the model increases. It also demonstrates that the smaller block model generally contains more gold than the larger block model. For example, at a cut-off grade of 1 g/t, the total gold contained in the models is 6,007,527, and 5,655,010 grams for 5-m and 20-m block models respectively. Table 7-5 Tonnage to be milled and gold production 5-m and 20-m block models Cut-off grade g/t Tonnages milled 5 m block 20 m block Average grad 5 m block 20 m e block Total Gold 5 m-g in Project 20 m-g 0.1 52,082,552 49,191,520 0.320 0.317 11,375,527 10,619,870 0.2 16,285,951 15,211,920 0.730 0.725 8,722,123 8,088,257 0.3 7,962,330 7,406,080 1.242 1.237 7,591,126 7,024,845 0.4 4,789,564 4,439,440 1.838 1.830 6,970,291 6,429,779 0.5 2,989,000 2,756,240 2.678 2.674 6,526,719 6,010,089 0.6 2,081,979 2,061,920 3.606 3.393 6,263,193 5,809,347 0.7 1,615,483 1,809,440 4.462 3.776 6,109,620 5,719,000 0.8 1,459,655 1,746,320 4.859 3.885 6,049,305 5,690,537 0.9 1,405,740 1,704,240 5.013 3.959 6,024,249 5,667,547 1 1,375,495 1,683,200 5.103 3.997 6,007,527 5,655,010 1.5 1,307,772 1,493,840 5.302 4.347 5,951,633 5,492,337 2 1,227,228 1,283,440 5.534 4.759 5,847,588 5,201,866 2.5 1,132,219 1,136,160 5.809 5.099 5,682,834 4,958,211 3 1,022,416 1,009,920 .6.136 5.400 5,442,429 4,687,213 3.5 934,311 904,720 6.406 5.645 5,208,455 4,403,643 4 850,151 778,480 6.669 5.966 4,948,289 4,021,114 4.5 758,101 589,120 6.962 6.542 4,620,477 3,358,625 5 663,092 399,760 7.280 7.351 4,239,273 2,582,651 5.5 570,056 399,760 7.611 7.351 3,822,211 2,582,651 6 476,362 357,680 7.978 7.556 3,359,210 2,379,755 6.5 387,599 273,520 8.378 7.953 2,880,442 1,922,578 7 311,658 189,360 8.776 8.500 2,434,026 1,429,245 Figure 7-8 shows the difference in the amount of the material that has to be delivered to the mill for each of the two scenarios, 5-m blocks and 20-m blocks. It shows 96 that, except for a cut-off grade of 1.0 g/t, the total material that has to be milled for the 5-m block model is greater than for the 20-m block model. Additional waste/low grade that must be milled due to large scale mining 600,000 i i i i i i l ! i i -400,000 J — — — I — — — — — — — — ' — Cut off grade g/t Figure 7-8 Additional waste/low grade materials that have to be milled when 5-m blocks are mined instead of 20-m blocks for different cut-off grades for gold deposit. Figure 7-9 shows the total amount of gold, product lost due to the mining of 20-m blocks instead of 5-meter blocks. It shows that the amount of lost product varies, based on the cut-off grade, which in turn depends on the model of the grade distribution of the deposit. In other words, this figure shows that more gold production is associated with small-block mining although the additional amount varies peaking at a cut-off grade of 5 g/t-97 Grams of gold lost due to employing 20m blocks instead of 5m blocks versus cut off grade o CD i + -o in E CD CD ,800,000 ,600,000 ,400,000 ,200,000 ,000,000 800,000 600,000 400,000 200,000 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Cut off Grade g/t Figure 7-9 Difference in gold production comparing 5-m block with 20-m block. Data from Table 7-5, which provided tonnages of the feed for the mill at different cut-off grades, is combined with the assumption that the milling cost for this deposit is 25 $/t, in order to calculate the milling cost for each block model. Table 7-5 also provides data regarding the total gold produced in each case. It is assumed that $ 8 net benefit is associated with every grams of gold produced, and this allows the total income for each case to be calculated. These results are tabulated in Table 7-6, where columns 2 and 3 show the total income for 5-m and 20-m block models. Column 3 is calculated by multiplying the total gold production in the 20-m block model by $ 8. Column 2 is calculated the same way except that the milling cost of the extra materials is also considered in the calculation. Column 4 shows the difference between columns 3 and 4 in millions of dollars. Column 5 shows the percentage of improvement in overall benefit for 5-m blocks compared to 20-m blocks. 9 8 Table 7-6 Total improvement in income for different cut-off grades - gold deposit Difference in Cut off Total benefit Total benefit benefit million Improve in grade g/t 5-m $ 20-m$ $ benefit % 0.1 18,728,418 84,958,957 -66.231 -78% 0.2 42,926,212 64,706,054 -21.780 -34% 0.3 46,822,756 56,198,764 -9.376 -17% 0.4 47,009,226 51,438,230 4.429 -9% 0.5 46,394,755 48,080,713 -1.686 4% 0.6 49,604,069 46,474,776 3.129 7% 0.7 53,725,881 45,751,997 7.974 17% 0.8 55,561,062 45,524,295 10.037 22% 0.9 55,656,491 45,340,378 10.316 23% 1 55,752,840 45,240,082 10.513 23% 1.5 52,264,768 43,938,694 8.326 19% 2 48,186,000 41,614,932 6.571 16% 2.5 45,561,195 39,665,685 5.896 15% 3 43,227,032 37,497,700 5.729 15% 3.5 40,927,867 35,229,146 5.699 16% 4 37,794,539 32,168,911 5.626 17% 4.5 32,739,288 26,868,997 5.870 22% 5 27,330,883 20,661,209 6.670 32«/o 5.5 26,320,292 20,661,209 5.659 27o/o 6 23,906,634 19,038,037 4.869 26o/o 6.5 20,191,564 15,380,626 4.811 31o/o 7 16,414,760 11,433,963 4.981 44% 7.5 16,324,827 11,634,785 4.690 40% 8 12,271,120 7,741,013 4.530 59% 8.5 9,994,855 6,391,289 3.604 56% 9 7,873,666 4,901,371 2.972 61% 9.5 5,995,921 3,380,567 2.615 77% 10 4,234,325 1,770,814 2.464 139o/o 10.5 3,282,871 1,770,814 1.512 85% Figure 7-10 depicts the improvements in net profit associated with smaller blocks using data from column 5 in Table 7-6. It can be seen that there is improvement in net benefit of the project for smaller mining block sizes for cut-off grades greater than 0.5 g/t. For example, at 1 g/t of cut-off grade the 5-m block model shows a 23% increase in overall net benefit. Assuming cut-off grades greater than 0.5 g/t this graph suggests that implementing smaller blocks may improve the economics of the project. This data would therefore support the implementation of smaller equipment. 99 Cut off grade gr/t Figure 7-10 Improvement in profit for gold deposit comparing 5-m block with 20-m block. 7.6 Summary This chapter introduced a methodology to evaluate the effect of equipment size on milling cost. Based on two real deposits different block models were created and the net profit for each scenario was calculated. The results show that from a revenue point of view that is without rigorous consideration of all aspects of project economics, there are improvements in ore recovery associated with the mining of small-scale blocks. These improvements result from more selectivity and less dilution associated with mining small blocks. There is also expected to be a higher recovery at the mill associated with the higher head grades provided by smaller blocks. The results obtained indicate the functionality of the proposed method for evaluating the ESSV- milling cost. They also demonstrate the importance of focusing on potential ore loss or negative returns when applying the model. However, it should be noted that the actual numbers in this study need refinement and definitive conclusion should be based on a more in depth study, which was beyond the scope of this work. 100 8 SIMULATION 8.1 Introduction In the previous chapters, numerous equipment size sensitive variables have been identified and discussed. Some ESSV have also been modeled individually and models have been validated and examined using field data from case studies. To look at the entire range of ESSVs a robust tool is needed. Simulation models have the capabilities to model the complexity of the whole mine system including ESSVs and their complex relationships. As discussed in chapter 1, the loading and haulage systems in open pit mines usually count for more than 65% of the total mining costs, therefore any improvements in this area have the potential to result in significant overall improvement in economics for the entire mine. In most open pit mines the haulage system consists of a complex system with several different components, such as loading machines, haulage machines, maintenance shops, and roads. The performance of these components depends on numerous variables. Since the performance of the haulage system is dependent on many different variables it is often difficult to predict its behavior. One approach that can account for the different variables and uncertainties is the use of discrete event simulation. Figure 8-1 shows the components of a possible integrated mine simulator. It consists of several components; each of which can be viewed as a separate simulator. This type of simulator is proposed as a useful tool for investigating a wide range of mining problems including the effects of larger or newer equipment on the economics of an open pit mine. 8.2 Objective This chapter describes the structure and results produced by a simulation model of the haulage system of an open pit mine. The first objective of this model is to investigate ESSV by modeling the haulage system of an open pit mine in a way that encompasses much of the complexity of the system. The second objective is to lay the foundation for an 101 integrated mine simulator as a multipurpose simulation program for assisting the industry in mine planning and equipment selection. The third objective is to quantify two ESSV in order to demonstrate the application of such a program. Production rate and lost production are the ESSV selected for quantification using simulation in this chapter. I Figure 8-1 Proposed integrated open pit mine simulator 102 8.3 Data Collection In order to build a realistic simulation, real world data is needed. Maintenance data, production data, and financial data from a large open pit mine were collected for the period between February 2000 and December of 2001. To achieve this data collection, it was necessary to spend time at the mine, which utilized a wide range of Information Technology (IT) including: - Modular Mining System - Vital Information Management System - Suspended Load Measurement System - J.D. Edwards System Data produced by these systems significantly aided the data collection process. 