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Technical amenability study of laboratory-scale sensor-based ore sorting on a Mississippi Valley type.. Tong, Yan 2012

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TECHNICAL AMENABILITY STUDY OF LABORATORY-SCALE SENSORBASED ORE SORTING ON A MISSISSIPPI VALLEY TYPE LEAD-ZINC ORE  by  Yan Tong  B.A., University of Science and Technology, Beijing, 2009  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF APPLIED SCIENCE  in  THE FACULTY OF GRADUATE STUDIES  (Mining Engineering)  THE UNIVERSITY OF BRITISH COLUMBIA  (Vancouver)  October, 2012  ©Yan Tong, 2012  Abstract Automatic sensor-based sorting is a clean preconcentration technique that can be used to separate valuable ore rock from waste rock based on the difference of the detected physical properties. This research evaluated the amenabilities of a Mississippi Valley type lead-zinc ore sample from Pend Oreille Mine to X-ray Fluorescence Sorting, X-ray Transmission Sorting, Optical Sorting and Microwave-Infrared Sorting using laboratoryscale bench-top sensing systems. A methodology for laboratory-scale quick evaluation of the amenability of an ore sample to automatic sensor-based sorting using bench-top sensor systems was generated as reference for future study. The preliminary testwork results showed that the two X-ray methods exhibited the best sorting results. About 37.7%~52.8% of the feed mass could be rejected as waste while above 95% of the lead and zinc was recovered in the product. The sorting feed (37.5+26.5 mm) could be upgraded by a factor of 1.5~2. The optical sorting method seemed not as effective as the X-ray methods. Only 18.8% of the sorting test feed (37.5+26.5 mm) was rejected to maintain above 95% metal recovery in the product. The test feed was upgraded by a factor of 1.2. Microwave-Infrared sorting results demonstrated that carbonate gangue mineral does not heat when exposed to microwave heating, while lead-zinc bearing sulfide does. Factors such as particle size, heating time and quantity of particles being heated at a time would influence microwave heating of rocks. Sorting feed of -19+13.2 mm presented the best segregation results after 10s of microwave heating. Above 95% of lead and zinc was recovered in a mass yield of 70% to the product. The test feed was upgraded by a factor of 1.4. The preconcentrate of X-ray Fluorescence sorting had a bond work index 12% smaller than that of the feed ore. The overall metal (lead and zinc) recoveries and grades in the flotation products were also improved after XRF sorting. The costs of both the grinding and the flotation reagent could also be reduced due to the reduction of the feed mass by rejecting the dolomitic gangue minerals up to 50%.  ii  Table of Contents Abstract ........................................................................................................................... ii Table of Contents ........................................................................................................... iii List of Tables ................................................................................................................. vii List of Figures................................................................................................................viii List of Abbreviations ....................................................................................................... xi Acknowledgements ....................................................................................................... xii CHAPTER 1 1.1  INTRODUCTION ................................................................................... 1  Automatic Sensor-Based Ore Sorting................................................................ 1  1.2 Objectives.............................................................................................................. 2 1.3 Mine and Mineralogy ............................................................................................. 2 1.4 Methodology .......................................................................................................... 3 CHAPTER 2  LITERATURE REVIEW ......................................................................... 4  2.1 Automatic Sensor-Based Sorting ........................................................................... 4 2.2 X-Ray Fluorescence Sorting Technology ............................................................... 8 2.2.1 X-Ray Fluorescence (XRF) Technology and XRF Analyzer ............................ 8 2.2.1.1 XRF basic theory ..................................................................................... 8 2.2.1.2 XRF analyzer ........................................................................................... 9 2.2.2 The XRF Sorter and Its Application ............................................................... 10 2.3 X-Ray Transmission Sorting Technology ............................................................. 14 2.3.1 X-Ray Transmission Theory ......................................................................... 14 2.3.2 DE-XRT Sorting Principles ............................................................................ 14 2.3.3 DE-XRT Sorter ............................................................................................. 17 2.3.4 Dual-Energy X-Ray Transmission Sorting Applications ................................. 19 2.3.5 Summary ...................................................................................................... 20 2.4 Optical Sorting Technology .................................................................................. 21  iii  2.4.1 Optical Sorting .............................................................................................. 21 2.4.2 The Color Ore Sorter and Its Applications in the Metal Mining Industry ......... 22 2.4.2.1 Color ore sorter ...................................................................................... 22 2.4.2.2 Applications of color ore sorting in the metal mining industry ................. 24 2.4.3 Summary ...................................................................................................... 25 2.5 Microwave-Infrared Sorting Technology .............................................................. 26 2.5.1 Microwaves and Heating Rates of Minerals .................................................. 26 2.5.2 Microwave Heating and Its Potential for Sorting ............................................ 27 2.6 Summary ............................................................................................................. 30 CHAPTER 3  SORTING TEST PROGRAM ............................................................... 31  3.1 Sample Preparation ............................................................................................. 31 3.2 X-Ray Fluorescence Sorting Test ........................................................................ 32 3.2.1 Materials ....................................................................................................... 32 3.2.2 Equipment .................................................................................................... 32 3.2.3 Experimental Procedures .............................................................................. 33 3.2.4 Results and Discussions ............................................................................... 33 3.2.4.1 Sortability of this ore using the Innov.X XRF analyzer ............................ 33 3.2.4.2 Grade-Recovery relationship.................................................................. 36 3.2.4.3 Summary of XRF sorting results ............................................................ 39 3.3 X-Ray Transmission Sorting Test ........................................................................ 41 3.3.1. Equipment ................................................................................................... 41 3.3.2. Experimental Procedures ............................................................................. 42 3.3.2.1 Sample preparation ................................................................................ 42 3.3.2.2 Image capture ........................................................................................ 42 3.3.2.3 Sample assay ........................................................................................ 42 3.3.2.4 Image processing and data extraction .................................................... 42 3.3.2.5 DE-XRT Sorting criterion determination ................................................. 45  iv  3.3.3 Results and Discussion................................................................................. 46 3.3.3.1 Simplified image analysis by GIMP ........................................................ 46 3.3.3.2 Image analysis by PACT software ......................................................... 49 3.3.3.3 Comparison of sorting results by two image analyzing methods ............ 51 3.4 Optical Sorting Test ............................................................................................. 53 3.4.1 Ore Characterization ..................................................................................... 53 3.4.2 Sample Preparation ...................................................................................... 53 3.4.3 Equipment .................................................................................................... 54 3.4.3.1 Hardware ............................................................................................... 54 3.4.3.2 Software ................................................................................................ 54 3.4.4 Color Sorting Potential Study ........................................................................ 55 3.4.5 Image Capture .............................................................................................. 56 3.4.6 Data Analysis ................................................................................................ 57 3.4.6.1 Generation of characteristic color data for waste rock and mineralized ore .......................................................................................................................... 57 3.4.6.2 Rejecting criteria determination .............................................................. 61 3.4.7 Sorting Results ............................................................................................. 66 3.4.8 Laboratory-Scale Color Ore Sorting Amenability Study Flowsheet Design .... 68 3.5 Microwave-Infrared Sorting Test .......................................................................... 69 3.5.1 Experimental Procedures .............................................................................. 69 3.5.1.1 Equipment.............................................................................................. 69 3.5.1.2 Testing procedures ................................................................................ 69 3.5.2 Results and Discussion................................................................................. 73 3.5.2.1 Factors influencing microwave heating of lead-zinc sulfide ore .............. 73 3.5.2.2 Distinguishability of waste rock from lead-zinc sulfide ore ...................... 82 3.5.2.3 Sorting results summary ........................................................................ 87 3.6 Ore Sorting Test Summary .................................................................................. 90  v  CHAPTER 4  SORTING IMPACT EVALUATION ...................................................... 93  4.1 Experimental Procedures .................................................................................... 94 4.1.1 X-Ray Diffraction Analysis ............................................................................ 94 4.1.2 Grinding Energy Consumption Test .............................................................. 94 4.1.3 Flotation Test ................................................................................................ 95 4.2 Results and Discussion ....................................................................................... 97 4.2.1 X-Ray Diffraction Analysis ............................................................................ 97 4.2.2 Grinding Energy Savings .............................................................................. 98 4.2.3 Flotation Test Results ................................................................................... 98 CHAPTER 5  CONCLUSIONS AND RECOMMENDATIONS .................................. 101  5.1 Conclusions ....................................................................................................... 101 5.1.1 Conclusions from the XRF Sorting Test ...................................................... 101 5.1.2 Conclusions from the XRT Sorting Test ...................................................... 102 5.1.3 Conclusions from the Optical Sorting Test .................................................. 103 5.1.4 Conclusions from the MW/IR Sorting Test .................................................. 104 5.1.5 Conclusions from the Impact Evaluation Test ............................................. 104 5.2 Recommendations............................................................................................. 105 5.2.1 Recommendations on Sensor-Based Ore Sorting of This Lead-Zinc Ore.... 105 5.2.2 Recommendations on Development of Laboratory-Scale Sensor-Based Ore Sorting Evaluation ............................................................................................... 105 REFERENCES ............................................................................................................ 107 APPENDICES ............................................................................................................. 113 APPENDIX A  X-RAY FLUORESCENCE SORTING DATA ................................... 113  APPENDIX B  X-RAY TRANSMISSION SORTING DAT ........................................ 132  APPENDIX C  OPTICAL SORTING DATA ............................................................. 140  APPENDIX D  MICROWAVE-INFRARED SORTING DATA ................................... 146  APPENDIX E  IMPACT EVALUATION DATA......................................................... 207  vi  List of Tables Table 2-1 Sensing Technologies Currently Available for Automatic Ore Sorters .............. 7 Table 2-2 Heating Rates of Common Sulphide Minerals and Gangue Minerals ............. 26 Table 2-3 Comparison of Different Sorting Technologies .............................................. 30 Table 3-1 Head Sample Size Analysis and Assays ....................................................... 31 Table 3-2 Grade-Recovery Relationship Test Results ................................................... 37 Table 3-3 Overall Sorting Results of 325 Rocks (Top Size: Bottom Size = 3:1) Sized Above 26.5 mm ............................................................................................................. 40 Table 3-4 Summary of XRF Sorting Results of Four Size Fractions .............................. 40 Table 3-5 XRT Sorting Results of -37.5+26.5 mm Lead-Zinc Ore Based on Average Brightness Value of the High Energy X-Ray Transmission Image ................................. 48 Table 3-6 XRT Sorting Results of -37.5+26.5 mm Lead-Zinc Ore Based on Average Brightness Value of the Low Energy X-Ray Transmission Image .................................. 49 Table 3-7 XRT Sorting Results of -37.5+26.5 mm Lead-Zinc Ore Based on Ore Indices ...................................................................................................................................... 51 Table 3-8 XRT Sorting Results Summary of Different Sorting Criteria ........................... 51 Table 3-9 Results of Quantitative Phase Analysis of Head Sample by XRD (Wt.%) ...... 53 Table 3-10 XRF Powder Analysis of Waste Rocks and Mineralized Ore ....................... 56 Table 3-11 Average Red, Green and Blue Value for the 10 Matched Patterns for Rock #26 ................................................................................................................................ 59 Table 3-12 Summary of Red, Green and Blue Value for Matched Patterns ................... 60 Table 3-13 Color Sorting Results Summary .................................................................. 66 Table 3-14 MW/IR Segregation (Individual 10s 50 Rocks) Results of -26.5+19 mm size fraction Based on Different Separation Average Surface Temperatures ........................ 86 Table 3-15 Summary of MW/IR Sorting Results of -26.5+19 mm Sample...................... 87 Table 3-16 MW/IR Sorting Results Summary ................................................................ 89 Table 3-17 Ore Sorting Results Summary ..................................................................... 92 Table 4-1 Bond Ball Charge .......................................................................................... 94 Table 4-2 Results of Quantitative Phase Analysis (Wt. %)............................................. 97 Table 4-3 P80, F80 and Work Indices for XRF Feed, XRF Concentrates and XRF Waste98 Table 4-4 Flotation Results Based on Calibrated XRF Powder Readings .................... 100  vii  List of Figures Figure 2-1 Two Sensor Set-Up Methods ......................................................................... 5 Figure 2-2 Two Types of Sorting Modes .......................................................................... 6 Figure 2-3 Typical XRF Energy Spectrum of a Soil Sample ............................................. 9 Figure 2-4 Typical XRF Analyzer Basic Components ...................................................... 9 Figure 2-5 Typical Layout of an XRF Ore Sorter ............................................................ 12 Figure 2-6 The Full-Scale RADOS XRF Ore Sorter Installed at Mintek.......................... 13 Figure 2-7 Transmission Curves of Mixtures with Different PbS Grades at 83kV/50kV .. 15 Figure 2-8 The Calibration Curve for Sorting ................................................................. 16 Figure 2-9 Transmission Graph for a Rock Sample ....................................................... 17 Figure 2-10 Layout of an X-Ray Transmission Sorter .................................................... 18 Figure 2-11 Principle of Single Energy and Dual-Energy X-Ray Transmission Sensing 19 Figure 2-12 Schematic Layout of a Color Ore Sorter ..................................................... 23 Figure 2-13 Schematic Diagram of MW/IR Sorting of Sulfide Ores ................................ 27 Figure 3-1 XRF Analysis Station at UBC ....................................................................... 32 Figure 3-2 Correlations between XRF Analyzer Surface Reading and Bulk Assays ...... 34 Figure 3-3 Correlations between Pb, Zn and Fe ............................................................ 35 Figure 3-4 Flowsheet of Grade-Recovery Relationship Test Procedures....................... 36 Figure 3-5 Grade-Recovery Relationship Curves for Pb and Zn .................................... 38 Figure 3-6 Relationship between Metal Recovery and Percentage Mass Rejected as Waste Based on Different Separation Threshold Values ............................................... 39 Figure 3-7 Pilot-Scale Dual-Energy XRT Scanner at CommoDas.................................. 41 Figure 3-8 Example of a DE-XRT Image Generated by the X-Ray Scanner .................. 42 Figure 3-9 Process of Value Image Generation ............................................................. 43 Figure 3-10 Screenshot of Brightness Value Extraction from the Image ........................ 44 Figure 3-11 Image Processing Flowsheet of PACT Software ........................................ 45 Figure 3-12 Separation Curve for Lead-Zinc Ore Sample Sized -37.5+26.5 mm Based on Average Brightness Values of High/Low Energy Images ............................................... 46 Figure 3-13 XRT Segregation Sortability Curve of -37.5+26.5 mm Lead-Zinc Ore Based on Average Brightness Values of High/Low Energy Images .......................................... 47 Figure 3-14 XRT Segregation Sortability Curve of -37.5+26.5 mm Lead-Zinc Ore Based on Ore Indices ............................................................................................................... 50  viii  Figure 3-15 Grade-Recovery Curves of XRT Sorting Based on Different Sorting Criteria ...................................................................................................................................... 52 Figure 3-16 Optical Bench-Top Image Acquisition System at CommoDas .................... 54 Figure 3-17 Images of Identified Waste Rock and Mineralized Ore ............................... 55 Figure 3-18 Schematic Set-Up of the Camera ............................................................... 57 Figure 3-19 Red Spectrum of Selected ROI Pattern for Rock #26-Waste ...................... 58 Figure 3-20 Patterns Matching of Rock #26 .................................................................. 59 Figure 3-21 Value Image Selection Methods ................................................................. 61 Figure 3-22 Sample of Threshold Images for Several Rocks ......................................... 62 Figure 3-23 Histograms of Red Value for the Value Image Before Threshold ................ 63 Figure 3-24 Histograms of Red Value for the Value Image after Threshold ................... 64 Figure 3-25 Correlation between Pb+Zn Grade and Avg. R133-255 .................................. 65 Figure 3-26 Color Sorting Grade-Recovery Curve for -37.5+26.5 mm Sample .............. 67 Figure 3-27 Flowsheet for Color Ore Sorting Amenability Study in Laboratory-Scale .... 68 Figure 3-28 BP-110 Lab Use Microwave Oven and FLIR T400 IR Camera ................... 69 Figure 3-29 Rock Position for Individual Testing ............................................................ 70 Figure 3-30 IR (Thermographic) Images of Two Sides of Rocks from -19+13.2 mm Size Fraction After 10s Microwave Heating ........................................................................... 71 Figure 3-31 Rock Positions for Group Testing ............................................................... 72 Figure 3-32 IR (Thermographic) Images of Two Sides of Rocks Tested in Groups of 4, 9 and 25 after 10s Microwave Heating ............................................................................. 72 Figure 3-33 Average Surface Temperatures of Rocks after 5s, 10s, and 15s Microwave Heating from -19+13.2 mm, -26.5+19 mm and -37.5+26.5 mm Size Fractions .............. 74 Figure 3-34 Average Surface Temperatures after Being Heated for 10s by Individual and Group Heating for Four Size Fractions .......................................................................... 77 Figure 3-35 Relationship between Microwave Heating Behaviour of Lead-Zinc Ore and Sample Weight .............................................................................................................. 78 Figure 3-36 Relationship between Microwave Heating Behaviour of Lead-Zinc Ore and Rock Weight: 5s, 10s and 15s ....................................................................................... 79 Figure 3-37 Relationship between Microwave Heating Behaviour of Lead-Zinc Ore and Rock Weight Heated Individually and Heated Together In a Group of Nine ................... 80 Figure 3-38 Average Surface Temperatures of Rocks from Four Size Fractions after Being Heated for 10s Individually and Together in a Group of Nine............................... 81 Figure 3-39 Average Surface Temperature vs. S /Pb+Zn Grades ................................. 83  ix  Figure 3-40 Relationship between Average Surface Temperature and S/Pb+Zn Grades after 5s, 10s and 15s Microwave Heating ...................................................................... 84 Figure 3-41 Flowsheet for Laboratory-scale Sensor-based Ore Sorting Study .............. 91 Figure 4-1 Standard Laboratory Bond Test Ball Mill ...................................................... 95  x  List of Abbreviations Avg.  Average  BWI  Bond Work Index  Conc.  Concentrate  Cum.  Cumulative  Distr.  Distribution  MLA  Mineral Liberation Analysis  MW/IR  Microwave-Infrared  POM  Pend Oreille Mine  Tph  Tonnes per hour  Wt.  Weight  XRD  X-Ray Diffraction  XRF  X-Ray Fluorescence  XRFC  X-Ray Fluorescence Concentrate  XRT  X-Ray Transmission  xi  Acknowledgements I would like to thank my supervisor Dr. Bern Klein for his support and guidance during this research. I also would like to thank Dr. Andrew Bamber, Dr.Maria Holuszko and Dr. Marek Pawlik for their help and instructions during the course of this study. Also I thank Dr. Henry Salomon-De-Friedberg and TECK CESL for their support and sponsorship as well as Pend Oreille Mine for providing the sample for this research. I thank Dr. Gus van Weert for the kind guidance he has given to me on MicrowaveInfrared Sorting and Olivia Wang for her help with the laboratory testing work. I want to thank PROrtech Ltd. as well for their support on the test work. I also would like to express my thanks to Matthew Kowalczyk for his help and useful suggestions during this research and CommoDas Ultrasort for providing me the laboratory-scale equipment and systems for this ore sorting study. I would like to thank Cindy Collins and Jeff Mabbutt for their support and help with XRF analyzing and related information. To my partners Amit Kumar, Avinash Tripathi, Chengtie Wang, Jeff Drozdiak, Stefan Nadolski and Zorig Davaanyam thanks a lot for all the help and assistance along the way through this research and the research tips we shared. Lastly, I would like to thank my husband Jia Wu and all my friends for their encouragement and support through my entire master’s program.  xii  CHAPTER 1  1.1  INTRODUCTION  Automatic Sensor-Based Ore Sorting  Automatic sensor-based sorting, together with hand sorting, comminution and size classification, dense media separation and coarse flotation, is the technology commonly used for preconcentration of ores. The automatic sensor-based sorting technique is a dry process needing no or very little water and energy and therefore shows its potential for energy saving and environmental improvement (Kleine, Wotruba, Robben, von Ketelhodt, & Kowalczyk, 2011). The automatic sensor-based sorting technique has developed rapidly in the recycling, food and industrial mineral industries during the last six decades. However, not until recent decades has it drawn the attention of the mining industry with the development of new sensors, improvement of data processing speed, and increase of the throughput of sorting machines so that it can meet the requirements of mining operations (Fitzpatrick, 2008). Sensors successfully applied to ore sorting include photometric, color (CCD), conductive/magnetic, thermal conductive (Microwave Infrared), radiometric, X-ray fluorescence and X-ray transmission. Ores preconcentrated by automatic sensor-based sorters are mainly uranium, nickel, copper, iron and gold (Kowalczyk & Bartram, 2008). One or a combination of sensors can be used to discriminate high-grade ore from lowgrade waste based on the difference of measured physical properties. A multi-sensor sorter, which is capable of measuring more than one property of the materials, is more suitable for novel mining applications. In a pilot-scale study by Fitzpatrick (2008), a general methodology for determination of an ore’s suitability for automatic sorting was developed using a multi-sensor automatic sorter (with both inductive and optical sensors) from CommoDas. This methodology was validated in that study by sorting of a nickel/copper sulfide ore and an iron ore (Fitzpatrick, 2008). The efficiency of an automatic sensor-based sorter was studied from aspects of the sorter’s identification and separation functions using a TiTech Combisense© (BSM 063) automatic sorter. Udoudo (2010) found that the separation efficiency decreased when particle size decreased and throughput increased. Poor separation efficiency was also due to the co-deflection losses (particles meant to go into the “accept” fraction were co-  1  deflected (deflected together) with the ones meant to go to the “reject” fraction). A separation efficiency model was also established using belt loading (a function of particle size, shape and throughput) and percentage deflected as variables. Using this model, the separation efficiency of the automatic sorter of a specific ore can be calculated (Udoudo, 2010). Prior to the evaluation of the amenability of an ore to automatic sensor-based sorting and the optimization of the efficiency of the selected sorter in pilot-scale, a preliminary study on the technical amenability of an ore sample to sensor-based sorting should be performed in the laboratory-scale to assess the potential for such application. This research set out to develop a methodology for laboratory-scale quick evaluation of the amenability of an ore sample to automatic sensor-based sorting using bench-top sensor systems. Also, the application of automatic sorting to a lead-zinc ore was investigated to add to the database of successful applications of automatic sorters in the mining industry. 1.2 Objectives The objectives of this research include:   to evaluate the amenabilities of this lead-zinc ore to sensor-based ore sorting using X-ray Fluorescence, X-ray Transmission, Optical and Microwave-Infrared sensing systems in the laboratory- scale; and    to assess the effect of sorting on grinding energy saving and flotation performance improvement.  1.3 Mine and Mineralogy The ore sample used for this research was from Pend Oreille Mine, one of the mines owned by Teck Resources Ltd. The Pend Oreille Mine is located in northeast Washington State, approximately two miles north of Metaline Falls, Washington and 95 miles north of Spokane, Washington. Pend Oreille Mine is a Mississippi Valley type metaline replacement high iron content zinc-lead formation (Huntting, 1966). It is an underground zinc-lead mine with a daily production rate of 2200 tons. The processing plant has a capacity of 80,000 tonnes annually with zinc concentrate grading at 60% (Canadian Mining Journal Website, 2004). In 2009, the mine was shut down due to the decreasing metal prices. Primary ore minerals in the Pend Oreille deposit are galena (PbS) and sphalerite (ZnS). Accessory minerals include pyrite, marcasite, pyrrhotite, calcite, palygorskite, fluorite and chalcopyrite. The host rock is mainly coarse dolomite.  2  1.4 Methodology This thesis is divided into five chapters and five appendices. Chapter 1 introduces the background and objectives of the research, mines where the materials are provided from, mineralogy of the ore and the methodology used. Chapter 2 reviews the literature on automatic sensor-based sorting. Four sensor-based sorting technologies, namely X-ray Fluorescence Sorting, X-ray Transmission Sorting, Optical Sorting and MicrowaveInfrared Sorting, are reviewed in detail in this chapter. Chapter 3 describes the sorting test of the Pend Oreille lead-zinc ore sample. The sorting test program used four sensorbased sorting technologies. The chapter summarizes the sorting results of each technology. Chapter 4 describes the impact of sorting on energy saving and flotation metallurgical performance by conducting a bond ball mill work index test and a flotation test. Chapter 5 presents the conclusions derived from this research and the recommendations for further study.  3  CHAPTER 2  LITERATURE REVIEW  This chapter reviews the literature on automatic sensor-based sorting technologies and their applications in the metal industry. A general introduction to the development and status of sensor-based sorting is provided, followed by detailed reviews of X-ray Fluorescence Sorting, X-Ray Transmission Sorting, Optical Sorting and MicrowaveInfrared Sorting technologies and their applications in the base metal industry. At the end of the chapter, a summary of the characteristics of these four sorting technologies is presented. 2.1 Automatic Sensor-Based Sorting Automatic sensor-based sorting is a method utilizing different sensing technologies to examine the physical properties of each particle and separate these particles into product and waste fractions based on the pre-defined sorting criteria. The reliability and efficiency of automatic sensor-based sorting is highly dependent on the discrimination of physical properties of each particle, such as conductivity, thermal conductivity, magnetic susceptibility, atomic density, reflectance, texture, etc. The particles are separated using an external force generated by air valves or mechanical arms. Automatic sensor-based sorting is capable of sorting materials sized from 2mm to 250mm depending on which physical property is detected (De Jong, 2012). Ore sorting using automatic sensor-based sorters can be used to preconcentrate the feed ore prior to the beneficiation plant, therefore providing a high grade feed and reducing the costs of grinding and reagents due to the reduction of feed mass. It also helps reduce the fine tailings generated and improves the metallurgical performance of mineral processing. The drawbacks of automatic ore sorting are mainly the preparation needed, such as feeding, sieving and surface cleaning, the large amount of capital, operating and maintenance costs, and the loss of valuable elements to the waste after sorting (Manouchehri, 2003). There are two ways of sensor set-up: on-belt and free-fall employed by the ore sorter (Figure 2-1). The chute-type sorter utilizes the free-fall sensor set-up method while the belt-type sorter employs the on-belt sensor set-up method. The sorter consists of five components: feed preparation system, feed presentation system, sensing system, data  4  processing and control system, and separation system. Wotruba and Harbeck (2010) provide a detailed description of these systems, which will be discussed in later sections on ore sorters using different sensing technologies.  