8.4 Data Analysis To input the data into the simulation, each data set was fitted to a statistical distribution. Prior to fitting the data to a distribution, the underlying assumption of independence and identically distributed failure times were verified using graphical tests. The results of these tests are presented in Appendix E. The statistical modeling/fitting package WeibulP™ (ReliaSoft, 2003) was used to analyze the data collected from the field. Weibull4-™ has the capability of fitting one and two-parameter exponential, two and three parameter Weibull, two parameter lognormal, and normal distributions. The program has a built-in routine that selects the best fit for the input data based on the Chi Square and Kolmogorov Smirnov (K-S) goodness of fit tests. Figure 8-2 shows the reliability versus time plot for the two fleets. The reliability of the larger trucks declines more rapidly over time than the reliability of the smaller trucks. This indicates that overall the larger trucks are less reliable. 103 Reliability vs Time Plot 1.00 0 60 0 60 01 £ 0.40 a 0.20 I | Large Tru< Trucks Smalt Trucks Weibul l Data 1 • P=2, A=RRX-S F=59 | S=0 Data 2 P=2. A=RRX-S F=90 | S=0 Anousri Ebrahimi Mining Department-27/12',2002 1:45:56 PM 120.0C (JI=0.96.TII=132.58. p=0.y* 3 2 - 0 . 6 ? , ^ 2 - 6 5 . 3 8 . p-0 93 240.00 360.00 Time, (t) 480.00 600.00 Figure 8-2 Reliability versus time plot for two truck fleets Table 8-1 shows the distribution parameters for the mean time to repair of the trucks (MTTR). MTTR data are fitted to either Lognormal or Weibull distributions. In the following tables the Weibull parameters B, r\, and y are the shape parameter, the scale parameter, and the location parameter respectively. For normal and lognormal distributions u. and rj are the mean and standard deviation respectively. For exponential distributions X equals 1/ct where a is the mean time between events. 104 Table 8-1 Distributions of the mean time to repair Distribution Weibull Lognormal Truck ID P Y CT MTTR 6160 Weibull 2 0.99 9.1 MTTR 6161 Lognormal 1.586 1.185 MTTR 6162 Weibull 2 0.97 9.7 MTTR 6163 Lognormal 1.802 1.056 MTTR 6164 Lognormal 1.693 1.265 MTTR 6165 Lognormal 1.791 1.209 MTTR 6114 Weibull 2 1.54 10 MTTR 6133 Weibull 3 1.04 8.7 1 MTTR 6134 Lognormal 1.75 0.87 Table 8-2 shows the distribution parameters for mean time between failures (MTBF) of the trucks. MTBF data are fitted to either Exponential or Weibull distributions. Table 8-2 Distributions of mean time between failures Distribution Weibull Exponential Truck ID P X M T B F 6160 Exponential 0.009 M T B F 6161 Weibull 2 0.66 66 M T B F 6162 Exponential 0.011 M T B F 6163 Exponential 0.008 M T B F 6164 Exponential 0.009 M T B F 6165 Exponential 0.009 M T B F 6114 Exponential 0.005 M T B F 6133 Exponential 0.007 M T B F 6134 Weibull 2 0.96 133 Table 8-3 shows the distribution parameters for loading time of the shovels based on truck size. The data are fitted with either normal, Lognormal or Weibull distributions. 105 Table 8-3 Load distributions for trucks Truck Shovel # Load Distribution u a P Large Small 9012 9012 Normal Normal 195.77 163.43 24.307 21.632 Large Small 9013 9013 Lognormal Normal 5.2495 166.65 0.1104 17.534 Large Small 9014 9014 Weibull2 Normal 173.98 18.360 20.112. 194.08 Large Small 9015 9015 Weibull2 Normal 180.16 16.344 22.051 228.33 8.5 Validation of Simulation Program The main simulation program was designed with the understanding that it would be validated using the haulage system of a large open pit mine. The selected mine utilizes seven shovels and a mixed fleet of large and small trucks in two pits. The mine uses a complex network of roads and ramps, connecting several loading points to three crushers and several dumping points. To simplify data collection for the validation, a large portion of the haulage system that contains three shovels and one crusher was selected. Figure 8-3 shows the layout of this part of the mine schematically. The main simulation model was modified based on this layout information. Figure 8-4 shows the modified simulation model. Production data for the month of May 2003 were collected and used to compare with the results of the simulation program for validation purposes. 106 program 8.5.1 Validation Constraints The constraints that relate to this part of the mine are as follows: 1- 60 minutes of breaks for every 12-hour shift consisting of a 30-minute lunch break and two 15-minute coffee breaks 2- The haulage system uses a mixed fleet of large and small trucks. For this particular route and in this period of time, 60% of trucks are large and 40% are small 3- There are three productive shovels for the model. All shovels work in ore. 107 4- On average, a shovel has three trucks assigned to it Figure 8-4 Model used for verification of the simulation program 8.5.2 Results of the Model Validation To validate the model, the simulation program is executed 200 times. Table 8-4 shows a summary of the results of these executions. The table shows that the average production of the haulage system calculated by the simulator is 574636 tonnes per week, which is very close to the actual production (574881 tonnes/week). This is an average of 200 runs. Figure 8-5 shows the frequency of the production for the validation model. These results also demonstrate that the model correctly duplicates the real world outcomes for this haulage system under the circumstances previously outlined. Table 8-4 Summary of 200 runs of the validation model Average production (tonne) 574636 Standard deviation (tonne) 20275.4 Minimum (tonne) 506598 Maximum (tonne) 612944 Median (tonne) 577951.5 Number of runs 200 108 Results of the validation model for 200 runs 506,250 518,750 531,250 543,750 556,250 568,750 581,250 593,750 606,250 618,750 Production (tonnes/week) Figure 8-5 Frequency of production for validation model 8.6 Case Study: Description of the Model In this case study the haulage system of an open pit mine is modeled using the simulation program to determine the impacts of equipment size on mine economics. Figure 8-6 shows the plan view of the haulage system model. The simulation model consists of three shovels, a fleet of haulage trucks, two dumpsites, one crusher with two feeders, one parking area, and one maintenance shop with 6 bays. The total length of hauls is more than 10km. A dispatch center controls the truck assignments with the objective of equalizing shovel utilization. Trucks fail and are repaired using the distributions obtained from analyses performed on field data (Table 8-1 and Table 8-2). The mine employs two 109 sizes of trucks CAT789, and CAT793. The payload of each truck is also randomly generated from distributions also obtained from field data (Table 8-3). Figure 8-6 Schematic of the open pit mine haulage system simulation model Figure 8-7 shows a close-up view of the parking area, crusher, and maintenance shop. The parking area has unlimited parking capacity. Another feature of the haulage layout is that two trucks can merge in the crusher area simultaneously as there are two crusher feeders. 