Figure 2-1 Two Sensor Set-Up Methods (De Jong, Van Houwelingen, & Kuilman, 2004) The ore sorter can be operated in particle-by-particle mode or recovery mode (Figure 22). In particle-by-particle mode, materials are present to sensors in a single layer and do not touch each other, for individual detection. When it comes to recovery mode, a layer of materials is transported to the sensing area due to the higher rate of feeding. In this mode, valuable materials together with diluted surrounding materials are ejected together to the product fraction to maximize the recovery. As a result, the particle-byparticle mode can provide a high grade and high recovery product in one run while the recovery mode can produce a diluted high recovery product (Kuilman, 2006). The particle-by-particle mode is used for this research since the sensing systems used in the laboratory-scale ore sorting amenability study are only suitable for individual detection.  5  Figure 2-2 Two Types of Sorting Modes (Kuilman, 2006) Various sensing technologies are currently available for sensor-based ore sorters, some of which are still under development. They are shown in Table 2-1. The present study is concentrated on the most common sorting technique, Optical Sorting, the well-developed X-ray Sorting method, both fluorescence and transmission, and the Microwave-Infrared Sorting technology which is still under development.  6  Table 2-1 Sensing Technologies Currently Available for Automatic Ore Sorters (Kleine, Wotruba, Robben, von Ketelhodt, & Kowalczyk, 2011; Van Weert, Kondos, & Gluck, 2009) Sensing Technologies  Physical Properties Detected  Applications  Radiometric  Natural Gamma Radiation  X-Ray Transmission (XRT)  Atomic Density  X-Ray Fluorescence (XRF)  Visible Fluorescence under XRays  Diamonds  X-Ray Fluorescence Spectroscopy (XRF-S)*  Elemental Composition  Base/Precious Metals  Near Infra-Red (NIR)  Reflection, Absorption  Industry Minerals, Base Metals  Color (CCD)  Colour, Reflection, Brightness, Transparency  Base/Precious Metals, Industrial Minerals, Gem Stones  Photometric (PM)  Monochromatic Reflection, Absorption and Transmission  Industry Minerals, Diamond  Electromagnetic (EM)  Conductivity/Magnetic Susceptibility  Base Metals  Microwave Absorption, Heat Conductivity  Base Metals, Carbonaceous Materials  Uranium, Precious Metals Base/Precious Metals, Coal, Diamonds, etc.  UNDER DEVELOPMENT Microwave-Infrared (MW/IR) Laser-Induced Fluorescence (LIF) Laser-Induced Breakdown Spectrometry (LIBS) * The XRF technology discussed in this research is XRF-S, expressed as XRF in short. Factors influencing the efficiency of the ore sorter include the size, density and liberation of the particle. The smaller the particle size, the better the liberation of the valuable minerals and therefore the better the sorting results are. On the other hand, sorting cost per ton of ore is a function of particle size and density. The larger and heavier the ore, the larger the throughput and therefore the cheaper the cost per ton of ore is (De Jong, 2012).  7  2.2 X-Ray Fluorescence Sorting Technology 2.2.1 X-Ray Fluorescence (XRF) Technology and XRF Analyzer It has been known for a long time that the X-ray Fluorescence (XRF) analysis technique can be applied in the mining industry to facilitate exploration quality control and mineral processing control. It is a simple and efficient method of qualitative and quantitative element analysis, with lower cost and higher speed compared to conventional chemical assays (Wood, 1959). XRF analyzers are available from Olympus (Innov.X), Thermo Scientific (Niton) and other manufacturers. Each of these analyzers has unique advantages but they all work in a similar way. They can quickly and non-destructively determine the element composition of materials with high accuracy and long-term precision. 2.2.1.1 XRF basic theory Elements in the sample can be analyzed simultaneously by measuring the emission of characteristic X-rays. An X-ray with sufficient energy bombards the atoms of the sample. X-rays collide with the electrons in the K and L shell resulting in the ejection of electrons from their atomic orbits. Electrons from the higher energy shell then fill the vacancies left and emit characteristic X-rays. The energy of characteristic X-rays is equal to the energy difference between the two shells of the elements. Thus, the emitted X-rays are identical when electrons are dislodged from atoms of the same element. In summary, the energy level of each emitted characteristic X-ray (fluorescence) represents the element being excited; the intensity of the fluorescent X-ray emitted represents the element concentration presence in the sample (Murphy, Maharaj, Lachapelle, & Yuen, 2010). Any element with an atomic number from 22 to 92 when present in sufficient concentration can be analyzed using the XRF analysis method without any special device. However, elements with atomic numbers 12 to 22 can also be analyzed by the XRF method with the help of special analyzing crystals and helium protection against air absorption (Wood, 1959). A typical XRF Energy Spectrum (Intensity versus Energy) is illustrated in Figure 2-3.The energy level of the peak represents the element presence while the height of the peak represents the concentration of the element detected in the sample.  8  Figure 2-3 Typical XRF Energy Spectrum of a Soil Sample (Murphy, Maharaj, Lachapelle, & Yuen, 2010) 2.2.1.2 XRF analyzer The basic components of a XRF analyzer are the X-ray tube, detector, multi-channel analyzer and computer (Figure 2-4).  Figure 2-4 Typical XRF Analyzer Basic Components (Murphy, Maharaj, Lachapelle, & Yuen, 2010) The X-ray tube is the source of the primary X-ray for element excitation. The detector detects fluorescent X-rays emitted from the sample and converts them to voltage pulses. The multi-channel analyzer then generates an energy spectrum of the characteristic Xrays by matching the voltage pulses with energy values and counting how many times each energy value occurs. The energy spectrum is expressed as intensity (counts per second) versus photon energy (keV). After that, the computer modifies the data obtained from the energy spectrum using a few factors and calculates the chemical composition of the sample based on the modified energy spectrum (Murphy, Maharaj, Lachapelle, & Yuen, 2010).  9  2.2.2 The XRF Sorter and Its Application Not only can XRF technique be used as a tool for chemical composition analysis, but also it provides a sensing method for automatic ore sorting and preconcentration. Fullscale XRF ore sorters are available from RADOS and CommoDas Ultrasort. Few studies can be found related to the application of XRF ore sorting. Fickling (2011) reviewed the design and operation of an XRF ore sorter from RADOS and demonstrated some preliminary study results on using it in the upgrading of a manganese ore. He indicated that the amenability of a specific ore to XRF sorting depended on the existence of particles with varied grades due to well liberation. He also developed a testwork approach on how to evaluate the amenability of a specific ore to XRF sorting, which provided some guidelines for developing our research methodologies. The three stages involved in his testwork approach are:   to determine the reliability of the XRF sorter in identifying particles with different grades by comparing the bulk assay and surface XRF analysis of each particle;    to determine a Grade-Recovery relationship graph characteristic to the specific ore; and    to determine the efficiency of the sorter. (Fickling, 2011)  The XRF ore sorter utilizes the same technology as the XRF analyzer. It analyzes and sorts rocks based on their chemical compositions by simultaneously measuring the concentration of elements of interest presented on the surface of the rock. A pre-set threshold is used to determine whether a rock reports to product or waste fractions (Fickling, 2011). The XRF ore sorter from RADOS has a capacity of 10-30t/h per module depending on the particle size and ore density. Each module can process particles with a size range of 20-250 mm, up to 8 particles per channel per second. The energy requirement for this ore sorter is from 0.2 to 0.4 kilowatt hour per ton (<6kW). Its detection limits vary from 0.05% to 0.1% for most metals and increase for lighter elements (Fickling, 2011). XRF sorters are also available from CommoDas Ultrasort with capacity up to 70 t/h. Figure 2-5 illustrates the typical layout of a XRF ore sorter. Figure 2-6 shows the full-scale RADOS XRF ore sorter installed at Mintek in Randburg. Materials are fed to a vibrating feeder from the feed hopper after removal of fines and misplaced particles by a static grizzly. Then the particles are transported to several parallel channels or chutes where the particles are prepared for X-ray detection when  10  they are falling off. During the free-fall of the particles, X-rays strike the surface of the particle resulting in the emission of characteristic fluorescent X-rays from the rock. The detector then analyzes the characteristic X-rays emitted and their concentration and then calculates the element composition present on the surface of the rock. After comparing the metal concentration of a specific particle with the pre-set sorting threshold value, the control unit will give orders to the mechanical ejector and eject the particle to either the product chute or the waste chute (Fickling, 2011).  11  Figure 2-5 Typical Layout of an XRF Ore Sorter (After Fickling, 2011)  12  Figure 2-6 The Full-Scale RADOS XRF Ore Sorter Installed at Mintek (Fickling, 2011) In summary, XRF sorting technology provides a green and dry sorting method for preconcentration of ores. A low level of energy is required (Fickling, 2011). However, although it is said that the XRF sorter has successful applications in sorting base metal ores, precious metal ores, ferrous metal ores and industrial minerals, barely any literature demonstrates any actual sorting results of specific ores, especially lead and zinc. More preliminary laboratory-scale and pilot-scale ore sorting tests are needed to prove the reliability of this sorting technique by providing some actual sorting results for specific ores. As a result, it is the goal of this research to evaluate the technical amenability of a lead-zinc ore to XRF sorting in the laboratory-scale using an Innov.X XRF analyzer.  13  2.3 X-Ray Transmission Sorting Technology Sorting technologies have developed rapidly in the past decade with the development of computing technology and improvement of the sensor resolution. One of these exciting technologies is Dual-Energy X-Ray Transmission Sorting (DE-XRT) which first found successful applications in coal separation and upgrading. The concept of image based DE-XRT Sorting was initially inspired by the X-ray detection unit used for airport security inspection. The dual-energy X-ray system is capable of identifying and evaluating materials based on the atomic number. It is a completely dry process and independent of dust, moisture, surface contamination, shape and size. 2.3.1 X-Ray Transmission Theory Materials will absorb part of the radiation when exposed to X-rays. The reduction of the initial X-ray intensity is defined as transmission damping. The amount of X-ray transmitted through materials depends on their densities: the heavier the material, the greater the absorption of the X-rays and the smaller the amount of transmission. Transmission damping is a function of density and thickness of material according to Lamber’s Law. The Lamber’s Law shows that the detected intensity of the X-ray transmitted is proportional to the exponential function of the material density and thickness. Materials with different thicknesses but the same density will transmit X-rays at different degrees. As a result, using single energy X-ray (monochromatic imaging) technique to recognize rocks with different thicknesses will be inaccurate (Strydom, 2010). De Jong and Harbeck applied Lambert’s Law to dual energy absorption and therefore eliminated the effect of thickness by using two energy levels of X-ray beam of different wavelengths (dual-energy X-ray transmission imaging)(De Jong & Harbeck, 2005). Detailed physics of X-ray transmission are referred to Strydom’s thesis, The application of dual energy X-ray transmission sorting to the separation of coal form torbanite. 2.3.2 DE-XRT Sorting Principles The concept of sorting by dual-energy X-ray transmission imaging was first established by Delft University of Technology in 2000. The dual-energy X-ray transmission imaging system could generate dual-energy images illustrating the shape of the object and the distribution of atomic number and thickness within the object image. A transmission curve characteristic to the specific material type could be generated by plotting the degree of transmission of X-rays of two energy levels (De Jong & Harbeck, 2005). For  14  mining applications, a transmission curve of each ore type with different grades could be obtained separately. The transmission curves for a PbS/SiO2 mixture of different PbS contents are shown in Figure 2-7 as an example.  Figure 2-7 Transmission Curves of Mixtures with Different PbS Grades at 83kV/50kV (De Jong & Harbeck, 2005) The above figure indicates that rocks with different grades can be separated according to their transmission curves if their curves do not coincide. Different software packages employ different methods of analysis of the transmission curve. The Mikrosort® simulation package used by Mikrosort DE-XRT sorter generates a calibration curve as sorting criterion. This curve is plotted using the linear regression of the plotted points from the transmission graph of the typical samples of the material. The curve indicates the average density distribution of the material within the size range for sorting (Figure 28). Individual pixels of the high and low energy X-ray images of each particle of the material will be evaluated and plotted on the calibration curve. The pixels are then assigned color according to their position on the curve. If a pixel is plotted in the high density zone it will be assigned as blue and otherwise it will be assigned red. A pre-set  15  percentage of pixels in the high density zone (blue) is then used as a sorting criterion. This criterion will be used for deciding whether the rock is to be rejected as waste or accepted as product (Strydom, 2010).  Figure 2-8 The Calibration Curve for Sorting (Strydom, 2010) The PACT Sorting Control System used by the CommoDas Ultrasort DE-XRT sorter generates an Ore Index for each particle and uses this Ore Index as the sorting criterion. For each particle, a 2D transmission histogram will be created from the low and high energy images showing the distribution of high/low energy brightness combinations of each pixel within the image. The 2D transmission histograms for each rock are then mapped against a weighted reference histogram, called an Ore Index Reference Map. This Ore Index Reference Map is created from the set of histograms and assay results for the sample. The ore index of each particle can be calculated by mapping the 2D histogram of the particle against the reference histogram. The larger the ore index, the more likely the particle reports to the product. Figure 2-9 shows an example of the 2D transmission histogram of a rock and its Ore Index Reference Mapping (Kowalczyk, 2012).  16  Figure 2-9 Transmission Graph for a Rock Sample (Kowalczyk, 2012) 2.3.3 DE-XRT Sorter The industrial DE-XRT sorter is composed of five major components just like other sorters. These five components are the feed system, feed presentation system, sensing system, data analysis system and separation system. The systematic layout of a DEXRT sorter is shown in Figure 2-10.  17  Figure 2-10 Layout of an X-Ray Transmission Sorter (CommoDas, 2004) 1. The materials are fed through the feed bin to the conveyor where they are transported to the sensing system. 2. The materials travel to the sensing area at a speed of 2-4 m/s. The belt width is normally 1-2 m. 3. The sensing system includes an X-ray tube and an X-ray line scanning sensor. X-rays generated from the X-ray tube strike the materials when they are travelling through the sensing area. The transmitted X-rays are then captured by the dualenergy X-ray line scanning sensor which is composed of an array of scintillation crystals. These crystals are used to capture the number of counts of each element in the array. Figure 2-11 shows the principle of the dual-energy X-ray transmission sensing system. An image of the particle is then generated line by line using a similar principle of color line scan camera (Strydom, 2010). 4. The scanning speed is normally 250-500 lines per second. The X-ray components inspection speed is between 0.5-2 m/s at about 1m inspection width. The sensor resolution is between 1.5×1.5 and 5×5 mm2 (De Jong, Dalmijn, & Kattubtudt, 2003).  18  5. High energy and low energy X-ray transmission images of each particle are generated and analyzed by the data processing system. 6. Depending on the analysis results, the data processing system will signal the separation system whether or not to eject the particle by the air valves.  Figure 2-11 Principle of Single Energy and Dual-Energy X-Ray Transmission Sensing (Von Ketelhodt & Bergmann, 2010) 2.3.4 Dual-Energy X-Ray Transmission Sorting Applications Von Ketelhode and Bergmann (2010) found that Dual Energy X-ray Transmission sorting was applicable to dry coarse coal separation in the size range of -120+12 mm with a capacity of 150-80 t/h. Their testwork of different coals was conducted at the XRT sorter pilot plant at Mintek in Randburg, Africa. Test results indicated that this technology was capable of segregating pyrite-bearing coal and shale to produce clean coal product and discriminating coal from torbanite, thereby producing two clean products (Von Ketelhodt & Bergmann, 2010). Kuilman (2006) also demonstrated the efficiency of the DE-XRT sorter in lowering the ash content of washed coal, preconcentration of Run-of-Mine coal and sorting of coal from waste dump. He indicated that the process should be optimized for sorting of ROM coal finer than 15mm. The sortability of washed coal was strongly dependent on the liberation of coal. DE-XRT Sorting was capable of sorting coal mine waste coarser than 5mm. However, the economy of coal sorting using this technology is strongly dependent  19  on the characteristics and origin of the material. The sortability of the coal could be only concluded on a case-by-case basis (Kuilman, 2006). The applications of XRT Sorting for base metal sulfides were first introduced by Harbeck (2004). Testwork using both single energy transmission and dual-energy transmission was conducted and dual-energy transmission indicated greater amenability (Harbeck, 2004). Other successful applications of DE-XRT Sorting include sorting of pentlandite nickel ore (Allen & Gordon, 2009), copper ores such as chalcopyrite and bornite, and pyrite bearing gold ores (Von Ketelhodt, 2009). It is important to know that the amenability of DE-XRT Sorting of any ore is strongly dependent on the degree of liberation of the ore. Conclusions could be specific to the case studied. 2.3.5 Summary DE-XRT sorting is a fast and reliable dry separation method with many successful applications in the base metal industry. It has several advantages, as follows:   suitability for high-speed identification, at several thousand particles per second (De Jong, Dalmijn, & Kattubtudt, 2003).    bulk volume inspection instead of surface (De Jong, Dalmijn, & Kattubtudt, 2003).    insensitivity to surface conditions, e.g., dust, moisture, and contaminants (De Jong, Dalmijn, & Kattubtudt, 2003).    on-line analysis of shape, texture, size distribution and composition (Kuilman, 2006).  The present preliminary laboratory DX-XRT testwork was conducted to evaluate the application potential of this technology to the preconcentration of a lead-zinc ore from dolomitic waste rocks. This work will extend the application of DE-XRT sorting technology to a new type of ore and also demonstrate the amenability of preconcentration of this lead-zinc ore using DE-XRT sorting technology.  20  2.4 Optical Sorting Technology 2.4.1 Optical Sorting Optical sorting is the oldest and most mature sorting technology in industry. It has been applied in the recycling, food and industrial mineral industries for a long time and many successful applications can be found. With the help of advanced computing technologies and enhancement of optical resolutions, the capacity of modern optical sorters has been much improved, up to 300t/h, and so has the reliability of this technology. It has only recently been introduced to the metal mining industry, however, and little evaluation of optical sorting of ores has been undertaken in the academic field. Optical sorting is a separation technique where materials are sorted based on discrimination in reflectance, transparency or color between particles by photo detector or digital cameras. Texture and shape recognition techniques are also available now and could be used in the process of optical sorting by facilitating the recognition of a specific material (Bamber, 2008). The present study is concentrated on the optical sorting under visible light. Two types of light source are commonly used for this application: continuous spectrum sources (e.g., tungsten bulbs) and discontinuous spectrum sources (e.g., fluorescent tubes and lasers). The detectors used for optical sorting under visible light are mainly photodiodes, photomultipliers (used with lasers) and Charged Couple Devices (CCDs). Optical sorting using photodiodes and photomultiplier detectors is called photometric sorting while that using a CCD detector (digital camera) is commonly called color sorting. Photodiodes and photomultipliers can only detect the intensity of light reflected from the material surface regardless of color while CCDs can analyze different colors. Two major color analyzing methods employing a Bayer filter mask and separate CCDs are used for CCD detector. The Bayer filter mask assigns red, green and blue to each pixel. This filter pattern includes two diagonally opposite green pixels, one red pixel and one blue pixel. The second analyzing method uses a RGB splitter prism to split light into red, green and blue components. Three CCDs are used to respond to each of the three colors (Fitzpatrick, 2008). There are two major methods to extract color information from the image: first-order statistical method using histogram parameters of RGB image and second-order statistical method, co-occurrence statistics from a gray-level image. The present study  21  does not aim to review different image processing technologies. For detailed information on image processing methods, readers are referred to Singh and Rao’s paper in 2005 (Singh & Rao, 2005) and Tessier, Duchesne and Bartolacci’s paper in 2007 (Tessier, Duchesne, & Bartolacci, 2007). The first-order statistical method using histogram parameters is used in this study to obtain color features. The RGB color model is commonly used for image analysis. An RGB image is composed of three channels of data—red, green and blue—recorded by three CCDs in the camera. Each individual pixel of the image has an intensity value for red, green and blue. This intensity value for each color channel is discrete and ranges from 0 to 255 for the 8-bit camera (Tessier, Duchesne, & Bartolacci, 2007). Sorters can analyze the data directly or convert these data to other color models such as YUV and HS, etc. These color models, unlike the RGB color model, evaluate an image using luminance and chrominance separately. As a result, after converting data from RGB color model to YUV model, sorters can separate materials based on pure color difference only. MikroSort and CommoDas color sorters both use the YUV color model for ore sorting (Fitzpatrick, 2008). 2.4.2 The Color Ore Sorter and Its Applications in the Metal Mining Industry 2.4.2.1 Color ore sorter Modern color sorters utilize line scan digital cameras together with complicated image processing and analyzing software to discriminate between colors of the materials. Color evaluation can be done within milliseconds (Fattori, 2009). High resolution images can be produced and used to identify small differences in brightness and color. This type of color sorter has many applications in the mining industry. The color ore sorter includes five components: feed preparation system, feed presentation system, sensing system, data processing system and separation system (Kidd, 1983). The schematic layout of a color ore sorter is shown in Figure 2-12.  22  Figure 2-12 Schematic Layout of a Color Ore Sorter (After Fitzpatrick, 2008; Keys, Met, Gordon, & Peverett, 1974) Preparation of feed is of great importance to achieving efficient sorting results, in two ways. First, rocks need to be present in a specific size range for machine sorting. Second, optical sorting is surface-dependent technology where a clean surface is needed for optical data generation. Therefore, rocks need to be washed to clean off fines prior to sensing. Moisture can also enhance the reflectance of the rock and therefore facilitates sorting based on color properties. Rocks need to travel through the sensing area in a monolayer and be separated from each other. In addition, individual particles should travel at the exact same speed prior to sensing in order to obtain images under constant conditions. Hence, a stable feed presentation system, normally consisting of a vibrating feeder and a conveying belt or a chute, is required for a color ore sorter. Color features of the rocks passing through the optical scanning area are recorded in the image captured by the line scan digital camera. This image is analyzed by the data processing system using sophisticated image processing software and then the decision is made whether the rock is ejected by air jets to the waste chute or continues its trajectory into the product chute. The separation system includes a series  23  of high-speed valves. When the processer sends a signal to the ejecting system that the rock should be ejected, the corresponding valve or valves will open for a certain time to generate a high-energy blast of air to blow the rock away from its trajectory across the splitter into one chute (Bulled, 1997). 2.4.2.2 Applications of color ore sorting in the metal mining industry Modern color ore sorters are available from CommoDas, MikroSort and Comex. They are capable of processing rocks sized from 2 to 300 mm with a capacity up to 300 t/h. Commercial applications of color sorting are mainly found for industrial minerals such as limestone, feldspar and talcum. Not many applications for base metal ore sorting are reviewed in the literature. Only two applications for gold and platinum ores are discussed. A containerized color sorter from CommoDas was installed at Kloof gold mine in South Africa in October 2003. This color ore sorter was used to sort gold reef from waste rock with a feed rate of about 80 tph. It could process rocks sized -80+16 mm. Five percent mass was rejected as waste after color sorting when concentrate grade was 5 g/t compared to 0.3 g/t in the feed (Von Ketelhodt, 2009). Another application of color ore sorting was found at Waterval platinum mine in South Africa. A solid CDX-120 chute color ore sorter was installed to upgrade chromite Run-ofMine ore based on color and brightness differences. The sensor used for this color ore sorter is 30-bit color line scan camera double-side detection. This color ore sorter has a throughput between 100 and 300 tph depending on the size, -300+50 mm. Approximately 20% to 65% mass could be rejected as waste after sorting (Von Ketelhodt, 2009). A preliminary study of color ore sorting was conducted using an industrial scale CommoDas Sorter installed at University of Exeter. This ore sorter employed multisensors, an optical sensor and an inductive sensor. The optical sensor used for this sorter is a Pricolor TVI 2048R. It is a 2048-bit line scan camera using CCDs. Sorting tests were done on an iron ore from the Marandoo deposit in Australia. The experimental results showed that the CommoDas sorter was able to upgrade this iron ore sample based on differences in optical properties. To be specific, a 2.8% increase of grade and a 3.88% decrease of acid oxides level were achieved by rejecting white siliceous waste. A color sorting test of a nickel/copper ore from Raglan mine in northern Canada was conducted for the same study. The experimental results indicated that this CommoDas  24  sorter could upgrade the nickel/copper ore sample based on color discriminations. Forty percent mass containing basalt and peridotite could be rejected as waste with 93% and 95% recoveries of copper and nickel respectively (Fitzpatrick, 2008). 2.4.3 Summary From the literature review, it can be concluded that optical sorting, and modern color sorting in particular, has demonstrated its potential in the applications of the metal mining industry through industrial applications for gold and platinum ores. Preliminary studies on iron and nickel/copper ores also indicated the potential application of color sorting technology. Color sorting is a proven technology with high throughput and reliability. However, optical sorting is a technique based on the detection of particle surface. It has no penetration power and is not capable of analyzing the bulk particle. In addition, adequate sample preparation and stable sample presentation are required for good sorting results.  25  2.5 Microwave-Infrared Sorting Technology 2.5.1 Microwaves and Heating Rates of Minerals Microwaves are one type of electromagnetic energy with an electric and magnetic field. Dielectrics that have dipoles can absorb the microwave radiation. Those dipoles will align and flip around under microwave radiation when the applied field is changing. As a result, such materials will be heated up when internal energy is stored because of friction (Kingman & Rowson, 1998). Assigned frequencies for microwave heating are 915 and 2450 MHz. Domestic microwave ovens are operating at 2450 MHz (Bradshaw, Van Wyk, & Swardt, 1998). Hua and Liu (1996) investigated heating rates of over 40 minerals and compounds. Their results showed that most sulphides minerals are good heaters which respond well to microwave radiation, while common gangue minerals such as quartz, calcite and feldspars are transparent to microwaves. This indicated that segregation of sulfide minerals from gangue based on temperature differences will be possible after microwave heating. Some heating rates of the minerals are summarized in the following Table 2-2. Table 2-2 Heating Rates of Common Sulphide Minerals and Gangue Minerals (Hua & Liu, 1996) Minerals  Chemical Composition  Heating Rate, ΔT: Δt /K·s-1  Chalcopyrite  CuFeS2  11.37  Ferrous sulphide  FeS  5.02  Pyrrhotite  Fe1-xS  16.43  Molybdenite  MoS2  5.08  Galena  PbS  5.93  Sphalerite  ZnS  0.89  Lead-zinc sulfide concentrate  PbS-ZnS  2.81  Quartz  SiO2  0.32  Calcium carbonate  CaCO3  1.33  26  2.5.2 Microwave Heating and Its Potential for Sorting The potential applications of selective microwave heating of minerals in mineral processing have been investigated in recent decades. It can assist the comminution process by weakening the ore through inducing differential expansion and increase mineral liberation by enhancing the intergranular fracture of the ore due to the different thermal behaviours of different mineral phases. Microwave heating also found applications in the refractory gold ore treatment process and leaching process to improve mineral separation. Readers are referred to Kobusheshe’s 2010 doctoral dissertation Microwave enhanced processing of ores for further information on this topic. The goal of this research is to explore infrared (IR) sensing of sulfide by means of noncontact IR imaging after exposure to microwave radiation. Microwave heating in this case functions as a pre-treatment process for machine sorting using IR sensors. Figure 2-13 illustrates such microwave infrared (MW/IR) sorting of sulfide ore.  