110 Maintenance shops Parking area Dispatch center Crusher Shovel Figure 8-7 Close up view of the parking area, maintenance shop and crusher. Figure 8-8 shows the flow chart of the simulation model used in this chapter. Three simulation models were programmed. In the first model there are 15 small trucks operating in the fleet, the second model consists of 11 large trucks and the third model utilizes 13 large trucks. The simulation was set to run for a period of 30 days. These programs were executed 200 times each. Results were stored as an output file including equipment performance, utilizations, maintenance information, queue information, production data, and traveled distances (empty and loaded). I l l Figure 8-8 Flow chart of the discrete event simulation program 112 8.7 Assumptions Due to a lack of information, the following assumptions were made for this simulation program: • Work conditions for all fleets are the same. • The owning and operating costs for large trucks are based on manufacturer's data and are assumed to be 0.70 $/t (Caterpillar, 1999). • The owning and operating costs for small trucks are based on manufacturer's data and are assumed to be 0.69 $/t (Caterpillar, 1999). • Traveling speed is assumed to be the same for both fleets • It is assumed that delays other than breakdowns are the same for both fleets. These are lunch break, coffee break, shift change, and standbys. 8.8 Simulation Results Results of the simulations show that, on average, the smaller trucks worked for a longer period of time before experiencing a breakdown. The average maintenance time for the smaller trucks was 34.28 hours a month, noticeably less than the larger trucks, which were 55.67 and 56.25 hours a month for an 11-trucks fleet and 13-trucks fleet respectively. In terms of percent of maintenance downtime for a fixed schedule of operating hours the smaller truck fleet has 4.76% maintenance time whereas the larger truck fleets have 7.73% and 7.81%. Table 8-5 shows these results. Table 8-5 Average maintenance time Average maintenance time for a truck (h/month) Average maintenance time for the fleet (%) Small Trucks 15 trucks 34.28 4.76% Large Trucks 11 trucks 55.67 7.73% 13 trucks 56.25 7.81% 113 Table 8-6 shows the number of breakdowns and total downtime that occurred for each of the three fleets. During the simulated one-month of operation, 67.7 and 68.9 breakdowns were recorded for the larger truck fleet compared with 47.6 breakdowns for the smaller trucks. The average total downtime for the larger truck fleet was 612.35 hours and 731.23 hours for the 11-truck fleet and 13-truck fleet respectively. The total downtime for the smaller trucks was 514.23 hours. Based on this information the effective utilizations are calculated. The effective utilization for the larger truck fleet was 92.27% and 92.19% for the 11-truck fleet and 13-truck fleet respectively. The effective utilization for the smaller truck is 95.24%. Table 8-6 Breakdowns for two fleets Large Trucks Small Trucks 11-trucks 13-trucks 15-trucks Total number of failures 67.67 68.91 47.55 Total downtime - fleet- hours 612.35 731.23 514.23 Effective Utilization 92.27% 92.190/0 95.24o/o 8.9 Production Figure 8-9 shows the production distributions for the three fleets from the 200 runs of the simulation model. This figure shows that for this case on average the 15 small trucks produce more than 11 or 13 large trucks. Frequency of the Production for Three Models - 200 runs 140 120 g. 100 § 80 60 40 20 0 a-v v <v <v v V v v v v v v <v <v v v v <v-Production - Tonnage (millions) • 11-large • 15-small • 13-large 1 1 n i 1 1 IT . 1 1 L 1 _ , . , n -114 Figure 8-9 Distribution of production from three fleets during a one month period Table 8-7 shows the average total production for the three fleets. This table also shows the average production based on t/hour for each fleet. Table 8-7 Average production for three fleets Large Trucks Small Trucks 11-truck 13-truck 15 truck Default payload used by mine tonne 218 218 165 Average Total Production 200 runs 2241701 2694405 2767842 Average Production per truck t/h 302.62 312.25 269.10 8.10 Lost Production Cost Using Equation 6 (section 3.3.1) the lost production cost is calculated. Table 8-8 shows the results for the three fleets. This table indicates that the lost production cost for the larger trucks is significantly higher; this is due to both a higher downtime rate and a higher production rate. Table 8-8 Lost production costs for small trucks versus larger trucks Fleet Size Production Rate per truck t/h Production Cost per truck $/t Downtime % Lost Production Cost per truck $/h Small Trucks 15 trucks 269.10 0.7 4.76% 8.87 Large Trucks 11- trucks 302.62 0.69 7.73% 16.14 13-trucks 312.25 0.69 7.81% 16.83 115 8.11 Summary and Interpretation of the Results In this case study the mine from which the data was taken uses 218 tonnes and 165 tonnes as the default capacity for the large and the small truck, respectively. This means a production ratio of 1.32 whereas the simulation model has shown that the actual production ratio between these two sizes of trucks is just 1.14. The data used in this simulation is obtained from a large open pit mine, where the actual production ratio for two truck sizes of this mine for a period of two years was 1.08 (section 5.2 and Appendix B). The fact that the simulation's payload ratio is close to that of the real data confirms the robustness of the model. It illustrates as well, the potential for performing sensitivity analysis on ESSVs using simulation such as production rates and the loading periods. This work demonstrates the benefits of using a simulation model for evaluating equipment performance and quantifying ESSVs such as lost production and the variability of the actual production. It has shown that using such a simulator enables the researcher to overcome the complexity of the equipment size selection issue. 8.12 Discussion and Overview In this research, several Equipment Size Sensitive Variables (ESSV) have been identified, discussed and classified as either direct or indirect. These variables capture the interactions between scale and mining performance. The research shows that the influence of the ESSV potentially extends beyond the central mine operations to encompass the mill, and possibly also the environment and community. The importance of these variables and the relationships defined between them are highlighted through quantitative and qualitative methods using case studies. A comprehensive approach has been presented for quantifying ESSVs in the overall mine planning and equipment selection context. 116 From the work done evaluating ESSV, some general conclusions related to deposit type are shown in Table 8-9. For example, this table suggests that larger equipment have a positive effect on the operator cost but have a negative effect on fragmentation regardless of the type of deposit. In terms of ore dilution and selectivity larger equipment have a negative effect on non-homogeneous deposits and have an insignificant effect on homogeneous deposits. The identification of these general effects signal opportunities for improvements in one or both of the mining process or technology used. For example, autonomous trucks and two-piece tires may improve the negative effects of larger equipment on tire cost. Table 8-9 also lists the areas of equipment size effects where the author believes opportunities exists for improving the technology and those that it is believed cannot be improved without the development of a new approach to the mining process. In this table (-) represents negative effects, (+) represents positive effects and (0) represents the area that no significant effect exists. 117 Table 8-9 Model to evaluate ESSV for trucks larger than 200 tonnes. Type of effects Possibility for technological ESSV description Homogeneous Non-homogeneous improvements for deposit deposit current systems Capital - - Yes Tire - - Yes Operator + + Yes Maintenance - - Yes Lost Production - - Yes Ore Dilution and selectivity 0 - No Fragmentation - - Yes Safety - - Yes Ground Condition - - No Minimum Pit bottom area - - No Flexibility and Versatility - - No Community / Environment - - No 118 9 CONCLUSIONS In this research, several Equipment Size Sensitive Variables (ESSV) have been identified, discussed and classified as either direct or indirect. These variables capture the interactions between scale and mining performance. The research shows that the influence of the ESSV potentially extends beyond the central mine operations to encompass the mill, and possibly also the environment and community. The importance of these variables and the relationships defined between them are highlighted through quantitative and qualitative methods using case studies. A comprehensive approach has been presented for quantifying ESSVs in the overall mine planning and equipment selection context. With respect to the specific knowledge about ESSV developed during this research, these have been divided into two categories: Those that have direct impacts and those that have indirect impacts. A summary of the specific knowledge derived for each type of impact follows. 9.1 Direct Impacts In terms of direct impact variables, the research shows the following: • A set of Long Range Average Cost curves can be generated for haulage trucks. These can be created from manufacturer's data and then verified using field data from an operating mine. These curves show that in open pit mines with trucks larger than 200-tonne payload, the conventional shovel-truck system is approaching a state of diseconomies of scale. • The reliability of the new generation of larger trucks has not been improved to the same degree that their payload capacity has. The selection of larger trucks is accompanied by a smaller fleet size. These two factors combine to create difficulties in fleet management and loss in production flexibility. 119 • A comparison of manufacturer's predicted production rates with actual rates achieved by trucks greater than 200-tonne revealed that the predicted rates were not often achieved. The literature review confirmed this and indicated that larger equipment is more sensitive to certain ESSV such as ground conditions. The significance of this conclusion is that equipment selection for trucks greater than 200-tonne payload capacity must be approached in a different manner. • An analysis of manufacturer's data and the analysis of case study data show that ownership costs ($/t) of ultra-large trucks are proportionately higher than other size ranges of trucks. This has significant influence on the final production cost. • Despite the fact that maintenance cost contributes the major portion of operating costs for trucks, research presented in this thesis shows that the proportion of maintenance costs is not influenced by the size of equipment significantly. This appears to be contradictory to the experience of other large truck users where certain components of the maintenance cost increase with larger equipment. It is recommended that further research is done before reaching a firm conclusion on this matter. 9.2 Indirect Impacts In terms of the indirect impacts on open pit production performance, it is evident that there is significant impact from haulage truck size in the following areas: • Larger trucks can dramatically influence mine geometry such as bench height, ramp width, and overall mine slope. In this thesis, a comprehensive methodology has been introduced to match the mine geometry and the mining equipment size to minimizing the overall mine operating costs. The grade distribution model, the commodity price and the recovery-head grade curves are important input variables in this proposed approach. This research produces the following conclusions: 120 - Larger equipment require greater bench heights, resulting in poorer dilution control and reduced selectivity, especially for non-homogenous deposits. The consequence is a higher milling unit cost per ore tonne processed due to delivering lower head grade ore to the mill. - Due to wider ramp requirements,- larger trucks usually result in a higher stripping ratio and shallower overall pit slope. A methodology has been developed to calculate the extra waste removal due to such ramp expansion. - Mining equipment size can significantly influence milling cost. Employing larger trucks usually leads to coarser ore fragmentation. This increases the ore size reduction ratio in the milling process, which increases one of the most costly operations in processing. The opportunity costs arising from machine failure are seen to increase with truck size. A methodology has been developed to quantify lost production associated with equipment scale. 121 10 FUTURE RESEARCH This thesis has identified a number of variables that are affected by equipment size and evaluated the impact of these variables on the overall productivity, performance, and economics of an open pit production system. Research in the following areas will help to develop the knowledge contributed by this work to make it more effective in assisting the mining industry with equipment selection and other critical decision-making processes. 10.1 Simulation This research has demonstrated the benefits of simulation for evaluating mine ESSV, however, several opportunities exist to improve this work, as follows: 1- More comprehensive data is required to model the behavior of larger equipment. For this reason an intensive data collection program should be undertaken for a variety of equipment from different manufacturers, technologies and different site conditions in order to produce robust simulation parameters and useful predictive simulation models. 2- The expansion of the simulation model is needed to include all mining operational systems such as drilling and blasting. 3- The simulation model must be developed to integrate mine design aspects such as scheduling and the mill operational systems. 10.2 Mine Design and Equipment Selection 1- This research has shown that mining equipment size has a significant impact on the mine geometry and the mine layouts. Many aspects of open pit mine design relate to the features of current production machines, trucks and shovels. A review on the 122 restrictions/constraints associated with the use-of trucks and shovels in the open pit mine design process will help to improve the mine design such as reducing the ore dilution. 2- This thesis has shown that the mining industry may have reached a state of diseconomies of scale with the scale of current equipment and mining methods. This would suggest that an intensive review of mining equipment and methods is needed. This includes developing new ideas about new loading machines and new mining methods. 10.3 ESSVs Not Considered in This Work As identified earlier the effects of scale on the environment and community were identified as important but beyond the scope of this thesis. Given their importance any expanded evaluation of the ESSVs should be considered then. 10.4 Maturing Technology As discussed in section 8.12, in order to get benefit of larger equipment, there are areas that need time to be matured. In this regard, a comprehensive study is needed to clarify the areas of the larger equipment that need more attention for improvements. This will help to direct the efforts in the right direction. For example, the quality of the tires and reliability of the larger equipment are two issues that should be a priority based on results from this work. It is also very important to monitor the effect of changing technology on the equipment efficiency. Even though this work showed that mining haulage trucks might be at a state of diseconomies of scale it must be remembered that these machines are quite new. Thus, as their technology matures they may be more economically viable. 123 11 REFERENCES Alamaro, M., Rethinking Technological Economy of Scale, Institute of Electrical and Electronics Engineers, Inc. (IEEE) Technology and Society Magazine, pp. 20-21, Winter 1994 Adam, W., and Brock J.W., The Bigness of Complex: Industry, Labor and Government in the American Economy, Pantheon Books, New York, 1986 Atkinson, T., Mining Engineering Handbook, Society for Mining, Metallurgy and Exploration Engineering (SME), Chapter 13.3, 1992 Banks, J., Carson, S. J., and Nelson L. B., Discrete-Event System Simulation, Prentice Hall International Series In Industrial and Systems Engineering, ISBN 0-13-217449-9, New Jersey USA 1999 Barton, J., The Impact of Larger Trucks on Tires, University of Alberta Learning seminar #6, 2001 Baumann, S. L., Review your assets to determine maximum fleet size - a case study history of Glamis Gold's U.S. Operation", http://www.glamis.com/. 