Figure 2-13 Schematic Diagram of MW/IR Sorting of Sulfide Ores (After Labbert, Baloun, Schoenherr, & Kuyumcu, 2012)  27  MW/IR ore sorting, the segregation of sulphides from barren rock, is still in the development stage compared to other sorting technologies such as X-ray sorting. It is also one of the few sorting techniques enabling entire rock analysis instead of surface. MW/IR was first studied to separate rocks containing graphite or carbonaceous matter, which causes “pre-robbing” problems in the gold milling process. Van Weert and Kondos’s paper, which was given during the 39th Annual Canadian Mineral Processors Conference in 2007 in Ottawa, showed that sulphides and carbonaceous materials bearing rocks responded readily to microwave radiation, which allows for sorting by an infrared sensor (Van Weert & Kondos, 2007). Limited samples were studied using a domestic microwave oven with a turntable. Effects of exposure time, orientation and size of rocks on microwave heating were evaluated. The results showed that the state of rock, i.e., whether it was cleaned, dusty or wet, did not have an impact on separation. Rocks cannot be heated up evenly due to the non-uniform distribution of those components responding to microwave radiation. However, due to the insufficient sample size tested, the conclusions need to be justified by large-scale laboratory tests. Further work on upgrading sulphide ores by infrared sensing after microwave radiation has demonstrated that successful upgrade of molybdenite or (chalco) pyrite by MW/IR sorting depends on the uneven distribution of sulphides in the host rock, making MW/IR sorting suitable for vein, stockwork or stringer-type sulphide deposits (Van Weert, Kondos, & Gluck, 2009). Maximum temperature as read by an IR sensor was used as sorting criterion in this study. However, average surface temperature could be a better parameter for sorting, which suggests further investigation. The temperature/weight relationship needed to be studied on adequate ore samples of different size range in a laboratory-scale test. Van Weert and Kondos (Van Weert & Kondos, 2008; Van Weert, Kondos, & Wang, 2011) have demonstrated that particle size has a great effect on microwave heating of pyrite crystals. The results from their research show that crushed clusters of pyrite crystals respond better to microwave radiation and heat up faster than evenly distributed ones. Solid specimens of different sulfide minerals including arsenopyrite, chalcocite, chalcopyrite, cobaltite, covellite, enargite, galena, molybdenite, pyrite, pyrrhotite and sphalerite were crushed to different sizes for MW/IR testing to investigate the effect of particle size and spatial distribution on microwave heating of sulfides (Van Weert, Kondos, & Wang, 2011). The results of their study showed that large sulphide  28  specimens greater than 1cm3 or small sulphide particles smaller than 100 microns responded poorly to 2450 MHz MWs. Sulfides can be heated up faster when touching than when separated, which indicates that corona discharging is of importance to microwave heating of sulfide. The research on the topic of recognition of sulfides by MW/IR technology has been conducted only on mineral samples and artificial rocks (made of pure minerals and cement) with limited sample size of specific particle sizes. Due to the inadequate sample size and the lack of actual ore samples used for the previous studies on MW/IR sorting, the effect on microwave heating of radiation time, size, surface area or weight of the rock, and quantity of rocks heated at a time needs further investigation. It is the goal of the present research to address the relationship between the average surface temperature and these other factors in a laboratory-scale test on a lead-zinc sulfide ore sample provided by industry. Average surface temperature instead of maximum surface temperature was used sorting criterion, since average surface temperature may represent the overall sulphide content in the rock better, especially when the latter is evenly distributed. A rock with a little sulfide content will be heated up to very high temperature at a spot where the sulfide occurs as aggregates, while a rock with a large sulphide content dispersed within the whole rock will be heated up evenly to a relatively high temperature. If those two rocks are compared using the maximum surface temperature, the one with dispersed sulfides will be considered as waste because of its low maximum surface temperature. This could lead to inefficiency of the separation. Actual sorting results based on different average surface temperatures will also be presented.  29  2.6 Summary Based on the review of the literatures regarding the four sorting technologies presented in the previous sections, a comparison of these four sorting methods is presented in Table 2-3. Table 2-3 Comparison of Different Sorting Technologies Sorting Methods  Physical Properties Detected  Applications  Sensor Set-up Mode  Bulk /Surface  Size Range  Capacity  XRF Sorting  Elemental Composition  Base/Precious Metals  Free-Fall  Surface  10-300mm  up to 70 tph  XRT Sorting  Atomic Density  Base/Precious Metals, Coal, Diamonds, etc.  On-Belt  Bulk  5-300 mm  up to 150 tph  Optical (Color) Sorting  Colour, Reflection, Brightness, Transparency  Base/Precious Metals, Industrial Minerals, Gem Stones  Free-Fall  Surface  5-250mm  up to 300 tph  MW/IR Sorting  Microwave Absorption, Heat Conductivity  Base Metals, Carbonaceous Materials  On-Belt  Bulk  N/A  Overall, these sensor-based sorting technologies provide potential applications for preconcentration ores and the technical amenabilities of these sorting methods for the lead-zinc ore can be examined in a laboratory-scale preliminary study prior to large scale test. The flowsheet for laboratory-scale ore sorting study, especially using these four sorting methods, is not yet established. Therefore, it is the aim of this present study to generate a methodology for laboratory-scale ore sorting amenability study using the lead-zinc ore as a case study.  30  CHAPTER 3  SORTING TEST PROGRAM  This chapter describes the sorting testwork done to evaluate the amenabilities of this lead-zinc ore to X-ray Fluorescence Sorting, X-ray Transmission Sorting, Optical Sorting and Microwave-Infrared Sorting in the laboratory-scale. The equipment and methodology used for these four sorting tests were described and sorting results obtained were calculated. 3.1 Sample Preparation Two drums of 720 kg ore sample from Pend Oreille Mine were shipped to UBC for this study. This ore sample was already hand-sorted with visible sulfide and meeting the size requirement for sorting study which may create a bias due to the preconcentration of Run of Mine ore. Ore samples were first screened using a US standard 75 mm sieve and the oversize products were fed to a laboratory Jaw Crusher. All the rocks were screened using standard US sieves; the sized fractions were weighted and assayed. The sieve sizes were 75 mm, 53 mm, 37.5 mm, 26.5 mm, 19 mm and 13.2 mm, producing seven size fractions. Subsamples for different ore sorting technologies were taken from each size fraction. Size analysis and assays are shown in Table 3-1. Table 3-1 Head Sample Size Analysis and Assays Calibrated Grade, % Pb Zn  mm  inch  Wt. Distribution, %  +75  +3  24.5  24.5  75.5  2.84  -75+53  -3+2.12  44.9  69.3  30.7  -53+37.5  -2.12+1.5  14.2  83.6  -37.5+26.5  -1.5+1.06  6.3  -26.5+19  -1.06+0.75  -19+13.2 -13.2  -0.75+0.5 -0.5 Total  Size Fraction  Cum. Wt. % Oversize  Undersize  Distribution, % Pb  Zn  5.58  16.9  14.2  4.66  11.80  50.9  55.1  16.4  5.85  9.97  20.2  14.7  89.9  10.1  3.65  9.52  5.6  6.3  2.1  92.0  8.0  1.49  12.80  0.8  2.8  2.0 6.0 100.0  94.0 100.0  6.0 0.0  2.23 3.10 4.11  7.78 8.61 9.63  1.1 4.5 100.0  1.6 5.3 100.0  31  3.2 X-Ray Fluorescence Sorting Test 3.2.1 Materials Fifty, seventy-five, one hundred and one hundred rocks from +75 mm, -75+53 mm, 53+37.5 mm and -37.5+26.5 mm size fractions respectively of Pend Oreille ore samples were collected. The quantity of rocks being used from each size fraction for XRF sorting test was randomly selected and it is only used to demonstrate the ore’s amenability to XRF sorting in laboratory-scale preliminary study. All 325 rocks were cleaned, dried, numbered and weighed before being exposed to XRF sorting tests. Primary ore minerals in this sample are galena and sphalerite. Therefore, elements including Pb, Zn and Fe need to be detected and analyzed by the XRF analyzer. 3.2.2 Equipment In this preliminary scope test, the amenability of the material to XRF Sorting was evaluated using the XRF analysis station at UBC. The XRF analysis station is mainly composed of a XRF analyzer from Olympus Innov.X, which is illustrated in Figure 3-1. The primary X-ray is produced by an X-ray gun. The rock is placed on the detection area with a diameter of 1cm. An X-ray will shoot on the sample resulting in production of characteristic fluorescence from the rock. This fluorescence will be analyzed and quantified by the detector. Elements and their concentration presented on the surface of the rock will be shown directly on the screen of the HP iPAQ analyzer. Data obtained from this system is used for separation of this lead-zinc ore sample. The analysis process simulates what a real XRF sorter does when sorting the materials.  Figure 3-1 XRF Analysis Station at UBC  32  3.2.3 Experimental Procedures In the XRF sorting test, the Innov.X XRF analyzer recorded readings of four, six or eight faces of individual rock, depending on particle size, in order to achieve maximum sorting potential. The average Pb, Zn and Fe grades for each rock were calculated by averaging the face readings; then anticipated XRF sorting results were calculated based on XRF surface readings. According to the anticipated sorting results, rocks in each size fraction were grouped by cut-off grades. They were then crushed in the Jaw Crusher and Gyratory Crusher to reduce size. A small portion of each grade range sample was pulverized and subdivided into two subsamples for XRF powder reading and assay. Pulverized samples from different grade ranges were sent for assay. Calibrations of XRF surface readings and XRF powder readings with real assays were conducted afterwards. Sorting results of four size fractions were finally calculated based on assays. 3.2.4 Results and Discussions 3.2.4.1 Sortability of this ore using the Innov.X XRF analyzer In order to determine whether the Innov.X XRF analyzer can be employed to identify rocks with different grades of different sizes, all 325 rocks were individually analyzed by the XRF analyzer and then grouped in different grade ranges. After that, the rocks in different grade ranges were crushed and pulverized. Representative samples from each grade range were prepared and sent for chemical assays, which were done at Teck CESL Lab. The typical results, which are illustrated in Figure 3-2, indicate that the Innov.X XRF analyzer can be used to separate ore from waste rock given that there is a good correlation between the analysis of the surface of the rock by the XRF analyzer and the analysis of the bulk rock by chemical assays.  33  35.00 y Zn= 0.6813x + 0.6939 R²= 0.9552  30.00 yPb = 1.7577x - 0.0893 R²= 0.9707  Assays, %  25.00 20.00 15.00 10.00  Pb grade Zn grade  5.00 0.00 0.00  10.00  20.00  30.00  40.00  50.00  XRF Analyzer Surface Readings, %  Figure 3-2 Correlations between XRF Analyzer Surface Reading and Bulk Assays Note: these results are not for individual rock analysis. Each point represents the average Pb and Zn grades of several rocks in a grade range.  From the results shown in Figure 3-2, it can be concluded that the valuable minerals in this lead-zinc ore samples are evenly distributed. Consequently, chemical composition analysis of the particle surface has enough confidence in indicating the grade of the bulk particle after calibration. Therefore, this lead-zinc ore is amenable to XRF sorting. Correlation between assays and XRF powder readings were also plotted. The perfect correlation indicated that calibrated XRF powder reading can be used to substitute chemical assays for other sorting tests. Detailed results are shown in Appendix A. Based on the XRD mineral analysis results, three sulfide minerals are found in this ore sample: galena, sphalerite and pyrite. Pb, Zn and Fe elements are the major metal elements for detection. Correlations between Pb, Zn and Fe grades presented on the rock surface are analyzed. The results are shown in Figure 3-3. It is evident from the figure that some of the rocks have low Pb content but high Zn content. If the Pb grade is used as threshold value, some high-grade zinc rocks will be rejected mistakenly as waste. In the lower part of Figure 3-3, the average Pb grades of the rocks with certain Zn grade range was plotted. It is clear that the larger the Zn grade in the rock, the larger the average Pb grade found. Therefore, the Pb grade has a good correlation with the Zn grade. Fe is also not a good indicator of Pb and Zn grades due to the fact that no evident  34  correlation is found between Pb/Fe and Zn/Fe. Consequently, the Zn grade is the most suitable threshold value for sorting this lead-zinc ore using XRF sensing system.  30.00 25.00  Pb Grade, %  20.00 15.00  11.71  10.00  5.91  5.00 0.00  0.03  0.06  0.17  0.25  0.47  1.04  2.18  2.89  6.57  6.93  3.55  Zn grade range, %  Figure 3-3 Correlations between Pb, Zn and Fe  35  3.2.4.2 Grade-Recovery relationship The second stage of this preliminary scope study of XRF sorting amenability is to discover the Grade-Recovery relationship for the lead-zinc ore by XRF Sorting using the Innov.X XRF Analyzer. All 325 rocks were analyzed by the XRF analyzer individually and then sorted in multi-stages using different threshold Zn grades. Threshold values used from Test 1 to Test 5 are 20.00% Zn, 10.00% Zn, 5.00% Zn, 2.00% Zn and 1.00% Zn respectively. The test procedures are illustrated in the flowsheet shown in Figure 3-4.  Figure 3-4 Flowsheet of Grade-Recovery Relationship Test Procedures The results of the Grade-Recovery relationship test are shown in Table 3-2. Based on the results, typical Grade-Recovery relationship curves for Pb and Zn can be plotted using the cumulative metal recoveries and concentrate grades in Figure 3-5.  36  Table 3-2 Grade-Recovery Relationship Test Results Grade, %  Distribution, %  Product  Mass, %  Pb  Zn  Pb  Zn  Test 1 Feed  100.0  3.91  8.50  100.0  100.0  Conc.1  20.4  11.85  21.67  61.7  52.0  Test 2 Feed  79.6  1.88  5.12  38.3  48.0  Conc.2  19.9  5.62  13.53  28.6  31.7  Test 3 Feed  59.7  0.63  2.32  9.7  16.3  Conc.3  12.4  1.71  7.56  5.4  11.0  Test 4 Feed  47.3  0.35  0.95  4.3  5.3  Conc.4  14.3  0.69  2.45  2.5  4.1  Test 5 Feed  33.0  0.21  0.29  1.7  1.2  Conc. 5  6.8  0.31  0.71  0.5  0.6  Discard  26.3  0.18  0.19  1.2  0.6  Threshold Value (Zn Grades), %  Cumulative, % Conc. Grade Zn of Pb Recovery 11.85 52.0  Conc. Grade of Zn 21.67  Mass Pulled out as Waste, %  20.00  Pb Recovery 61.7  10.00  90.3  8.77  83.7  17.65  59.7  5.00  95.7  7.11  94.7  15.28  47.3  2.00  98.3  5.74  98.9  12.54  33.0  1.00  98.8  5.25  99.4  11.46  26.3  79.6  37  Pb grade - recovery curve 14.00  Conc. Grade (%)  12.00 10.00 8.00 6.00 4.00 2.00 0.00 40.0  50.0  60.0  70.0  80.0  90.0  100.0  90.0  100.0  Recovery (%)  Zn grade - recovery curve 25.00  Conc. Grade (%)  20.00 15.00 10.00 5.00 0.00 40.0  50.0  60.0  70.0  80.0  Recovery (%)  Figure 3-5 Grade-Recovery Relationship Curves for Pb and Zn Data from the above Grade-Recovery relationship curves can be used as reference for selection of threshold value for sorting this lead-zinc ore sample using the Innov.X XRF Analyzer. Figure 3-6 shows the relationship between metal recovery and % mass rejected as waste based on different threshold Zn grades.  38  Recovery and Mass Pulled out (%)  100.0 90.0 80.0 70.0 60.0 50.0 40.0 30.0 20.0 10.0 0.0 0.00  2.00  4.00  6.00  8.00  10.00 12.00 14.00 16.00 18.00 20.00  Threshold Value (Separation at Zn Grade of) (%)  Mass pulled out as waste  Pb recovery  Zn recovery  Figure 3-6 Relationship between Metal Recovery and Percentage Mass Rejected as Waste Based on Different Separation Threshold Values From Figure 3-6, it can be concluded that above 45% barren waste can be rejected by mass, while above 95% of metals can be recovered in the concentrate. This indicates that XRF sorting of this lead-zinc ore using the Innov.X XRF Analyzer exhibits great potential. 3.2.4.3 Summary of XRF sorting results The overall sorting results of these 325 rocks regardless of size (top size: bottom size = 3:1) are shown in Table 3-3. Sorting tests of +75 mm, -75+53 mm, -53+37.5 mm and 37.5+26.5 mm size fractions were also conducted separately to determine the optimum size for XRF sorting. Optimum results for each size fraction (to reject as much waste as possible with above 95% metal recovery) are shown in summary in Table 3-4. The detailed sorting results for each size fraction can be found in Appendix A. In summary, in this preliminary scope test sorting of this lead-zinc ore using the Innov.X XRF Analyzer exhibits great potential. Table 3-4 shows that the optimum size for XRF sorting is -37.5+26.5 mm with regard to percentage of mass rejected as waste. At this size, 52.8% waste could be rejected at 97.1% Zn and 96.5% Pb recoveries, with calculated concentrate grades of 21.04% in Zn and 7.61% in Pb. All the other size fractions also showed good sorting results, from 35% to 50% of waste rejected by mass with above 95% metal recoveries.  39  Table 3-3 Overall Sorting Results of 325 Rocks (Top Size: Bottom Size = 3:1) Sized Above 26.5 mm Separation at Zn Grade of,%  Conc.,%  1.00 2.00 5.00 10.00 20.00  73.7 67.0 52.7 40.3 20.4  Mass Rejected as Waste, %  Metal Recovery, %  Conc. Grade, %  Pb Zn Pb 26.3 98.8 99.4 5.25 33.0 98.3 98.9 5.74 47.3 95.7 94.7 7.11 59.7 90.3 83.7 8.77 79.6 61.7 52.0 11.85 Calculated Head Grade: 3.91% Pb and 8.50% Zn  Waste Grade, %  Zn 11.46 12.54 15.28 17.65 21.67  Pb 0.18 0.21 0.35 0.63 1.88  Zn 0.19 0.29 0.95 2.32 5.12  Table 3-4 Summary of XRF Sorting Results of Four Size Fractions Separation at Zn grade of, %  Conc., %  +75 mm  2.00  64.1  Mass Rejected as Waste, % 35.9  -75+53 mm  5.00  62.8  -53+37.5 mm  5.00  -37.5+26.5 mm  5.00  Size Fraction  Metal Recovery, %  Conc. Grade, %  Waste Grade, %  Calculated Head Grade, %  Zn  Pb  Zn  Pb  Zn  Pb  Zn  Pb  98.2  96.1  9.68  3.27  0.31  0.24  6.32  2.18  37.2  96.1  97.6  15.87  8.72  1.09  0.36  10.37  5.61  52.2  47.8  95.1  98.5  18.82  10.49  1.06  0.17  10.33  5.56  47.2  52.8  97.1  96.5  21.04  7.61  0.56  0.25  10.21  3.72  40  3.3 X-Ray Transmission Sorting Test 3.3.1. Equipment The equipment used for this X-ray Transmission Sorting testwork was from CommoDas’s facility in Surrey, British Columbia. It is a dual energy (140kV/77kV) Heimann 6040i X-ray scanner. The scanner was originally designed for airport package security checking and modified for mineral detection purpose (Figure 3-7). A box with four rocks can be scanned at one time with the X-ray scanner and dual energy grayscale images of these four rocks can be obtained.  Figure 3-7 Pilot-Scale Dual-Energy XRT Scanner at CommoDas The image processing software used for this study is GIMP. GIMP (GNU Image Manipulation Program) is a free image editing and analyzing software. This software features functions like fuzzy selection and free selection for shape contouring, threshold and histogram distribution of color intensity, etc. It can be used as a simplified image analysis tool for DE-XRT image processing, which was developed through this study. Sophisticated image analysis software used in the industrial DE-XRT sorter was inaccessible. However, CommoDas Ultrasort provided analysis results using their sorting control system for comparison purposes.  41  3.3.2. Experimental Procedures 3.3.2.1 Sample preparation One hundred rocks from -37.5+26.5 mm size fraction of Pend Oreille lead-zinc ore samples after primary crushing and screening were prepared for this sorting testwork. Individual rocks were marked and weighed prior to testing. 3.3.2.2 Image capture A group of four rocks was scanned simultaneously in a wood box using the X-ray scanner described in the previous section. A high energy and low energy gray-scale image was obtained for each rock, four rocks in one image. A sample of a dual-energy image was shown in the Figure 3-8.  Figure 3-8 Example of a DE-XRT Image Generated by the X-Ray Scanner 3.3.2.3 Sample assay After imaging, the rocks were individually crushed through a Gyrotary Crusher and Cone Crusher, pulverized and assayed by XRF analyzing of powders. Lead and zinc grades of each rock were obtained for further analysis. 3.3.2.4 Image processing and data extraction The author analyzed the DE-XRT images of each rock using GIMP, which is a simple image processing software, and CommoDas Ultrasort analyzed the image using PACT sorting control system. Results based on these two image analysis methods were then compared.  42  Simplified image processing by GIMP 1. Value image generation The image obtained from the X-ray scanner is a tif. format 1024×768 16-bit image with two layers (high and low energy). This image needed to be separated into two layers so that a high energy and a low energy image could be obtained for further analysis. Using GIMP software, the image of each rock was cropped out from the initial image and two layers of the image were saved as separate high energy and low energy images. Since GIMP can only process 8-bit images, images were depressed to 8-bit for this analysis. Data will be lost due to this depression. However, the depression of image will not significantly influence the analysis results since all images are processed and analyzed using the same method. The high energy and low energy images of each rock had a resolution of 320×240. This value image for each rock was used for further analysis. Figure 3-9 shows the process of value image generation.  Figure 3-9 Process of Value Image Generation 2. Brightness value extraction Individual gray-scale high energy and low energy images of each rock were processed by GIMP software. The shape of the rock was selected using GIMP’s fuzzy selection function. The distribution of brightness value within the selected rock image was shown in the histogram. The brightness value ranged from 0 to 255 for an 8-bit image where 0 is blank, 255 is white and numbers between 0 and 255 represent different degrees of gray color. The larger the brightness value, the brighter the image, and the more X-rays  43  are transmitted. The average brightness value of the rock image (shape contour) was shown in the histogram. Figure 3-10 is a screenshot showing the processing of images using GIMP. In this simplified data extraction process, the average brightness value of the image was used as criterion instead of the brightness distribution for all pixels.  Figure 3-10 Screenshot of Brightness Value Extraction from the Image Image processing by PACT The initial images obtained by the X-ray scanner were also processed and analyzed by CommoDas Ultrasort using the PACT Sorting Control system. A high energy image and a low energy image of each rock were automatically obtained by the software and a 2D transmission histogram for each rock was also obtained showing the distribution of brightness value of the pixels within the image. The 2D transmission histogram and assay of each rock were then analyzed and correlations between them were studied for the generation of the Ore Index Mapping Reference. After that, the Ore Index of each rock was then calculated by mapping the 2D histogram of each rock against the Ore Index Mapping Reference histogram. The image processing flowsheet of PACT software is illustrated in Figure 3-11. The algorithm behind this Ore Index calculation is inaccessible to the author. Therefore, the DE-XRT sorting results based on the Ore Index are used only for comparison. The Ore Index could also demonstrate the actual  44  technical amenability of DE-XRT sorting of this lead-zinc ore using an industrial-scale DE-XRT sorter.  Figure 3-11 Image Processing Flowsheet of PACT Software 3.3.2.5 DE-XRT Sorting criterion determination The data information extracted from the DE-XRT images and assays were studied and analyzed for sorting criterion determination by plotting the correlation curve between the characteristic image data and the chemical composition of the rocks. Two sorting criteria were generated based on two data processing methods.  45  3.3.3 Results and Discussion 3.3.3.1 Simplified image analysis by GIMP Sorting potential based on average brightness value The average brightness value of the high energy and low energy images of the 100 rocks were read from the histogram through GIMP software. They are shown in Appendix B. The correlation between the total atomic number for valuable elements and the average brightness values of the high/low energy X-ray transmission images of the rock were analyzed by plotting the average brightness values of the high energy image and the low energy image at the X and Y axis respectively (Figure 3-12). The total atomic number for valuable elements is defined as: total atomic number for valuable elements (Ztotal) = atomic number of Pb * Pb grade + atomic number of Zn* Zn grade. For example, if the Pb and Zn grades of the rock are 3.72% and 10.45%, the Ztotal is equal to 80* 0.0372+ 32* 0.1045 = 6. Each spot shown in Figure 3-12 represents a rock. Rocks were plotted using different colors based on their total valuable atomic numbers. The larger the Ztotal value, the heavier the rock is.  Low Energy Inmage Average Brightness  120.0  Barren waste rock 100.0 80.0  Ztotal  60.0  0-1  40.0  1-10  20.0  10-20 >20  0.0 0.0  20.0  40.0  60.0  80.0  100.0  120.0  140.0  160.0  High Energy Image Average Brightness  Figure 3-12 Separation Curve for Lead-Zinc Ore Sample Sized -37.5+26.5 mm Based on Average Brightness Values of High/Low Energy Images  46  Figure 3-12 shows rocks with smaller total valuable atomic number (low grade) positioned in the upper right while those with bigger total valuable atomic number are at bottom left, demonstrating good separation potential based on average brightness values of both the high energy image and the low energy image. This indicates that single energy X-ray transmission will also work for this lead-zinc ore sample, sized 37.5+26.5 mm, due to the small variance of rock thickness. The relationship between the total lead and zinc grades (Pb+Zn) and the average brightness values of the high/low energy images is shown in Figure 3-13. Figure 3-13 also indicates that sorting of this lead-zinc ore based on the average brightness value of the X-ray transmission images would achieve satisfactory results.  60.00  160.0 140.0  50.00 120.0 40.00  30.00  80.0  Intensity  Metal grade, %  100.0  60.0 20.00 40.0 10.00 20.0 0.0  1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97  0.00  Rock # Pb+Zn grade  Avg intensity of high engergy image  Avg intensity of low energy image  Figure 3-13 XRT Segregation Sortability Curve of -37.5+26.5 mm Lead-Zinc Ore Based on Average Brightness Values of High/Low Energy Images  47  Sorting results Individual rocks with an average brightness value of high/low energy images higher than the separation value will be considered as waste. The sorting results of the 100 rocks based on different separation average brightness values of the high energy and low energy images are shown in Table 3-5 and Table 3-6. Table 3-5 XRT Sorting Results of -37.5+26.5 mm Lead-Zinc Ore Based on Average Brightness Value of the High Energy X-Ray Transmission Image Separation at the Avg. Brightness of High Energy Image 130  Conc. (%)  Mass Rejected (%)  97.2  110  Metal Recovery (%)  Conc. Grade (%) Zn  Waste Grade (%)  Pb  Zn  Pb  Pb  Zn  2.8  100.0  99.9  6.38  8.97  0.08  0.37  81.0  19.0  99.6  99.3  7.63  10.70  0.13  0.33  90  67.9  32.1  99.3  98.8  9.07  12.71  0.13  0.32  80  65.0  35.0  99.2  98.8  9.46  13.25  0.14  0.31  70  63.3  36.7  99.1  97.6  9.71  13.46  0.15  0.56  65  61.9  38.1  99.0  96.1  9.92  13.56  0.17  0.88  60  58.3  41.7  96.7  93.5  10.29  14.01  0.49  1.36  50  44.9  55.1  93.4  72.9  12.91  14.18  0.74  4.29  40  33.0  67.0  87.9  60.7  16.50  16.04  1.12  5.12  30  20.4  79.6  61.4  36.9  18.67  15.80  3.00  6.92  Calculated Head Grade: 6.20% Pb and 8.73% Zn  As shown in Table 3-5, above 96% of Pb and Zn could be recovered in 62% mass of the feed after XRT sorting based on the average brightness value of the high energy image. The sorting product had calculated Pb and Zn grades of 9.92% and 13.56% (6.20% and 8.73% respectively in the feed) when sorting at an average brightness value of 65 for the high energy X-ray transmission image.  48  Table 3-6 XRT Sorting Results of -37.5+26.5 mm Lead-Zinc Ore Based on Average Brightness Value of the Low Energy X-Ray Transmission Image Separation at the Avg. Brightness of Low Energy Image 90  Conc. (%)  Mass Rejected (%)  96.4  80  Metal Recovery (%)  Conc. Grade (%)  Waste Grade (%)  Pb  Zn  Pb  Zn  Pb  Zn  3.6  99.9  99.8  6.43  9.04  0.10  0.37  85.1  14.9  99.7  99.4  7.26  10.20  0.12  0.32  70  76.5  23.5  99.5  99.1  8.07  11.32  0.12  0.33  65  72.3  27.7  99.5  99.0  8.53  11.94  0.12  0.32  60  68.6  31.4  99.4  98.9  8.98  12.57  0.12  0.31  50  64.5  35.5  99.2  98.7  9.54  13.35  0.13  0.29  45  63.1  36.9  98.1  98.2  9.64  13.57  0.31  0.43  40  59.4  40.6  97.8  93.6  10.20  13.74  0.34  1.38  35  46.3  53.7  92.9  77.1  12.44  14.53  0.81  4.32  30  33.0  67.0  79.2  59.0  14.86  15.60  1.93  6.37  Calculated Head Grade: 6.20% Pb and 8.73% Zn  As shown in Table 3-6, above 98% of Pb and Zn could be recovered in 63% mass of the feed after XRT sorting based on the average brightness value of the low energy image. The sorting product had calculated Pb and Zn grades of 9.64% and 13.57% (6.20% and 8.73% respectively in the feed) when sorting at an average brightness value of 45 for the low energy X-ray transmission image. Based on these findings, it can be concluded that sorting based on the average brightness value of the high/low energy image of this lead-zinc ore sample with a narrow size range demonstrated great amenability. Above 35% mass could be rejected as waste while upgrading the feed based on the discrimination of average brightness of the XRT image. The results also indicated that single energy X-ray transmission image analysis was also adequate for segregation of this lead-zinc ore sample in this size range. 3.3.3.2 Image analysis by PACT software Sorting potential based on Ore Index The original dual-energy XRT images were also analyzed using CommoDas Ultrasort PACT software in order to demonstrate the technical amenability of DE-XRT Sorting of this lead-zinc ore by simulating the real separation process of pilot-scale sorter. Ore  49  Indices for each rock were calculated using the PACT software by investigating correlations between the weighted Pb+Zn grades and the shape of the transmission curve for the 100 rocks. The algorithm of the Ore Index is inaccessible to the author. The calculated ore indices are shown in Appendix B. The sortability curve base on Ore Indices is illustrated in Figure 3-14. Figure 3-14 indicates that sorting based on Ore Indices will successfully upgrade this lead-zinc ore by rejecting the low grade barren waste rocks.  50.00  100  45.00  90  40.00  Ore Index  80  35.00  70  30.00  60  25.00  50  20.00  40  15.00  30 20  10.00  10  5.00  0  0.00  Pb+Zn Grades, %  110  -37.5+26.5 mm POM Ore XRT Segregation sortability curve based on ORE INDEX  Rock #  Pb+Zn grade  Ore Index  Figure 3-14 XRT Segregation Sortability Curve of -37.5+26.5 mm Lead-Zinc Ore Based on Ore Indices Sorting results Individual rocks with an Ore Index smaller than the separation Ore Index will be considered as waste and rejected. Sorting results based on different separation Ore Indices were calculated and are shown in Table 3-7. Results shown in Table 3-7 confirm the technical amenability of XRT sorting of this lead-zinc ore sample. Above 95% Pb and Zn could be recovered in 62% mass of the feed. The product grades of Pb and Zn were calculated to be 9.86% and 13.39% respectively.  50  Table 3-7 XRT Sorting Results of -37.5+26.5 mm Lead-Zinc Ore Based on Ore Indices Separation Ore Index at  Conc. (%)  Mass Rejected (%)  5  74.9  10  Conc. Grade (%)  Metal Recovery (%)  Waste Grade (%)  Pb  Zn  Pb  Zn  Pb  Zn  25.1  8.24  11.56  99.5  99.2  0.12  0.30  72.2  27.8  8.54  11.98  99.5  99.0  0.12  0.30  15  68.4  31.6  9.01  12.62  99.4  98.9  0.12  0.31  20  66.3  33.7  9.29  13.00  99.3  98.8  0.12  0.30  30  65.6  34.4  9.39  13.15  99.3  98.8  0.13  0.31  33  62.3  37.7  9.86  13.39  99.1  95.6  0.14  1.02  35  59.7  40.3  9.35  12.38  98.9  93.0  0.18  1.51  40  54.5  45.5  11.17  13.92  98.1  86.9  0.25  2.51  50  40.9  59.1  14.44  15.16  95.2  70.9  0.51  4.29  60  36.0  64.0  15.69  15.45  91.1  63.7  0.86  4.95  Calculated Head Grade: 6.20% Pb and 8.73% Zn  3.3.3.3 Comparison of sorting results by two image analyzing methods The optimum sorting results based on different sorting criteria are summarized in Table 3-8. The optimum sorting results were achieved for each sorting criterion when as much waste as mass could be rejected while achieving 95% metal recovery. The GradeRecovery curves for each sorting criterion are shown in Figure 3-15. Table 3-8 XRT Sorting Results Summary of Different Sorting Criteria Conc. Grade (%) Pb Zn  Metal Recovery (%) Pb Zn  Waste Grade (%) Pb Zn  Separation Criteria  Conc. (%)  Mass Rejected (%)  Ore Index at 33  62.3  37.7  9.86  13.39  99.1  95.6  0.14  1.02  61.9  38.1  9.92  13.56  99.0  96.1  0.17  0.88  63.1  36.9  9.64  13.57  98.2  98.2  0.31  0.43  Avg. Brightness of High Energy Image at 65 Avg. Brightness of Low Energy Image at 45  Calculated Head Grade: 6.20% Pb and 8.73% Zn  51  100.00  Metal Recovery, %  95.00 90.00 85.00 80.00 75.00 70.00 Ore Index - Pb Avg. Brightness of High Energy Image - Pb Avg. Brightness of Low Energy Image - Pb  65.00 60.00 0.00  5.00  10.00  15.00  20.00  Concentrate Grade, % 100.00 90.00  Metal Recovery, %  80.00 70.00 60.00 50.00 Ore Index - Zn Avg. Brightness of High Energy Image - Zn Avg. Brightness of Low Energy Image - Zn  40.00 30.00 0.00  5.00  10.00  15.00  Concentrate Grade, % Figure 3-15 Grade-Recovery Curves of XRT Sorting Based on Different Sorting Criteria From the results listed in Table 3-8, it can be concluded that both single and dual energy X-ray transmission imaging technique could be used for sorting of this lead-zinc sample sized -37.5+26.5 mm, due to the negligible effect of thickness. In addition, the satisfactory DE-XRT Sorting results based on different Ore Indices also demonstrate the technical amenability of DE-XRT Sorting of this lead-zinc ore sample using a pilot-scale X-ray sorter.  52  3.4 Optical Sorting Test 3.4.1 Ore Characterization Ore samples for this optical sorting test are from Pend Oreille Lead Zinc mine. The primary minerals in this ore are sphalerite and galena. Host rock is mainly light-gray bedded dolomite. Other minerals found in this ore include pyrite, cerussite, calcite and quartz. Mineral composition of this ore sample was analyzed by quantitative X-ray Diffraction (XRD). The results of XRD analysis are shown in Table 3-9. Table 3-9 Results of Quantitative Phase Analysis of Head Sample by XRD (Wt.%) Mineral  Ideal Formula  Pyrite  FeS2  38.5  Sphalerite  (Zn,Fe)S  10.2  Galena  PbS  4.6  Cerussite  PbCO3  0.1  Dolomite-Ankerite  CaMg(CO3)2Ca(Fe2+,Mg,Mn)(CO3)2  Calcite  CaCO3  2.2  Plagioclase ?  NaAlSi3O8 – CaAlSi2O8  0.8  Quartz  SiO2  0.9  Muscovite ?  KAl2(AlSi3O10)(OH)2  Total  Distribution  42.6  100.0  It can be seen from the analysis results that only three sulfide minerals are present in this sample: pyrite, sphalerite and galena. Gangue mineral is mainly dolomite. The aim of this study is then to separate lead-zinc bearing sulfides from dolomitic waste based on the differences in color properties. 3.4.2 Sample Preparation A subsample of 100 rocks from -37.5+26.5 mm were marked, weighed, cleaned and dried for the color ore sorting test.  53  3.4.3 Equipment 3.4.3.1 Hardware In this laboratory-scale color sorting amenability study, an optical bench-top image acquisition system at CommoDas’s Surrey facility was used for imaging. This system is equipped with a RGB camera (Opteon USB 1024×768 resolution) mounted with 25mm lens. The camera is installed 50cm above the background surface. The area being imaged is 8.6×6.8 cm. The lighting system is composed of a rectangular arrangement of four standard fluorescent lamps (Philips F39T12/CW 29 Watt Alto). A white blanket is used to protect the imaging from ambient light. The system layout can be seen in Figure 3-16. This system can generate 1024x768 RGB images under different exposure times, e.g., 240 ms, 300 ms, 360 ms.  Figure 3-16 Optical Bench-Top Image Acquisition System at CommoDas 3.4.3.2 Software National Instrument (NI) Vision Mine Objective Detection and Analysis Software The NI Vision Mine Objective Detection and Analysis Software developed at UBC was used for image analysis so as to study the characteristic color data of barren rocks and mineralized ores and to determine the rejection criteria. This software features a function of pattern recognition using fuzzy logic match instead of exact match, which includes pattern learning and pattern matching (Bamber, 2008). This function is used in this study to generate optical features in terms of the RGB values of the object, either mineralized ore or waste rock, therefore providing the sorting threshold for this ore.  54  GIMP GIMP is used in this study for image editing and data extraction using the first-order statistical histogram method. This software features a free selection function, which can be used for region of interest (ROI) selection, contouring rock shape in our case. It also provides functions such as generating a histogram of each color channel, threshold image, etc. It is a simple, handy and useful software we can get access to for our simplified image analysis. 3.4.4 Color Sorting Potential Study From the subsample of 100 rocks, five light rocks with white and light gray or black green color, and two brass color heavy rocks were picked for a color sorting potential study. The rocks were wetted using water prior to imaging. Images of these rocks were obtained individually by the optical bench-top image acquisition system mentioned earlier. The exposure time was set to 300 ms, which was found most suitable for this sample to prevent overexposure. The rocks are shown in Figure 3-17.  Figure 3-17 Images of Identified Waste Rock and Mineralized Ore  55  Most of the waste rock appears in white or light gray color (Rock #26, 51 and 97) while some waste rock with banded texture appears in bedded white and black (Rock #17). Another type of waste appears in very dark green (Rock #39). Mineralized ores can be divided into two groups based on color property. One type of ore appears in brass yellow with light gray veins (Rock #80). The other type appears in brass yellow with spotted shiny lead gray color (Rock #72). The rocks were then crushed and pulverized for XRF readings. Previous study of XRF sorting showed that calibrated XRF powder reading, which is calculated using the Assay vs. XRF powder reading correlation function, had enough confidence to predict the chemical composition of the rock. XRF powder analysis of the seven rocks is shown in Table 3-10. Table 3-10 XRF Powder Analysis of Waste Rocks and Mineralized Ore  Category  Waste  Ore  Rock ID 17 26 39 51 97 72 80  Pb grade, % 0.37 0.13 0.05 0.19 0.09 19.63 0.10  Zn grade, % 0.39 0.17 0.24 0.33 0.32 18.32 24.47  It is evident from Table 3-10 that rocks with light gray and white or dark green color shown as waste rock in Figure 3-17 contain little lead and zinc. The rock with brass yellow color with spotted shiny lead grey contains a high grade of lead and zinc. The rock with brass yellow and light gray color contains a high grade of zinc but a low grade of lead. This indicates the potential of color sorting to remove the barren dolomitic waste rocks from mineralized ores and therefore produce an upgraded lead-zinc preconcentrate. 3.4.5 Image Capture The images of the other 93 rocks were taken with the optical bench-top image acquisition system under wet conditions. The exposure time was 300ms. The rock was placed at the same position each time and was photographed individually. The schematic set-up of the camera is illustrated in Figure 3-18. A 1024×768 revolution RGB image was generated for each of the 100 rocks.  56  Figure 3-18 Schematic Set-Up of the Camera 3.4.6 Data Analysis 3.4.6.1 Generation of characteristic color data for waste rock and mineralized ore The NI Vision Mine Object Detection and Analysis Software was used to generate characteristic color data in terms of red, green and blue for the waste rock and mineralized ore selected for the color sorting potential study shown in Figure 3-17. Open the image in this software and the red, green and blue value for each pixel can be read directly. The software also features a function of pattern matching with which we can generate the color properties of rock by finding matched patterns (with similar color properties) in the image. This software can only deal with 8-bit coded images with 640×480 pixels resolution therefore the images were resized from the original image size of 1024×768 pixel resolutions to 640×480 pixels resolution prior to analysis. For the waste rock (Rock #26) image, a region of interest (ROI) was chosen as one pattern. Once the pattern is chosen, a 3D color spectrum of the ROI will be displayed and the color data for red, green and blue for each individual pixel within the interest area will be recorded. Figure 3-19 shows the red spectrum of the selected pattern. The red value of each pixel is recorded in the table on the right side of the figure.  57  Figure 3-19 Red Spectrum of Selected ROI Pattern for Rock #26-Waste For an 8-bit coded image, the value of red, green and blue varies from 0 to 255. In the above figure, the red value of the selected ROI varies from about 130 to 150. Once this pattern is selected, we can use the pattern-match function of the software to find similar color patterns within the same rock image. A maximum number of 20 matches could be done at one time. However, only 10 color patterns could be recorded. Figure 3-20 shows the matched pattern within the image of Rock #26. The average red, green and blue values of the ten patterns are listed in Table 3-11. The average red, green and blue values for these ten patterns can be used as characteristic color features for this rock.  58  Figure 3-20 Patterns Matching of Rock #26 Table 3-11 Average Red, Green and Blue Value for the 10 Matched Patterns for Rock #26 Rock #26  1  2  3  4  5  6  7  8  9  10  Area  551  551  551  551  551  551  551  551  551  551  Mean  154  158  130  111  128  118  158  137  98  112  Red  Standard Deviation  16  23  17  15  23  14  20  14  24  16  Mean  170  175  144  123  141  132  175  152  108  125  Green  Standard Deviation  18  25  18  16  26  16  22  15  24  18  Mean  95  97  79  67  78  73  98  84  59  67  Standard Deviation  12  16  12  10  17  10  14  9  24  10  Blue  Average  130  144  80  Using the same method, two patterns each for rock #51 and rock #97 were selected and matched. All the rocks appearing in light gray and white color are assumed to be waste. One pattern was selected for the dark green rock #39 in order to obtain the color features for this type of waste. However no pattern matching was done for this rock since only five rocks could be found of this type out of one hundred. The selected pattern  59  could adequately demonstrate the color features of this type of waste. The average characteristic color features in terms of red, green and blue values for these five pattern sets are used for describing and identifying waste rock. Two patterns were selected and matched for rock #72 and one for rock #80, which are assumed to be mineralized ore. The average characteristic color features in terms of red, green and blue values for these three pattern sets are used to describe and identify mineralized ore. The average characteristic red, green and blue value for each pattern set for the rocks are shown in full in Appendix C. Table 3-12 summarizes these values for each of eight pattern sets for the rocks. Table 3-12 Summary of Red, Green and Blue Value for Matched Patterns Index  1  2  3  4  5  Ave  Max  Min  Rock 39  Red  133 149 124 131 130  133  149  124  40  Green  145 163 137 143 144  146  163  137  42  Blue  79  90  77  81  90  77  25  Index  1  2  3  Ave  Max  Min  Red  88  70  69  76  88  69  Green  97  78  77  84  97  77  Blue  49  41  41  44  49  41  Waste  79  80  Ore  It is clearly shown in the above table that the patterns of most waste rocks have an average red value of 133 while those of mineralized ore average 76. Differences exist as well for green and blue color. One type of gangue mineral that appears in dark green has a red value of 40. It could also be discriminated from the ores with an average red of 76. As a result, the featured color values could be used to sort this ore. In this laboratory-scale research, the potential of color sorting of this lead-zinc ore was investigated based on the differences of red color only.  60  3.4.6.2 Rejecting criteria determination All the 100 images were processed in four stages using GIMP software:   contouring the value image;    filtering the image by threshold function;    extracting the average red value from histogram after threshold; and    determining the rejection criterion.  Contouring the value image There are three methods to select the value image for analysis: bounding box, centroid and contouring (Kowalczyk & Bartram, 2008). They are shown in Figure 3-21.  Figure 3-21 Value Image Selection Methods In this study, the contouring method was employed. The value image for each rock was contoured manually using free select tool in the GIMP software. Filtering the image by threshold function According to the characteristic color data study for the waste rock and mineralized ore discussed in the previous section, it is clear that the waste rock image has an average red value of 133 while the mineralized ore image has an average red value of 76. All the rocks were visually similar as a whole, but it is easy to identify light gray or white gangue minerals in the rock. The percentage of “waste pattern” present in the rock image can be used as criterion for sorting the waste rock from mineralized ore. It is also observed that most of the dolomitic gangue minerals in the image have a red value larger than 133. Therefore it is assumed that any pixel that has a red value between 133 and 255  61  belongs to the waste pattern. The more the pixels with red value between 133 and 255, the more “waste patterns” present in the rock image, and that rock is more related to waste. Using the threshold function in GIMP, each value image was color coded with a threshold red value of 133 to 255. Individual pixels with a red value from 133 to 255 will appear in red in the image after threshold and will be given a red value of 1. Any pixel which has a red value between 0 and 132 will appear in green in the image after threshold and will be given a red value of 0. Figure 3-22 shows a sample of threshold images. It is shown clearly in the figure that rocks have great differences in red color after threshold.  Figure 3-22 Sample of Threshold Images for Several Rocks Extraction of average red value from histogram after threshold The percentage of red area shown in the threshold image represents the correlation of this rock to waste; the more red, the greater the correlation. The percentage of the red area can be expressed as the average red value of the value image after threshold, since pixels were given a red value of 1(shown in red) or 0 (shown in green) in the image  62  after threshold. More specifically, the larger the average red value of the threshold image, the more percentage of red area in the threshold image, and the more the rock is related to waste. The average red value of the value image after threshold was obtained using the first-order of statistical method using histogram parameters. GIMP features a function of histogram showing the spectrum of red value, the mean, standard deviation, medium of red value, and pixels counted. Figure 3-23 and Figure 3-24 show the histograms of red value for the value images before and after threshold respectively.  Figure 3-23 Histograms of Red Value for the Value Image Before Threshold Note: left: rock #72, right: rock #26  63  Figure 3-24 Histograms of Red Value for the Value Image after Threshold Note: left: rock #72, right: rock #26 It is evident from Figure 3-23 that the average red values read from the histogram of the value image after threshold differ greatly between rocks. As result, the average red value of the value image after threshold is used as separation criterion. Determination of rejecting criteria Correlations between Pb+Zn grade and the average red value of the value image after threshold (133-255), Avg. R133-255, are shown in Figure 3-25. It is clearly shown in the figure that rocks with Avg. R133-255 larger than 50 are totally waste rock. When the Avg. R133-255 is smaller than 50, two types of error occurred only use the Avg. R133-255 as criterion to separate barren rock from mineralized ore.  64  Figure 3-25 Correlation between Pb+Zn Grade and Avg. R133-255   error 1: some barren rocks have a smaller Avg. R133-255 similar to that of mineralized ore.    error 2: some mineralized ore rocks have a larger Avg. R133-255 similar to that of barren rock.  The first type of error exists partly because there is another type of waste pattern present in the sample appearing in dark green color, which has a red value of approximately 40. Another reason for the first type of error is the existence of barren waste rocks with bedded texture appearing in bedded light grey and dark colors. The second type of error exists because some high zinc grade ore appears in brass yellow color with light grey veins (waste pattern). These light grey veins will contribute much to the Avg. R133-255. The first type of error could be partly corrected by an additional step of separation using the average red value of the value image after threshold (0-40), Avg. 040.  The second type of error could not be effectively corrected after several trials.  Consequently, the rejection criteria were set as follows: If Avg. R133-255 > X, then waste If Avg. R133-255 < X, then second judgement If Avg. 0-40 > Y, then waste  65  If Avg. 0-40 < Y, then ore where X and Y can be optimized based on the sorting results. 3.4.7 Sorting Results Color sorting results based on different Avg. R133-255 values of the rock image, using the rejection criteria described in the previous section, are shown in Table 3-13. The X value varies while the Y value is set as 7. Table 3-13 Color Sorting Results Summary Metal Recovery, % Pb Zn  Conc. Grade, % Pb Zn  Waste Grade, % Pb Zn  Conc. %  Waste Rejection %  R40-7  84.4  15.6  99.6  99.5  7.44  10.29  0.14  0.29  R133-22  R40-7  81.2  18.8  98.5  97.0  7.64  10.43  0.50  0.50  R133-21  R40-7  79.6  20.4  98.4  93.9  7.79  10.30  0.48  2.60  R133-15  R40-7  75.8  24.2  98.3  93.2  8.17  10.74  0.43  2.45  R133-11  R40-7  70.7  29.3  98.1  92.6  8.73  11.43  0.41  2.20  R133-8  R40-7  58.6  41.4 89.1 77.1 9.57 11.47 Calculated Head Grade: 6.20% Pb and 8.73% Zn  1.66  4.84  Sorting Criteria R133-30  Based on the sorting results shown in Table 3-13, the optimum rejection criterion is as follows: If Avg. R133-255 > 22, then waste If Avg. R133-255 < 22, then second judgement If Avg. 0-40 > 7, then waste If Avg. 0-40 < 7, then ore Using this rejection criterion, color sorting of this lead-zinc ore sample could successfully recover 98.5% lead and 97.0% zinc in 81% of the feed mass. Lead and zinc grades were upgraded to 7.64% and 10.43% respectively from 6.30% and 8.73% calculated in the feed. Figure 3-26 shows the Grade-Recovery curve for color sorting of -37.5+26.5 mm sample.  66  14.00  Conc. Grade (%)  12.00 10.00 8.00 6.00 4.00 2.00 0.00 0.0  10.0  20.0  30.0  40.0  50.0  60.0  70.0  80.0  90.0 100.0  Metal Recovery (%) Pb  Zn  Figure 3-26 Color Sorting Grade-Recovery Curve for -37.5+26.5 mm Sample  67  3.4.8 Laboratory-Scale Color Ore Sorting Amenability Study Flowsheet Design The major steps in this color ore sorting amenability study of an ore sample in the laboratory-scale were sample preparation, sorting potential determination, image capture, data analysis, and sorting result calculation according to the testwork done in this study. The flowsheet for this evaluation process can be seen in Figure 3-27. This flowsheet can be used as a guideline for color ore sorting studies of other ore types in laboratory-scale.  Characterization of Ore     Geology Mineralogy  Sample Preparation     Screening Surface cleaning  Sorting Potential Determination      Visible traits XRF analysis Correlation  Image Capture     Bench-top image acquisition Exposure time determination  Characteristic Color Determination     Narrow size range Remove particles too large/small     Remove dust Wetting    Featured red, green, blue data for ore minerals Featured red, green, blue data for gangue mineral    Data Analysis  Rejection Criteria Decision     Sorting Results   Contouring value image Filtering the image by threshold Extraction of avg. color value (R,G,B) from histogram after threshold Rejection criteria determination  Figure 3-27 Flowsheet for Color Ore Sorting Amenability Study in LaboratoryScale  68  3.5 Microwave-Infrared Sorting Test 3.5.1 Experimental Procedures 3.5.1.1 Equipment The microwave oven used in this test was a model BP-110 (2.45 GHz, 12.2 cm wavelength, 1000 w output microwave power) from Microwave Research & Applications, Inc., designed for laboratory use. All tests were conducted using its high energy level setting (maximum output 1000 w). The infrared camera used in this test was a FLIR T400 Infrared Camera with a 320×240 IR resolution. It can generate an IR picture and a regular digital picture at the same time. The equipment used for this test is shown in Figure 3-28.  Figure 3-28 BP-110 Lab Use Microwave Oven and FLIR T400 IR Camera 3.5.1.2 Testing procedures Fifty rocks, each sized from -53+37.5 mm, -37.5+26.5 mm, -26.5+19 mm and -19+13.2 mm, were prepared for the microwave/infrared (MW/IR) sorting test. Individual rock samples were marked from 1 to 50 for each size fraction.  Individual testing A batch of fifty rocks from each of the four size fractions were weighed and exposed individually to microwave radiation for different periods of time to evaluate the effect of weight and exposure time on microwave heating. For the -53+37.5 mm size fraction, 10s, 20s and 30s of microwave heating were conducted. For the other three size fractions, rocks were exposed to microwave radiation for 5s, 10s and 15s respectively. One rock at a time was put on the same spot, as shown in Figure 3-29. This is necessary because the MW energy distribution in a MW oven is inhomogeneous. However, each spot shown  69  in the picture has the same heating effect of individual specimens according to previous studies (Van Weert, Kondos, & Gluck, 2009).  Figure 3-29 Rock Position for Individual Testing An IR image was taken using the FLIR T400 IR camera for two sides of each rock right after microwave heating by the oven. One could argue that it takes time with the larger rocks for internal heat to diffuse to the surface and that the surface temperature may actually increase with time. Work at Process Research ORTECH has shown this not to occur with 50 mm rocks (Wang, 2012) and this variant was not pursued in this work. The average surface temperature of each rock was obtained by contouring the shape of the rock and averaging the temperatures of both sides using FLIR Quick Reporter 9.0. Figure 3-30 shows the IR (Thermographic) images of both sides of rock samples (one mineralized ore and one waste). XRF surface reading of each rock was done for Pb, Zn, Fe and S analysis. Calibrated XRF surface readings were used as assays for data analysis since assays and XRF surface reading have a strong correlation according to XRF sorting studies.  70  Figure 3-30 IR (Thermographic) Images of Two Sides of Rocks from -19+13.2 mm Size Fraction After 10s Microwave Heating Note: the left appears to be mineralized ore and the right appears to be waste) Average surface temperature of both sides were calculated.  Group testing Rock samples from each size fraction were grouped and exposed to microwave radiation for 10s in order to discover the microwave heating performance of different quantities of rock being heated at the same time. Assume that individual rock has a cubic shape with the side being the particle size (13.2 mm, 19 mm, 26.5 mm and 37.5 mm) and each rock has the same density, which is the average density of the sample. As a result, the weight of each rock is proportional to the third power of the particle size. Therefore, the weight of a rock with a side of 37.5 mm approximately equals the weights of four, nine and thirty rocks with a side of 26.5 mm, 19 mm and 13.2 mm, respectively. However, only a maximum of 25 rocks can be treated at the same time in the lab microwave oven due to its size. Consequently, after being exposed to microwave for 10s, the surface temperatures of separate groups of four, nine and twenty-five rocks from 37.5+26.5 mm, -26.5+19 mm and -19+13.2 mm size fraction were measured.  71  Figure 3-31 Rock Positions for Group Testing Also, the surface temperatures of a group of nine rocks from each of the four size fractions were measured after 10 seconds microwave heating so that a comparison of heating performance of a group of nine rocks from different size fractions could be made. Rock positioning in the oven for grouped sample tests is shown in Figure 3-31. A glass plate, which is used in the domestic microwave oven, was put inside the Lab BP-110 microwave oven for conveniently moving the rocks for IR imaging. The rocks were then placed on the glass plate according to the positions illustrated in the above figure. After being heated for 10s, the plate was quickly and carefully taken out and photographed using the IR camera. The rocks were then turned around and an IR image was taken on the second side of the rocks. Figure 3-32 shows an example of the digital and IR images for the group test.  Figure 3-32 IR (Thermographic) Images of Two Sides of Rocks Tested in Groups of 4, 9 and 25 after 10s Microwave Heating Note: -19+13.2 mm rocks were grouped in 9 and 25 while -26.5+19 mm rocks are tested 4 rocks at a time as shown in this figure.  72  3.5.2 Results and Discussion 3.5.2.1 Factors influencing microwave heating of lead-zinc sulfide ore Effect of heating time In order to illustrate the effect of heating time on microwave heating of lead-zinc sulfide ore, 50 rock samples from each of the four size fractions were exposed to microwave radiation for different periods of time. It takes approximately five seconds for this type of microwave oven to come to full power. Hence, a minimum of five seconds heating was used in this study. Figure 3-32 shows the average surface temperatures of the fifty rocks each from -19+13.2 mm, -26.5+19 mm and -37.5+26.5 mm size fractions after being heated by microwave for 5s, 10s and 15s. The rocks were numbered in ascending order of temperature after exposure. It is interesting to note that the 10 seconds microwave exposure did yield a more irregular curve. Future work should explore the repeatability of the readings as a function of rock position on the plate, to gain information for scale-up.  73  Average Surface Temperature, ℃  320 300 280 260 240 220 200 180 160 140 120 100 80 60 40 20 0  -19+13.2 mm Avg Surface Temperature - 15S Avg Surface Temperature - 10S Avg Surface Temperature - 5S  1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49  Rock  Average Surface Temperature, ℃  -26.5+19 mm 320 300 280 260 240 220 200 180 160 140 120 100 80 60 40 20 0  Avg Suface Temperature - 15S Avg Surface Temperature - 10S Avg Surface Temperature - 5S  1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49  Average Surface Temperature, ℃  Rock 320 300 280 260 240 220 200 180 160 140 120 100 80 60 40 20 0  -37.5+26.5 mm Avg Surface Temperature - 15S Avg Surface Temperature - 10S Avg Surface Temperature - 5S  1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49  Rock  Figure 3-33 Average Surface Temperatures of Rocks after 5s, 10s, and 15s Microwave Heating from -19+13.2 mm, -26.5+19 mm and -37.5+26.5 mm Size Fractions  74  It is shown clearly in Figure 3-33 that microwave heating of this lead-zinc ore is a function of heating time; the longer the heating time, the higher the increase in temperature. Also evident is that the smaller the particle size, the greater the influence of heating time on microwave heating of this material, based on the fact that microwave power was in excess of that needed (details are shown in Appendix D). Effect of quantity of rocks heated at the same time The effect of quantity of rocks heated at the same time on microwave heating was investigated by exposing one, four, nine and twenty-five rocks at a time to microwave radiation in the lab microwave oven. Figure 3-34 shows the average surface temperatures of rocks after being heated for 10s individual and group heating. The rocks were numbered in ascending order of temperature after individually exposure to 10 seconds microwaving.  75  -19+13.2 mm POM 45 Rocks  Average Surface Temperature, ℃  260 240 220 200 180 160 140 120 100 80 60 40 20 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 Rock # -19+13.2 mm -10S Individual -19+13.2 mm -10S Group in 9 -19+13.2 mm -10S Group in 25  Average Surface Temperature, ℃  -26.5+19 mm POM 45 Rocks 180 170 160 150 140 130 120 110 100 90 80 70 60 50 40 30 20 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 Rock # -26.5+19 mm - 10s individual -26.5+19 mm - 10s Grouped in 9  76  -37.5+26.5 mm POM 45 Rocks  Average Surface Temperature, ℃  180 170 160 150 140 130 120 110 100 90 80 70 60 50 40 30 20 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 Rock # -37.5+26.5 mm - 10S Individual -37.5+26.5 mm - 10S Grouped in 4 -37.5+26.5 mm - 10S Grouped in 9  -53+37.5 mm POM 45 Rocks 100 Average Surface Temperature, ℃  90 80 70 60 50 40 30 20 10 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 Rock # -53+37.5 mm -10s individual  -53+37.5 mm -10s Grouped in 9  Figure 3-34 Average Surface Temperatures after Being Heated for 10s by Individual and Group Heating for Four Size Fractions As seen in Figure 3-34, the highest temperatures are measured for samples being heated individually for 10s for all of the four size fractions. The rocks being heated together, the lower the temperature to which the rocks were heated.  77  Effect of particle size/weight It has been found that microwave heating of sulfide minerals is a function of their size. In order to investigate the effect of particle size on microwave heating of this lead-zinc sulfide ore, two hundred rocks between 53 and 13.2 mm were exposed to microwave heating for 10s. In this study, the relationship between average surface temperature and weight of the rocks was assessed to address the effect of particle size on microwave heating, based on the assumptions that weight is proportional to the third power of particle size and all tests had sufficient MW power. The results are shown in Figure 3-35. -53+13.2 mm 200 Rocks - Individual 10S 260  Average Surface Temperature, ℃  240 220 200 180 160 140 120 100 80 60 40 20 0 0  40  80  120  160  200  240  280  320  360  400  Weight, g  Figure 3-35 Relationship between Microwave Heating Behaviour of Lead-Zinc Ore and Sample Weight As shown in Figure 3-35, microwave heating of rocks containing sulfides is a function of weight. The heavier the sample, the lower temperature it can be heated up to in 10s. The temperatures of rocks barely containing sulfides remain almost the same regardless of weight. This is most significant, because it allows efficient rejection of waste. It is also evident that the smaller the rock is, the greater the difference of average surface temperatures between valuable ore and waste rock. Figure 3-36 shows clearly that the longer the heating time, the greater effect of weight on microwave heating of rocks containing sulfides.  78  Average Surface Temperature, ℃  -37.5+13.2 mm 150 rocks - 5S 260 240 220 200 180 160 140 120 100 80 60 40 20 0 0  20  40  60  80  100  120 140 Weight, g  160  180  200  220  240  180  200  220  240  Average Surface Temperature, ℃  -37.5+13.2 mm 150 rocks -10S 260 240 220 200 180 160 140 120 100 80 60 40 20 0 0  20  40  60  80  100 120 140 Weight, g  160  Average Surface Temperature, ℃  -37.5+13.2 mm 150 rocks - 15S 260 240 220 200 180 160 140 120 100 80 60 40 20 0 0  20  40  60  80  100 120 140 Weight, g  160  180  200  220  240  Figure 3-36 Relationship between Microwave Heating Behaviour of Lead-Zinc Ore and Rock Weight: 5s, 10s and 15s  79  -53+13.2 mm POM 180(45*4) Rocks MW/IR Segregation 10S - comparision 260  Average Surface Temperature, ℃  240 220 200 180 160 140 120 100 80 60 40 20 0  0  40  80  120  individual  160  200 Weight, g  240  280  320  360  400  Group in 9  Figure 3-37 Relationship between Microwave Heating Behaviour of Lead-Zinc Ore and Rock Weight Heated Individually and Heated Together In a Group of Nine Figure 3-37 illustrates that rocks can be heated to higher temperature when being exposed to microwave individually rather than being heated together. It is evident that the finer the particle, the higher the average surface temperature after 10s microwave heating regardless of the quantity of rocks being heated at the same time (see Figure 3-38).  80  Average Surface Temperature, ℃  Individual - 10s (45 rocks from each size fraction) 260 250 240 230 220 210 200 190 180 170 160 150 140 130 120 110 100 90 80 70 60 50 40 30 20 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 Rock # -19+13.2 mm  -26.5+19 mm  -37.5+26.5 mm  -53+37.5 mm  Average Surface Temperature, ℃  Group in 9 - 10s (45 rocks from each size fraction) 180 170 160 150 140 130 120 110 100 90 80 70 60 50 40 30 20 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 Rock # -19+13.2 mm  -26.5+19 mm  -37.5+26.5 mm  -53+37.5 mm  Figure 3-38 Average Surface Temperatures of Rocks from Four Size Fractions after Being Heated for 10s Individually and Together in a Group of Nine  81  Figure 3-33 to Figure 3-38 prove that microwave heating of lead-zinc sulfide ores is a function of particle size, weight, heating time and quantity of rocks being heated at one time. All these factors affect the segregation of barren waste from lead-zinc ore. 3.5.2.2 Distinguishability of waste rock from lead-zinc sulfide ore Since sulfides respond well to microwave heating while waste rocks such as dolomite and quartz do not, it is possible to distinguish valuable mineralized sulfide ores from waste rock by measuring the average surface temperature of rocks after being selectively heated by microwave. MW/IR sorting tests were done on four size fractions. In this section only the sorting results of the -26.5+19 mm size fraction are discussed to demonstrate the distinguishability of waste rock from lead-zinc sulfide ore by means of MW/IR sensing. Results for all the four size fractions are shown in Appendices D. Figure 3-39 shows that the higher the S content, the higher the average surface temperature after 10s microwave heating. The XRD results indicate that only three kinds of sulfide are found in this ore sample, namely galena, sphalerite and pyrite. Sulfur content in our case represents the overall sulfide content present in this sample. However, Pb from galena and Zn from sphalerite are the only elements of interest that need to be recovered during the MW/IR segregation. It is also evident that some rocks containing a low Pb+Zn grade were heated to above 100°C after 10s microwave heating. It is believed that this is related to the high content in the rock of pyrite, which also responds well to microwave radiation. Figure 3-40 also illustrates the fact that microwave can selectively heat up the rocks with higher sulfide content but not those with higher Pb+Zn content. Figure 3-34 and Figure 3-38 also show a perfect rejection of waste because temperatures of ore vary as a function of MW time/power, but the temperatures of the waste rock do not. Therefore, sorting of waste from this lead-zinc ore by means of MW/IR sensing is much easier than sorting of lead-zinc sulfides from waste.  