1999 Biz/ed.http://www.bized.ac.uk/ stafsup/options/ aec/phppt/business/chap09.ppt. Accessed on May 12, 2003 Blackwell, G. H , Estimation of large open pit haulage truck requirements, Canadian Institute of Mining, Metallurgy and Petroleum (CIM) Bulletin, Vol. 92 No. 1028 Pages 143-148, 1999 124 Blackwell, G. H., Simulated Grades and Open Pit Mine Planning - Resolving Opposed Positions, Computer Applications in the Minerals Industries, 28th International Symposium (APCOM 99), Colorado USA, pp. 205- 216, 1999 Bozorgebrahimi, E., A., Hall, R., and Blackwell, G.H., Economies of Scale in Surface Mining Operations; A Case Study of the Impact of Haul Truck Size on Maintenance Cost, Society for Mining, Metallurgy and Exploration Engineering (SME), Annual Meeting and Exhibit, Cincinnati Ohio, USA, Preprint 03-020, 2003. Brooks Automation™, http://www.autosim.com. official website for AutoMod, 2003 Brooks Automation, Inc., AutoMod User's Manual v 10.0, 2001 Buhl, B., MMPE410-University of British Columbia, Surface Mine Planning and Design course, Industrial Presentations, 2002 Campbell, J.D., Global maintenance benchmarking, Presentation at UOA/MIAC Mining Learning Seminar #3-Mine Maintenance, University of Alberta, Edmonton, 1998 Caterpillar Performance Handbook, Edition 30, 1999 Chiasson, L., Truck/Shovel matching issues as haul truck sizes increase, University of Alberta Learning seminar, 2001 Christensen, L. R and Greene, W. H Economies of Scale in U.S. Electric Power Generation, Journal of Political Economy, University of Chicago Press Vol. 84, pp. 655-676, 1976 Couzens, T.R., Aspects of production planning: operating layout and phase plans in: open pit mine planning and design, (J.T. Crawford and W.A. Hustrulid, editors), pp. 217-232 Society for Mining, Metallurgy and Exploration Engineering (SME), 1979 125 Coombes, J., Conditional simulation - which method for mining?, Conference Geostats 2000, Cape Town, 2000 Commodity charts: http://futures.tradingcharts.com/hist CP.html, Accessed 2003 Commodity charts: http://futures.tradingcharts.com/hist GD.html. Accessed 2003 Copper: Technology and Competitiveness, U.S. Congress, Office of Technology Assessment, Washington DC: U.S. Government Printing Office, 1988 Dance, A., The Benefits of Mine-Mill Integration, The 3rd international conference of Intelligent Processing Manufacturing of Materials (IPMM), Vancouver, 2001 Dimitrakopoulos, R., Notes for the book Geostatistical Simulation for Mining, The University of Queens land, Australia, Chapter 8, 2003 Djan-Sampson, M. and Daneshmend, L., Practical Failure Analysis and Reliability Assessment of Mining Equipment, 10th Canadian Institute of Mining, Metallurgy and Petroleum (CIM), Maintenance/Engineering Conference, Saskatoon, 1998 Doob, J. L., The Development of Rigor in Mathematical Probability (1900-1950), Amer. Math. Monthly 103, 586-595, 1996. Dunbar, W. S., Dessureault, S., and Scoble, M., Analysis of Flexible Mining Systems, Mineral Resources Engineering, Vol. 8, pp. 165-179, 1999 El-Alfy, S.E., and Atkinson, T, The Carol Lake Mine of The Iron Ore Company of Canada, Transactions-Institution of Mining and Metallurgy-Section A, pp. A1-A14, 1993 126 Ellis, D.W., Mine Design Using Simulation, First International Symposium on Mine Simulation Via the Internet, Greece, Athens, 1996 Eloranta, J., Downstream costs and their relationship to blasting J Eloranta & associates, http://www.elorantaassoc.com/propapers.htm, 1999 Erdem, B., Celebi, N., and Pasamehmetoglu, A., G. A computer simulation model for dragline stripping in surface coal mines with one flat-lying seam, First international symposium on mine simulation via the internet, Proceeding CD 1996 Fytas, K.G., "An Interactive Computer Simulation Model of Open Pit Haulage Systems", M.Sc. Thesis, Department of Mining Engineering, Queen's University, Kingston, Ontario, Canada, 1983 Getzen, T.E., "Health Economics: Fundamentals and Flow of Funds", Temple University, ISBN: 0-471-5648-X, 1997 Gilewicz, P., Large Truck Report, World Mining Equipment Magazine, July/Aug, www.parkerbaymining.com, 2001 Gilewicz, P., Large Excavators e> Haul Trucks in Surface Mining What Mines Are Using Today & Where They May Be Headed, Conference Haulage 2002, Tuscan Arizona, 2002 Hall R. A., Daneshmend L. K., Lipsett G., and Wong J., Reliability Analysis as a Tool for Surface Mining Equipment Evaluation and Selection, Canadian Mining, Metallurgy and Petroleum (CIM) Bulletin, Vol. 93 (1044), pp.78-82, 2000 Hall R. A., Reliability Analysis and Discrete Event Simulation As Tools For Mining Equipment Management, doctoral thesis submitted to the Department of Mining Engineering, Queen's University, Kingston Canada, 2000 127 Haidar A.D., and Naoum, S.G., Opencast mine equipment selection using genetic algorithms, Journal of Construction Engineering and Management, vol.125, pp. 32-38, 1999 Hustrulid, W., and Kuchta, M., Open pit mine planning and design, Volume 1 Fundamentals, pp. 259, ISBN9054101733, 1998 IOCC, Iron Ore Company of Canada, http://www.ironore.ca/products/process.htm. Accessed Nov. 2003 Klein, B., Hall, R., Scoble, M., and Morin, M., Total System Approach to Design for Underground Mine-Mill Integration, Canadian Institute of Mining, Metallurgy and Petroleum (CIM) Bulletin, Vol. 96, No. 1067, pp.65-71, 2003 Klemke, D., Klemke experience, KMC mining, University of Alberta Learning seminar, 2001 Krause, G., Large Heavy Haulers: is bigger better?, University of Alberta Learning seminar #6, 2001 Lewis, B.M., Andrews, E.P., Koen, J.E., and Mickleborough, R.O. Design and Manufaturing Concepts for 300 Ton Class Mining Trucks, 10th Canadian Institute of Mining, Metallurgy and Petroleum, Maintenance/Engineering Conference, Saskatoon, 1998 Lizotte, Y., Economic and technical relations between open-pit design and equipment selection, Mine Planning and Equipment Selection, Singhal (ed.) Balkema, Rotterdam. ISBN 9061918197, 1988 McCann, P., A proof of the relationship between optimal vehicle size, haulage length and the structure of distance-transport costs, Transportation Research Part A 35 (2001), p. 671-693, 1999 128 Mining Association of B.C., http://wvvw.bcminerals.ca. Accessed 2003 Minister of Public Works and Government Services, ISBN0-662-65627-X Catalogue, No. PF3-2/34, 2001 Miron, Michel, "Nonferrous Metal Outlook", Natural Resources Canada, www.nrcan.gc.ca/mms/cmy/2002revu/gol e.htm, 2002 Michelin, http://www.michelin.com, accessed 2003 McTurk, J., Batty, P., Ellis, D.W., Hydrotransport system simulation modeling for Syncrude's North Mine, International Jurnal of Surface Mining Reclamation and Environment, pp 97-102, 1996 Odell, C. J., Bozorgebrahimi, A. E., Scoble, M., and Veiga, M., Integrating Sustainability into Mining Engineering Practice: Tools and Implications, Canadian Institute of Mining, Metallurgy and Petroleum (CIM) Annual Meeting, Montreal, 2003 O'Hara, T. A., and Suboleski, S.C, Cost and Cost Estimation, Society for Mining, Metallurgy and Exploration Engineering (SME) Mining Engineering Handbook, Chapter 6.3, p. 412, 1992 Panagiotou, G. N., and Michalakopoulos T. N., STRAPAC2 a tool for planning and analysis of shovel-truck operations, First international symposium on mine simulation via the internet, Proceeding CD, 1996 Powers, B., The development of welded structures for mining equipment" Harnischfeger Corporation, Milwaukee, WI, USA, A.A. balkema, P.O. Box 1975, 3000 BR Rotterdam, Netherlands, 1996 129 P&H company, http://www.phmining.com/equipment/shovels.html accessed June 30, 2003 RAND Institute, New Forces at Work in Mining, www.rand.org, 2001 ReliaSoft™, http://www.reliasoft.com. 2003 Richards, M., 2001- A mining odyssey- New equipment and technology in mining at Highland Valley Copper, Canadian Institute of Mining, Metallurgy and Petroleum (CIM) Bulletin, Vol. 92 No. 1032, pp. 71-75, 2001 Runge Ian C , Mining Economics and Strategy, Society for Mining, Metallurgy and Exploration Engineering (SME), 1998 Schafrik S. J., and Karmis M., A novel, web-driven continuous mining simulator, Society for Mining, Metallurgy and Exploration Engineering (SME) annual meeting, Denver Preprint 01-32, 2001 Michaud P., Scoble M., and Lizotte Y., Rock Fragmentation and Mining Productivity: Characterization and Case Studies, International Society of Explosives Engineers, p.61, 1997 Scoble, M., Head, Mining Engineering Department, University of British Columbia, Personal Communication, 2003 Singhal, R.K., Fytas, K., and Collins, J.L., Optimization Loading and Hauling Equipment Productivity in Surface Mining, Society for Mining, Metallurgy and Exploration Engineering (SME) Fall Meeting, St. Louis, 1986 130 Srajer, V., Stuart, N.J., Kolada, R., and Szymanski, J., Selection of Loading and Hauling Equipment: User Practices, 21st Application of Computer and Application of Computers and Operations Research, pp. 638-645, 1989 Sturgul, J.R., "Annotated Bibliography of Mine System Simulation (1961-1995)", First International Symposium on Mine Simulation via the Internet, 1996 Sturgul, J. R., Mine design-examples using simulation, Published by Society for Mining, Metallurgy and Exploration Engineering (SME), 1999. Sturgul, J.R., "Advances in Simulation for the 21s' Century" Regional APCOM, Kalgoorlie, Western Australia, Published by Australia Institute of Metallurgy and Mining, pp 41-44, 1998 Sturgul, J.R., and Jacobsen, W.L., "A Simulation Model for Testing a Proposed Mining Operation: Phase I", Proceeding of the Third International Symposium on Mine Planning and Equipment Selection, Istanbul, Turkey, P. 281-287, 1994 Sturgul, J.R., and Hensley, P. "Simulation Studies of Ore Haulage from the Face to the Conveyor Belt in an Underground Mine", Computer Systems in the Australian Mining Industry, Wollongong, NSW, Australia, Published by Australia Institute of Metallurgy and Mining, 1989 Sullivan, T.W., New technology and economies of scale in shovel-truck sizing The proceedings of the second international symposium on the mine planning and equipment selection, Calgary, 1990 Sutter Gold Project, Amador County California, Presentation can be accessed at: http://www.unr.edu/mines/mine-eng/presentations/, 2003 131 Wohlgemuth, P., Syncrude haul truck experience, University of Alberta Learning seminar #6, June 2001 Wusaty, E., Is Bigger Better?, presented to MINExpo 96, Las Vegas, Nevada, 1996 Winckers A., Overview of Recent Development in Flotation Technology and Plant Practice for Copper Gold Ores, Proceedings of the Mineral Processing Plant Design, Practice, and Control, Vol. 1, pp. 1124-1141, Society for Mining, Metallurgy and Exploration Engineering (SME), 2003 Wolverine Software, http://www.wolverinesoftware.com/h 1 .htm. Accessed 2003 132 APPENDIX A- TRUCK SIZE POPULATION AROUND THE WORLD A survey on large trucks showed that there were 253 ultra-trucks (capacity bigger than 272 tonnes) around the world by the end of 2000, and it is estimated that there is a potential for 1200 more of these trucks to be introduced in the next ten years (Gilewicz, 2001). Table A- 1 (Gilewicz, 2002) shows the results of a more recent survey on the worldwide haul truck population in 2002. Table A- 1 Haul Truck Population by Size (Gilewicz, 2002) Size Class Tonne # Mines # Units Fleet Capacity (000 t.) % Total 90 282 2743 247 15% 110 82 888 98 6% 140 188 1442 202 12% 154-190 224 2993 539 33% 220 122 2099 462 28% 290+ 32 371 111 7% Total 600* 10536 1658 100% * This is not the sum of this column as some mines have more than one size of truck. Based on Gilewicz's report there are 10,536 large haul trucks (payload of 90t or more) operating in 600 mines around the world. More than 90% of these trucks work in just 4 types of mines. The distribution of large haul trucks by mine type is shown in Figure A- 1. Figure A- 2 shows the truck distribution by regions. This figure shows that the larger trucks are not uniformly distributed around the world. 134 The rest including oil Figure A- 1 Distribution of large trucks by mine type (Gilewicz, 2002) Former Soviet Union/China 25% Figure A- 2 Large tuck distribution by regions (Gilewicz, 2002) Worldwide, there are 5 companies that manufacture large trucks for the mining industry. Table A- 2 (Gilewicz, 2002) shows the manufacturers, the haul truck sizes, and 135 models. This table is dated early 2002. Some new models were introduced to the market afterward. For example, Caterpillar introduced its new 797B model with 345-tonne payload. Table A- 2 Haul Trucks: Manufacturers and Models (Gilewicz, 2002) Size Class (Metric ton) Cat Hitachi Komatsu Liebherr Unit Rig 90 777D EH1600 HD785-5 TR100 110 EH1700 HD985-5 MT3000 140 785C HD1500 MT3300 170 EH3000 630E MT3600 190 789C EH35000 730E T252 MT2700 220 793C EH4000 830E T262 MT4400 290 EH45000 930E-2 TI272 327 797 T282 MT5500 136 APPENDIX B- COST ANALYSIS OF TWO DIFFERENT TRUCK SIZES 137 bO CTi In g I D _ G 0 0 ro O O CN O o o <N o - a o • >—< 1H (U O H <D , G G - a 1 G u 3 T3 O - a G 3 o oo G <u O H O O - G CO CTS H o o 2 o o CN - a a , <u _ G •4—> a _ o *H—» u 3 - a o T3 G CTi >H 3 O _ G bO G • »H H-» CTS >H (U O H o CO CTS G <u O bO -£3 > - a « < 8 O H G O O G G bO s <*> ••3 3 CT! 3 ^ O c i X o G o !< - a o G G O H o o o «N t-i CTS oO G P H J * 3 SO so Os IT) O O r>.' o LO so o o so co oo" CN o c5~ o o Os OO o LO LO so" so 0~ O O Os Os CN uo CN CO | I ON I ON T—I SOI SO CO On" CN O O O O o o OO r>." ON On CO CN CN On OO r>. 53 CO On S3, On OO S3 U CO ON U - a g <u o. - G G g * H - » u 3 - a o as -*-» O bO G C*H I G u O H CTS O H -13 G CTS -4-t CTS o (J "ns H - » o •4-t _G O -G to CN I CO -a a! o ns O H <u bO CTS in - G u o G O • —< (J 3 - d o G G O CO I so so CN "25" co LO" o uo LO O o H - » CTS - a G CTS CTS C H CN CO (U H 3 O - G 601 G oo CO o " ON T3 3 a CTS - G - a G as O O G O O CN O O O CN 3 V H oo o ON o d CN ON o [>." |CN |CN r>T LO o CN O N On O H U 0 o o " o CTS O H <u - G O u ON ON O o O o o o " ^ 1 o CN uo O OO co" CO I CN oo sO_ co" On CN" so CO so" CN <N"I On 0 0 co CO r>." CN I CN SOI uo"| T - H U O CO On B O O ^ 1 Os CO LO CO LO o o CN ON O 0 CO ON o CJ <u W) al U l I cu J3 -4-1 -a _o 'G cu a , <u s al (U -3 *-4-» cj •3 -a o ai -4-1 O 3 <u <U 03 I U l S3 oj a , o a! cu U C ai 3 cu 3 'ai cu -3 O u "ai -4-1 o - 4 J cu -3 -4—1 o J3 P Q cu ,3 ai H -a cu 3 ai cj -3 -a s al G O J cu u a ai G cu •4-1 3 'al cu -3 O u U l O -O .2 cu u S3 a! S3 cu S3 'ai P Q cu a! H SO 00 0^ 0^ 0 0 0-cn CN 0 OS :ost nni 0 0 O O 0 T—t O OS 0 bo < ^ al cu OO t—1 . 0 60 OS so o~ 0" a! 0 OO U l cu -£3 so 1 so 1 0 r>.' OS 0 Os ^tS ^ s cn O o~ 0 CN roducti< tonnes O i>. CN 0 1—1 LTl roducti< tonnes 1055 l>. u~> to 1^- O O O U l 00 CN O 0-3 cn OS O O O m O O -G Os OS r—i LO W> 1 3 *-4-< ai U  cu &, O LT) Os CN osts so so O O cn osts UO *—1 O T—1 Os' CJ cn cn —_l m os ai CN O cn T—1 H m Os ~§ cn t>. cy 0 00 t-^  0 0 so-00 00 T—1 T—1 -3- 00 O 00 00 O T—1 00 < ^ CN 0 CO r—1 r—i to 0 U CN ~° O 00 0 O 0 00 -Q 00 cn O !>•' ai 0. T—1 —1 2000 18145 O CN OS cn Os cn OO OS 00 Os CJ H H H H 3 U l < < < < H U U U U at U .1 cj cu o -a cu 3 al cu S3 g *-t-l (J 3 -a o cu -S3 S3 O •fl 3 ai 3 CT1 cu M> S3 cu cu « 1 U l O u u 3 T3 O cu -3 O -S3 P Q al H o 3 cu - G T 3 cu O S3 al J3 <u 4-1 cu 6 ai U l al O H S3 O u -ui ai J3 3 O '-4-1 ai N Os cn 4-1 <_> ct! cu <-Ul O ~0 ai cu cu N CJ 3 S3 cu U l • i-H -a p al a , o CJ al -c 3 CU -a "u S3 o S3 cu cj S3 al 3 cu -4-1 S3 'al -a cu 3 3 al .5 -a ai -S3 3 ai cu 2 Z rl ±i -u^  -a cu ai ai -a <u 3 3 al " a , 3 3 cu -3 O u 3 O CJ 3 -a o 3 1 P Q <u ai H 'oduction $ 982,766 486,616 100% 49.5% lost pi cost "ai 101 Operating hours 190,384 95,920 %00T ty 0 Lost production cost $/h CN u- i 100% 98.3% so cn O s t3~ Cost $/t 0 0 0 0 -—1 iction ^h LT) cN so so Os Ul 100% 107.7% 1 3 ^ O U l P!H ility % O t3~ Os 0^ <3~ 00 Os 100% 100.5% liabi Rel Truck # CAT789 CAT793 CAT789 CAT793 cu O - Q G O C U -4-t "3 u u C U G O '•4-t as 3 cr C U cn o u <u 1-1 ( U •4-t G a, as -G CQ ai H <u < s -G u as < U 60 G >-| G O 2 -G S cu 60 as I-I as C U f2 <u JG I U <u CG <u -G <o .S2 <« G •3 H •« 3 O ,G -a cu 4-1 u <u a, X! C U 3 O -G G cu OH O cu J 3 cu 3 G *3 .12 O -G 60 G G O G G g *H-t as 3 cr cu 60 G cu C U CG -G CJ ai ( U o -a G as •2 £ ii .G C U C H "as C H 60 .5 -G 1-1 -t-t -2 w 3 as O -a cu G C U -G cu -O 3 G  ~ -a C U 3 u ai O u C U Ui C U -4-1 G O H a! u PQ C U 3 aS H OH as u -G C U C U OG 3 C U U H cu -4-1 G cu -a cu OH U 3 cu l-i -G cu G "as " * H „ 3 0 0"T3 cu • -H t——« P^ 3 o u * 3 < OH ai u JG cu G O Q 3 O o OH al -a' G as G O • i-H tj 3 T3 O aS O, O as 60 G *3 3 13 G o cu <_> G as G C U ai o 4-1 % O CO S O 1 PQ cu as H o u <u u G aS G cu B2 CQ cu 3 oo -G C U _ T H -4-t S T * 2 8 'a 0 as u G O -a o -G aS PH -G 00 O o o J3 U 3 H CO Os o o so LO LO o O O o o Os LO I o o Os so Os o Os SO O O CO 00 Os , ^ 00 C*sl 3 " o •^ 1 o o so so O O O0 Os Os 00 Os OO 'I CO Os C U sH cu 60 a! I C U -G O -G LO CM O o to a! -a * n C U OH C U G cu _G L2 O u C U 60 ai 1 1 I C U _G O OH cu I-H as OH O as G G aS G O -a <u to as PQ O cu cu CG C U G O OH G O u 3 <u 3 o OH a o u oo c 'H-I OH o o 3 l_l P H <U oo 60 <u CTi -u» >H CTi P ^ < 3 O 1 3 oo 3 O u 3 P H O O LO O o 60 3 O u <u OH o ,3 PH , C 3 a <u o CTi 3 <U IU 60 <u -3 <u -a c o u CTi Ul a. (U N ^ H U 3 3 <u Ul tin o u . 3 O O tj Ul O +H CTi Ul <u OH O 00 I JU ,o E-H o CN CTi -U» UO o CO H UO UO a •*r -3 emit Ul P H H-l OO eg  i-3 Ul oo O -*-» CT! Ui <u On C H OO O OO H-t NO U3 (U 3 IU P H NO l>-• H CO -U» • ^H CN 3 i—i IU OH O (U u 3 <u o -3 H <u <u qG O u o -3 3 <u 3 O OH a o O u -a T 3 CTi -a s CTi 3 o 02 3 i o CTS <u .3 O CTS <U J3 1% O -3 0 0 On P H IU H o ,2 On P H IU CTi H CTi O O 60 3 -HJ OO <u U 3 4H CTi \ 3 oo TH oo .a o 3 <-> oo o u 3 P H O u Ul u Q oo oo O u "CT! -UI CTi u u 3 u, H * CN ro O O On O NO o O !>.' On On UO O O O O 00 00 -CNl o O On U0 O o ro On O O O 1^ o On CN On CN CN CN ro ON B ON OO B ro ON s -3 oo -a 3 CTi APPENDIX C - DETAILED INFORMATION ABOUT COPPER DEPOSIT 142 This is a porphyry deposit with an average grade of 0.25 % copper. It is located in a valley at an average altitude of 1200 m. In the database there are 143 vertical diamond drill boreholes with total a length of 60,166 meters. The length of boreholes varies from 122.83 meter to 593.45 meters. Table C- 1 shows a summary of the database for this deposit. Table C- 1 A summary of diamond drill boreholes in copper deposit HOLE ID NORTHING EASTING ELEVATION DEPTH 10-117 27295.1 36226 1194.4 396.54 Oct-93 27281.3 35451.1 1198.8 374.9 10N-165E 27242.4 37602.1 1202.7 408.43 11-187 27337.1 38348.9 1200.1 316.08 16N-129E 27483.3 36537.6 1192.9 352.04 22-21 27694.3 33260.4 1218.6 225.86 22-69 27652.3 34719 1198.1 359.05 2S-117E 27041.9 36182 1198.3 225.25 30-99 27888 35636.7 1197 492.25 34-103 28010 35768.9 1202.7 425.81 3445 28015.9 33990.2 1197.5 418.49 34-87 28016 35281 1200.6 323.7 34-91 28039.9 35388.8 1199 358.75 Results Table C- 2 shows the statistics of the composite file created from this database and used to perform the variogram modeling. In total, there are 33,436 samples in the database, and ore grades are between 0.01% to 4.32% of copper. Although the majority of the samples show very low-grade copper, there is enough high-grade ore that may make it viable. 