82  Average Surface Temperature, ℃  -26.5+19 mm - Individual 10s  160 140 120 100 80 60 40 20 0 0.00  5.00  10.00  15.00  20.00  25.00  30.00  35.00  50.0  60.0  70.0  S grade, %  (1)  Average Surface Temperature, ℃  -26.5+19 mm - Individual 10s  160 140 120 100 80 60 40 20 0 0.0  10.0  20.0  30.0  40.0  Pb+Zn grade, %  (2) Figure 3-39 Average Surface Temperature vs. S /Pb+Zn Grades  83  -26.5+19 mm POM MW/IR Segregation 27.00  300  S grade Avg Surface Temperature - 15S  270  Avg Surface Temperature - 10S  S grade, %  24.00  240  Avg Surface Temperature - 5S  21.00  210  18.00  180  15.00  150  12.00  120  9.00  90  6.00  60  3.00  30  0.00  Average Surface Temperature, ℃  30.00  0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 Rock  -26.5+19 mm POM MW/IR Segregation - Calibrated  Metal Grade, %  50.00  300  Pb+Zn grade Avg Surface Temperature - 15S Avg Surface Temperature - 10S Avg Surface Temperature - 5S  270 240 210  40.00  180 30.00  150 120  20.00  90 60  10.00  Average Surface Temperature, ℃  60.00  30 0.00  0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 Rock  Figure 3-40 Relationship between Average Surface Temperature and S/Pb+Zn Grades after 5s, 10s and 15s Microwave Heating  84  Sorting results of MW/IR sensing of -26.5+19 mm after 10s microwave heating are taken as an example to demonstrate the capability of selective microwave heating (Table 3-14). A series of separation temperatures were chosen to separate waste rock from mineralized ore. Table 3-14 shows that 99.9% of the Pb and 95% of the Zn in the head sample were sorted into 77% of the weight after 10s microwave heating. Table 3-14 lists the MW/IR sorting results of rocks from -26.5+19 mm size fraction under different testing conditions.  .  85  Table 3-14 MW/IR Segregation (Individual 10s 50 Rocks) Results of -26.5+19 mm size fraction Based on Different Separation Average Surface Temperatures Wt.% in Hot Fraction Concentrate Grades, %  Wt.% in Cold Fraction  % in Hot Fraction  Waste Grades, %  Metal Recovery, %  % in Cold Fraction Metal Recovery, %  Hot Fraction  Cold Fraction Waste Wt. % 10.2  Pb  Zn  S  Pb  Zn  S  Pb  Zn  S  Pb  Zn  S  32  Conc. Wt. % 89.8  10.31  14.18  14.44  0.03  0.82  1.64  100.0  99.3  99.3  0.0  0.7  0.7  33  85.8  14.2  10.79  14.81  15.05  0.03  0.83  1.62  100.0  99.1  99.0  0.0  0.9  1.0  45  76.9  23.1  12.03  15.84  16.36  0.04  2.77  2.43  99.9  95.0  97.0  0.1  5.0  3.0  60  73.7  26.3  12.53  16.42  16.88  0.08  2.73  2.66  99.8  94.4  96.3  0.2  5.6  3.7  80  69.5  30.5  11.09  16.62  16.69  5.09  4.17  5.06  83.2  90.1  90.0  16.8  9.9  10.0  95  54.5  45.5  12.05  16.25  16.16  5.92  8.72  9.53  70.9  69.1  70.2  29.1  30.9  29.8  100  35.8  64.2  14.39  15.44  16.48  6.40  11.36  11.28  55.6  43.1  45.3  44.4  56.9  54.7  105  27.8  72.2  16.42  16.67  17.02  6.50  11.34  11.65  49.4  36.2  37.7  50.6  63.8  62.3  110  19.5  80.5  16.52  16.71  17.12  7.50  11.88  12.18  34.8  25.5  27.8  65.2  74.5  72.2  120  11.3  88.7  13.75  18.02  15.83  8.69  12.16  12.80  16.8  15.9  15.1  83.2  84.1  84.9  130  4.7  95.3  11.73  16.45  18.02  9.14  12.64  12.90  5.9  6.0  7.1  94.1  94.0  92.9  Separation Temperature, °C  Calculated Head Grade: 9.26% Pb and 12.82% Zn, 13.10% S  86  Table 3-15 Summary of MW/IR Sorting Results of -26.5+19 mm Sample Pb in Hot Fraction, % Grade Recovery  Zn in Hot Fraction, % Grade Recovery  21.0  11.71  99.9  16.00  98.6  45  23.1  12.03  99.9  15.84  95.0  60  23.1  12.03  99.9  15.84  95.0  Test Condition  Separation at °C  Mass % of Cold  5s  30  10s 15s  Calculated Head Grade: 9.26% Pb and 12.82% of Zn 50 rocks being tested Pb in Hot Zn in Hot Test Separation Mass % Fraction, % Fraction, % Condition at, °C of Cold Grade Recovery Grade Recovery Individual  45  20.9  12.75  99.9  16.60  95.2  Group in 9  26  16.6  12.09  100.0  16.38  99.0  Calculated Head Grade: 10.09% Pb and 13.79% Zn  45 rocks being tested  It is clearly shown in the above table that MW/IR sensing has the same segregation effect on this lead-zinc ore after 5s and 15s microwave heating. However, at 15s rocks are heated to higher temperatures, meaning that a higher separation temperature is needed for distinguishing waste rock from valuable mineralized ore. Based on these results, it appears that a heating time of 10s will be quite enough for effective elimination of waste rock from this ore sample. However, it also appears that batch heating of rocks, especially the larger ones, for short periods (<10 seconds) introduces errors, which can be avoided by heating the rocks in continuous microwave device. This should be the subject of further testwork. 3.5.2.3 Sorting results summary All of the samples from the four size fractions exhibited good MW/IR sorting results. Microwave heating time of 10s and 15s achieved a similar segregation effect while heating for 5s did not provide as great a difference in average surface temperature as did 10s, probably due to the fact that the oven needs about 5 seconds to warm up and reach full power. As a result, the optimum heating time for MW/IR segregation of this lead-zinc ore on a batch laboratory-scale is 10 seconds. Table 3-16 summarizes the optimum sorting results for each test condition. For individual testing, samples from the -19+13.2 mm size fraction yield the best MW/IR sorting results, with lead and zinc recoveries exceeding 95% and mass rejection exceeding 30%. The other three larger size fractions exhibited similarly good sorting  87  results. About 20-25% mass could be rejected with above 95% metal recovery of lead and zinc. Whether the larger ore particles contained more lead and zinc still needs to be established but appears likely, in which case less mass can be rejected, setting a 95% metal recovery. For group testing, microwave heating of the samples in a group of nine yielded similar sorting results as the individual test. Although lower average surface temperatures were obtained for all the rocks when heated together in a group of nine rather than individually, no significant improvement or inefficiency of the optimum MW/IR sorting of this material was identified. Only a lower separation temperature needed to be chosen for similar sorting results. About 20-30% mass could be rejected in the cold fraction depending on the size of rock in order to recover above 95% of the element of interest, Pb and Zn in our case. However, for the large rocks sized -53+37.5 mm, sorting results varied a lot when separation was at 26°C and 29°C (metal recoveries dropped from 99.8% to 59.8% for Pb and 97.5% to.66.7% for Zn). Previous study by Olive showed that duplicate MW heating test on the same rock could give 2-3°C difference. For the rocks with higher average temperature, the error could be more when duplicate tests were done (Wang, O., personal communication, July 18, 2012). Thus, sorting results of 26°C and 29°C should be of less difference in order to justify the reliability of the MW/IR segregation. Therefore, it can be concluded that heating individually achieved better segregation result than group heating for -53+37.5 mm rocks after 10s microwave heating since consistent sorting results were achieved based on different separation temperatures by individual heating. It is also evident that the finer the particle size, the more consistent the sorting results of group heating were based on separation temperatures with a difference of 2-3°C. Similar optimum sorting results in terms of mass rejection were achieved when exposing the same weight of rocks, in groups of one, four, nine and twenty-five rocks based on different rock size, to microwave heating. This indicates that high capacity of microwave treatment will be possible, which enables the application of this sorting technology to base metal sulfide ore sorting. Shape recognition technologies now available will also allow scanners to determine the size of the individual rocks and adjust the separation temperature for each rock, eliminating the need for elaborate sizing of the sorting machine feed.  88  Table 3-16 MW/IR Sorting Results Summary Pb, % Size, mm  Separation Limit, °C  Mass % of Cold  Zn, %  -53+37.5  26  24.4  Grade in Head 11.10  14.66  99.8  Grade in Head 8.30  10.70  97.5  -37.5+26.5  31  17.3  5.13  6.20  99.8  10.51  12.13  95.4  -26.5+19  30  23.4  10.09  13.15  99.8  13.79  17.06  94.8  -19+13.2  28  31.1  4.46  6.45  99.7  7.12  9.82  95.0  Grade in Hot  Recovery in Hot  Grade in Hot  Recovery in Hot  45 rocks grouped in 9 -10s Pb, % Particle # Individual (-53+37.5 mm) Group in 4 (-37.5+26.5 mm) Group in 9 (-26.5+19 mm) Group in 25 (-19+13.2 mm)  Separation Limit, °C  Mass % of Cold  Grade in Head  Grade in Hot  35  23.5  11.10  37  17.3  30  26  Zn, % Recovery in Hot  Grade in Head  Grade in Hot  Recovery in Hot  14.49  99.8  8.30  10.59  97.7  5.13  6.20  99.8  10.51  12.13  95.4  23.4  10.09  13.15  99.8  13.79  17.06  94.8  35.1  4.46  6.84  99.7  7.12  10.37  94.6  45 rocks -10S same weight being heated at the same time Pb, % Size, mm  Separation Limit, °C  Mass % of Cold  -53+37.5  35  21.3  Grade in Head 11.06  -37.5+26.5  50  24.3  -26.5+19  45  23.1  -19+13.2  31  29.7  Zn, %  Grade in Hot  Recovery in Hot  14.03  99.8  Grade in Head 8.54  4.82  6.23  98.0  9.26  12.03  99.9  4.16  5.90  99.7  Grade in Hot  Recovery in Hot  10.63  98.0  10.06  12.76  96.1  12.82  15.84  95.0  7.30  9.92  95.6  50 rocks individual - 10s  89  3.6 Ore Sorting Test Summary The amenabilities of the four sorting technologies X-ray fluorescence sorting, X-ray transmission sorting, optical sorting and microwave-infrared sorting to this ore have been investigated in laboratory-scale in this chapter. Sorting results are summarized in Table 3-17, except for the microwave-infrared sorting which is still under development. Table 317 indicates that pilot-scale sorting tests of the other three sorting techniques could be performed to further confirm the technical feasibilities of sorting this lead-zinc ore using automatic sensor-based ore sorters, since promising results were obtained in the laboratory-scale evaluation. Technically speaking, XRF sorting provided the most satisfactory sorting results in terms of waste rejection. Above 50% mass could be rejected as waste and above 96% Pb and Zn could be recovered from 47% of the test feed. XRT sorting also presents almost 40% waste mass rejections. Color sorting could only achieve about 20% waste mass rejection, which is not as promising as the X-ray technologies. A flowsheet for a technical amenability study of sensor-based ore sorting in laboratoryscale was generated and provides a reference for similar studies. The flowsheet can be seen in Figure 3-41.  90  Sample  Crushing and Screening Ore  Sub-Sampling  Characterization  Sorting Test Feed  Cleaning  Sample Preparation  Marking  XRD Mineral Analysis Element Analysis  Weighing Sorting Tests  Liberation Study  XRF        Sortability study (Liberation, correlation between the assays and XRF analyzer readings) Grade-yield relationship study Sorting criteria (threshold value) determination Sorting test of different sized feed  XRT       Dual-energy image capture Image processing and analysis (simple and sophisticated image analysis software) Sorting criteria determination Sorting test of different sized feed  Preconcentrate    Optical         Sorting potential study (visible traits, XRF assay and correlation) Image capture Characteristic color data generation Sorting criteria determination Sorting test of different sized feed  Impact Evaluation  Bond work index test Downstream metallurgical performance test  MW/IR       Sorting potential study (respond to microwave heating differently) Sorting criteria determination (find the best heating condition and threshold value for separation) Sorting test of different sized feed  Waste Utilization of the waste e.g. Backfill  Figure 3-41 Flowsheet for Laboratory-scale Sensor-based Ore Sorting Study  91  Table 3-17 Ore Sorting Results Summary Sample Size, mm  -37.5+26.5  -37.5+26.5  Sorting Technique  XRF  XRT  Sorting Criteria  Conc., %  Mass Rejected as Waste, %  Metal Recovery, %  Calculated Head Grade  Conc. Grade, %  Waste Grade, %  Pb  Zn  Pb  Zn  Pb  Zn  Pb  Zn  Note  5.00% Zn Grade  47.2  52.8  96.5  97.1  3.72  10.21  7.61  21.04  0.25  0.56  Assays  Avg. Brightness Value of High Energy Image of 65  61.9  38.1  99.0  96.1  6.20  8.73  9.92  13.56  0.17  0.88  Calibrated XRF Powder  Ore Index of 33  62.3  37.7  99.1  95.6  6.20  8.73  9.86  13.39  0.14  1.02  Calibrated XRF Powder  -37.5+26.5  Optical (Color)  R133-22, R40-7  81.2  18.8  98.5  97.0  6.20  8.73  7.64  10.43  0.50  0.50  Calibrated XRF Powder  -19+13.2  MW/IR  31°C (Individual Heating)  70.3  29.7  99.7  95.6  4.16  7.30  5.90  9.92  0.04  1.09  Calibrated XRF Surface  92  CHAPTER 4  SORTING IMPACT EVALUATION  It has been demonstrated that automatic sensor-based ore sorting is beneficial to the ore beneficiation process in terms of energy saving and metallurgical performance improvement. Bamber (2008) demonstrated that preconcentration of ores by sorting had three benefits regarding power savings at the mill: reduction in tonnage of feed to the mill; rejection of hard siliceous waste rock which has high grinding work index; and providing finer feed to mill plant. Therefore, energy consumption could be considered as one of the indices used for sorting impact evaluation. It can be calculated by measuring the Bond Ball Mill Work Index for the material. Better metallurgical performance may also be achieved with higher grade and finer feed due to the upgrading effect of ore sorting and the removal of hard gangue minerals. In order to evaluate the sorting impact of this lead-zinc ore, the Bond Ball Mill Work Indices for the head ore, X-ray fluorescence (XRF) sorting concentrate and XRF waste were calculated for the evaluation of grinding energy savings. Flotation tests were also conducted with the feed of head sample and XRF concentrate using similar reagent conditions to those employed by the mine site.  93  4.1 Experimental Procedures 4.1.1 X-Ray Diffraction Analysis Sub-samples of head ore, XRF concentrate and XRF waste were prepared for the Quantitative X-Ray Diffraction analysis for mineral composition characterization. 4.1.2 Grinding Energy Consumption Test The impact of ore sorting on grinding energy consumption can be calculated from the Bond equation as follows (Bamber, 2008): ( Where:  √  √  )  (1)  = Waste Rejection (tph) = Change in Bond Work Index = 80% passing size of the product (µm) = 80% passing size of the feed (µm)  Waste rejection data can be achieved from different sorting tests. Bond Ball Mill Work Indices for the materials can be determined by Standard Laboratory Bond Ball Mill Index Test. Representative samples from -37.5+26.5 mm size fraction Pend Oreille ore head sample, XRF sorting concentrate and XRF sorting waste were crushed by gyratory crusher and cone crusher. Then the crushed products were screened at 2800 µm and prepared for the Laboratory Bond Ball Mill Index test. The Bond Ball Mill (Figure 4-1) is 305 mm in length and 305 mm in diameter. Ball charge used is shown in Table 4-1. Table 4-1 Bond Ball Charge Ball, Inches 1 1/2 1 1/4 1 3/4 5/8 Sum  Diameter, Inches 1.5 1.25 1 0.75 0.625  Number 43 67 10 71 94  Unit Weight, lbs 0.500555 0.289673 0.148313 0.062569 0.036209  Total Weight, lbs 21.5 19.4 1.5 4.4 3.4 50.3  Steel SG=7.85 Calculated surface area: 842 sq. in.  94  Figure 4-1 Standard Laboratory Bond Test Ball Mill The Bond Work Index of the POM feed was determined through a full Standard Bond Ball Mill Work Index test with a closing screen of 180 µm and considered as the reference. Tests were done according to the standard Bond Ball Mill Work Index Test procedures developed by Bond (1961). The work indices of the XRF sorting concentrate and waste were then determined by a comparative Bond Ball Mill Work Index test (Berry & Bruce, 1966). The Bond Ball Mill Work Indices can be calculated employing equations (2) and (3). ( (  √  √  )⁄(  √  √  √ √  )  )  (2) (3)  4.1.3 Flotation Test Flotation tests of different feed samples (Head and XRF concentrate) were conducted to evaluate the impact of ore sorting on the downstream flotation performance. One 1 kg subsample of the head ore and two 1 kg subsamples of XRF concentrate were prepared as the feed for the flotation tests. According to the grindability test results shown in Appendix E, a 500 g sample could be ground dry for 5 min in a laboratory rod mill to have a product of P80 being about 100 microns. As a result, each flotation feed was divided into two portions and dry ground to have a grinding product size of 80%  95  passing 100 microns. Then the ground materials were transferred into a batch scale Denver laboratory flotation cell (4L). The flotation test was conducted at 20% solid content with tap water. The temperature under which this test was conducted is room temperature. The reagents used in sequence were pH modifier, activator or depressant and collector. The pH of the slurry was adjusted by adding lime until it was 10.5 in the lead flotation circuit. After the pH of the slurry was stabilized, the depressant sodium cyanide with a concentration of 100 g/t was added in the lead flotation circuit to depress pyrite and sphalerite. After three min conditioning, the Potassium Amyl Xanthate (PAX) with a concentration of 50g/t was added as collector. Conditioning time for the pH modifier, activator or depressant and collector was one min each. The methylisobutylcarbinol (MIBC) was used as frother. In the zinc flotation circuit, the pH was set as 11. The activator used for Zn flotation was copper sulphate with a concentration of 700 g/t. 100 g/t PAX was used in the Zn flotation circuit. Froth was collected from the cell by scraping by hand at an interval of three seconds in the first minute and five seconds thereafter. At the very beginning, froth was allowed to build up for five seconds before scraping. Fresh tap water was added to slurry during the test in order to maintain pulp volume and clean sides of the cell. Each product was then dried in the oven and prepared for XRF powder reading for assays.  96  4.2 Results and Discussion 4.2.1 X-Ray Diffraction Analysis The quantitative X-Ray Diffraction analysis of the head ore, XRF concentrate and XRF waste are shown in Table 4-2. Results indicate that XRF sorting rejected many hard dolomitic gangue minerals that are hard to grind. Lead and zinc were upgraded in the XRF concentrate (XRFC) which meant that feed with higher grades was reported to flotation circuit. Almost no valuable metal contents of lead and zinc were lost in the XRF waste. Table 4-2 Results of Quantitative Phase Analysis (Wt. %) Mineral  Ideal Formula  Feed  XRFC  Waste  Pyrite  FeS2  38.5  48.4  3.2  Sphalerite  (Zn,Fe)S  10.2  19.5  Galena  PbS  4.6  4.9  Cerussite  PbCO3  0.1  Dolomite-Ankerite  CaMg(CO3)22+ Ca(Fe ,Mg,Mn)(CO3)2  Calcite  42.6  23.7  88.4  CaCO3  2.2  1.9  6.8  Plagioclase ?  NaAlSi3O8 – CaAlSi2O8  0.8  1.0  0.2  Quartz  SiO2  0.9  0.6  0.7  Muscovite ?  KAl2(AlSi3O10)(OH)2  Total  0.7 100.0  100.0  100.0  97  4.2.2 Grinding Energy Savings Results of the Work Index tests are shown in Table 4-3. Table 4-3 P80, F80 and Work Indices for XRF Feed, XRF Concentrates and XRF Waste Sample  F80, microns  P80, microns  BWI, kwh/tonne  % Saving of Wi  Head Ore  2338  99  8.33  XRF Conc.  2230  77  7.20  13.6  XRFConc.-2  2362  82  7.40  11.2  XRF Waste  2365  101  8.43  Results shown in Table 4-3 indicated that the Bond Work Index of the XRF concentrate was smaller than the head ore which meant that ore sorting could provide softer feed to the mill. Even though the Bond Work Indices were similar for the head ore and XRF concentrate, considerable amount of energy could still be saved due to the large mass rejection rate by XRF sorting. The grinding energy savings can be calculated based on the following conditions as an example. The F80 and P80 of the ball mill at the mine site were 4.17 mm and 0.47 mm respectively (Lin, D., personal communication, October 27, 2011). The waste rejection achieved by XRF sorting of -37.5+26.5 mm size fraction was 52.8%. The average calculated Bond Work Index for the XRF concentrate was 7.30 kwh/tonne. The Bond Work Index for head ore was 8.33 kwh/tonne. The capacity of the XRF sorter was assumed to be 60 tph. The grinding energy savings after ore sorting can be calculated then using the Equation (1). (  √  √  )  (  √  √  )  kW  4.2.3 Flotation Test Results Flotation results are shown in Table 4-4 based on XRF powder readings. The metal recoveries for Pb and Zn after XRF sorting were 96.5% and 97.1% respectively. As a result, the overall metal recoveries of Pb and Zn for XRF concentrate after flotation process were 73.9% Pb in the lead concentrate and 73.0% Zn in the zinc  98  concentrate. The overall metal recoveries of Pb and Zn for XRFconcentrate-2 were 75.6% Pb in the lead concentrate and 85.4% Zn in the zinc concentrate. It is clearly shown that the overall metal recoveries of Pb and Zn both increased after preconcentration by XRF sorting. The grades of the lead concentrate and zinc concentrate were also higher after preconcentration. However, the results obtained in this test could only be seen as tentative. Repeated flotation tests should be conducted in the future work to justify the reliability of the results obtained in this study  99  Table 4-4 Flotation Results Based on Calibrated XRF Powder Readings POM Pb Conc. Zn Conc. Tailings Total XRF Pb Conc. Zn Conc. Tailings XRF2 Pb Conc. Zn Conc. Tailings Total  Weight, g 101.85 200.66 656.1 958.61 Weight, g 109.28 212.9 635.1 957.28 Weight, g 98.36 258.6 609.74 966.7  Weight Distr. % 10.6 20.9 68.4 100.0 Weight Distr. % 11.4 22.2 66.3 100.0 Weight Distr. % 10.2 26.8 63.1 100.0  Pb Grade, % 23.2 4.9 0.7 4.0 Pb Grade, % 30.6 2.4 0.8 4.6 Pb Grade, % 50.7 3.2 0.9 6.6  Zn Grade, % 11.4 30.4 1.9 8.9 Zn Grade, % 18.1 44.2 1.8 13.1 Zn Grade, % 4.5 46.1 1.9 14.0  Fe Grade, % 7.3 5.6 17.7 14.0 Fe Grade, % 6.0 5.5 27.5 20.1 Fe Grade, % 6.1 5.0 24.9 17.7  Pb Recovery, % 62.3 25.8 11.8 100.0 Pb Recovery, % 76.6 11.8 11.6 100.0 Pb Recovery, % 78.3 13.0 8.7 100.0  Zn Recovery, % 13.6 71.8 14.6 100.0 Zn Recovery, % 15.8 75.2 9.0 100.0 Zn Recovery, % 7.0 88.0 5.0 100.0  100  CHAPTER 5  CONCLUSIONS AND RECOMMENDATIONS  5.1 Conclusions This thesis summarized the research testwork done on the technical evaluation of sensor-based ore sorting (XRF, XRT, Color and MW/IR) of a Mississippi Valley type lead-zinc ore sample from Pend Oreille Mine. A literature review found that automatic sensor-based ore sorting has been successfully applied to many base metal cases such as copper, nickel and gold. This laboratory-scale ore sorting amenability study of a leadzinc ore, using different sensing technologies, will add valuable knowledge to the study of sensor-based ore sorting and will provide guidelines for ore sorting evaluation of other ore types. Several conclusions can be drawn according to the testwork described in this thesis. The most valuable contribution of this work is the generation of a detailed evaluation process and methodology for ore sorting amenability study at laboratory-scale using certain sensor-based sorting techniques. The X-ray sorting methods of fluorescence and transmission showed greatest potential for preconcentration of this lead-zinc ore based on the sorting results obtained in this study and therefore are recommended for large-scale testing and justification of the reliability. Optical sorting seemed to be not very effective for sorting this lead-zinc ore and hence not studied further. MW/IR sorting of lead-zinc bearing sulfide ore is promising but the sorting technique itself needs more development until industrial application. 5.1.1 Conclusions from the XRF Sorting Test 1. The procedures for laboratory-scale XRF sorting amenability study using a XRF analyzer were established. The grades of elements of interest are read using the XRF analyzer by averaging different faces’ readings; sorting potential using this XRF analyzer is demonstrated by plotting the chemical assay results and XRF analyzer surface readings of the rocks on a sortability curve; a Grade-Recovery curve is obtained by separation at different threshold metal grades for sorting criterion determination; sorting results using this XRF analyzer are calculated.  101  2. X-Ray Fluorescence Sorting has been applied successfully in the separation of dolomitic waste rock from lead-zinc mineralized ore in this preliminary scope study using the Innov.X XRF Analyzer. The results have demonstrated that discrimination of element compositions on the surface of rock could be used and detected by a XRF analyzer to sort ores from barren gangues when:   a degree of “liberation” at certain particle size provides particles with different grades to be sorted (liberation here means the enrichment of valuable minerals/gangue minerals in the rock); and    a good correlation exists between the analysis of particle surface by XRF analyzer/XRF sorter and the analysis of bulk particle by chemical assay (a strong correlation between XRF surface reading and assays existed for our material).  3. It is observed that XRF sorting provided satisfactory results in rejecting barren waste rocks and therefore upgrading the mill feed. The overall sorting results of the 325 rocks sized above 26.5 mm (top size: bottom size = 3:1) demonstrated that above 95% of the Pb and Zn reporting to the feed could be recovered when rejecting 47.3% of mass as waste. Pb and Zn grades in the concentrate were calculated to be 7.11% and 15.28% compared to those in the head being 3.91% and 8.50% respectively (upgrade the head by a factor of approximate 1.8 for both Pb and Zn). 4. The finer the particle size, the better the sorting results are in terms of percentage mass rejected as waste. The -37.5+26.5 mm size fraction showed the best sorting results. When a Zn grade of 5.00% was selected as sorting threshold (cut-off grade), 96.5% Pb and 97.1% Zn could be recovered in the concentrate product with calculated grades of 7.61% of Pb and 21.04 % of Zn at a waste rejection of 52.8% by mass. 5.1.2 Conclusions from the XRT Sorting Test 1. A simple X-ray transmission image processing and analyzing method was developed in this study using GIMP software to determine the sorting criterion for XRT sorting of an ore sample. This method can be used as laboratory quick evaluation of an ore’s amenability to XRT sorting. 2. Sorting criteria used in this study included threshold average brightness value of single energy X-ray transmission image (high/low energy) and Ore Index  102  generated from dual-energy X-ray images (by CommoDas). The smaller the average brightness value of the X-ray image and the larger the Ore Index of the rock, the more likely the rock reported to the product fraction. 3. Clear distinction between lead-zinc bearing sulfide and dolomitic waste rock were observed using the XRT sorting technique. It is observed from the sorting results that above 36% mass of the feed can be rejected as waste when above 95% of Pb and Zn were recovered in the product by X-Ray transmission sorting. The calculated product grades of Pb and Zn were about 10% Pb and 14% Zn compared to 6% Pb and 9% Zn in the feed. 4. Similar sorting results were obtained using single energy X-ray transmission imaging (high/low energy) and dual-energy transmission imaging techniques primarily because the sample tested for this sorting technique had a narrow size range and therefore the effect of thickness on X-ray transmission could be neglected. The average brightness value of the X-ray image could be used as sorting criterion instead of the characteristic distribution of the brightness for all pixels within the image (each pixel is analyzed) because the ore sample sized – 37.5+26.5 mm was well “liberated” (the rock either enriched in valuable minerals or gangue minerals) The average brightness value of the image could represent the average density composition (atomic number) of the rock due to the well liberation of the rock at this size. The sortability of the lead-zinc ore using X-Ray Transmission sorting could only be concluded for this specific case. The technical amenability of XRT sorting of other types of lead-zinc ore could be evaluated using similar procedures as this study. 5.1.3 Conclusions from the Optical Sorting Test 1. A methodology for laboratory-scale color sorting amenability studies was established based on the process of this color sorting study. The major steps are characterization of ore, sample preparation, sorting potential determination, image capture, data analysis and sorting result calculation. This methodology provides useful guidelines for designing and conducting future laboratory-scale color ore sorting amenability studies using optical bench-top image acquisition systems. 2. The results of color sorting of the Pend Oreille lead-zinc ore using simple and handy image analysis software indicated that color sorting was capable of  103  upgrading a sample of this lead-zinc ore sized -37.5+26.5 mm based on color differences. More specifically, the rejection of light grey dolomitic waste rocks led to about 19% reduction of the mass while recovering 98.5% and 97% of lead and zinc. Lead and zinc grades were upgraded to 7.64% and 10.43% respectively from 6.30% and 8.73% in the feed. 5.1.4 Conclusions from the MW/IR Sorting Test 1. The effect on microwave heating behaviour of heating time and weight/size of rock, as well as quantity of rocks being heated at the same time was investigated using a lead-zinc ore from industry. 2. The longer the heating time, the higher the average surface temperature of the rock. The finer the rock size, the smaller the rock weight, the less time the rocks are heated, and the smaller the quantity of rocks being heated at the same time, the more efficiently the microwave heating performs in terms of ore sorting. 3. This MW/IR sorting test work has indicated that carbonate gangue minerals in mineralized sulfide ore does not heat when exposed to microwave heating. This suggests that MW exposure of this type of ore, followed by IR sensing, is a possible method to metallurgically concentrate this lead-zinc ore by rejecting waste rock. While performance of samples from different size fractions varied a little, they all showed high metal recoveries as well as about 20-30% barren mass rejection. The smaller the size, the better the segregation of valuable ore and barren rock, and therefore the better the sorting results were in terms of grade/recovery. 5.1.5 Conclusions from the Impact Evaluation Test 1. The results of sorting impact evaluation testwork showed that the preconcentrate of X-ray fluorescence sorting had a Bond Work Index 12% smaller than that of the feed ore, which indicated potential grinding energy saving due to the removal of hard waste rock. The reduction of feed mass by up to 50% will also contribute to energy savings. 2. Flotation test results also presented the improvement of the overall lead and zinc recovery in the lead rougher flotation concentrate and zinc rougher flotation concentrate as well as the concentrate grade after XRF sorting. The reduction of feed mass by removing the dolomitic gangue minerals up to 50% by mass could also contribute to reagent savings in the flotation process.  104  5.2 Recommendations Several recommendations are proposed for further study in two directions: recommendations on further evaluation of this lead-zinc ore’s amenability to sensorbased ore sorting and recommendations on development of laboratory-scale sensorbased ore sorting evaluation. 5.2.1 Recommendations on Sensor-Based Ore Sorting of This Lead-Zinc Ore 1. Further tests using representative Run-of-Mine ore sample from different mineralization zones in the deposit should be conducted to evaluate the amenabilities of this lead-zinc ore to sensor-based ore sorting due to the limitation of the sample used in this study. 2. Large-scale testwork should include sorting tests of different particle sizes and degree of liberation and therefore optimizing the top size and top/bottom size ratio for sorting process using different sensing technologies. 3. Pilot-scale or full-scale sorting tests by commercial ore sorters should be conducted to justify the reliability of the sorting results obtained by this laboratoryscale preliminary study. 5.2.2 Recommendations on Development of Laboratory-Scale Sensor-Based Ore Sorting Evaluation 1. Ore sample mineral liberation study by optical thin section study or MLA should also be conducted to characterize the ore. 2. Advanced and updated XRF analyzer should be used in future XRF sorting study in order to achieve more accurate results. 3. Integration of advanced image analysis software into the bench-top image acquisition system to extract color information and determine sorting criterion for color sorting study. 4. Advanced DE- XRT image analysis system, which is capable of automated analyzing individual pixel in both of the high and low energy images and generating sorting criterion, should be considered for interpretation of the dualenergy image obtained in future DE-XRT sorting study. 5. Continuous microwave heating device should be used for further MW/IR study to eliminate the microwave oven warm-up characteristic, creating uncertainty in this work whether heating selectivity is lost for larger rocks (5 cm).  105  6. Limitations such as high cost of microwave generation, lack of reliable design of continuous microwave generator meeting the processing requirements of the automated MW/IR sorter and slow data processing speed of IR image are major problems need to be considered in the development of the MW/IR sorting technology in future study.  106  REFERENCES  Allen, A., & Gordon, H. (2009, May). X-ray sorting and other technologies for upgrading nickel ore. 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Retrieved August 10th, 2011 from http://www.canadianminingjournal.com/news/zinc-mining-news--pend-oreillemine-to-reopen/1000013128/  112  APPENDICES APPENDIX A  X-RAY FLUORESCENCE SORTING DATA  Surface Analysis of Rocks from Four Size Fractions by the Innov.X XRF Analyzer -37.5+26.5 mm Size Fraction  Sample ID  Weight, g  PO106-1  Metal Grade, % Pb  Zn  Fe  83.7  1.54  4.70  2.83  PO106-2  125.5  4.01  28.89  16.11  PO106-3  140.2  3.03  49.22  6.10  PO106-4  62.6  0.05  0.38  2.68  PO106-5  72.7  0.04  0.29  0.87  PO106-6  86.7  9.75  17.20  17.14  PO106-7  68.2  0.06  0.38  1.24  PO106-8  107.5  21.66  41.42  4.39  PO106-9  70.1  0.06  0.28  1.49  PO106-10  59.8  0.07  0.29  1.37  PO106-11  68.9  0.09  12.58  1.10  PO106-12  47.9  0.21  33.67  7.23  PO106-13  55.6  0.03  0.13  1.08  PO106-14  149.9  0.05  1.74  2.02  PO106-15  54.7  0.03  0.14  1.05  PO106-16  57.9  11.54  40.07  2.98  PO106-17  118.4  0.52  0.85  27.81  PO106-18  55.2  0.02  0.15  2.40  PO106-19  87.9  0.87  3.13  34.78  PO106-20  74.3  0.07  0.52  0.92  PO106-21  115.5  2.31  53.06  10.78  PO106-22  78.7  0.36  38.36  5.96  PO106-23  186.6  12.55  20.09  18.07  PO106-24  190.7  14.72  62.55  2.52  PO106-25  103.7  13.59  57.45  3.46  PO106-26  53.7  0.06  0.45  2.36  PO106-27  59.2  3.67  5.68  9.37  PO106-28  92.5  0.60  1.71  41.92  PO106-29  63.7  9.33  33.96  14.40  PO106-30  115.0  1.33  25.70  15.51  PO106-31  120.9  0.04  0.21  3.77  113  Sample ID  Weight, g  PO106-32  Metal Grade, % Pb  Zn  Fe  101.6  0.57  23.44  19.75  PO106-33  45.9  0.03  0.15  3.12  PO106-34  36.6  0.30  28.96  6.01  PO106-35  64.5  3.10  13.56  20.91  PO106-36  56.7  1.43  19.86  17.37  PO106-37  52.7  0.68  45.51  12.16  PO106-38  101.3  0.05  0.29  3.66  PO106-39  103.9  0.04  0.18  1.67  PO106-40  78.9  0.18  1.93  3.92  PO106-41  41.9  0.03  1.83  1.53  PO106-42  101.8  0.10  1.37  3.31  PO106-43  71.7  0.03  0.15  0.71  PO106-44  79.6  0.31  25.69  10.92  PO106-45  47.3  0.05  0.59  1.39  PO106-46  71.1  0.08  0.72  1.13  PO106-47  80.6  0.04  0.24  2.47  PO106-48  70.6  0.21  16.28  9.51  PO106-49  83.5  1.60  3.97  3.34  PO106-50  137.1  0.79  46.72  2.91  PO106-51  93.0  0.29  16.64  13.95  PO106-52  142.0  0.04  0.29  1.93  PO106-53  53.7  0.20  2.11  9.82  PO106-54  109.9  0.31  9.03  13.15  PO106-55  150.7  1.21  44.39  1.97  PO106-56  55.5  0.05  3.55  1.66  PO106-57  164.7  0.19  28.54  3.49  PO106-58  47.0  0.03  0.22  1.44  PO106-59  79.0  0.04  0.24  1.17  PO106-60  50.0  0.03  0.17  9.50  PO106-61  125.9  0.30  15.01  11.99  PO106-62  118.5  0.29  14.78  7.70  PO106-63  40.8  0.09  2.37  17.57  PO106-64  45.6  0.07  4.90  0.89  PO106-65  62.8  0.03  0.20  4.48  PO106-66  156.5  0.31  1.73  26.31  PO106-67  89.2  0.20  20.40  8.13  PO106-68  49.2  0.15  3.12  12.31  114  Sample ID  Weight, g  PO106-69  Metal Grade, % Pb  Zn  Fe  109.0  0.30  23.91  8.72  PO106-70  85.0  0.04  0.38  8.32  PO106-71  62.9  25.99  28.30  10.35  PO106-72  50.1  0.05  0.32  1.14  PO106-73  40.5  0.07  0.39  1.39  PO106-74  79.1  0.81  5.87  36.08  PO106-75  44.6  0.07  1.43  3.79  PO106-76  53.7  0.02  0.14  0.73  PO106-77  37.2  0.02  0.11  0.84  PO106-78  52.9  0.42  0.94  38.94  PO106-79  166.7  0.30  41.55  4.72  PO106-80  85.4  0.06  0.28  1.66  PO106-81  80.5  0.55  3.14  32.20  PO106-82  79.6  0.73  2.07  37.06  PO106-83  60.1  0.19  1.30  13.31  PO106-84  39.3  0.53  7.82  36.34  PO106-85  41.8  0.15  11.38  4.64  PO106-86  132.3  0.72  1.35  25.62  PO106-87  67.0  0.24  28.71  13.22  PO106-88  73.3  0.06  0.41  2.69  PO106-89  55.1  0.05  0.32  1.18  PO106-90  77.2  0.10  0.33  6.75  PO106-91  50.9  0.25  21.44  8.55  PO106-92  62.0  0.02  0.15  1.41  PO106-93  63.2  0.04  1.31  2.78  PO106-94  85.1  0.59  1.67  24.82  PO106-95  44.8  0.02  0.12  7.58  PO106-96  55.7  0.11  26.02  3.00  PO106-97  76.8  2.95  23.35  8.50  PO106-98  59.6  0.10  1.53  15.53  PO106-99  77.9  0.02  0.18  0.89  PO106-100  80.0  6.27  27.32  15.16  115  -53+37.5 mm Size Fraction  Sample ID  Weight, g  PO150-1  Metal Grade, % Pb  Zn  Fe  91.2  0.07  0.29  1.06  PO150-2  165.8  0.05  0.38  10.33  PO150-3  197.4  0.05  1.26  1.13  PO150-4  254.4  0.44  7.52  21.40  PO150-5  229.1  0.02  0.20  1.03  PO150-6  231.6  0.04  0.27  1.00  PO150-7  150.1  0.09  1.29  1.86  PO150-8  337.5  0.22  20.03  8.26  PO150-9  254.1  0.54  3.51  31.02  PO150-10  261.8  0.33  1.95  13.64  PO150-11  93.5  2.29  43.80  2.09  PO150-12  184.3  0.04  0.33  1.37  PO150-13  281.5  0.06  0.39  2.24  PO150-14  129.2  0.31  31.41  5.49  PO150-15  619.1  12.82  46.03  4.67  PO150-16  234.9  15.27  45.66  5.50  PO150-17  136.9  0.51  0.78  25.17  PO150-18  166.7  0.10  1.73  1.15  PO150-19  358.0  9.42  24.73  13.91  PO150-20  105.3  0.10  1.29  3.44  PO150-21  285.4  0.08  0.54  1.32  PO150-22  324.9  0.35  16.94  16.36  PO150-23  154.9  0.08  0.38  9.18  PO150-24  179.9  12.10  27.04  10.10  PO150-25  134.9  0.08  0.39  1.61  PO150-26  397.1  16.23  54.14  4.78  PO150-27  292.4  10.04  26.15  12.03  PO150-28  204.6  0.56  28.56  13.69  PO150-29  225.0  10.33  27.11  15.38  PO150-30  92.9  0.09  3.75  1.53  PO150-31  150.4  0.03  0.22  0.86  PO150-32  444.5  0.13  3.96  3.89  PO150-33  223.1  0.08  0.58  1.78  PO150-34  93.4  0.04  0.15  1.12  PO150-35  221.4  0.33  38.33  7.48  PO150-36  298.3  0.03  0.12  0.61  PO150-37  207.0  0.04  0.17  0.90  116  Sample ID  Weight, g  PO150-38  Metal Grade, % Pb  Zn  Fe  127.7  0.08  0.37  0.91  PO150-39  142.9  0.05  0.23  0.89  PO150-40  200.2  0.28  16.32  10.88  PO150-41  239.0  0.07  0.30  2.84  PO150-42  106.6  0.05  0.35  9.40  PO150-43  379.4  5.37  14.59  5.55  PO150-44  218.0  0.69  4.13  23.87  PO150-45  122.6  0.12  0.45  1.66  PO150-46  85.4  0.09  6.47  4.41  PO150-47  150.3  0.11  4.24  5.47  PO150-48  324.3  0.25  2.22  15.58  PO150-49  268.5  0.13  1.31  5.12  PO150-50  288.1  22.22  29.02  7.99  PO150-51  153.0  0.33  15.30  19.54  PO150-52  119.3  0.06  0.26  1.03  PO150-53  88.4  0.07  0.95  2.74  PO150-54  143.8  0.06  0.23  1.01  PO150-55  273.2  0.09  0.23  7.80  PO150-56  253.4  7.99  31.32  10.98  PO150-57  306.8  0.40  1.20  33.13  PO150-58  149.8  0.14  19.19  7.16  PO150-59  209.5  3.01  24.71  13.10  PO150-60  93.9  0.17  8.21  6.58  PO150-61  182.7  0.54  45.57  2.07  PO150-62  146.3  0.10  12.70  3.41  PO150-63  345.6  0.46  1.53  19.20  PO150-64  235.2  0.60  12.29  5.09  PO150-65  199.7  0.72  29.19  8.51  PO150-66  153.1  0.04  0.28  7.15  PO150-67  252.9  0.82  8.74  36.06  PO150-68  250.1  0.42  2.11  27.23  PO150-69  156.1  0.17  8.01  8.32  PO150-70  93.1  0.06  0.25  0.84  PO150-71  165.2  8.07  11.81  22.68  PO150-72  568.4  17.21  36.46  8.69  PO150-73  131.1  0.28  17.75  3.15  PO150-74  89.4  0.07  0.25  2.25  PO150-75  95.3  0.07  0.27  1.40  PO150-76  138.2  0.05  0.21  1.06  117  Sample ID  Weight, g  PO150-77  Metal Grade, % Pb  Zn  Fe  20.2  0.07  0.29  1.08  PO150-78  147.1  0.08  9.20  1.38  PO150-79  111.2  0.07  0.28  1.33  PO150-80  225.0  0.44  42.58  4.67  PO150-81  109.6  0.07  0.60  2.59  PO150-82  246.5  1.35  22.44  3.30  PO150-83  100.6  0.05  0.30  1.78  PO150-84  211.6  0.09  2.49  4.03  PO150-85  322.2  3.02  9.25  9.03  PO150-86  218.3  3.22  12.41  17.29  PO150-87  133.1  0.09  0.49  1.96  PO150-88  137.7  0.14  18.09  1.44  PO150-89  190.8  0.11  0.46  1.63  PO150-90  146.7  0.07  0.36  2.84  PO150-91  271.5  19.03  32.38  8.87  PO150-92  236.1  4.37  15.08  13.88  PO150-93  185.2  2.38  19.23  18.07  PO150-94  305.2  0.78  2.75  19.60  PO150-95  286.4  0.24  13.16  10.03  PO150-96  137.3  0.13  7.16  6.30  PO150-97  302.9  3.81  10.78  14.10  PO150-98  84.9  0.07  0.39  1.13  PO150-99  285.7  0.44  14.82  7.59  PO150-100  73.8  0.13  1.40  6.99  118  -75+53 mm Size Fraction  Sample  Weight, g  PO212-1  Metal Grade, % Pb  Zn  Fe  363  3.32  18.64  22.87  PO212-2  750.1  0.21  0.67  6.41  PO212-3  766.9  0.22  27.59  4.11  PO212-4  673.1  0.08  0.71  2.82  PO212-5  667.5  8.86  40.34  7.06  PO212-6  208.3  0.11  1.23  1.65  PO212-7  598.5  0.77  8.17  25.66  PO212-8  340.1  0.08  0.66  6.14  PO212-9  543.3  0.43  7.69  16.16  PO212-10  857.6  0.42  2.15  21.57  PO212-11  617.9  2.24  41.06  8.28  PO212-12  1104.7  6.29  12.93  7.72  PO212-13  535.5  11.45  16.43  10.32  PO212-14  421.4  0.39  27.69  6.45  PO212-15  782.7  0.03  0.15  0.92  PO212-16  454.3  0.04  0.20  1.04  PO212-17  475.6  0.03  0.15  0.97  PO212-18  855.3  0.03  0.27  1.24  PO212-19  454.8  0.03  0.48  0.70  PO212-20  820.5  0.30  20.90  10.94  PO212-21  492.8  0.34  3.91  5.54  PO212-22  436.6  22.23  46.02  4.92  PO212-23  917.6  0.03  0.24  0.99  PO212-24  919.4  0.67  15.60  24.56  PO212-25  409.0  1.59  33.16  6.94  PO212-26  438.5  1.02  10.17  3.62  PO212-27  273.0  0.03  0.31  0.86  PO212-28  528.5  0.34  38.30  5.65  PO212-29  411.8  0.14  17.88  5.54  PO212-30  930.7  0.51  4.39  23.18  PO212-31  916.9  0.72  16.09  21.88  PO212-32  438.1  5.99  19.00  19.70  PO212-33  769.9  15.73  27.89  7.98  PO212-34  355.1  0.24  0.36  1.75  PO212-35  439.5  0.65  27.50  9.73  PO212-36  340.0  0.09  0.35  2.43  PO212-37  262.4  1.24  13.97  5.73  119  Sample  Weight, g  PO212-38  Metal Grade, % Pb  Zn  Fe  393.2  0.15  12.55  2.10  PO212-39  388.1  0.35  9.70  4.61  PO212-40  457.8  0.19  0.61  10.14  PO212-41  323.1  3.26  35.18  7.38  PO212-42  684.3  0.56  33.42  8.76  PO212-43  348.1  0.04  5.03  4.55  PO212-44  462.9  0.81  1.64  34.41  PO212-45  668.1  0.10  1.10  4.36  PO212-46  517.0  0.06  1.23  4.72  PO212-47  382.6  0.04  0.30  2.09  PO212-48  532.0  7.68  23.96  12.97  PO212-49  740.7  1.03  14.48  11.47  PO212-50  663.1  4.74  7.13  8.42  PO212-51  422.2  0.05  0.30  1.77  PO212-52  827.9  13.60  36.08  6.08  PO212-53  733.3  6.21  18.62  21.14  PO212-54  214.9  0.12  0.49  1.89  PO212-55  425.5  0.20  1.09  10.04  PO212-56  1025.6  0.66  2.24  27.88  PO212-57  542.6  0.10  0.66  3.56  PO212-58  786.4  0.68  16.00  12.74  PO212-59  454.9  0.61  13.66  14.76  PO212-60  405.8  0.07  0.40  2.41  PO212-61  335.2  17.82  37.58  8.65  PO212-62  631.0  2.28  21.67  7.13  PO212-63  1148.5  11.16  20.21  7.67  PO212-64  719.4  0.67  8.37  31.15  PO212-65  786.0  12.32  37.69  9.23  PO212-66  474.0  0.10  0.71  3.29  PO212-67  566.4  14.23  16.86  8.69  PO212-68  506.8  0.19  15.00  6.40  PO212-69  482.0  3.54  17.09  16.13  PO212-70  664.4  0.93  11.83  16.06  PO212-71  682.3  5.44  19.98  6.61  PO212-72  770.3  7.29  12.33  14.41  PO212-73  860.7  0.14  2.99  8.59  PO212-74  1024.9  0.40  4.43  10.67  PO212-75  937.6  0.70  35.55  14.27  120  +75 mm Size Fraction  Sample  Weight, g  PO300-1  Metal Grade,% Pb  Zn  Fe  1081.9  0.24  6.34  11.47  PO300-2  1570.5  0.59  1.02  18.43  PO300-3  897.1  0.07  0.47  6.36  PO300-4  2509.9  0.86  16.21  5.44  PO300-5  1237.3  0.14  19.26  4.75  PO300-6  1281.3  0.29  3.14  23.30  PO300-7  898.4  0.93  7.65  18.30  PO300-8  1097.1  0.08  27.52  2.87  PO300-9  1551.3  0.03  0.22  6.87  PO300-10  801.7  0.03  0.14  2.26  PO300-11  1106.9  1.46  11.09  8.38  PO300-12  820.1  0.01  0.10  0.91  PO300-13  1326.9  0.56  9.11  26.68  PO300-14  1907.9  0.68  5.73  33.30  PO300-15  925.9  6.87  6.10  7.85  PO300-16  1461.6  0.09  7.05  3.15  PO300-17  1143.2  0.49  2.69  24.41  PO300-18  977.0  0.02  0.22  1.71  PO300-19  874.9  0.26  0.67  21.77  PO300-20  979.8  0.13  6.68  5.85  PO300-21  1049.1  0.03  0.28  9.40  PO300-22  1000.3  0.09  0.62  4.09  PO300-23  933.5  0.69  6.87  8.27  PO300-24  1380.6  8.12  27.54  13.44  PO300-25  1048.7  0.03  0.16  1.16  PO300-26  1381.5  6.81  36.19  7.12  PO300-27  1477.0  0.47  2.83  29.96  PO300-28  885.6  1.75  5.90  6.03  PO300-29  1063.4  1.01  22.82  4.29  PO300-30  1753.8  0.48  2.09  31.05  PO300-31  769.5  0.03  7.45  3.02  PO300-32  794.4  0.04  0.18  1.95  PO300-33  1659.8  0.69  2.55  14.55  PO300-34  881.6  0.05  0.28  2.67  PO300-35  1291.6  4.87  14.38  15.48  PO300-36  1100.0  0.12  0.75  2.41  PO300-37  1010.6  0.01  0.10  10.11  121  Sample  Weight, g  PO300-38  Metal Grade,% Pb  Zn  Fe  1824.2  0.64  3.20  21.34  PO300-39  1813.8  10.17  28.00  13.23  PO300-40  1266.6  0.04  0.14  9.69  PO300-41  1307.2  0.66  15.39  19.17  PO300-42  854.6  0.03  0.17  1.61  PO300-43  1381.6  0.56  4.30  13.35  PO300-44  1349.9  0.52  1.01  19.37  PO300-45  1322.7  6.70  18.65  18.26  PO300-46  1135.6  1.74  14.82  11.81  PO300-47  588.4  0.02  0.17  0.88  PO300-48  656.9  0.03  1.73  1.41  PO300-49  1060.9  0.15  0.10  0.96  PO300-50  1305.4  0.24  0.95  19.23  122  XRF Surface Readings, XRF Powder Readings and Assays for Grouped Rocks for Four Size Fractions -37.5+26.5 mm Size Fraction Separation at Zn Grade of, % 0.20  XRF Surface Readings -37.5+26.5 mm  Weight, g  0-0.20  0.50  XRF Powder Readings  Assay Results  Pb Grade, %  Zn Grade, %  Fe Grade, %  Pb Grade, %  Zn Grade, %  Fe Grade, %  Pb Grade, %  Zn Grade, %  Fe Grade, %  775.4  0.03  0.16  2.73  0.04  0.07  2.76  0.03  0.03  1.38  0.20-0.50  1424.2  0.05  0.31  2.50  0.12  0.12  5.60  0.09  0.06  4.32  1.00  0.50-1.00  364.1  0.26  0.74  14.04  0.40  0.63  19.99  0.28  0.31  16.70  2.00  1.00-2.00  1066.3  0.29  1.59  13.74  0.37  1.42  27.13  0.24  0.53  24.40  5.00  2.00-5.00  660.0  0.71  3.34  15.25  1.13  4.24  18.87  0.84  2.42  17.70  10.00  5.00-10.00  287.5  1.17  7.30  23.74  2.50  5.14  21.85  2.02  3.12  20.60  15.00  10.00-15.00  293.5  0.84  13.51  8.59  1.23  13.75  19.06  1.00  9.33  20.10  20.00  15.00-20.00  432.9  2.45  16.64  13.99  4.32  17.13  20.10  3.59  12.70  21.30  30.00  20.00-30.00  1401.0  3.98  25.10  11.03  7.46  26.92  15.58  6.34  20.00  18.00  +30.00  1413.1  6.31  47.35  6.12  13.65  37.69  9.76  12.60  30.70  11.30  8118.0  2.11  14.79  9.24  4.21  13.39  14.16  3.72  10.21  13.95  Total/Head Grade, %  123  -53+37.5 mm Size Fraction Separation at Zn Grade of, % 0.25  XRF Surface Readings  XRF Powder Readings  Assay Results  -53+37.5 mm  Weight, g  Pb Grade, %  Zn Grade, %  Fe Grade, %  Pb Grade, %  Zn Grade, %  Fe Grade, %  Pb Grade, %  Zn Grade, %  Fe Grade, %  0-0.25  1858.8  0.05  0.20  1.97  0.06  0.42  2.87  0.03  0.19  1.56  0.50  0.25-0.50  2995.3  0.07  0.35  3.12  0.19  0.56  2.84  0.14  0.23  1.53  1.00  0.50-1.00  843.4  0.15  0.64  5.63  0.23  0.93  7.37  0.16  0.48  6.99  2.00  1.00-2.00  1876.0  0.25  1.46  12.43  0.23  1.42  16.46  0.16  0.70  16.30  5.00  2.00-5.00  2251.0  0.36  3.18  15.32  0.49  6.29  22.54  0.35  3.41  23.40  10.00  5.00-10.00  1449.3  0.95  8.29  14.37  2.70  12.70  18.72  2.39  8.31  21.50  20.00  10.00-20.00  3537.4  2.08  14.74  11.15  5.50  21.93  15.15  4.42  15.60  19.00  30.00  20.00-30.00  2541.2  7.28  25.57  10.59  12.39  25.93  15.01  11.10  19.40  20.60  +30.00  3196.2  11.09  41.52  6.32  22.38  32.57  10.26  20.40  26.70  12.30  20548.6  3.13  13.32  8.90  6.27  13.92  12.33  5.56  10.33  13.95  Total/Head Grade, %  124  -75+53 mm Size Fraction XRF Surface Readings Pb Zn Fe Grade, % Grade, % Grade, % 0.07 0.37 8.12  XRF Powder Readings Pb Zn Fe Grade, % Grade, % Grade, % 0.32 0.41 11.37  Assay Results Pb Zn Fe Grade, % Grade, % Grade, % 0.24 0.18 8.94  Separation at Zn Grade of, %  -75+53 mm  Weight, g  1.00  0-1.0  8148.4  2.00  1.0-2.0  2281.8  0.25  1.25  9.85  0.21  0.81  12.26  0.17  0.43  11.5  5.00  2.0-5.0  5192.3  0.43  3.32  8.11  0.79  4.71  24.21  0.63  2.81  24.6  10.00  5.0-10.0  3260.5  1.37  7.77  10.43  3.05  12.00  21.41  2.47  7.46  24.3  15.00  10.0-15.0  4829.1  3.12  12.76  10.41  7.97  14.00  16.45  6.96  10.0  20.2  20.00  15.0-20.0  6978.9  4.12  17.03  10.50  10.14  19.25  21.00  8.80  13.5  24.1  30.00  20.0-30.0  4762.8  6.54  23.52  11.79  10.52  28.78  13.88  8.52  20.4  16.7  +30.00  6553.6  7.15  37.52  9.91  17.57  33.37  12.71  13.2  23.6  14.3  42007.4  3.09  13.97  9.75  6.94  14.91  16.46  5.61  10.37  17.74  Total/Head Grade, %  125  +75 mm Size Fraction XRF Surface Readings Pb Zn Fe Grade, % Grade, % Grade, % 0.04 0.14 11.24  XRF Powder Readings Pb Zn Fe Grade, % Grade, % Grade, % 0.10 0.28 3.25  Assay Results Pb Zn Fe Grade, % Grade, % Grade, % 0.06 0.10 1.34  Separation at Zn Grade of, %  +75 mm  Weight, g  0.20  0-0.20  8246.00  0.50  0.20-0.50  5356.10  0.04  0.28  12.33  0.15  0.27  7.74  0.13  0.15  3.72  1.00  0.50-1.00  4280.60  0.18  0.77  9.27  0.76  0.88  38.92  0.51  0.39  33.30  2.00  1.00-2.00  3577.30  0.46  1.15  12.51  0.64  1.82  31.14  0.49  0.95  28.50  5.00  2.00-5.00  10520.90  0.53  2.94  10.46  1.01  3.57  22.82  0.79  2.07  22.20  10.00  5.00-10.00  11171.00  1.07  6.86  10.67  1.76  11.54  18.02  1.39  7.61  18.90  15.00  10.00-15.00  3534.10  2.80  13.49  13.84  4.70  14.93  22.62  3.63  9.65  23.90  20.00  15.00-20.00  6377.10  1.89  17.14  13.14  3.74  23.83  16.09  3.26  17.50  18.90  +20.00  6736.40  5.97  28.69  6.50  12.83  25.65  16.05  10.10  17.60  17.60  59799.5  1.38  7.82  10.85  2.75  9.33  18.03  2.18  6.32  17.45  Total/Head Grade, %  126  Sorting Results of Each Size Fraction Based on Different Threshold Values (Zn Grades) Separation at Zn Grade of, %  Conc., %  0.20  90.4  0.50  72.9  1.00  68.4  2.00  Waste Rejection, %  Metal Recovery, %  Conc. Grade, %  Zn  Pb  Zn  Pb  9.6  100.0  99.9  11.29  4.11  27.1  99.9  99.5  13.99  5.07  31.6  99.7  99.2  14.89  5.39  55.3  44.7  99.1  98.3  18.30  6.61  5.00  47.2  52.8  97.1  96.5  21.04  7.61  10.00  43.6  56.4  96.0  94.6  22.49  8.06  15.00  40.0  60.0  92.7  93.6  23.68  8.70  20.00  34.7  65.3  86.1  88.4  25.37  9.48  30.00  17.4  82.6  52.3  59.0  30.70  12.60  Calculated Head Grade: 3.72% Pb and 10.21 % Zn (-37.5+26.5 mm size fraction 100 rocks)  Metal Recovery, %  Conc. Grade, %  Separation at Zn Grade of, %  Conc., %  Waste Rejection, %  Zn  Pb  Zn  Pb  0.25  91.0  9.0  99.8  100.0  11.34  6.11  0.50  76.4  23.6  99.5  99.6  13.46  7.25  1.00  72.3  27.7  99.3  99.5  14.20  7.65  2.00  63.1  36.9  98.7  99.2  16.15  8.73  5.00  52.2  47.8  95.1  98.5  18.82  10.49  10.00  45.1  54.9  89.4  95.5  20.47  11.76  20.00  27.9  72.1  63.4  81.8  23.47  16.28  30.00  15.6 84.4 40.2 57.1 26.70 Calculated Head Grade: 5.56% Pb and 10.33 % Zn (-53+37.5 mm size fraction 100 rocks)  20.40  127  Separation at Zn Grade of, %  Conc., %  Waste Rejection, %  1.00  80.6  2.00  Metal Recovery, %  Conc. Grade, %  Zn  Pb  Zn  Pb  19.4  99.7  99.2  12.82  6.91  75.2  24.8  99.4  99.0  13.72  7.39  5.00  62.8  37.2  96.1  97.6  15.87  8.72  10.00  55.0  45.0  90.5  94.2  17.05  9.61  15.00  43.6  56.4  79.4  79.9  18.91  10.30  20.00  26.9  73.1  57.8  53.9  22.25  11.23  30.00  15.6 84.4 35.5 36.7 23.60 Calculated Head Grade: 5.61% Pb and 10.37 % Zn (-75+53 mm size fraction 75 rocks)  Separation at Zn Grade of, %  Conc., %  Waste Rejection, %  0.20  86.2  0.50  Metal Recovery, %  13.20  Conc. Grade, %  Zn  Pb  Zn  Pb  13.8  99.8  99.6  7.31  2.52  77.3  22.7  99.6  99.1  8.14  2.80  1.00  70.1  29.9  99.1  97.4  8.93  3.04  2.00  64.1  35.9  98.2  96.1  9.68  3.27  5.00  46.5  53.5  92.5  89.7  12.56  4.21  10.00  27.8  72.2  70.0  77.8  15.87  6.11  15.00  21.9  78.1  60.9  68.0  17.55  6.77  20.00  11.3 88.7 31.4 52.1 17.60 Calculated Head Grade: 2.18% Pb and 6.32 % Zn (+75 mm size fraction 50 rocks)  10.10  All sorting results were calculated based on chemical assays.  128  Correlations Between XRF Analyzer Surface Readings and Bulk Assays for Each Size Fraction  -37.5+26.5 mm size fraction  35.00  -53+37.5 mm size fraction 35.00  25.00  yPb = 1.9228x - 0.3921 R² = 0.9825  30.00  20.00  yZn = 0.685x - 0.0279 R² = 0.9831  15.00 10.00 5.00  Assay results, %  Assay results, %  30.00  yPb = 1.7553x + 0.0069 R² = 0.9874  25.00 20.00  yZn = 0.6748x + 1.1418 R² = 0.9548  15.00 10.00 5.00  0.00  0.00 0.00  10.00  20.00  30.00  40.00  XRF analyzer surface readings, % Pb Grade  Zn Grade  50.00  0.00  10.00  20.00  30.00  40.00  50.00  XRF analyzer surface readings, % Pb Grade  Zn Grade  129  -75+53 mm size fraction  +75 mm size fraction 25.00  30.00 25.00  20.00  yPb = 1.6636x + 0.3324 R² = 0.9207  20.00 15.00  Assay results, %  Assay results, %  15.00  yZn = 0.677x + 1.0365 R² = 0.9532  10.00  y = 1.6489x - 0.1156 R² = 0.9854  10.00  5.00  y = 0.6969x + 0.6916 R² = 0.9047  5.00  0.00  0.00 0.00  10.00  20.00  30.00  XRF analyzer surface readings, % Pb Grade Zn Grade  40.00  0.00  5.00  10.00  15.00  20.00  25.00  30.00  35.00  XRF surface readings, % Pb Grade  Zn Grade  It is evident from the above graphs, the finer the particle size, the better the correlation between XRF analyzer surface readings and assays was. This indicated that a better sorting result will be achieved by the finer size fraction. This is primarily due to the limitation of our XRF analyzer. The detection window for our XRF analyzer is about 1cm2. Although eight faces (8 cm2 detection area) of the rock sized above 75 mm (about 400 cm2 of the total surface area) were analyzed by the XRF analyzer, which account for 1/50 of the rock surface, the average reading of these faces was still not adequate for precisely predicting the grades of the bulk rock. In addition, the coarser the particle size, the less precisely the surface analysis can be used to predict the grades of the bulk rock. Consequently, a poorer correlation exhibited.  130  Correlation between XRF Powder Readings and Assays 35.00 30.00 25.00  Assays, %  20.00  yZn = 0.761x - 0.5648 R² = 0.9912  yPb = 0.8598x - 0.0798 R² = 0.9913  15.00  10.00 5.00 0.00 0.00  5.00  10.00  Pb grade  15.00  20.00  25.00  30.00  35.00  40.00  XRF Powder Readings, %  Zn grade  It is clearly shown in the above figure that XRF powder readings and assays have a very strong correlation between each other. All samples were prepared following the same procedures (jaw and cone crushing and pulverizing for 45s) to obtain same particle size for consistent analysis results from XRF analyzer. The calibrated XRF powder readings using the above calibration function have enough confidence in predicting real assays. Therefore, other sorting tests using XRT and optical sorting technologies were calculated and analyzed based on calibrated XRF powder reading. MW/IR sorting tests were calculated and analyzed based on XRF surface reading. Rock samples from -19+13.2 mm are too small to have enough pulverized powder for XRF power analysis. In order to have consistent results for MW/IR sorting tests, calibrated XRF surface readings were used as assays which are also quite confident in predicting real assays.  131  APPENDIX B  X-RAY TRANSMISSION SORTING DAT  Initial Data Set of Avg. Brightness Value High Energy Image  Low Energy Image  1  Avg. Intensity (0,255) 55.5  1627  Avg. Intensity (0,255) 39.3  2  22.7  1491  3  82.2  4  Rock ID  320*240 Zn, %  Zzn  Ztotal  82.00  30.00  112.00  Pixels Counted  Total Pixels  Weight, g  1667  76800  105.84  5.68  10.56  16.23  465.41  316.68  782.09  21.2  1514  76800  141.79  21.17  11.48  32.66  1736.27  344.45  2080.72  1323  56.8  1361  76800  108.18  0.18  0.21  0.39  14.58  6.27  20.85  100.7  1154  68.2  1190  76800  70.11  0.03  0.17  0.20  2.49  5.18  7.68  5  45.6  1519  30.1  1556  76800  140.78  2.58  1.24  3.81  211.42  37.09  248.52  6  30.7  1362  27.6  1402  76800  129.54  12.87  12.07  24.94  1055.70  362.01  1417.71  7  93.0  1204  59.2  1241  76800  68.04  0.60  0.74  1.34  49.00  22.25  71.24  8  133.5  1988  93.9  2057  76800  77.99  0.17  0.55  0.72  13.97  16.59  30.56  9  95.4  1427  65.0  1483  76800  91.09  0.04  0.17  0.21  3.58  5.05  8.63  10  100.6  1374  65.5  1407  76800  82.25  0.09  0.11  0.20  7.22  3.24  10.46  11  28.7  1495  25.9  1524  76800  93.26  32.82  10.65  43.47  2691.27  319.46  3010.73  12  116.7  1601  80.9  1656  76800  81.09  0.51  0.27  0.79  42.19  8.19  50.38  13  125.8  1095  87.1  1130  76800  48.65  0.05  0.09  0.14  4.11  2.82  6.93  14  32.7  1539  26.4  1578  76800  138.02  7.91  24.08  31.99  648.72  722.25  1370.98  15  25.3  1491  23.5  1519  76800  137.01  8.50  16.51  25.01  697.05  495.29  1192.35  16  36.2  990  32.8  1018  76800  97.66  19.01  8.37  27.38  1559.20  251.00  1810.20  17  112.7  1966  77.6  2025  76800  110.2  0.37  0.39  0.75  29.96  11.56  41.52  18  126.1  1461  87.6  1496  76800  65.62  0.08  0.17  0.25  6.45  5.16  11.61  19  111.9  2071  72.9  2130  76800  101.88  0.08  0.20  0.29  6.88  6.13  13.01  Pixels Counted  Pb, %  ZPb Pb+Zn  132  High Energy Image  Low Energy Image  20  Avg. Intensity (0,255) 27.9  1190  Avg. Intensity (0,255) 26.7  21  29.2  1325  22  31.2  23  Rock ID  320*240 Zn, %  Zzn  Ztotal  82.00  30.00  112.00  Pixels Counted  Total Pixels  Weight, g  1219  76800  89.88  26.07  16.92  43.00  2138.09  507.63  2645.72  27.2  1347  76800  128.33  16.94  9.21  26.15  1389.46  276.17  1665.63  1054  30.3  1095  76800  95.65  21.08  16.37  37.45  1728.41  491.20  2219.61  30.9  1492  26.4  1527  76800  89.97  30.67  14.73  45.40  2514.58  442.03  2956.61  24  121.0  1092  84.7  1131  76800  52.92  0.29  0.34  0.64  24.07  10.32  34.39  25  86.6  1350  56.8  1391  76800  98.03  0.18  0.18  0.36  15.04  5.37  20.41  26  104.7  1421  68.8  1452  76800  82.63  0.13  0.17  0.31  10.72  5.25  15.96  27  96.1  1395  60.2  1440  76800  75.72  0.22  0.26  0.48  18.33  7.81  26.14  28  43.1  1183  29.0  1213  76800  98.39  0.98  12.98  13.96  80.30  389.54  469.84  29  28.6  942  28.3  972  76800  75.53  24.94  16.42  41.36  2045.03  492.68  2537.71  30  60.6  1435  39.0  1473  76800  68.51  8.10  8.07  16.17  664.38  242.17  906.54  31  28.9  1831  23.7  1879  76800  161.29  5.62  17.21  22.83  461.00  516.30  977.30  32  57.8  1526  36.4  1552  76800  96.08  3.31  7.59  10.90  271.32  227.60  498.92  33  56.0  1010  39.3  1046  76800  75.2  0.35  31.66  32.01  28.70  949.77  978.47  34  28.9  1305  25.7  1341  76800  125.26  8.03  12.73  20.76  658.53  381.80  1040.33  35  60.8  1084  41.3  1122  76800  83.05  0.53  5.87  6.39  43.07  176.05  219.13  36  51.9  1638  33.4  1693  76800  135.41  0.67  2.15  2.82  55.25  64.45  119.70  37  131.0  1316  92.8  1359  76800  56.55  0.00  0.33  0.33  0.37  9.86  10.23  38  125.5  1581  84.5  1613  76800  68.01  0.08  0.25  0.34  6.91  7.62  14.53  39  119.6  1474  83.2  1527  76800  71.12  0.05  0.24  0.30  4.31  7.33  11.64  40  32.6  1043  28.0  1067  76800  85.88  15.60  16.89  32.49  1279.57  506.70  1786.26  41  31.8  1088  29.7  1130  76800  111.87  6.21  10.55  16.76  509.60  316.37  825.97  42  103.1  2256  68.5  2327  76800  131.62  0.14  0.40  0.54  11.84  11.88  23.72  Pixels Counted  Pb, %  ZPb Pb+Zn  133  High Energy Image  Low Energy Image  43  Avg. Intensity (0,255) 28.0  1090  Avg. Intensity (0,255) 26.7  44  55.1  1430  45  57.4  46  Rock ID  320*240 Zn, %  Zzn  Ztotal  82.00  30.00  112.00  Pixels Counted  Total Pixels  Weight, g  1116  76800  105.99  21.52  23.88  45.40  1764.66  716.30  2480.96  34.7  1473  76800  104.12  0.72  9.70  10.42  59.34  291.00  350.33  871  40.9  906  76800  56.49  0.44  19.32  19.76  36.48  579.52  616.00  110.6  1509  76.3  1553  76800  85.55  0.05  0.48  0.53  4.23  14.45  18.69  47  121.3  1045  86.3  1087  76800  51.21  0.09  0.20  0.29  7.02  5.99  13.00  48  49.3  1127  32.7  1155  76800  101.87  0.32  9.63  9.95  26.45  288.86  315.31  49  127.6  1543  88.9  1588  76800  68.72  0.00  0.50  0.50  0.25  14.88  15.13  50  25.8  1120  26.2  1149  76800  121.39  19.75  14.32  34.07  1619.68  429.58  2049.26  51  95.4  1578  64.7  1622  76800  105.46  0.19  0.27  0.45  15.18  8.02  23.20  52  40.8  1190  31.7  1234  76800  95.35  5.84  13.81  19.65  479.10  414.36  893.46  53  37.0  1206  28.3  1237  76800  82.08  5.68  25.18  30.86  465.47  755.45  1220.93  54  106.7  1242  73.9  1283  76800  74.18  0.19  0.33  0.53  15.76  10.03  25.79  55  107.5  1501  72.2  1536  76800  85.47  0.04  0.13  0.17  3.29  3.94  7.23  56  38.4  1704  26.8  1752  76800  123.07  7.76  29.29  37.05  635.98  878.73  1514.71  57  118.2  1211  81.4  1260  76800  58.57  0.16  0.80  0.95  12.72  23.96  36.68  58  68.5  1013  43.4  1051  76800  62.74  0.47  4.30  4.77  38.60  129.02  167.62  59  82.9  1042  47.6  1077  76800  46.52  0.50  0.15  0.65  40.77  4.50  45.27  60  126.3  1602  89.1  1660  76800  72.8  0.39  0.28  0.67  32.27  8.41  40.68  61  45.7  1338  34.1  1372  76800  86.23  6.27  14.69  20.97  514.30  440.84  955.15  62  50.7  958  34.5  992  76800  81.98  0.77  21.90  22.67  63.40  656.85  720.26  63  120.8  2212  81.4  2291  76800  97.74  0.02  0.55  0.57  2.05  16.39  18.44  64  128.9  1664  91.3  1730  76800  71.44  0.17  0.35  0.53  14.27  10.58  24.85  65  123.8  1382  86.9  1425  76800  66.73  0.00  0.16  0.16  0.02  4.75  4.77  Pixels Counted  Pb, %  ZPb Pb+Zn  134  High Energy Image  Low Energy Image  66  Avg. Intensity (0,255) 37.9  1155  Avg. Intensity (0,255) 30.0  67  27.8  1290  68  28.9  69  Rock ID  320*240 Zn, %  Zzn  Ztotal  82.00  30.00  112.00  Pixels Counted  Total Pixels  Weight, g  1186  76800  98.78  5.57  6.88  12.46  457.00  206.50  663.50  25.8  1319  76800  86.85  21.85  22.53  44.38  1791.40  675.97  2467.37  1438  26.7  1474  76800  146.86  13.27  14.44  27.71  1088.47  433.12  1521.59  24.9  1322  24.1  1348  76800  133.86  25.88  19.15  45.03  2122.31  574.53  2696.84  70  43.6  1702  29.7  1748  76800  170.94  1.30  4.51  5.81  106.64  135.39  242.03  71  60.7  1095  46.8  1129  76800  79.19  7.29  5.49  12.78  598.13  164.60  762.73  72  27.4  1460  25.2  1492  76800  131.7  19.63  18.32  37.95  1610.03  549.46  2159.49  73  40.4  1312  31.2  1351  76800  95.31  7.49  13.16  20.65  614.36  394.67  1009.03  74  50.6  1127  35.6  1163  76800  100.17  0.56  4.14  4.70  45.76  124.21  169.97  75  115.6  1027  80.3  1068  76800  52.06  0.09  0.21  0.30  7.43  6.17  13.59  76  94.0  911  63.8  945  76800  59.25  0.06  0.51  0.57  5.31  15.22  20.53  77  46.5  975  36.0  1007  76800  97.89  0.58  14.88  15.46  47.32  446.52  493.84  78  109.8  1663  72.1  1715  76800  82.66  0.05  0.98  1.03  3.95  29.43  33.38  79  118.5  1370  82.4  1420  76800  67.64  0.05  0.36  0.41  4.31  10.83  15.14  80  52.9  1197  35.4  1252  76800  89.23  0.10  24.47  24.57  7.91  734.07  741.98  81  71.9  1074  43.2  1117  76800  62  0.85  13.79  14.64  69.72  413.67  483.39  82  27.9  931  29.5  960  76800  71.41  30.42  17.43  47.85  2494.25  522.94  3017.20  83  54.8  1118  36.3  1155  76800  90.55  1.41  10.34  11.75  116.03  310.18  426.21  84  115.7  1432  78.4  1480  76800  71.62  0.13  0.24  0.37  10.34  7.30  17.64  85  117.1  1642  81.2  1706  76800  82.36  0.02  0.13  0.15  1.68  3.82  5.50  86  78.1  1181  52.4  1226  76800  92.89  0.14  0.29  0.43  11.42  8.79  20.22  87  68.2  1118  44.6  1164  76800  65.93  0.73  13.44  14.17  59.59  403.20  462.79  88  63.3  1344  38.0  1382  76800  91.9  0.84  6.27  7.11  68.73  188.23  256.96  Pixels Counted  Pb, %  ZPb Pb+Zn  135  High Energy Image  Low Energy Image  89  Avg. Intensity (0,255) 45.6  1052  Avg. Intensity (0,255) 35.3  90  28.1  795  91  57.6  92  Rock ID  320*240 Zn, %  Zzn  Ztotal  82.00  30.00  112.00  Pixels Counted  Total Pixels  Weight, g  1089  76800  71.53  6.03  8.16  14.19  494.65  244.85  739.50  28.9  814  76800  73.43  23.87  14.27  38.14  1957.54  428.10  2385.64  1153  37.8  1197  76800  82.05  0.65  7.51  8.16  53.21  225.21  278.41  58.9  864  39.4  904  76800  62.51  0.77  9.93  10.70  62.91  298.04  360.95  93  51.3  735  36.3  762  76800  61.34  0.69  4.99  5.68  56.63  149.58  206.20  94  51.2  1109  34.5  1148  76800  78.59  0.67  34.12  34.79  54.67  1023.73  1078.40  95  38.4  1081  31.5  1107  76800  74.25  16.83  12.84  29.67  1379.72  385.23  1764.95  96  58.1  1189  38.2  1223  76800  77.87  3.67  4.32  7.99  301.12  129.66  430.78  97  140.2  1667  101.4  1731  76800  64.19  0.09  0.32  0.41  7.28  9.65  16.93  98  142.0  1511  100.2  1559  76800  53.95  0.01  0.21  0.22  0.75  6.24  6.99  99  107.3  1268  71.5  1323  76800  66.87  0.05  0.13  0.18  4.03  3.89  7.92  100  107.6  1707  73.0  1757  76800  96.44  0.04  0.13  0.17  2.89  3.91  6.80  Pixels Counted  Pb, %  ZPb Pb+Zn  136  Initial Data Set of Ore Index  Rock ID  Weight, g  Pb, %  Zn, %  SUM Pb + Zn (%)  Weighted by Atomic Number Pb + Zn (%)  Ore Index SPECTRAL Correl = 0.8944  1  105.8  5.68  10.56  16.23  6.98  54  2  141.8  21.17  11.48  32.66  18.58  97  3  108.2  0.18  0.21  0.39  0.19  20  4  70.1  0.03  0.17  0.20  0.07  5  5  140.8  2.58  1.24  3.81  2.22  46  6  129.5  12.87  12.07  24.94  12.66  87  7  68.0  0.60  0.74  1.34  0.64  21  8  78.0  0.17  0.55  0.72  0.27  0  9  91.1  0.04  0.17  0.21  0.08  11  10  82.3  0.09  0.11  0.20  0.09  2  11  93.3  32.82  10.65  43.47  26.88  95  12  81.1  0.51  0.27  0.79  0.45  2  13  48.7  0.05  0.09  0.14  0.06  2  14  138.0  7.91  24.08  31.99  12.24  78  15  137.0  8.50  16.51  25.01  10.65  91  16  97.7  19.01  8.37  27.38  16.16  87  17  110.2  0.37  0.39  0.75  0.37  4  18  65.6  0.08  0.17  0.25  0.10  2  19  101.9  0.08  0.20  0.29  0.12  5  20  89.9  26.07  16.92  43.00  23.62  94  21  128.3  16.94  9.21  26.15  14.87  95  22  95.7  21.08  16.37  37.45  19.82  89  23  90.0  30.67  14.73  45.40  26.40  88  24  52.9  0.29  0.34  0.64  0.31  1  25  98.0  0.18  0.18  0.36  0.18  11  26  82.6  0.13  0.17  0.31  0.14  3  27  75.7  0.22  0.26  0.48  0.23  16  28  98.4  0.98  12.98  13.96  4.19  43  29  75.5  24.94  16.42  41.36  22.66  94  30  68.5  8.10  8.07  16.17  8.09  51  31  161.3  5.62  17.21  22.83  8.73  77  32  96.1  3.31  7.59  10.90  4.45  47  33  75.2  0.35  31.66  32.01  8.74  42  34  125.3  8.03  12.73  20.76  9.29  84  137  Rock ID  Weight, g  Pb, %  Zn, %  SUM Pb + Zn (%)  Weighted by Atomic Number Pb + Zn (%)  Ore Index SPECTRAL Correl = 0.8944  35  83.1  0.53  5.87  6.39  1.96  35  36  135.4  0.67  2.15  