143 Table C- 2 Statistics for the sample file of the copper deposit Number of samples: 33,436 Minimum: 0.01 Maximum: 4.32 Mean: 0.263 Variance: 0.073 Standard deviation: 0.269 Coefficient of variation: 1.023 Skewness: 3.05 Median: 0.19 Using SURPAC software, variogram models were determined for different directions. Figure C-1 shows the variogram towards north. It shows a very small amount of nugget effect 0.001, and a range of about the 150 meters. Table C- 3 shows the values for this variogram. Using these numbers and ordinary Kriging method, grades for blocks were estimated. 0.16 -Q.14 - + Q.12 -0.1 -gam math) 0.0 6 -0.06 -0.04 -+ + + + 1 + + + 0.02 -n • 0 0 lfl'O 20'0 30'0 4 0'0 50'0 0 -> 90 (20) d i s t a n c e Normal variogram Figure C- 1 Variogram for copper deposit in direction of north 144 Table C- 3 Variogram parameters for copper deposit Structure Sill Range Nugget 1 0.04926 149.15 0.01344 A set of sections in different directions has been created throughout the block model. Cross-sections show that mineralization continues beyond the current exploration depth, which is 500 meters. This suggests further exploration for deeper parts of the region. The majority of the deposit takes place between 300 to 400 meters of alluvial materials. Figure C- 2 shows a view of the topography of the region with three cross sections. It clearly shows that high-grade materials are concentrated in the center of the deposit while low-grade ores are in the outer shells. Figure C- 2 Topography and three sections of the copper porphyry deposit Figure C- 3 shows an image of the blocks containing grades greater than 0.0% of copper. This model consists of over 5 million block units. The model shows some traces of the mineralization (outcropping) on surface. 145 Figure C- 3 Block model of the copper porphyry deposit Table C- 4 lists tonnages of ore in different grade categories for the 5-m block model. This deposit contains 729 millions tonnes of copper ore at a cut-off grade of 0.2% with an average grade of 0.35%. It is estimated the deposit contains 3.6 million tonnes of ore over 1% grade with an average grade of 1.25%. The highest ore grade that has been found in this block model is 2.33%. 146 Table C- 4 Reports on copper deposit block model (5-m blocks) Classes of Cumulative Cumulative Cu % Tonnes Grade % Tonnes Grade % 0.0-0.1 6,020,805,414 0.004 7,290,360,003 0.049 0.1-0.2 540,064,254 0.148 1,269,554,589 0.264 0.2-0.3 352,797,405 0.245 729,490,335 0.350 0.3-0.4 186,629,731 0.344 376,692,930 0.449 0.4-0.5 89,852,306 0.444 190,063,199 0.552 0.5-0.6 49,359,183 0.545 100,210,893 0.648 0.6-0.7 26,639,599 0.643 50,851,710 0.748 0.7-0.8 12,447,790 0.742 24,212,111 0.864 0.8-0.9 5,306,354 0.848 11,764,321 0.992 0.9-1.0 2,858,153 0.941 6,457,967 1.111 1.0-1.1 1,108,216 1.047 3,599,814 1.246 1.1-1.2 759,413 1.144 2,491,598 1.334 1.2-1.3 688,074 1.248 1,732,185 • 1.417 1.3-1.4 416,198 1.345 1,044,111 1.529 1.4-1.5 165,033 1.457 627,913 1.651 1.5-1.6 189,689 1.551 462,880 1.720 1.6-1.7 84,818 1.64 273,191 1.837 1.7-1.8 40,436 1.748 188,373 1.926 1.8-1.9 103,556 1.845 147,937 1.975 1.9-2.0 5,260 1.936 44,381 2.279 2.0-2.1 0 0 39,121 2.325 2.1-2.2 0 0 39,121 2.325 2.2-2.3 0 0 39,121 2.325 2.3-2.4 39,121 2.325 39,121 2.325 Table C- 5 lists the tonnages of ore in different grade categories for the 10-m block model. As predicted, when compared to 5-m blocks, this model shows less variation in ore grades. Overall, it can be seen that the grades have shifted toward smaller values. There are 3.3 millions tonnes of ore at a grade greater than 1.0% with an average grade of 1.24%. The highest grade for ore in this block model is 2.1%. 147 Table C- 5 reports on copper deposit block model (10-m blocks) Classes of Cumulative Cumulative Cu % Tonnes Grade % Tonnes Grade % 0.0-0.1 6,012,051,130 0.004 7,290,360,000 0.049 0.1-0.2 554,714,340 0.148 1,278,308,870 0.261 0.2-0.3 355,428,720 0.245 723,594,530 0.348 0.3-0.4 184,223,610 0.344 368,165,810 0.447 0.4-0.5 87,555,330 0.444 183,942,200 0.549 0.5-0.6 48,094,810 0.546 96,386,870 0.645 0.6-0.7 26,347,340 0.644 48,292,060 - 0.743 0.7-0.8 11,503,620 0.745 21,944,720 0.862 0.8-0.9 4,847,090 0.846 10,441,100 0.992 0.9-1.0 2,288,100 0.939 5,594,010 1.118 1.0-1.1 957,320 1.046 3,305,910 1.242 1.1-1.2 836,340 1.146 2,348,590 1.322 1.2-1.3 497,070 1.249 1,512,250 1.420 1.3-1.4 286,670 1.351 1,015,180 1.504 1.4-1.5 370,830 1.448 728,510 1.564 1.5-1.6 131,500 1.546 357,680 1.684 1.6-1.7 136,760 1.629 226,180 1.764 1.7-1.8 26,300 1.749 89,420 1.970 1.8-1.9 7,890 1.865 63,120 2.063 1.9-2.0 0 0 55,230 2.091 2.0-2.1 55,230 2.091 55,230 2.091 2.1-2.2 0 0 0 0 2.2-2.3 0 0 0 0 2.3-2.4 0 0 0 0 Table C- 6 lists the tonnages of ore in different grade categories for the 20-m block model. As predicted, when compared to the 5-m blocks, this model shows even less grade variation. Similar to the 10-m block model, the grades have shifted toward smaller values. There are 2.7 millions tonnes of ore at a grade greater than 1.0% with an average grade of 1.24%. The highest grade for ore in this block model is 1.85%. 148 Table C- 6 reports on copper deposit block model (20-m blocks) Classes of Cumulative Cumulative Cu % Tonnes Grade % Tonnes Grade % 0.0-0.1 6,004,332,080 0.004 . 7,290,360,000 0.049 0.1-0.2 569,679,040 0.147 1,286,027,920 0.257 0.2-0.3 355,449,760 0.246 716,348,880 0.344 0.3-0.4 184,962,640 0.343 360,899,120 0.441 0.4-0.5 85,674,880 0.445 175,936,480 0.543 0.5-0.6 47,718,720 0.544 90,261,600 0.637 0.6-0.7 23,017,760 0.645 42,542,880 0.741 0.7-0.8 10,477,920 0.742 19,525,120 0.853 0.8-0.9 4,607,760 0.849 9,047,200 0.982 0.9-1.0 1,746,320 0.94 4,439,440 1.121 1.0-1.1 862,640 1.04 2,693,120 1.238 1.1-1.2 631,200 1.145 1,830,480 1.332 1.2-1.3 273,520 1.239 1,199,280 1.430 1.3-1.4 273,520 1.338 925,760 1.486 1.4-1.5 315,600 1.451 652,240 1.548 1.5-1.6 168,320 1.536 336,640 1.639 1.6-1.7 63,120 1.66 168,320 1.743 1.7-1.8 63,120 1.753 105,200 1.793 1.8-1.9 42,080 1.852 42,080 1.852 1.9-2.0 0 0 0 0.000 2.0-2.1 0 0 0 0.000 2.1-2.2 0 0 0 0.000 2.2-2.3 0 0 0 0.000 2.3-2.4 0 0 0 0.000 APPENDIX D - DETAILED INFORMATION ABOUT GOLD DEPOSIT 150 In the gold deposit database, there are 1197 diamond drill boreholes with a total length of 99,519 meters. While the majority of holes are drilled vertically, there are many non-vertical holes. The length of borehole varies from 10 meters to 353 meters. Table D- 1 shows a summary of the database. Table D- 1 a summary of diamond drill boreholes in gold deposit HOLE ID NORTHING EASTING ELEVATION DEPTH 1-001 43881.9 49149 7093.91 300 1-002 43906.9 49194.4 7102.71 300 1-003 43922.5 49242.6 7110.55 300 1-004 44001.8 49149.2 7114.3 300 1-005 43987 49234 7117.48 300 1-006 44080.5 49144.6 7119.97 300 1-007 44073.4 49189 7120.02 300 1-008 44037.8 49226.8 7119.4 235 1-009 44606.84 49626.29 7192.02 405 1-010 44740.42 49489.19 7161.19 405 1-011 44838 49381.31 7149.94 405 1-012 44793.22 49365.17 7155.14 405 1-013 44628.94 49518.83 7175.18 400 1-014 44572.61 49581.59 7182.46 400 1-015 44705 49530.97 7169.61 480 1-016 44686.48 49376.48 7160XJ8 390 1-017 44644.52 49427.16 7163.69 405 1-018 44607.7 49469.39 7164.31 405 1-019 44792.32 49435.37 7158.31 405 1-020 44233.56 49246.01 7148.73 300 1-021 44995.98 49713.72 7165.39 405 1-022 44968.7 49806.13 7179.74 445 Results Table D- 2 shows the statistics of the composite file created from this database and used to perform variogram modeling. In total, there are 43,490 samples in this file, and ore grades are between 0 and 1.364 grams per tonne of gold. The majority of the samples show very low-grade ore. 151 Table D- 2 Statistics for the sample file of the gold deposit Number of samples: 43490 Minimum: 0 Maximum: 1.364 Mean: 0.019 Variance: 0.003 Standard deviation: 0.057 Coefficient of variation 2.928 Skewness: 10.448 Median: 0.002 Using SURPAC software, the variography was performed in different directions. Figure D- 1 shows one of the variograms (north). It shows the value for nugget effect to be 0.025, with about 50 meters range. D.Q4 -0 .035 -0.03 -Q.025 -gamma(ri) 0 .02 -0 .015 -+ 0.01 -0 .005 -i ° 0 „ + + + + + + + + + + + + + + + + lO'O 20'0 30'0 4 0'0 50'fl d i s t a n c e 0 -> 2 2 . 5 (10) Normal variogram Figure D- 1 Variogram for gold deposit in direction of north Table D- 3 shows the values for this variogram. Using these numbers and ordinary Kriging method, grades for blocks are estimated. 