2.82  1.07  44  37  56.6  0.00  0.33  0.33  0.09  2  38  68.0  0.08  0.25  0.34  0.13  2  39  71.1  0.05  0.24  0.30  0.10  2  40  85.9  15.60  16.89  32.49  15.95  91  41  111.9  6.21  10.55  16.76  7.37  86  42  131.6  0.14  0.40  0.54  0.21  4  43  106.0  21.52  23.88  45.40  22.15  95  44  104.1  0.72  9.70  10.42  3.13  36  45  56.5  0.44  19.32  19.76  5.50  36  46  85.6  0.05  0.48  0.53  0.17  4  47  51.2  0.09  0.20  0.29  0.12  3  48  101.9  0.32  9.63  9.95  2.82  47  49  68.7  0.00  0.50  0.50  0.14  0  50  121.4  19.75  14.32  34.07  18.30  95  51  105.5  0.19  0.27  0.45  0.21  9  52  95.4  5.84  13.81  19.65  7.98  69  53  82.1  5.68  25.18  30.86  10.90  59  54  74.2  0.19  0.33  0.53  0.23  7  55  85.5  0.04  0.13  0.17  0.06  3  56  123.1  7.76  29.29  37.05  13.52  65  57  58.6  0.16  0.80  0.95  0.33  1  58  62.7  0.47  4.30  4.77  1.50  32  59  46.5  0.50  0.15  0.65  0.40  31  60  72.8  0.39  0.28  0.67  0.36  0  61  86.2  6.27  14.69  20.97  8.53  77  62  82.0  0.77  21.90  22.67  6.43  45  63  97.7  0.02  0.55  0.57  0.16  3  64  71.4  0.17  0.35  0.53  0.22  1  65  66.7  0.00  0.16  0.16  0.04  1  66  98.8  5.57  6.88  12.46  5.92  70  67  86.9  21.85  22.53  44.38  22.03  94  68  146.9  13.27  14.44  27.71  13.59  90  69  133.9  25.88  19.15  45.03  24.08  97  70  170.9  1.30  4.51  5.81  2.16  50  71  79.2  7.29  5.49  12.78  6.81  59  72  131.7  19.63  18.32  37.95  19.28  94  138  Rock ID  Weight, g  Pb, %  Zn, %  SUM Pb + Zn (%)  Weighted by Atomic Number Pb + Zn (%)  Ore Index SPECTRAL Correl = 0.8944  73  95.3  7.49  13.16  20.65  9.01  67  74  100.2  0.56  4.14  4.70  1.52  44  75  52.1  0.09  0.21  0.30  0.12  1  76  59.3  0.06  0.51  0.57  0.18  7  77  97.9  0.58  14.88  15.46  4.41  53  78  82.7  0.05  0.98  1.03  0.30  12  79  67.6  0.05  0.36  0.41  0.14  2  80  89.2  0.10  24.47  24.57  6.62  33  81  62.0  0.85  13.79  14.64  4.32  36  82  71.4  30.42  17.43  47.85  26.94  100  83  90.6  1.41  10.34  11.75  3.81  38  84  71.6  0.13  0.24  0.37  0.16  1  85  82.4  0.02  0.13  0.15  0.05  1  86  92.9  0.14  0.29  0.43  0.18  31  87  65.9  0.73  13.44  14.17  4.13  34  88  91.9  0.84  6.27  7.11  2.29  39  89  71.5  6.03  8.16  14.19  6.60  62  90  73.4  23.87  14.27  38.14  21.30  104  91  82.1  0.65  7.51  8.16  2.49  34  92  62.5  0.77  9.93  10.70  3.22  43  93  61.3  0.69  4.99  5.68  1.84  38  94  78.6  0.67  34.12  34.79  9.63  41  95  74.3  16.83  12.84  29.67  15.76  85  96  77.9  3.67  4.32  7.99  3.85  42  97  64.2  0.09  0.32  0.41  0.15  1  98  54.0  0.01  0.21  0.22  0.06  0  99  66.9  0.05  0.13  0.18  0.07  11  100  96.4  0.04  0.13  0.17  0.06  3  139  APPENDIX C  OPTICAL SORTING DATA  Initial Data for Color Sorting  Sample ID  Weight, g  Pb, %  Zn, %  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41  105.8 141.8 108.2 70.1 140.8 129.5 68.0 78.0 91.1 82.3 93.3 81.1 48.7 138.0 137.0 97.7 110.2 65.6 101.9 89.9 128.3 95.7 90.0 52.9 98.0 82.6 75.7 98.4 75.5 68.5 161.3 96.1 75.2 125.3 83.1 135.4 56.6 68.0 71.1 85.9 111.9  5.68 21.17 0.18 0.03 2.58 12.87 0.60 0.17 0.04 0.09 32.82 0.51 0.05 7.91 8.50 19.01 0.37 0.08 0.08 26.07 16.94 21.08 30.67 0.29 0.18 0.13 0.22 0.98 24.94 8.10 5.62 3.31 0.35 8.03 0.53 0.67 0.00 0.08 0.05 15.60 6.21  10.56 11.48 0.21 0.17 1.24 12.07 0.74 0.55 0.17 0.11 10.65 0.27 0.09 24.08 16.51 8.37 0.39 0.17 0.20 16.92 9.21 16.37 14.73 0.34 0.18 0.17 0.26 12.98 16.42 8.07 17.21 7.59 31.66 12.73 5.87 2.15 0.33 0.25 0.24 16.89 10.55  Red Threshold 133-255 0-40 Avg. R133-255 Avg. R0-40 4.0 5.7 5.5 0.4 15.3 26.1 84.1 0.0 2.8 1.8 6.4 1.6 3.4 11.6 5.7 10.2 9.2 0.8 5.3 41.2 4.5 1.8 39.8 0.0 87.5 0.0 3.8 0.2 3.6 0.2 6.6 0.0 14.9 0.0 8.8 0.1 2.3 135.1 4.3 0.4 5.3 3.0 7.7 0.1 5.7 0.0 26.9 0.1 3.0 6.2 152.0 0.0 4.0 0.0 5.8 0.1 6.8 0.1 6.3 0.0 5.0 0.1 5.9 0.0 21.7 0.0 3.8 0.8 19.1 0.9 2.6 0.0 28.4 0.0 10.1 0.0 2.3 61.0 4.0 0.3 8.1 0.0  140  Sample ID  Weight, g  Pb, %  Zn, %  42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87  131.6 106.0 104.1 56.5 85.6 51.2 101.9 68.7 121.4 105.5 95.4 82.1 74.2 85.5 123.1 58.6 62.7 46.5 72.8 86.2 82.0 97.7 71.4 66.7 98.8 86.9 146.9 133.9 170.9 79.2 131.7 95.3 100.2 52.1 59.3 97.9 82.7 67.6 89.2 62.0 71.4 90.6 71.6 82.4 92.9 65.9  0.14 21.52 0.72 0.44 0.05 0.09 0.32 0.00 19.75 0.19 5.84 5.68 0.19 0.04 7.76 0.16 0.47 0.50 0.39 16.27 0.77 0.02 0.17 0.00 5.57 21.85 13.27 25.88 1.30 7.29 19.63 7.49 0.56 0.09 0.06 0.58 0.05 0.05 0.10 0.85 30.42 1.41 0.13 0.02 0.14 0.73  0.40 23.88 9.70 19.32 0.48 0.20 9.63 0.50 14.32 0.27 13.81 25.18 0.33 0.13 29.29 0.80 4.30 0.15 0.28 14.69 21.90 0.55 0.35 0.16 6.88 22.53 14.44 19.15 4.51 5.49 18.32 13.16 4.14 0.21 0.51 14.88 0.98 0.36 24.47 13.79 17.43 10.34 0.24 0.13 0.29 13.44  Red Threshold 133-255 0-40 Avg. R133-255 Avg. R0-40 14.5 2.0 4.7 0.1 2.8 2.6 8.1 0.0 17.4 2.3 20.0 0.0 10.1 0.0 21.4 0.0 5.4 0.0 81.8 0.2 6.5 0.0 4.8 0.8 14.3 0.5 55.9 0.0 4.8 0.0 7.6 0.0 4.8 0.0 3.6 0.0 11.0 0.2 5.6 0.4 9.2 0.0 2.1 66.2 9.1 11.9 20.4 0.1 5.7 0.0 5.9 0.1 9.1 0.0 6.7 2.0 5.5 0.6 28.1 0.0 4.3 0.0 6.3 0.1 7.7 0.3 15.2 0.1 37.3 0.0 24.1 0.0 4.5 0.3 4.8 1.2 8.8 0.0 8.3 0.0 9.9 0.0 4.9 0.1 8.6 0.4 4.3 0.0 3.2 5.3 6.6 0.1  141  Sample ID  Weight, g  Pb, %  Zn, %  88 89 90 91 92 93 94 95 96 97 98 99 100  91.9 71.5 73.4 82.1 62.5 61.3 78.6 74.3 77.9 64.2 54.0 66.9 96.4  0.84 6.03 23.87 0.65 0.77 0.69 0.67 16.83 3.67 0.09 0.01 0.05 0.04  6.27 8.16 14.27 7.51 9.93 4.99 34.12 12.84 4.32 0.32 0.21 0.13 0.13  Red Threshold 133-255 0-40 Avg. R133-255 Avg. R0-40 7.4 0.0 4.7 1.4 5.6 0.2 3.4 2.6 10.0 0.0 13.9 0.0 3.7 0.0 4.3 0.1 5.1 0.7 157.6 0.0 73.3 0.0 4.0 68.3 5.0 0.1  142  Characteristic Color Data for Each Selected Pattern for Selected Rocks Waste rock-26  1  2  3  4  5  6  7  8  9  10  Area  551  551  551  551  551  551  551  551  551  551  Mean  154  158  130  111  128  118  158  137  98  112  Standard Deviation  16  23  17  15  23  14  20  14  24  16  Mean  170  175  144  123  141  132  175  152  108  125  Standard Deviation  18  25  18  16  26  16  22  15  24  18  Mean  95  97  79  67  78  73  98  84  59  67  Standard Deviation  12  16  12  10  17  10  14  9  24  10  Waste rock -51 pattern 1  1  2  3  4  5  6  7  8  9  10  Area  368  368  368  368  368  368  368  368  368  368  119  150  145  139  130  121  109  111  104  115  Red Green Blue  Red Green Blue  Mean Standard Deviation  29  26  17  22  34  18  16  16  9  18  Mean  132  164  161  152  143  134  121  124  115  129  Standard Deviation  31  28  18  24  37  19  17  17  10  20  Mean  74  93  91  85  80  75  68  69  65  72  Standard Deviation  17  17  12  14  22  11  10  10  7  12  Waste rock -51-pattern 2  1  2  3  4  5  6  7  8  9  10  Area  378  378  378  378  378  378  378  378  378  378  Mean  152  123  129  127  143  147  112  121  118  134  Standard Deviation  8  24  13  11  24  15  15  20  18  26  Red Green Blue  Mean  165  136  141  140  156  160  123  133  130  146  Standard Deviation  9  26  15  13  27  17  16  23  19  27  Mean  92  76  79  77  86  90  68  74  72  82  Standard Deviation  6  15  9  8  17  10  10  13  11  15  Average 130 144 80  Average 124 137 77 Average 131 143 79  143  Waste rock-97-pattern 1  1  2  3  4  5  6  7  8  9  10  Area  1452  1452  1452  1452  1452  1452  1452  1452  1452  1452  Mean  145  144  139  155  127  140  124  139  97  120  Standard Deviation  21  21  26  20  22  22  24  22  13  25  Mean  157  157  150  168  139  152  135  151  106  131  Standard Deviation  23  22  28  22  22  24  27  24  14  27  Mean  86  86  82  91  77  81  73  83  57  72  Standard Deviation  14  13  17  13  22  14  16  19  8  16  Waste rock-97-pattern 2  1  2  3  4  5  6  7  8  9  10  Area  609  609  609  609  609  609  609  609  609  609  Red Green Blue  Red Green Blue  Mean  171  153  164  162  149  141  127  142  136  143  Standard Deviation  16  23  21  18  23  14  27  19  28  24  Mean  187  166  179  179  164  153  140  156  147  156  Standard Deviation  17  26  22  19  25  15  30  21  30  26  Mean  103  92  99  99  91  84  77  85  81  85  Standard Deviation  12  15  14  13  15  10  18  13  17  16  Average 133 145 79  Average 149 163 90  144  Mineralized rock-80  1  2  3  4  5  6  7  8  9  10  Area  840  840  840  840  840  840  840  840  840  840  Mean  97  90  94  79  82  80  93  83  95  92  Standard Deviation  9  8  9  10  12  12  10  10  17  12  Mean  106  98  102  87  89  87  101  92  105  101  Standard Deviation  10  9  9  10  13  12  11  11  18  14  Mean  53  50  51  44  45  45  52  47  54  51  Standard Deviation  6  5  5  5  6  7  6  6  11  8  Mineralized rock -72-pattern 1  1  2  3  4  5  6  7  8  9  10  Area  456  456  456  456  456  456  456  456  456  456  Mean  88  77  55  78  63  81  66  64  59  67  Standard Deviation  10  12  6  11  8  14  8  11  16  11  Mean  97  85  61  87  71  90  74  72  65  74  Standard Deviation  12  13  6  12  8  16  9  12  18  11  Mean  53  46  33  46  37  49  40  38  35  38  Standard Deviation  6  8  4  7  4  9  5  7  10  5  Mineralized rock-72-pattern 2  1  2  3  4  5  6  7  8  9  10  Area  540  540  540  540  540  540  540  540  540  540  Mean  82  60  54  69  71  55  82  57  85  79  Standard Deviation  14  8  7  11  17  6  13  6  10  20  Mean  91  66  60  77  78  60  92  63  96  87  Standard Deviation  15  9  7  12  18  7  14  6  11  21  Mean  49  36  32  40  42  32  50  33  52  45  Standard Deviation  8  6  5  6  17  4  7  4  7  11  Red Green Blue  Red Green Blue  Red Green Blue  Average 88 97 49  Average 70 78 41  Average 69 77 41  145  APPENDIX D  MICROWAVE-INFRARED SORTING DATA  Calculation of the Absorbed Microwave Energy The weight of rock #50 (RockID #37) in the following figure for -37.5+26.5 mm size fraction is 40.4 g. Grades for Pb, Zn, Fe and Light elements are 13.85%, 61.67%, 3.68% and 20.80%. The specific heat capacities for Pb, Zn, Fe, S and Ca (S and Ca are major light elements in this ore sample) are 0.16, 0.39, 0.45, 0.73 and 0.65(Engineering Toolbox) respectively. The specific heat capacity for light elements is 0.70 used for the calculation. The average specific heat capacity for this rock is then Cp=0.42.  Average Surface Temperature, ℃  P*t=Cp*ΔT*M=0.42*(168-23)*40.4=2460.36J  320 300 280 260 240 220 200 180 160 140 120 100 80 60 40 20 0  -37.5+26.5 mm POM  Rock ID # 37 of -  Avg Surface Temperature - 15S Avg Surface Temperature - 10S  37.5+26.5 mm  Avg Surface Temperature - 5S  1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49  Rock  Efficiency of microwave heating of 15s =2460.36/(1000*15)=16% Based on the results derived from the above calculation, it can be concluded that the large rock (-37.5+26.5 mm) can be only heated up to 168 °C (compared to 300 °C for the rock sized -19+13.2 mm) after 15s microwave heating is not because of shortage of microwave energy. The calculation above is based on the XRF surface reading of metal grades. Assumptions are made that the average temperature of measured surface equals to the average temperature of the bulk rock. This assumption may be valid for small rocks with mass of 30 g -50 g, but may cause bias on the large rocks with mass of 300 g-400 g due to the limit of penetration depth. Thus, the energy calculation here is only approximate and is only used for the above conclusion.  146  -53+37.5 mm Size Fraction MW/IR Sorting Initial Data Set Sample ID  Weight, g  1  Metal Content, %  Average Temperature, °C  Pb  Zn  Pb+Zn  10s  20s  30s  G9-10s  201.1  56.87  20.04  76.91  56  98  141  32  2  287.9  16.13  9.85  25.98  48  81  111  29  3  187.1  3.26  8.36  11.62  49  80  113  30  4  140.1  0.02  0.84  0.86  33  49  62  26  5  124.2  23.79  10.08  33.87  63  106  154  32  6  115.2  0.52  21.84  22.37  62  114  148  34  7  161.2  18.87  9.64  28.51  50  92  122  28  8  159.7  0.01  0.82  0.83  31  39  50  24  9  231.1  11.41  18.43  29.84  47  75  106  30  10  243.8  0.02  0.75  0.77  34  47  60  25  11  120.5  44.05  23.91  67.96  65  114  158  31  12  256.4  0.05  0.81  0.86  34  47  61  25  13  140.2  6.06  3.98  10.04  56  101  139  30  14  197.7  4.50  9.92  14.42  50  89  120  28  15  261.3  0.85  8.19  9.05  50  81  115  31  16  113.4  0.48  8.10  8.58  63  107  149  31  17  198.0  0.71  1.39  2.10  51  82  117  28  18  153.9  0.53  12.26  12.79  59  101  138  31  19  144.4  33.11  7.32  40.42  61  102  141  33  20  77.8  0.09  1.67  1.75  38  58  76  26  21  123.0  0.05  0.77  0.82  33  46  56  25  22  138.3  0.39  1.20  1.59  54  94  127  34  23  169.2  3.74  10.44  14.18  57  97  138  32  24  282.3  0.80  1.14  1.94  45  73  99  31  25  148.3  0.02  0.76  0.78  32  44  61  25  147  Metal Content, %  Average Temperature, °C  Sample ID  Weight, g  Pb  Zn  Pb+Zn  10s  20s  30s  G9-10s  26  212.0  3.68  5.85  9.53  51  85  122  32  27  90.9  52.80  19.71  72.51  75  138  181  31  28  145.0  31.81  17.90  49.72  58  102  146  35  29  160.2  67.84  16.27  84.11  60  112  151  30  30  125.1  0.88  10.00  10.88  60  103  140  35  31  143.6  6.28  10.97  17.24  59  98  128  31  32  336.2  13.33  8.58  21.92  49  77  107  29  33  166.0  0.15  0.79  0.95  32  42  56  25  34  317.3  53.58  14.81  68.38  48  76  106  29  35  121.9  1.71  14.33  16.05  56  98  136  30  36  110.6  2.00  2.37  4.37  59  102  138  30  37  280.8  16.92  15.29  32.21  48  72  101  29  38  210.4  2.28  2.36  4.63  48  79  104  29  39  374.5  6.17  7.07  13.24  42  69  85  28  40  168.8  0.10  0.83  0.94  32  45  55  25  41  275.8  0.58  14.08  14.65  47  76  107  30  42  183.6  0.22  0.93  1.15  33  48  59  25  43  195.4  0.09  0.81  0.90  35  51  68  26  44  207.6  0.15  0.87  1.02  35  52  63  26  45  282.6  13.18  16.67  29.85  47  74  102  30  46  156.8  3.73  21.17  24.90  59  100  146  47  184.7  43.55  26.98  70.53  52  90  116  48  232.8  0.18  0.89  1.07  40  61  79  49  117.8  2.17  5.41  7.58  61  111  142  50  168.7  1.21  1.55  2.76  56  92  132  148  Sortability Graph -53+37.5 mm POM MW/IR Segregation - Calibrated  90.00  300  Pb+Zn grade Avg Surface Temperature - 30s  270  Avg Surface Temperature - 20S  Metal Grade, %  80.00  Avg Surface Temperature - 10S  240  70.00  210  60.00  180  50.00  150  40.00  120  30.00  90  20.00  60  10.00  30  0.00  0  Average Surface Temperature, ℃  100.00  1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 Rock  Individual and Group Tests -53+37.5 mm POM 45 Rocks 100  Average Surface Temperature, ℃  90 80 70 60 50 40 30 20 10 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 Rock # -53+37.5 mm -10s individual -53+37.5 mm -10s Grouped in 9  149  -53+37.5 mm MW/IR Sorting Results –Individual 10s (Calibrated XRF Surface Readings) Wt. % in Hot Fraction Concentrate Grades, % Pb Zn  Hot Fraction  Cold Fraction  Conc. %  Waste %  32  93.1  6.9  11.87  33  88.3  11.7  35  78.7  45  Separation Temperature, °C  Wt. % in Cold Fraction Waste Grades, %  % in Hot Fraction Metal Recovery, % Pb Zn  % in Cold Fraction Metal Recovery, % Pb Zn  Pb  Zn  9.11  0.07  0.80  100.0  99.4  0.0  0.6  12.51  9.56  0.09  0.83  99.9  98.9  0.1  1.1  21.3  14.03  10.63  0.08  0.82  99.8  98.0  0.2  2.0  68.3  31.7  15.75  11.72  0.93  1.67  97.3  93.8  2.7  6.2  47  59.9  40.1  16.81  11.08  2.47  4.74  91.0  77.7  9.0  22.3  48  48.1  51.9  14.93  11.04  7.46  6.21  65.0  62.3  35.0  37.7  49  42.5  57.5  15.62  11.38  7.68  6.44  60.1  56.7  39.9  43.3  50  35.9  64.1  17.27  11.79  7.58  6.72  56.1  49.6  43.9  50.4  55  28.1  71.9  18.66  12.55  8.09  6.97  47.3  41.2  52.7  58.8  56  21.3  78.7  18.20  13.22  9.13  7.27  35.1  33.0  64.9  67.0  60  8.8  91.2  22.04  13.37  9.99  8.07  17.6  13.8  82.4  86.2  Calculated Head Grade: 11.06% Pb and 8.54% Zn  150  24.00 23.00 22.00 21.00 20.00 19.00 18.00 17.00 16.00 15.00 14.00 13.00 12.00 11.00 10.00 9.00 8.00 7.00 6.00 5.00 4.00 3.00 2.00 1.00 0.00 30  35  40  45  50  55  60  100.0 95.0 90.0 85.0 80.0 75.0 70.0 65.0 60.0 55.0 50.0 45.0 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0  Pb recovery in fraction, % & % mass of fraction  Pb grade in fraction, %  Segregation of Pb in Cold and Hot fractions of -53+37.5 mm size on Average temperature under 10s Microwave radioation - Calibrated  65  Separation limit (average temperature), ℃ Pb grade in cold % mass in hot Pb recovery in hot Pb recovery in cold  Pb grade in hot % mass in cold  15.00  100.0 95.0 90.0 85.0 80.0 75.0 70.0 65.0 60.0 55.0 50.0 45.0 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0  14.00 13.00 12.00  Zn grade in fraction, %  11.00 10.00 9.00 8.00 7.00 6.00 5.00 4.00 3.00 2.00 1.00 0.00 30  35  Zn grade in hot % mass in cold  40  45  50  55  60  Zn recovery in fraction, % & % mass of fraction  Segregation of Zn in Cold and Hot fractions of -53+37.5 mm size on Average temperature under 10s Microwave radioation - Calibrated  65  Separation limit (average temperature), ℃ Zn grade in cold % mass in hot Zn recovery in hot Zn recovery in cold  151  -53+37.5 mm MW/IR Sorting Results –Individual 20s (Calibrated XRF Surface Readings) Wt. % in Hot Fraction Concentrate Grades, % Pb Zn  Hot Fraction  Cold Fraction  Conc. %  Waste %  45  93.1  6.9  11.87  47  86.5  13.5  52  78.7  70  Separation Temperature, °C  Wt. % in Cold Fraction Waste Grades, %  % in Hot Fraction Metal Recovery, % Pb Zn  % in Cold Fraction Metal Recovery, % Pb Zn  Pb  Zn  9.11  0.07  0.80  100.0  99.4  0.0  0.6  12.78  9.75  0.06  0.79  99.9  98.7  0.1  1.3  21.3  14.03  10.63  0.08  0.82  99.8  98.0  0.2  2.0  71.3  28.7  15.12  11.28  0.94  1.72  97.6  94.2  2.4  5.8  75  59.8  40.2  16.00  11.02  3.69  4.85  86.6  77.2  13.4  22.8  80  45.6  54.4  15.65  11.27  7.20  6.24  64.6  60.2  35.4  39.8  85  35.4  64.6  18.45  12.58  7.02  6.33  59.0  52.1  41.0  47.9  90  31.3  68.7  17.80  11.85  7.99  7.03  50.3  43.4  49.7  56.6  92  27.7  72.3  18.82  12.66  8.08  6.96  47.2  41.1  52.8  58.9  100  17.8  82.2  21.04  12.17  8.90  7.75  33.8  25.3  66.2  74.7  105  9.0  91.0  28.85  14.96  9.30  7.90  23.5  15.8  76.5  84.2  Calculated Head Grade: 11.06% Pb and 8.54% Zn  152  32.00 30.00 28.00 26.00 24.00 22.00 20.00 18.00 16.00 14.00 12.00 10.00 8.00 6.00 4.00 2.00 0.00  100.0 95.0 90.0 85.0 80.0 75.0 70.0 65.0 60.0 55.0 50.0 45.0 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0 30  40  50  60  70  80  90  100  110  Pb recovery in fraction, % & % mass of fraction  Pb grade in fraction, %  Segregation of Pb in Cold and Hot fractions of -53+37.5 mm size on Average temperature under 20s Microwave radioation - Calibrated  120  Separation limit (average temperature), ℃ Pb grade in cold % mass in hot Pb recovery in hot Pb recovery in cold  Pb grade in hot % mass in cold  16.00 15.00 14.00 13.00 12.00 11.00 10.00 9.00 8.00 7.00 6.00 5.00 4.00 3.00 2.00 1.00 0.00  100.0 95.0 90.0 85.0 80.0 75.0 70.0 65.0 60.0 55.0 50.0 45.0 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0 30  40  Zn grade in hot % mass in cold  50  60  70  80  90  100  110  Zn recovery in fraction, % & % mass of fraction  Zn grade in fraction, %  Segregation of Zn in Cold and Hot fractions of -53+37.5 mm size on Average temperature under 20s Microwave radioation - Calibrated  120  Separation limit (average temperature), ℃ Zn grade in cold % mass in hot Zn recovery in hot Zn recovery in cold  153  -53+37.5 mm MW/IR Sorting Results –Individual 30s (Calibrated XRF Surface Reading) Wt. % in Hot Fraction Concentrate Grades, % Pb Zn  Hot Fraction  Cold Fraction  Conc. %  Waste %  56  93.4  6.6  11.83  60  88.8  11.2  70  78.7  90  Separation Temperature, °C  Wt. % in Cold Fraction Waste Grades, %  % in Hot Fraction Metal Recovery, % Pb Zn  % in Cold Fraction Metal Recovery, % Pb Zn  Pb  Zn  9.09  0.08  0.81  100.0  99.4  0.0  0.6  12.44  9.51  0.09  0.81  99.9  98.9  0.1  1.1  21.3  14.03  10.63  0.08  0.82  99.8  98.0  0.2  2.0  71.3  28.7  15.12  11.28  0.94  1.72  97.6  94.2  2.4  5.8  105  60.0  40.0  16.33  11.65  3.13  3.87  88.7  81.9  11.3  18.1  110  47.6  52.4  15.13  11.15  7.35  6.16  65.2  62.2  34.8  37.8  120  33.5  66.5  16.85  11.45  8.14  7.07  51.1  45.0  48.9  55.0  130  26.5  73.5  16.43  10.72  8.06  7.06  46.5  39.3  53.5  60.7  140  15.9  84.1  30.69  15.85  7.33  7.15  44.2  29.6  55.8  70.4  150  5.3  94.7  48.27  17.20  8.97  8.05  23.2  10.7  76.8  89.3  160  1.0  99.0  52.80  19.71  10.65  8.43  4.6  2.2  95.4  97.8  Calculated Head Grade: 11.06% Pb and 8.54% Zn  154  52.00 48.00  Pb grade in fraction, %  44.00 40.00 36.00 32.00 28.00 24.00 20.00 16.00 12.00 8.00 4.00 0.00 40  50  60  70  80  90  100.0 95.0 90.0 85.0 80.0 75.0 70.0 65.0 60.0 55.0 50.0 45.0 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0  Pb recovery in fraction, % & % mass of fraction  56.00  Segregation of Pb in Cold and Hot fractions of -53+37.5 mm size on Average temperature under 30s Microwave radioation - Calibrated  100 110 120 130 140 150 160 170 180  Separation limit (average temperature), ℃  22.00  Pb grade in cold  % mass in hot  Segregation of Zn in Cold and Hot fractions of -53+37.5 mm size on Average temperature under 30s Microwave radioation Calibrated  20.00 18.00  Zn grade in fraction, %  16.00 14.00 12.00 10.00 8.00 6.00 4.00 2.00 0.00 40  50  60  Zn grade in hot % mass in cold  70  80  90  100.0 95.0 90.0 85.0 80.0 75.0 70.0 65.0 60.0 55.0 50.0 45.0 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0  Zn recovery in fraction, % & % mass of fraction  Pb grade in hot  100 110 120 130 140 150 160 170 180  Separation limit (average temperature), ℃ Zn grade in cold % mass in hot Zn recovery in hot Zn recovery in cold  155  -53+37.5 mm MW/IR Sorting Results –Individual 10s for 45 Rocks (Calibrated XRF Surface Reading) Wt. % in Hot Fraction Concentrate Grades, % Pb Zn  Hot Fraction  Cold Fraction  Conc. %  Waste %  32  92.4  7.6  12.01  33  87.2  12.8  35  76.5  45  Separation Temperature, °C  Wt. % in Cold Fraction Waste Grades, %  % in Hot Fraction Metal Recovery, % Pb Zn  % in Cold Fraction Metal Recovery, % Pb Zn  Pb  Zn  8.91  0.07  0.80  99.9  99.3  0.1  0.7  12.73  9.40  0.09  0.83  99.9  98.7  0.1  1.3  23.5  14.49  10.59  0.08  0.82  99.8  97.7  0.2  2.3  67.9  32.1  15.89  11.41  0.99  1.73  97.1  93.3  2.9  6.7  47  58.5  41.5  17.11  10.63  2.62  5.00  90.2  75.0  9.8  25.0  48  45.6  54.4  15.01  10.46  7.83  6.48  61.7  57.5  38.3  42.5  49  39.5  60.5  15.83  10.76  8.02  6.69  56.3  51.2  43.7  48.8  50  32.1  67.9  17.91  11.14  7.88  6.95  51.9  43.2  48.1  56.8  55  25.7  74.3  21.97  13.17  7.35  6.61  50.8  40.8  49.2  59.2  56  20.2  79.8  20.62  13.03  8.69  7.10  37.6  31.8  62.4  68.2  60  8.4  91.6  25.34  14.70  9.81  7.71  19.1  14.8  80.9  85.2  Calculated Head Grade: 11.10% Pb and 8.30% Zn  156  28.00  100.0 95.0 90.0 85.0 80.0 75.0 70.0 65.0 60.0 55.0 50.0 45.0 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0  26.00 24.00  Pb grade in fraction, %  22.00 20.00 18.00 16.00 14.00 12.00 10.00 8.00 6.00 4.00 2.00 0.00 30  40  50  Pb recovery in fraction, % & % mass of fraction  Segregation of Pb in Cold and Hot fractions of -53+37.5 mm size on Average temperature under 10s Microwave radioation (45 rocks) - Calibrated  60  Separation limit (average temperature), ℃ Pb grade in cold % mass in hot Pb recovery in hot Pb recovery in cold  Pb grade in hot % mass in cold  16.00  100.0 95.0 90.0 85.0 80.0 75.0 70.0 65.0 60.0 55.0 50.0 45.0 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0  15.00 14.00 13.00  Zn grade in fraction, %  12.00 11.00 10.00 9.00 8.00 7.00 6.00 5.00 4.00 3.00 2.00 1.00 0.00 30  40  50  Zn recovery in fraction, % & % mass of fraction  Segregation of Zn in Cold and Hot fractions of -53+37.5 mm size on Average temperature under 10s Microwave radioation (45 rocks) - Calibrated  60  Separation limit (average temperature), ℃ Zn grade in hot % mass in cold  Zn grade in cold Zn recovery in hot  % mass in hot Zn recovery in cold  157  -53+37.5 mm MW/IR Sorting Results –Group in 9 10s for 45 Rocks (Calibrated XRF Surface Reading) Wt. % in Hot Fraction Concentrate Grades, % Pb Zn  Hot Fraction  Cold Fraction  Conc. %  Waste %  24  98.1  1.9  11.32  25  82.9  17.1  26  75.6  28  Separation Temperature, °C  Wt. % in Cold Fraction Waste Grades, %  % in Hot Fraction Metal Recovery, % Pb Zn  % in Cold Fraction Metal Recovery, % Pb Zn  Pb  Zn  8.44  0.01  0.82  100.0  99.8  0.0  0.2  13.38  9.84  0.08  0.81  99.9  98.3  0.1  1.7  24.4  14.66  10.70  0.08  0.85  99.8  97.5  0.2  2.5  64.6  35.4  15.99  11.34  2.18  2.73  93.0  88.4  7.0  11.6  29  47.7  52.3  13.90  11.60  8.55  5.28  59.8  66.7  40.2  33.3  30  29.9  70.1  14.57  10.78  9.62  7.23  39.3  38.9  60.7  61.1  31  16.2  83.8  18.50  11.55  9.67  7.67  27.0  22.6  73.0  77.4  32  7.9  92.1  14.40  11.36  10.82  10.57  10.2  10.8  89.8  89.2  Calculated Head Grade: 11.10% Pb and 8.30% Zn  158  20.00 19.00 18.00 17.00 16.00 15.00 14.00 13.00 12.00 11.00 10.00 9.00 8.00 7.00 6.00 5.00 4.00 3.00 2.00 1.00 0.00  100.0 95.0 90.0 85.0 80.0 75.0 70.0 65.0 60.0 55.0 50.0 45.0 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0 22  23  24  25  26  27  28  29  30  31  32  Pb recovery in fraction, % & % mass of fraction  Pb grade in fraction, %  Segregation of Pb in Cold and Hot fractions of -53+37.5 mm size on Average temperature under 10s Microwave radioation grouped in 9 (45 rocks) - Calibrated  33  Separation limit (average temperature), ℃ Pb grade in hot % mass in cold  Pb grade in cold Pb recovery in hot  % mass in hot Pb recovery in cold  13.00  100.0 95.0 90.0 85.0 80.0 75.0 70.0 65.0 60.0 55.0 50.0 45.0 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0  12.00 11.00  Zn grade in fraction, %  10.00 9.00 8.00 7.00 6.00 5.00 4.00 3.00 2.00 1.00 0.00 22  23  24  Zn grade in hot % mass in cold  25  26  27  28  29  30  31  32  Zn recovery in fraction, % & % mass of fraction  Segregation of Zn in Cold and Hot fractions of -53+37.5 mm size on Average temperature under 10s Microwave radioation grouped in 9 (45 rocks) - Calibrated  33  Separation limit (average temperature), ℃ Zn grade in cold % mass in hot Zn recovery in hot Zn recovery in cold  159  -53+37.5 mm Size Fraction Sorting Results Summary Test Condition  Separation Limit, °C  Mass % of Cold  10s  35  20s 30s  Pb in Hot Fraction, %  Zn in Hot Fraction, %  Grade  Recovery  Grade  Recovery  21.3  14.03  99.8  10.63  98.0  70  28.7  15.12  97.6  11.28  94.2  90  28.7  15.12  97.6  11.28  94.2  Calculated Head Grade: 11.06% Pb and 8.54% Zn Test Condition  Separation Limit, °C  Mass % of Cold  Individual  35  Group in 9  26  50 rocks being tested  Pb in Hot Fraction, %  Zn in Hot Fraction, %  Grade  Recovery  Grade  Recovery  23.5  14.49  99.8  10.59  97.7  24.4  14.66  99.8  10.70  97.5  Calculated Head Grade: 11.10% Pb and 8.30% Zn  45 rocks being tested  160  -37.5+26.5 mm Size Fraction MW/IR Sorting Initial Data Set Sample ID  Weight, g  1  Metal Content, %  Average Temperature, °C  Pb  Zn  Pb+Zn  5s  10s  15s  G4-10s  G9-10s  213.2  1.47  1.57  3.04  33  48  67  38  33  2  146.1  1.01  1.05  2.06  35  52  71  41  35  3  86.3  46.55  24.71  71.26  44  83  114  42  37  4  97.6  1.11  2.10  3.21  37  58  82  38  36  5  75.4  0.06  0.87  0.93  29  36  42  32  29  6  70.1  8.12  37.79  45.90  46  88  124  68  44  7  77.2  4.03  2.30  6.33  40  70  94  52  39  8  64.5  0.19  4.60  4.79  32  55  61  31  30  9  39.9  0.02  0.79  0.80  27  36  43  27  26  10  42.1  0.04  0.80  0.84  28  35  37  28  27  11  143.4  0.38  7.41  7.79  35  55  77  45  38  12  87.0  0.21  13.66  13.87  36  60  82  46  39  13  40.2  0.41  13.41  13.82  56  111  162  73  52  14  31.7  0.03  0.79  0.82  28  32  38  26  27  15  83.0  1.24  30.71  31.94  42  73  104  50  42  16  94.0  0.08  0.81  0.89  39  64  88  49  41  17  40.7  0.02  0.78  0.80  27  32  39  27  26  18  34.3  0.03  0.78  0.81  28  42  50  29  27  19  91.1  2.11  11.94  14.05  41  69  98  58  41  20  49.0  0.41  2.99  3.40  50  93  116  68  40  21  87.4  0.04  0.87  0.91  38  64  87  51  38  22  95.9  0.12  9.08  9.19  36  64  79  46  35  23  68.3  60.92  14.35  75.27  45  82  110  44  32  24  39.3  0.76  1.75  2.50  54  106  149  53  40  25  98.7  0.85  11.89  12.74  40  67  93  49  37  161  Metal Content, %  Average Temperature, °C  Sample ID  Weight, g  Pb  Zn  Pb+Zn  5S  10s  15s  G4-10s  G9-10s  26  89.8  29.92  11.11  41.03  41  69  103  48  37  27  65.1  8.86  28.08  36.94  49  91  128  55  39  28  64.7  0.45  16.29  16.74  46  83  125  51  47  29  30.4  0.03  0.82  0.85  27  33  43  27  27  30  37.5  0.01  0.88  0.89  27  32  38  27  27  31  134.0  0.60  0.95  1.54  39  60  75  51  39  32  35.7  0.18  8.10  8.28  51  93  145  62  43  33  41.2  0.02  0.80  0.81  26  27  29  26  27  34  62.0  0.83  23.03  23.87  46  81  116  64  42  35  98.1  0.64  9.05  9.69  38  61  84  45  36  36  59.8  0.28  5.52  5.80  48  88  125  62  44  37  40.4  24.25  42.71  66.96  61  135  168  85  51  38  33.0  0.04  0.81  0.85  31  39  49  29  27  39  31.4  0.04  0.84  0.88  34  54  76  37  31  40  42.7  0.16  1.10  1.26  45  74  108  62  40  41  54.4  0.04  16.45  16.50  38  74  79  37  31  42  63.2  22.99  40.72  63.70  49  97  130  57  43  43  65.5  3.75  28.65  32.41  48  86  115  55  47  44  57.3  0.19  28.18  28.37  48  93  127  65  48  45  107.8  0.68  17.95  18.64  41  71  89  66  44  46  36.9  0.03  0.82  0.85  27  32  37  27  47  27.1  0.02  0.87  0.90  26  31  32  28  48  62.0  0.02  9.04  9.06  28  34  35  28  49  42.4  0.03  0.80  0.83  26  29  31  50  43.7  0.02  0.75  0.76  27  32  35  162  Sortability Graph  -37.5+26.5 mm POM MW/IR Segregation - Calibrated 70.00 60.00  300  Pb+Zn grade  270  Avg Surface Temperature - 15S Avg Surface Temperature - 10S  240  Avg Surface Temperature - 5S  Metal Grade, %  210 50.00  180  40.00  150 120  30.00  90 20.00 60 10.00  Average Surface Temperature, ℃  80.00  30  0.00  0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49  Rock  Individual and Group Tests  Average Surface Temperature, ℃  -37.5+26.5 mm POM 45 Rocks 180 170 160 150 140 130 120 110 100 90 80 70 60 50 40 30 20 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 -37.5+26.5 mm - 10S Individual -37.5+26.5 mm - 10S Grouped in 9  Rock #  -37.5+26.5 mm - 10S Grouped in 4  163  -37.5+26.5 mm MW/IR Sorting Results –Individual 5s (Calibrated XRF Surface Readings) Wt. % in Hot Fraction  Hot Fraction  Cold Fraction  Conc. %  Waste %  26  96.8  3.2  4.98  27  90.1  9.9  28  85.1  33  Separation Temperature, °C  Concentrate Grades, % Pb Zn  Wt. % in Cold Fraction Waste Grades, %  % in Hot Fraction Metal Recovery, % Pb Zn  % in Cold Fraction Metal Recovery, % Pb Zn  Pb  Zn  10.36  0.02  0.82  100.0  99.7  0.0  0.3  5.34  11.08  0.02  0.81  100.0  99.2  0.0  0.8  14.9  5.65  11.50  0.02  1.80  99.9  97.3  0.1  2.7  73.8  26.2  6.39  12.97  0.38  1.83  97.9  95.2  2.1  4.8  35  64.4  35.6  7.22  14.30  0.45  2.37  96.7  91.6  3.3  8.4  37  56.2  43.8  8.20  15.20  0.46  3.44  95.8  85.0  4.2  15.0  38  49.2  50.8  9.33  16.26  0.43  4.03  95.4  79.6  4.6  20.4  40  37.4  62.6  11.9  20.18  0.58  4.00  92.5  75.1  7.5  24.9  42  26.6  73.4  13.4  21.20  1.71  6.02  74.0  56.0  26.0  44.0  45  20.8  79.2  5.62  22.63  4.60  6.75  24.3  46.8  75.7  53.2  48  9.7  90.3  9.26  21.54  4.34  8.82  18.7  20.8  81.3  79.2  50  4.5  95.5  6.64  16.85  4.73  9.73  6.3  7.6  93.7  92.4  Calculated Head Grade: 4.82% Pb and 10.06% Zn  164  15.00 14.00 13.00 12.00 11.00 10.00 9.00 8.00 7.00 6.00 5.00 4.00 3.00 2.00 1.00 0.00  100.0  Pb recovery in fraction, % & % mass of fraction  Pb grade in fraction, %  Segregation of Pb in Cold and Hot fractions of -37.5+26.5 mm size on Average temperature under 5s Microwave radioation - Calibrated 90.0 80.0 70.0 60.0 50.0 40.0 30.0 20.0 10.0 0.0 20  25  30  35  40  45  50  55  Separation limit (average temperature), ℃ Pb grade in hot % mass of cold  Pb grade in cold Pb recovery in hot  % mass of hot Pb recovery in cold  40.00  100.0  36.00  90.0  32.00  80.0  28.00  70.0  24.00  60.0  20.00  50.0  16.00  40.0  12.00  30.0  8.00  20.0  4.00  10.0  0.00  0.0  20  25  Zn grade in hot % mass of cold  30  35  40  45  Separation limit (average temperature), ℃ Zn grade in cold Zn recovery in hot  50  Zn recovery in fraction, % & % mass of fraction  Zn grade in fraction, %  Segregation of Zn in Cold and Hot fractions of -37.5+26.5 mm size on Average temperature under 5s Microwave radioation - Calibrated  55  % mass of hot Zn recovery in cold  165  -37.5+26.5 mm MW/IR Sorting Results –Individual 10s (Calibrated XRF Surface Readings) Wt. % in Hot Fraction  Hot Fraction  Cold Fraction  Conc. %  Waste %  31  96.8  3.2  4.98  32  91.2  8.8  40  82.9  50  Separation Temperature, °C  Concentrate Grades, % Pb Zn  Wt. % in Cold Fraction Waste Grades, %  % in Hot Fraction Metal Recovery, % Pb Zn  % in Cold Fraction Metal Recovery, % Pb Zn  Pb  Zn  10.36  0.02  0.82  100  99.7  0.0  0.3  5.28  10.95  0.02  0.81  100  99.3  0.0  0.7  17.1  5.80  11.78  0.03  1.69  99.9  97.1  0.1  2.9  75.7  24.3  6.23  12.76  0.40  1.62  98.0  96.1  2.0  3.9  55  64.4  35.6  7.22  14.30  0.45  2.37  96.7  91.6  3.3  8.4  60  55.1  44.9  8.33  15.90  0.49  2.87  95.4  87.2  4.6  12.8  65  44.2  55.8  10.35  18.59  0.44  3.31  94.9  81.6  5.1  18.4  70  33.7  66.3  10.71  21.36  1.81  4.30  75.1  71.7  24.9  28.3  80  25.3  74.7  14.05  22.19  1.68  5.94  73.9  55.9  26.1  44.1  85  17.1  82.9  6.70  23.29  4.43  7.32  23.8  39.6  76.2  60.4  90  11.4  88.6  7.93  22.51  4.41  8.45  18.8  25.5  81.2  74.5  95  5.3  94.7  13.54  26.80  4.32  9.11  15.0  14.3  85.0  85.7  Calculated Head Grade: 4.82% Pb and 10.06% Zn  166  15.00 14.00 13.00 12.00 11.00 10.00 9.00 8.00 7.00 6.00 5.00 4.00 3.00 2.00 1.00 0.00  100.0 90.0 80.0 70.0 60.0 50.0 40.0 30.0 20.0 10.0 0.0 20  30  Pb grade in hot % mass of cold  40  50  60  70  80  Separation limit (average temperature), ℃ Pb grade in cold Pb recovery in hot  90  100  Pb recovery in fraction, % & % mass of fraction  Pb grade in fraction, %  Segregation of Pb in Cold and Hot fractions of -37.5+26.5 mm size on Average temperature under 10s Microwave radioation - Calibrated  % mass of hot Pb recovery in cold  40.00  100.0  36.00  90.0  32.00  80.0  28.00  