152 Table D- 3 variogram parameters for gold deposit Structure Sill Range Nugget 1 0.005466 35.85 0.001384 Figure D- 2 show the topography of the region plus a general view of the diamond drill holes. The intensity of the exploration work in the region implies the complexity of the gold deposit. General study on the exploration work suggests that the whole deposit consists of two separate mineralization zones. Since the database was very large, in order to reduce the processing time, only part of the database was used for this study. For this study one of these zones is chosen to work with. Some deep exploration works in this field, shown in Figure D- 2, show that there is not sufficient mineralization at depth. Figure D- 2 A general view to the north showing diamond drill holes- gold deposit Figure D- 3 shows the block model of the deposit. It shows the blocks with ore grade greater than 0.0 gram gold per tonne. As can be seen the majority of the deposit consists of very low-grade ore. There is some high-grade ore in the center that cannot be seen in this figure. 153 Figure D- 3 Block model and diamond drill holes - gold deposit Table D- 4 shows the volume, tonnage and grades for the 5-meter block model. In this model there are about 8 million tonnes of ore with an average grade of 1.24 grams per tonne of gold at a cut-off grade of 0.3 g/t. This is the highest resolution (smallest mining block) for this deposit. It shows ore grades greater than 17.0 g/t. 154 Table D- 4 Tonnage and grades of the gold deposit for the 5-meter block model Au (g/t) Volume m3 Tonnes Cumulative Au (g/t) 0.0-0.1 1,905,196,750 5,010,667,453 0.003 0.1-0.2 13,610,875 35,796,601 0.134 0.2-O.3 3,164,875 8,323,621 0.239 0.3-0.4 1,206,375 3,172,766 0.344 0.4-0.5 684,625 1,800,564 0.444 0.5-0.6 344,875 907,021 0.546 0.6-0.7 177,375 466,496 0.642 0.7-0.8 59,250 155,828 0.744 0.8-0.9 20,500 53,915 0.845 0.9-1.0 11,500 30,245 0.948 1.0-1.5 25,750 67,723 1.252 1.5-2.0 30,625 80,544 1.769 2.0-2.5 36,125 95,009 2.258 2.5-3.0 41,750 109,803 2.761 3.0-3.5 33,500 88,105 3.273 3.5-4.0 32,000 84,160 3.748 4.04.5 35,000 92,050 4.258 4.5-5.0 36,125 95,009 4.746 5.0-5.5 35,375 93,036 5.252 5.5-6.0 35,625 93,694 5.745 6.0-6.5 33,750 88,763 6.229 6.5-7.0 28,875 75,941 6.745 7.0-7.5 22,750 59,833 7.245 7.5-8.0 25,500 67,065 7.739 8.0-8.5 16,250 42,738 8.244 8.5-9.0 13,500 35,505 8.741 9.0-9.5 10,500 27,615 9.244 9.5-10.0 8,875 23,341 9.742 10.0-10.5 6,375 16,766 10.219 10.5-11.0 4,250 11,178 10.723 11.0-11.5 2,750 7,233 11.255 11.5-12.0 2,125 5,589 11.751 12.0-12.5 1,625 4,274 12.133 12.5-13.0 1,125 2,959 12.722 13.0-13.5 1,000 2,630 13.190 13.5-14.0 625 1,644 13.843 14.0-14.5 500 1,315 14.200 14.5-15.0 0 0 0.000 15.0-15.5 125 329 15.084 17.0-17.5 500 1,315 17.118 17.5-18.0 125 329 17.646 Total 1,925,000,000 5,062,750,000 0.007 155 Table D- 5 shows the volume, tonnage and grades for the 10-meter block model. In this model there are about 7.7 million tonnes of ore with an average grade of 1.23 grams per tonne of gold at the cut-off grade of 0.3 g/t. This is the second highest resolution for this deposit and shows ore grades greater than 12.0 g/t. Table D- 5 Tonnage and grades of the gold deposit for the 10-meter block model Cumulative Au (g/t) Volume m3 Tonnes Au (g/t) 0.0-0.1 1,905,714,000 5,012,027,820 0.004 0.1-0.2 13,211,000 34,744,930 0.134 0.2-0.3 3,134,000 8,242,420 0.240 0.3-0.4 1,198,000 3,150,740 0.344 0.4-0.5 633,000 1,664,790 0.445 0.5-0.6 301,000 791,630 0.545 0.6-0.7 141,000 370,830 0.645 0.7-0.8 57,000 149,910 0.742 0.8-0.9 30,000 78,900 0.839 0.9-1.0 13,000 34,190 0.962 1.0-1.5 50,000 131,500 1.236 1.5-2.0 38,000 99,940 1.738 2.0-2.5 51,000 134,130 2.249 2.5-3.0 50,000 131,500 2.736 3.0-3.5 35,000 92,050 3.213 3.54.0 32,000 84,160 3.785 4.04.5 48,000 126,240 4.263 4.5-5.0 38,000 99,940 4.715 5.0-5.5 34,000 89,420 5.227 5.5-6.0 35,000 92,050 5.735 6.0-6.5 23,000 60,490 6.286 6.5-7.0 32,000 84,160 6.744 7.0-7.5 21,000 55,230 7.275 7.5-8.0 20,000 52,600 7.716 8.0-8.5 17,000 44,710 8.282 8.5-9.0 15,000 39,450 8.772 9.0-9.5 4,000 10,520 9.216 9.5-10.0 8,000 21,040 9.771 10.0-10.5 9,000 23,670 10.196 10.5-11.0 3,000 7,890 10.706 11.0-11.5 2,000 5,260 11.273 11.5-12.0 0 0 0.000 12.0-12.5 1,000 2,630 12.149 12.5-13.0 2,000 5,260 12.648 Total 1,925,000,000 5,062,750,000 0.007 156 Table D- 6 shows the volume, tonnage and grades for the 20-meter block model. In this model there are about 7.4 million tonnes of ore with the average grade of 1.23 grams per tonne of gold at the cut-off grade of 0.3 g/t. This is the smallest resolution for this deposit and shows ore grades greater than 10.0 g/t. Table D- 6 Tonnage and grades of the gold deposit for the 20-meter block model Au (g/t) Volume m3 Tonnes Cumulative Au(g/t), 0.0-0.1 1,906,296,000 5,013,558,480 0.004 0.1-0.2 12,920,000 33,979,600 0.134 0.2-0.3 2,968,000 7,805,840 0.239 0.3-0.4 1,128,000 2,966,640 0.349 0.4-0.5 640,000 1,683,200 0.447 0.5-0.6 264,000 694,320 0.539 0.6-0.7 96,000 252,480 0.649 0.7-0.8 24,000 63,120 0.767 0.8-0.9 16,000 42,080 0.878 0.9-1.0 8,000 21,040 0.936 1.0-1.5 72,000 189,360 1.237 1.5-2.0 80,000 210,400 1.827 2.0-2.5 56,000 147,280 2.142 2.5-3.0 48,000 126,240 2.690 3.0-3.5 40,000 1.05,200 3.293 3.5-4.0 48,000 126,240 3.662 4.0-4.5 72,000 189,360 4.177 4.5-5.0 72,000 189,360 4.832 5.0-5.5 0 0 0.000 5.5-6.0 16,000 42,080 5.612 6.0-6.5 32,000 84,160 6.264 6.5-7.0 32,000 84,160 6.723 7.0-7.5 8,000 21,040 7.380 7.5-8.0 24,000 63,120 7.711 8.0-8.5 8,000 21,040 8.019 8.5-9.0 8,000 21,040 8.852 9.0-9.5 8,000 21,040 9.035 9.5-10.0 8,000 21,040 9.564 10.0-10.5 0 0 0.000 10.5-11.0 8,000 21,040 10.521 Total 1,925,000,000 5,062,750,000 0.007 157 APPENDIX E - STATISTICAL DISTRIBUTION FITTING, TREND TEST AND TEST FOR CORRELATION \ 158 Reliability Versus Time Plot for Different Trucks Sizes 0*. 1 • • | W:r.lfkj DllUMT (IB • IS! > - s . bHDHH P-i A-RRXS I p.i AHMU B . M n j p m XJM JTOOOGC jooocao 4ixwr..-x Small truck #6134 Large truck #6164 - K \ s M A.RRX-S • • f»i, AtWX-S \ \ \ \ v>* M M •. • L- :*>.•: r.»*%' .. .... • • Small truck #6133 :: M A--HRXS fiSt i . Anauti: Enrjiwn • M l L>no8nr.»nnj« __ . 2 21 U UM •• • conoonc Large truck #6163 159 Trend Test Test for Correlation Truck-6160 500.00 450.00 400.00 350.00 u. 300.00 P 250.00 - 200.00 150.00 100.00 50.00 0.00 St • • 200.00 300.00 i - U h T B F 400.00 500.00 Large truck #6160 Test for Correlation Truck-6161 700.00 600.00 500.00 & 400.00 * § 300.00 200.00 100.00 0 00 • • • • 400.00 i -1thTBF Large truck #6161 Trend Test for Truck-6160 1 4 0 0 0 120.00 _ 100.00 • — • ^^0^——— 80.00 — 60.00 - —^m-— 40 oo • -m-20 00 M 0.00 >P , , r • • 0.00 200.00 400 .00 600 .00 800.00 1000.00 Cumulative T B F Hours Large truck #6160 Trend Test for Truck-6161 0.00 2000.00 4000 .00 6 0 0 0 0 0 Cumulative T B F Hours Large truck #6161 Test for Correlation Truck-6162 700.00 600 00 500.00 ^ 400.00 ).00 2 0 0 0 0 400.00 600.00 800.00 i-1th TBF Large truck #6162 Trend Test for Truck-6162 0 00 1000 00 2000.00 3 O X 0 0 4 0 X 0 0 5000.00 5000.00 7000 00 CumjIat iveTBFHou-s Large truck #6162 160 Trend Test for Truck-6114 0.00 1000.00 2000.00 3000.00 4000.00 5000.00 6000 00 7000.00 Currul at i ve T BF Hour s Small truck #6114 Trend Test for Truck-6133 2500 00 -I 0 0 0 200000 4000.00 6000.00 8000.00 10000 00 Q j m j J a t i v e T B F U o u s Small truck #6133 Test for Correlation Truck-6114 400.00 M t h T B F Small truck #6114 Test for Correlation Truck-6134 600.00 + 500.00 400.00 300.00 200.00 100.00 0.00 • • ! • r • m • 0.00 100.00 200.00 300.00 400.00 500.00 600.00 i -1thTBF Small truck #6134 Test for Correlation Truck-6133 • • 0.00 100.00 200.00 300.00 400.00 500.00 600 00 M t h T B F Small truck #6133 Trend Test for Truck-6134 4000.00 6000.00 Currul a t i veTBF Hours Small truck #6134 161 

Cite

Citation Scheme:

        

Citations by CSL (citeproc-js)

Usage Statistics

Share

Embed

Customize your widget with the following options, then copy and paste the code below into the HTML of your page to embed this item in your website.
                        
                            <div id="ubcOpenCollectionsWidgetDisplay">
                            <script id="ubcOpenCollectionsWidget"
                            src="{[{embed.src}]}"
                            data-item="{[{embed.item}]}"
                            data-collection="{[{embed.collection}]}"
                            data-metadata="{[{embed.showMetadata}]}"
                            data-width="{[{embed.width}]}"
                            async >
                            </script>
                            </div>
                        
                    
IIIF logo Our image viewer uses the IIIF 2.0 standard. To load this item in other compatible viewers, use this url:
http://iiif.library.ubc.ca/presentation/dsp.831.1-0081125/manifest

Comment

Related Items