70.0  24.00  60.0  20.00  50.0  16.00  40.0  12.00  30.0  8.00  20.0  4.00  10.0  0.00  0.0 20  30  Zn grade in hot % mass of cold  40  50  60  70  80  Separation limit (average temperature), ℃ Zn grade in cold Zn recovery in hot  90  100  Zn recovery in fraction, % & % mass of fraction  Zn grade in fraction, %  Segregation of Zn in Cold and Hot fractions of -37.5+26.5 mm size on Average temperature under 10s Microwave radioation - Calibrated  % mass of hot Zn recovery in cold  167  -37.5+26.5 mm MW/IR Sorting Results –Individual 15s (Calibrated XRF Surface Readings) Wt. % in Hot Fraction  Wt. % in Cold Fraction  % in Hot Fraction Metal Recovery, %  % in Cold Fraction Metal Recovery, %  Concentrate Grades, %  Waste Grades, % Zn  Pb  Zn  Pb  Zn  0.02  3.16  100.0  98.0  0.0  2.0  11.13  0.02  2.07  99.9  97.6  0.1  2.4  5.73  11.65  0.03  1.74  99.9  97.2  0.1  2.8  26.2  6.39  12.97  0.38  1.83  97.9  95.2  2.1  4.8  64.7  35.3  7.18  14.66  0.47  1.62  96.5  94.3  3.5  5.7  80  56.1  43.9  8.24  15.43  0.43  3.18  96.1  86.1  3.9  13.9  85  47.9  52.1  9.55  16.69  0.46  3.96  95.0  79.5  5.0  20.5  95  34.3  65.7  12.93  20.38  0.58  4.67  92.1  69.5  7.9  30.5  105  26.6  73.4  13.40  21.20  1.71  6.02  74.0  56.0  26.0  44.0  115  18.9  81.1  5.81  22.03  4.58  7.27  22.8  41.4  77.2  58.6  125  10.0  90.0  9.01  25.32  4.35  8.37  18.6  25.1  81.4  74.9  130  4.5  95.5  6.64  16.85  4.73  9.73  6.3  7.6  93.7  92.4  Hot Fraction  Cold Fraction  Conc. %  Waste %  Pb  Zn  Pb  35  93.7  6.3  5.14  10.52  40  88.2  11.8  5.46  45  83.9  16.1  70  73.8  76  Separation Temperature, °C  Calculated Head Grade: 4.82% Pb and 10.06% Zn  168  15.00 14.00 13.00 12.00 11.00 10.00 9.00 8.00 7.00 6.00 5.00 4.00 3.00 2.00 1.00 0.00  100.0  Pb recovery in fraction, % & % mass of fraction  Pb grade in fraction, %  Segregation of Pb in Cold and Hot fractions of -37.5+26.5 mm size on Average temperature under 15s Microwave radioation - Calibrated  90.0 80.0 70.0 60.0 50.0 40.0 30.0 20.0 10.0 0.0 20  30  40  50  60  70  80  90  100  110  120  130  140  Separation limit (average temperature), ℃ Pb grade in hot % mass of cold  Pb grade in cold Pb recovery in hot  % mass of hot Pb recovery in cold  40.00  100.0  36.00  90.0  32.00  80.0  28.00  70.0  24.00  60.0  20.00  50.0  16.00  40.0  12.00  30.0  8.00  20.0  4.00  10.0  0.00  0.0  20  30  40  50  60  70  80  90  100  110  120  130  Zn recovery in fraction, % & % mass of fraction  Zn grade in fraction, %  Segregation of Zn in Cold and Hot fractions of -37.5+26.5 mm size on Average temperature under 15s Microwave radioation - Calibrated  140  Separation limit (average temperature), ℃ Zn grade in hot % mass of cold  Zn grade in cold Zn recovery in hot  % mass of hot Zn recovery in cold  169  -37.5+26.5 mm MW/IR Sorting Results –Individual 10s 45 rocks (Calibrated XRF Surface Readings) Wt. % in Hot Fraction  Hot fraction  Cold fraction  Conc. %  Waste %  32  95.3  4.7  5.38  40  88.4  11.6  52  76.2  55  Separation Temperature, °C  Concentrate Grades, % Pb Zn  Wt. % in Cold Fraction Waste Grades, %  % in Hot Fraction Metal Recovery, % Pb Zn  % in Cold Fraction Metal Recovery, % Pb Zn  Pb  Zn  10.99  0.02  0.81  100.0  99.6  0.0  0.4  5.80  11.78  0.03  0.82  99.9  99.1  0.1  0.9  23.8  6.54  13.87  0.62  1.07  97.1  97.6  2.9  2.4  68.7  31.3  7.22  14.30  0.54  2.19  96.7  93.5  3.3  6.5  60  58.8  41.2  8.33  15.90  0.57  2.82  95.5  89.0  4.5  11.0  65  47.1  52.9  10.35  18.59  0.49  3.32  94.9  83.3  5.1  16.7  70  36.0  64.0  10.71  21.36  2.00  4.41  75.1  73.1  24.9  26.9  80  27.0  73.0  14.05  22.19  1.83  6.19  74.0  57.0  26.0  43.0  85  18.2  81.8  6.70  23.29  4.78  7.66  23.8  40.4  76.2  59.6  90  12.2  87.8  7.93  22.51  4.74  8.85  18.8  26.0  81.2  74.0  95  5.7  94.3  13.54  26.80  4.62  9.52  15.0  14.5  85.0  85.5  100  3.7  96.3  8.56  19.46  5.00  10.16  6.2  6.9  93.8  93.1  Calculated Head Grade: 5.13% Pb and 10.51% Zn  170  15.00 14.00 13.00 12.00 11.00 10.00 9.00 8.00 7.00 6.00 5.00 4.00 3.00 2.00 1.00 0.00  100.0  Pb recovery in fraction, % & % mass of fraction  Pb grade in fraction, %  Segregation of Pb in Cold and Hot fractions of -37.5+26.5 mm size on Average temperature under 10s Microwave radioation - Calibrated - Individual 45 rocks  90.0 80.0 70.0 60.0 50.0 40.0 30.0 20.0 10.0 0.0 20  30  40  50  60  70  80  90  100  110  Separation limit (average temperature), ℃ Pb grade in hot % mass of cold  Pb grade in cold Pb recovery in hot  % mass of hot Pb recovery in cold  40.00  100.0  36.00  90.0  32.00  80.0  28.00  70.0  24.00  60.0  20.00  50.0  16.00  40.0  12.00  30.0  8.00  20.0  4.00  10.0  0.00  Zn recovery in fraction, % & % mass of fraction  Zn grade in fraction, %  Segregation of Zn in Cold and Hot fractions of -37.5+26.5 mm size on Average temperature under 10s Microwave radioation - Calibrated - Individual 45 rocks  0.0 20  30  Zn grade in hot % mass of cold  40  50  60  70  80  Separation limit (average temperature), ℃ Zn grade in cold Zn recovery in hot  90  100  110  % mass of hot Zn recovery in cold  171  -37.5+26.5 mm MW/IR Sorting Results – Group in 4 – 10s 45 rocks (Calibrated XRF Surface Readings) Wt. % in Hot Fraction  Hot Fraction  Cold Fraction  Conc. %  Waste %  27  93.1  6.9  5.51  30  89.7  10.3  37  82.7  40  Separation Temperature, °C  Concentrate Grades, % Pb Zn  Wt. % in Cold Fraction  % in Hot Fraction  % in Cold Fraction  Waste Grades, %  Metal Recovery, %  Metal Recovery, %  Pb  Zn  Pb  Zn  Pb  Zn  11.23  0.02  0.81  100.0  99.5  0.0  0.5  5.72  11.62  0.03  0.80  99.9  99.2  0.1  0.8  17.3  6.20  12.13  0.05  2.78  99.8  95.4  0.2  4.6  73.0  27.0  6.84  13.51  0.52  2.41  97.3  93.8  2.7  6.2  42  65.7  34.3  5.62  13.91  4.20  3.98  72.0  87.0  28.0  13.0  45  56.1  43.9  4.21  14.68  6.31  5.18  46.0  78.3  54.0  21.7  50  39.0  61.0  3.73  15.63  6.03  7.23  28.4  58.0  71.6  42.0  52  27.7  72.3  4.78  20.40  5.27  6.72  25.8  53.8  74.2  46.2  55  22.4  77.6  4.72  19.97  5.25  7.78  20.6  42.6  79.4  57.4  60  17.6  82.4  3.10  18.95  5.57  8.71  10.6  31.7  89.4  68.3  65  9.6  90.4  5.40  22.75  5.10  9.21  10.1  20.7  89.9  79.3  Calculated Head Grade: 5.13% Pb and 10.51% Zn  172  10.00  100.0  9.00  90.0  8.00  80.0  7.00  70.0  6.00  60.0  5.00  50.0  4.00  40.0  3.00  30.0  2.00  20.0  1.00  10.0  0.00  Pb recovery in fraction, % & % mass of fraction  Pb grade in fraction, %  Segregation of Pb in Cold and Hot fractions of -37.5+26.5 mm size on Average temperature under 10s Microwave radioation - Calibrated - 45 rocks grouped in 4  0.0  20  30  40 50 Separation limit (average temperature), ℃  Pb grade in hot % mass of cold  60  Pb grade in cold Pb recovery in hot  70 % mass of hot Pb recovery in cold  40.00  100.0  36.00  90.0  32.00  80.0  28.00  70.0  24.00  60.0  20.00  50.0  16.00  40.0  12.00  30.0  8.00  20.0  4.00  10.0  0.00  0.0 20  30  Zn grade in hot % mass of cold  40  50  Separation limit (average temperature), ℃ Zn grade in cold Zn recovery in hot  60  70  Zn recovery in fraction, % & % mass of fraction  Zn grade in fraction, %  Segregation of Zn in Cold and Hot fractions of -37.5+26.5 mm size on Average temperature under 10s Microwave radioation - Calibrated - 45 rocks grouped in 4  % mass of hot Zn recovery in cold  173  -37.5+26.5 mm MW/IR Sorting Results – Group in 9 – 10s 45 rocks (Calibrated XRF Surface Readings)  Separation Temperature, °C  Hot Fraction  Cold Fraction  Wt. % in Hot Fraction Concentrate Grades, % Pb Zn  Wt. % in Cold Fraction  % in Hot Fraction  % in Cold Fraction  Waste Grades, %  Metal Recovery, %  Metal Recovery, %  Conc. %  Waste %  Pb  Zn  Pb  Zn  Pb  Zn  26  97.5  2.5  5.26  10.76  0.02  0.78  100.0  99.8  0.0  0.2  27  89.7  10.3  5.72  11.62  0.03  0.80  99.9  99.2  0.1  0.8  31  82.7  17.3  6.20  12.13  0.05  2.78  99.8  95.4  0.2  4.6  33  73.9  26.1  5.05  13.01  5.37  3.42  72.7  91.5  27.3  8.5  35  66.4  33.6  5.54  14.01  4.32  3.60  71.7  88.5  28.3  11.5  36  60.3  39.7  6.02  14.86  3.79  3.90  70.7  85.2  29.3  14.8  37  51.7  48.3  2.92  14.73  7.49  5.99  29.5  72.5  70.5  27.5  39  33.2  66.8  3.58  18.76  5.90  6.41  23.1  59.3  76.9  40.7  41  23.4  76.6  4.74  24.76  5.25  6.17  21.6  55.0  78.4  45.0  42  18.8  81.2  5.63  24.12  5.02  7.35  20.6  43.2  79.4  56.8  45  8.4  91.6  4.78  25.40  5.16  9.15  7.8  20.2  92.2  79.8  50  2.5  97.5  12.36  28.10  4.95  10.05  6.0  6.7  94.0  93.3  Calculated Head Grade: 5.13% Pb and 10.51% Zn  174  15.00 14.00 13.00 12.00 11.00 10.00 9.00 8.00 7.00 6.00 5.00 4.00 3.00 2.00 1.00 0.00  100.0  Pb recovery in fraction, % & % mass of fraction  Pb grade in fraction, %  Segregation of Pb in Cold and Hot fractions of -37.5+26.5 mm size on Average temperature under 10s Microwave radioation - Calibrated - 45 rocks grouped in 9  90.0 80.0 70.0 60.0 50.0 40.0 30.0 20.0 10.0 0.0 20  25  Pb grade in hot % mass of cold  30  35  40  45  Separation limit (average temperature), ℃ Pb grade in cold Pb recovery in hot  50  55  % mass of hot Pb recovery in cold  40.00  100.0  36.00  90.0  32.00  80.0  28.00  70.0  24.00  60.0  20.00  50.0  16.00  40.0  12.00  30.0  8.00  20.0  4.00  10.0  0.00  0.0 20  25  Zn grade in hot % mass of cold  30  35  40  45  Separation limit (average temperature), ℃ Zn grade in cold Zn recovery in hot  50  Zn recovery in fraction, % & % mass of fraction  Zn grade in fraction, %  Segregation of Zn in Cold and Hot fractions of -37.5+26.5 mm size on Average temperature under 10s Microwave radioation - Calibrated - 45 rocks grouped in 9  55  % mass of hot Zn recovery in cold  175  -37.5+26.5 mm Size Fraction Soring Results Summary Test Condition  Separation Limit, °C  Mass % of Cold  5s  33  10s 15s  Pb in Hot Fraction, %  Zn in Hot Fraction, %  Grade  Recovery  Grade  Recovery  26.2  6.39  97.9  12.97  95.2  50  24.3  6.23  98.0  12.76  96.1  70  26.2  6.39  97.9  12.97  95.2  Calculated Head Grade : 4.82% Pb and 10.06% Zn Test Condition  Separation Limit, °C  Mass % of Cold  Individual  52  Group in 4 Group in 9  50 rocks being tested  Pb in Hot Fraction, %  Zn in Hot Fraction, %  Grade  Recovery  Grade  Recovery  23.8  6.54  97.1  13.87  97.6  37  17.3  6.20  99.8  12.13  95.4  31  17.3  6.20  99.8  12.13  95.4  Calculated Head Grade : 5.13% Pb and 10.51% Zn  45 rocks being tested  176  -26.5+19 mm Size Fraction MW/IR Sorting Initial Data Set Metal Content, %  Average Temperature, °C  Sample ID  Weight, g  Pb  Zn  Pb+Zn  5s  10s  15s  G9-10s  1  66.1  36.10  13.03  49.13  40  65  90  49  2  32.2  0.54  12.10  12.65  60  108  152  58  3  36.1  42.82  17.04  59.85  57  112  173  46  4  34.5  0.17  3.60  3.77  46  98  146  48  5  25.2  0.02  0.77  0.78  25  30  33  24  6  22.1  0.03  0.79  0.82  29  41  51  25  7  21.7  2.37  3.17  5.54  66  135  186  62  8  22.7  22.25  15.04  37.29  67  128  183  53  9  45.6  31.86  17.57  49.43  48  89  122  46  10  32.2  4.47  22.62  27.10  62  129  187  52  11  52.2  0.43  15.46  15.89  48  91  114  58  12  27.7  6.46  15.13  21.59  51  115  138  52  13  50.3  0.36  33.88  34.24  47  95  120  52  14  18.7  0.03  0.80  0.83  34  58  76  27  15  11.5  0.05  0.86  0.90  27  32  38  25  16  43.3  20.17  23.91  44.08  49  100  129  55  17  23.3  0.04  0.81  0.86  27  36  42  26  18  36.6  0.16  0.83  0.99  50  102  143  55  19  37.0  1.09  1.06  2.15  48  95  135  55  20  31.8  37.17  21.13  58.30  57  110  159  54  21  31.7  0.02  0.75  0.77  27  34  40  26  22  33.4  0.11  21.53  21.63  34  45  60  30  23  25.3  0.05  17.85  17.90  56  110  146  49  24  18.5  0.05  0.94  0.99  26  33  36  26  25  28.1  0.08  1.20  1.28  29  43  50  28  177  Metal Content, %  Average Temperature, °C  Sample ID  Weight, g  Pb  Zn  Pb+Zn  5s  10s  15s  G9-10s  26  48.0  5.02  17.60  22.62  49  94  121  57  27  53.2  4.93  34.27  39.20  53  96  159  56  28  27.4  5.34  24.60  29.94  62  133  170  66  29  12.6  0.04  0.81  0.85  26  32  36  26  30  16.4  0.02  0.75  0.77  28  33  42  26  31  45.3  17.16  24.13  41.28  52  101  136  54  32  45.8  20.96  17.99  38.95  50  100  128  47  33  40.0  22.36  15.77  38.12  54  107  145  66  34  37.3  2.23  11.51  13.74  52  100  144  58  35  35.2  24.50  20.40  44.90  54  111  152  61  36  23.3  0.03  0.79  0.82  26  32  36  26  37  15.8  0.49  3.85  4.34  33  51  64  29  38  23.8  17.55  13.32  30.87  65  129  172  76  39  28.1  0.04  0.86  0.90  27  33  38  26  40  22.2  0.00  0.84  0.84  28  32  38  26  41  41.6  0.25  19.79  20.04  46  96  112  44  42  23.7  27.70  19.19  46.89  76  150  191  80  43  29.3  0.64  5.52  6.16  61  114  164  67  44  14.7  0.54  3.13  3.67  34  55  77  33  45  24.4  20.40  23.97  44.36  58  123  183  65  46  24.2  0.01  0.82  0.83  27  32  42  47  35.7  0.36  3.44  3.80  50  99  135  48  17.8  0.03  0.88  0.91  27  32  37  49  21.4  0.04  0.85  0.89  26  32  37  50  41.9  2.79  6.08  8.86  51  103  129  178  Sortability Graph  -26.5+19 mm POM MW/IR Segregation - Calibrated  50.00  300  Pb+Zn grade Avg Surface Temperature - 15S Avg Surface Temperature - 10S Avg Surface Temperature - 5S  270 240 210  Metal Grade, %  40.00  180 30.00  150 120  20.00  90 60  10.00  Average Surface Temperature, ℃  60.00  30 0.00  0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49  Rock  Individual and Group Tests  Average Surface Temperature, ℃  -26.5+19 mm POM 45 Rocks 180 170 160 150 140 130 120 110 100 90 80 70 60 50 40 30 20 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 Rock # -26.5+19 mm - 10s individual -26.5+19 mm - 10s Grouped in 9  179  -26.5+19 mm MW/IR Sorting Results – Individual 5s (Calibrated XRF Surface Readings) Wt. % in Hot Fraction Concentrate Grades, % Pb Zn  Hot Fraction  Cold Fraction  Conc. %  Waste %  26  93.5  6.5  9.90  27  84.7  15.3  30  79.0  35  Separation Temperature, °C  Wt. % in Cold Fraction  % in Hot Fraction  % in Cold Fraction  Waste Grades, %  Metal Recovery, %  Metal Recovery, %  Pb  Zn  Pb  Zn  Pb  Zn  13.65  0.04  0.82  100.0  99.6  0.0  0.4  10.92  14.98  0.03  0.82  99.9  99.0  0.1  1.0  21.0  11.71  16.00  0.03  0.85  99.9  98.6  0.1  1.4  73.7  26.3  12.53  16.42  0.08  2.73  99.8  94.4  0.2  5.6  40  69.5  30.5  11.09  16.62  5.09  4.17  83.2  90.1  16.8  9.9  47  61.4  38.6  12.53  16.05  4.08  7.70  83.0  76.8  17.0  23.2  48  52.7  47.3  12.74  16.68  5.39  8.53  72.5  68.5  27.5  31.5  50  39.3  60.7  13.68  17.71  6.41  9.66  58.0  54.2  42.0  45.8  55  21.2  78.8  15.68  16.51  7.53  11.83  36.0  27.3  64.0  72.7  60  11.6  88.4  10.73  15.19  9.07  12.51  13.5  13.8  86.5  86.2  65  4.4  95.6  17.81  12.70  8.87  12.83  8.4  4.3  91.6  95.7  Calculated Head Grade : 9.26% Pb and 12.82% Zn  180  100.0 90.0 80.0 70.0 60.0 50.0 40.0 30.0 20.0 10.0 0.0 20  30  Pb grade in hot % mass of cold  30.00  40  50  60  Separation limit (average temperature), ℃ Pb grade in cold Pb recovery in hot  70  % mass of hot Pb recovery in cold  Segregation of Zn in Cold and Hot fractions of -26.6+19 mm size on Average temperature under 5s Microwave radioation - Calibrated  100.0  28.00  90.0  26.00 24.00  80.0  22.00  Zn grade in fraction, %  Pb recovery in fraction, % & % mass of fraction  20.00 19.00 18.00 17.00 16.00 15.00 14.00 13.00 12.00 11.00 10.00 9.00 8.00 7.00 6.00 5.00 4.00 3.00 2.00 1.00 0.00  70.0  20.00 18.00  60.0  16.00  50.0  14.00 12.00  40.0  10.00  30.0  8.00 6.00  20.0  4.00  10.0  2.00 0.00  Zn recovery in fraction, % & % mass of fraction  Pb grade in fraction, %  Segregation of Pb in Cold and Hot fractions of -26.5+19 mm size on Average temperature under 5s Microwave radioation - Calibrated  0.0  20 Zn grade in hot % mass of cold  30  40  50  Separation limit (average temperature), ℃ Zn grade in cold Zn recovery in hot  60  70 % mass of hot Zn recovery in cold  181  -26.5+19 mm MW/IR Sorting Results – Individual 10s (Calibrated XRF Surface Readings)  Separation Temperature, °C  Hot fraction  Cold Fraction  Wt. % in Hot Fraction Concentrate Grades, %  Wt. % in Cold Fraction  % in Hot Fraction  % in Cold Fraction  Waste Grades, %  Metal Recovery, %  Metal Recovery, %  Conc., %  Waste, %  Pb  Zn  Pb  Zn  Pb  Zn  Pb  Zn  32  89.8  10.2  10.31  14.18  0.03  0.82  100.0  99.3  0.0  0.7  33  85.8  14.2  10.79  14.81  0.03  0.83  100.0  99.1  0.0  0.9  45  76.9  23.1  12.03  15.84  0.04  2.77  99.9  95.0  0.1  5.0  60  73.7  26.3  12.53  16.42  0.08  2.73  99.8  94.4  0.2  5.6  80  69.5  30.5  11.09  16.62  5.09  4.17  83.2  90.1  16.8  9.9  95  54.5  45.5  12.05  16.25  5.92  8.72  70.9  69.1  29.1  30.9  100  35.8  64.2  14.39  15.44  6.40  11.36  55.6  43.1  44.4  56.9  105  27.8  72.2  16.42  16.67  6.50  11.34  49.4  36.2  50.6  63.8  110  19.5  80.5  16.52  16.71  7.50  11.88  34.8  25.5  65.2  74.5  120  11.3  88.7  13.75  18.02  8.69  12.16  16.8  15.9  83.2  84.1  130  4.7  95.3  11.73  16.45  9.14  12.64  5.9  6.0  94.1  94.0  Calculated Head Grade : 9.26% Pb and 12.82% Zn  182  100.0 90.0 80.0 70.0 60.0 50.0 40.0 30.0 20.0 10.0  Pb recovery in fraction, % & % mass of fraction  20.00 19.00 18.00 17.00 16.00 15.00 14.00 13.00 12.00 11.00 10.00 9.00 8.00 7.00 6.00 5.00 4.00 3.00 2.00 1.00 0.00  0.0 20  30  40  Pb grade in hot % mass of cold  50  60  70  80  90  100  110  120  Separation limit (average temperature), ℃ Pb grade in cold Pb recovery in hot  130  140  % mass of hot Pb recovery in cold  Segregation of Zn in Cold and Hot fractions of -26.5+19 mm size on Average temperature under 10s Microwave radioation - Calibrated 30.00 28.00 26.00 24.00 22.00 20.00 18.00 16.00 14.00 12.00 10.00 8.00 6.00 4.00 2.00 0.00  100.0 90.0 80.0 70.0 60.0 50.0 40.0 30.0 20.0 10.0  Zn recovery in fraction, % & % mass of fraction  Zn grade in fraction, %  Pb grade in fraction, %  Segregation of Pb in Cold and Hot fractions of -26.5+19 mm size on Average temperature under 10s Microwave radioation - Calibrated  0.0 20  30  40  Zn grade in hot % mass of cold  50  60  70  80  90  100  110  Separation limit (average temperature), ℃ Zn grade in cold Zn recovery in hot  120  130  140  % mass of hot Zn recovery in cold  183  -26.5+19 mm MW/IR Sorting Results – Individual 15s (Calibrated XRF Surface Readings) Wt. % in Hot Fraction Concentrate Grades,% Pb Zn  Hot Fraction  Cold Fraction  Conc. %  Waste %  36  94.9  5.1  9.76  40  86.4  13.6  60  76.9  80  Separation Temperature, °C  Wt. % in Cold Fraction  % in Hot Fraction  % in Cold Fraction  Waste Grades, %  Metal Recovery, %  Metal Recovery, %  Pb  Zn  Pb  Zn  Pb  Zn  13.47  0.03  0.82  100.0  99.7  0.0  0.3  10.72  14.72  0.03  0.83  100.0  99.1  0.0  0.9  23.1  12.03  15.84  0.04  2.77  99.9  95.0  0.1  5.0  73.7  26.3  12.53  16.42  0.08  2.73  99.8  94.4  0.2  5.6  90  69.5  30.5  11.09  16.62  5.09  4.17  83.2  90.1  16.8  9.9  120  60.2  39.8  12.74  15.62  3.99  8.58  82.9  73.4  17.1  26.6  125  54.2  45.8  12.15  15.40  5.84  9.76  71.1  65.1  28.9  34.9  135  41.1  58.9  12.89  16.75  6.73  10.08  57.2  53.7  42.8  46.3  145  29.1  70.9  13.92  17.92  7.34  10.73  43.8  40.7  56.2  59.3  155  21.0  79.0  16.63  19.74  7.31  10.99  37.6  32.3  62.4  67.7  170  11.9  88.1  20.68  16.85  7.72  12.28  26.5  15.6  73.5  84.4  185  5.0  95.0  10.98  16.14  9.17  12.65  5.9  6.3  94.1  93.7  Calculated Head Grade : 9.26% Pb and 12.82% Zn  184  Segregation of Pb in Cold and Hot fractions of -26.5+19 mm size on Average temperature under 15s Microwave radioation - Calibrated  100.0  24.00  90.0  22.00 80.0  Pb grade in fraction, %  20.00 18.00  70.0  16.00  60.0  14.00  50.0  12.00 10.00  40.0  8.00  30.0  6.00  20.0  4.00 10.0  2.00 0.00  Pb recovery in fraction, % & % mass of fraction  26.00  0.0 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200  30.00  % mass of hot  Segregation of Zn in Cold and Hot fractions of -26.5+19 mm size on Average temperature under 15s Microwave radioation - Calibrated  28.00  90.0  26.00 24.00  80.0  22.00  Zn grade in fraction, %  100.0  70.0  20.00 18.00  60.0  16.00  50.0  14.00 12.00  40.0  10.00  30.0  8.00 6.00  20.0  4.00  10.0  2.00 0.00  Zn recovery in fraction, % & % mass of fraction  Separation limit (average temperature), ℃ Pb grade in hot Pb grade in cold  0.0 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200  Zn grade in hot % mass of cold  Separation limit (average temperature), ℃ Zn grade in cold Zn recovery in hot  % mass of hot Zn recovery in cold  185  -26.5+19 mm MW/IR Sorting Results – Individual 10s 45 rocks (Calibrated XRF Surface Readings) Wt. % in Hot Fraction Concentrate Grades, % Pb Zn  Hot Fraction  Cold Fraction  Conc. %  Waste %  32  93.3  6.7  10.81  35  86.6  13.4  45  79.1  65  Separation Temperature, °C  Wt. % in Cold Fraction  % in Hot Fraction  % in Cold Fraction  Waste Grades, %  Metal Recovery, %  Metal Recovery, %  Pb  Zn  Pb  Zn  Pb  Zn  14.73  0.02  0.81  100.0  99.6  0.0  0.4  11.65  15.80  0.03  0.81  100.0  99.2  0.0  0.8  20.9  12.75  16.60  0.04  3.18  99.9  95.2  0.1  4.8  70.9  29.1  11.82  17.53  5.87  4.68  83.1  90.1  16.9  9.9  95  54.5  45.5  13.09  17.39  6.50  9.49  70.7  68.7  29.3  31.3  98  45.3  54.7  15.30  16.58  5.77  11.49  68.7  54.5  31.3  45.5  100  36.4  63.6  15.33  16.20  7.09  12.42  55.3  42.8  44.7  57.2  110  21.5  78.5  16.52  16.71  8.33  13.00  35.2  26.0  64.8  74.0  120  12.4  87.6  13.75  18.02  9.57  13.20  16.9  16.2  83.1  83.8  130  5.1  94.9  11.73  16.45  10.00  13.65  6.0  6.1  94.0  93.9  Calculated Head Grade: 10.09% Pb and 13.79% Zn  186  20.00 19.00 18.00 17.00 16.00 15.00 14.00 13.00 12.00 11.00 10.00 9.00 8.00 7.00 6.00 5.00 4.00 3.00 2.00 1.00 0.00  100.0 90.0 80.0 70.0 60.0 50.0 40.0 30.0 20.0 10.0 0.0 20  30  40  Pb grade in hot % mass of cold  30.00  50  60  70  80  90  100  110  120  Separation limit (average temperature), ℃ Pb grade in cold Pb recovery in hot  130  140  % mass of hot Pb recovery in cold  Segregation of Zn in Cold and Hot fractions of -26.5+19 mm size on Average temperature under 10s Microwave radioation - 45 rocks - Calibrated  28.00  100.0 90.0  26.00 24.00  80.0  22.00  Zn grade in fraction, %  Pb recovery in fraction, % & % mass of fraction  Pb grade in fraction, %  Segregation of Pb in Cold and Hot fractions of -26.5+19 mm size on Average temperature under 10s Microwave radioation - 45 rocks - Calibrated  70.0  20.00 18.00  60.0  16.00  50.0  14.00 12.00  40.0  10.00  30.0  8.00 6.00  20.0  4.00  10.0  2.00 0.00  0.0 20  30  40  Zn grade in hot % mass of cold  50  60  70  80  90  100  110  Separation limit (average temperature), ℃ Zn grade in cold Zn recovery in hot  120  130  140  % mass of hot Zn recovery in cold  187  -26.5+19 mm MW/IR Sorting Results – Group in 9- 10s 45 rocks (Calibrated XRF Surface Readings) Wt. % in Hot Fraction Concentrate Grades, % Pb Zn  Hot Fraction  Cold Fraction  Conc. %  Waste %  25  95.8  4.2  10.53  26  83.4  16.6  30  76.6  45  Separation Temperature, °C  Wt. % in Cold Fraction  % in Hot Fraction  % in Cold Fraction  Waste Grades, %  Metal Recovery, %  Metal Recovery, %  Pb  Zn  Pb  Zn  Pb  Zn  14.36  0.03  0.79  100.0  99.8  0.0  0.2  12.09  16.38  0.03  0.81  100.0  99.0  0.0  1.0  23.4  13.15  17.06  0.06  3.08  99.8  94.8  0.2  5.2  72.7  27.3  13.85  17.15  0.10  4.88  99.7  90.3  0.3  9.7  50  54.8  45.2  10.18  18.01  9.98  8.69  55.3  71.5  44.7  28.5  53  45.4  54.6  10.97  16.77  9.36  11.32  49.3  55.2  50.7  44.8  55  31.7  68.3  9.30  17.63  10.46  12.02  29.2  40.5  70.8  59.5  60  15.9  84.1  15.72  16.01  9.02  13.38  24.8  18.5  75.2  81.5  65  10.2  89.8  14.79  15.52  9.56  13.60  14.9  11.5  85.1  88.5  70  3.4  96.6  22.61  16.25  9.66  13.71  7.5  4.0  92.5  96.0  Calculated Head Grade: 10.09% Pb and 13.79% Zn  188  20.00 19.00 18.00 17.00 16.00 15.00 14.00 13.00 12.00 11.00 10.00 9.00 8.00 7.00 6.00 5.00 4.00 3.00 2.00 1.00 0.00  100.0 90.0 80.0 70.0 60.0 50.0 40.0 30.0 20.0 10.0  Pb recovery in fraction, % & % mass of fraction  Pb grade in fraction, %  Segregation of Pb in Cold and Hot fractions of -26.5+19 mm size on Average temperature under 10s Microwave radioation - 45 rocks grouped in 9 - Calibrated  0.0 20  30  Pb grade in hot % mass of cold  40  50  60  70  Separation limit (average temperature), ℃ Pb grade in cold Pb recovery in hot  80  % mass of hot Pb recovery in cold  Segregation of Zn in Cold and Hot fractions of -26.5+19 mm size on Average temperature under 10s Microwave radioation - 45 rocks grouped in 9 - Calibrated 30.00  100.0  28.00  90.0  26.00 24.00  80.0  Zn grade in fraction, %  22.00  70.0  20.00 18.00  60.0  16.00  50.0  14.00 12.00  40.0  10.00  30.0  8.00 6.00  20.0  4.00  10.0  2.00 0.00  0.0 20  30  Zn grade in hot % mass of cold  40  50  60  Separation limit (average temperature), ℃ Zn grade in cold Zn recovery in hot  70  80  % mass of hot Zn recovery in cold  189  -26.5+19 mm Size Fraction Sorting Results Summary Pb in Hot Fraction, % Grade Recovery  Zn in Hot Fraction, % Grade Recovery  21.0  11.71  99.9  16.00  98.6  45  23.1  12.03  99.9  15.84  95.0  60  23.1  12.03  99.9  15.84  95.0  Test Condition  Separation Limit, °C  Mass % of Cold  5s  30  10s 15s  Calculated Head Grade:9.26% Pb and 12.86% of Zn 50 rocks being tested Pb in Hot Zn in Hot Test Separation Mass % Fraction, % Fraction, % Condition Limit, °C of Cold Grade Recovery Grade Recovery Individual  45  20.9  12.75  99.9  16.60  95.2  Group in 9  26  16.6  12.09  100.0  16.38  99.0  Calculated Head Grade: 10.09% Pb and 13.79% Zn  45 rocks being tested  190  -19+13.2 mm Size Fraction MW/IR Sorting Results Initial Data Set  Sample ID  Weight, g  1  Metal Content, %  Average Temperature, °C  Pb  Zn  5s  10s  15s  G9-10s  G25-10s  7.6  29.79  2.92  49  105  156  75  48  2  5.2  11.17  6.30  66  150  194  90  70  3  8.2  0.35  1.07  62  165  223  82  56  4  4.6  0.01  1.53  26  28  31  26  25  5  5.3  0.02  0.86  28  34  39  27  26  6  5.8  4.88  29.45  97  187  280  140  82  7  13.0  0.24  13.92  45  101  122  84  48  8  6.8  0.04  0.77  26  29  33  26  25  9  4.9  0.19  1.28  26  29  32  26  25  10  5.7  15.63  9.76  70  115  173  105  47  11  7.1  0.05  1.04  26  31  32  27  26  12  4.6  1.33  0.90  76  155  244  116  85  13  7.5  0.04  0.84  27  32  33  27  26  14  8.7  0.04  3.63  28  31  35  26  25  15  7.7  0.05  0.89  27  30  35  28  26  16  8.2  17.99  30.70  64  130  194  97  60  17  12.1  6.35  2.21  65  132  181  84  55  18  9.5  1.54  1.26  49  105  146  65  47  19  6.4  0.23  0.80  46  88  121  50  45  20  10.1  1.13  1.41  48  122  127  73  55  21  7.1  8.18  7.95  85  194  299  177  74  22  10.4  0.03  0.77  26  28  31  27  26  23  13.2  1.00  8.97  58  115  166  76  64  24  10.0  0.04  0.76  26  29  32  28  26  25  4.5  0.03  0.79  29  30  37  30  27  191  Sample ID  Weight, g  26  Metal Content, %  Average Temperature, °C  Pb  Zn  5s  10s  15s  G9-10s  G25–10s  7.0  0.02  0.75  28  28  33  27  25  27  4.4  9.33  4.15  59  128  203  89  69  28  8.2  7.45  31.30  53  113  148  92  75  29  5.2  24.86  25.51  115  239  271  162  113  30  4.9  0.04  15.77  37  61  86  46  34  31  4.5  42.06  28.24  70  164  197  116  92  32  5.8  0.01  1.22  27  39  32  28  26  33  6.1  0.03  1.01  28  30  34  28  26  34  4.6  0.04  0.81  28  29  33  28  26  35  7.4  10.89  28.38  49  105  149  63  47  36  7.6  3.11  26.65  64  134  202  95  81  37  11.1  0.62  1.97  63  127  186  95  64  38  9.0  0.12  13.03  47  88  153  64  61  39  9.4  21.58  1.77  68  124  190  89  52  40  7.6  0.03  0.75  27  31  37  29  26  41  6.8  0.02  0.75  28  28  28  27  26  42  5.6  0.02  0.76  28  30  32  29  26  43  8.8  0.01  0.89  34  42  55  34  31  44  3.7  0.02  0.76  36  56  102  42  33  45  10.1  0.16  7.99  70  122  225  99  73  46  7.7  2.09  3.94  67  123  191  65  47  5.9  0.82  17.52  50  97  159  48  48  4.7  1.98  30.00  41  70  105  32  49  7.3  0.04  0.97  28  32  36  27  50  5.2  0.03  0.77  27  31  37  27  192  Sortability Graph -19+13.2 mm POM MW/IR Segregation -Calibrated assays 75.00 Pb+Zn grade  65.00  Avg Surface Temperature - 15S  60.00  Avg Surface Temperature - 10S  240  Avg Surface Temperature - 5S  210  50.00  270  45.00  180  40.00  150  35.00 30.00  120  25.00  90  20.00 15.00  60  10.00  Average Surface Temperature, ℃  55.00  Metal Grade, %  300  70.00  30  5.00 0.00  0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49  Rock  Individual and Group Tests  Average Surface Temperature, ℃  -19+13.2 mm POM 45 Rocks 260 240 220 200 180 160 140 120 100 80 60 40 20 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 Rock # -19+13.2 mm -10S Individual -19+13.2 mm -10S Group in 9  193  -19+13.2 mm MW/IR Sorting Results – Individual- 5s (Calibrated XRF Surface Readings) Wt. % in Hot Fraction Concentrate Grades, % Pb Zn  Hot Fraction  Cold Fraction  Conc. %  Waste %  26  87.9  12.1  4.73  27  78.6  21.4  28  64.4  40  Separation Temperature, °C  Wt. % in Cold Fraction  % in Hot Fraction  % in Cold Fraction  Waste Grades, %  Metal Recovery, %  Metal Recovery, %  Pb  Zn  Pb  Zn  Pb  Zn  8.17  0.05  0.95  99.8  98.4  0.2  1.6  5.28  9.04  0.05  0.92  99.8  97.3  0.2  2.7  35.6  6.44  10.73  0.04  1.08  99.7  94.7  0.3  5.3  58.4  41.6  7.10  11.41  0.04  1.53  99.6  91.3  0.4  8.7  45  53.5  46.5  7.68  10.79  0.11  3.28  98.8  79.1  1.2  20.9  47  49.3  50.7  8.33  11.04  0.11  3.67  98.7  74.5  1.4  25.5  50  38.1  61.9  8.33  11.66  1.60  4.62  76.3  60.9  23.7  39.1  60  31.0  69.0  9.21  10.83  1.89  5.71  68.6  46.0  31.4  54.0  65  18.0  82.0  11.93  10.83  2.46  6.53  51.6  26.7  48.4  73.3  70  6.3  93.7  9.77  16.04  3.79  6.72  14.7  13.7  85.3  86.3  Calculated Heat Grade : 4.16% Pb and 7.30% Zn  194  13.00 12.50 12.00 11.50 11.00 10.50 10.00 9.50 9.00 8.50 8.00 7.50 7.00 6.50 6.00 5.50 5.00 4.50 4.00 3.50 3.00 2.50 2.00 1.50 1.00 0.50 0.00  100.0 95.0 90.0 85.0 80.0 75.0 70.0 65.0 60.0 55.0 50.0 45.0 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0 20  30  40  50  60  70  Pb recovery in fraction, % & % mass of fraction  Pb grade in fraction, %  Segregation of Pb in Cold and Hot fractions of -19+13.2 mm size on Average temperature under 5s Microwave radioation (Calibrated)  80  Separation limit (average temperature), ℃ Pb grade in hot % mass in cold  Pb grade in cold Pb recovery in hot  % mass in hot Pb recovery in cold  17.00 16.00 15.00 14.00 13.00 12.00 11.00 10.00 9.00 8.00 7.00 6.00 5.00 4.00 3.00 2.00 1.00 0.00  100.0 95.0 90.0 85.0 80.0 75.0 70.0 65.0 60.0 55.0 50.0 45.0 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0  20  30  Zn grade in hot % mass in cold  Zn recovery in fraction, % & % mass of fraction  Zn grade in fraction, %  Segregation of Zn in Cold and Hot fractions of -19+13.2 mm size on Average temperature under 5s Microwave radioation (Calibrated)  40 50 60 70 80 Separation limit (average temperature), ℃ Zn grade in cold % mass in hot Zn recovery in hot Zn recovery in cold  195  -19+13.2 mm MW/IR Sorting Results – Individual- 10s (Calibrated XRF Surface Readings) Wt. % in Hot Fraction Concentrate Grades, % Pb Zn  Hot Fraction  Cold Fraction  Conc. %  Waste %  28  92.1  7.9  4.52  29  84.8  15.2  30  78.2  31  Separation Temperature, °C  Wt. % in Cold Fraction  % in Hot Fraction  % in Cold Fraction  Waste Grades, %  Metal Recovery, %  Metal Recovery, %  Pb  Zn  Pb  Zn  Pb  Zn  7.85  0.02  0.88  100.0  99.0  0.0  1.0  4.90  8.45  0.04  0.88  99.8  98.2  0.2  1.8  21.8  5.31  9.09  0.04  0.87  99.8  97.4  0.2  2.6  70.3  29.7  5.90  9.92  0.04  1.09  99.7  95.6  0.3  4.4  40  63.2  36.8  6.56  10.93  0.04  1.07  99.7  94.6  0.3  5.4  60  61.5  38.5  6.94  11.51  0.04  1.05  99.6  94.2  0.4  5.8  70  57.1  42.9  7.22  10.99  0.10  2.39  99.0  86.0  1.0  14.0  100  51.2  48.8  8.00  11.03  0.13  3.38  98.5  77.4  1.5  22.6  105  40.9  59.1  7.84  10.96  1.62  4.77  77.0  61.4  23.0  38.6  125  23.2  76.8  9.14  12.55  2.66  5.72  50.8  39.8  49.2  60.2  130  16.6  83.4  9.49  12.64  3.10  6.24  37.9  28.8  62.1  71.2  155  8.5  91.5  13.24  16.10  3.32  6.48  27.0  18.7  73.0  81.3  Calculated Heat Grade : 4.16% Pb and 7.30% Zn  196  14.00  100.0 95.0 90.0 85.0 80.0 75.0 70.0 65.0 60.0 55.0 50.0 45.0 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0  13.00 12.00 11.00  Pb grade in fraction, %  10.00 9.00 8.00 7.00 6.00 5.00 4.00 3.00 2.00 1.00 0.00  20  30  40  50  60  Pb grade in hot % mass in cold  Pb recovery in fraction, % & % mass of fraction  Segregation of Pb in Cold and Hot fractions of -19+13.2 mm size on Average temperature under 10s Microwave radioation (Calibrated)  70 80 90 100 110 120 130 140 150 160 170 Separation limit (average temperature), ℃ Pb grade in cold % mass in hot Pb recovery in hot Pb recovery in cold  17.00 16.00 15.00 14.00 13.00 12.00 11.00 10.00 9.00 8.00 7.00 6.00 5.00 4.00 3.00 2.00 1.00 0.00  100.0 95.0 90.0 85.0 80.0 75.0 70.0 65.0 60.0 55.0 50.0 45.0 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0 20  30  40  50  60  70  80  Zn recovery in fraction, % & % mass of fraction  Zn grade in fraction, %  Segregation of Zn in Cold and Hot fractions of -19+13.2 mm size on Average temperature under 10s Microwave radioation (Calibrated)  90 100 110 120 130 140 150 160 170  Separation limit (average temperature), ℃ Zn grade in hot % mass in cold  Zn grade in cold Zn recovery in hot  % mass in hot Zn recovery in cold  197  -19+13.2 mm MW/IR Sorting Results – Individual- 15s (Calibrated XRF Surface Readings) Wt. % in Hot Fraction Concentrate Grades, % Pb Zn  Hot Fraction  Cold Fraction  Conc. %  Waste %  31  94.0  6.0  4.43  32  84.8  15.2  33  77.6  36  Separation Temperature, °C  Wt. % in Cold Fraction  % in Hot Fraction  % in Cold Fraction  Waste Grades, %  Metal Recovery, %  Metal Recovery, %  Pb  Zn  Pb  Zn  Pb  Zn  7.71  0.02  0.92  100.0  99.2  0.0  0.8  4.90  8.44  0.04  0.96  99.8  98.0  0.2  2.0  22.4  5.35  9.14  0.04  0.90  99.8  97.2  0.2  2.8  69.4  30.6  5.98  10.02  0.04  1.13  99.7  95.3  0.3  4.7  50  63.2  36.8  6.56  10.93  0.04  1.07  99.7  94.6  0.3  5.4  100  60.6  39.4  6.98  11.23  0.04  1.55  99.6  91.4  0.4  8.6  120  57.1  42.9  7.22  10.99  0.10  2.39  99.0  86.0  1.0  14.0  150  42.1  57.9  8.67  10.47  0.89  5.00  87.6  60.3  12.4  39.7  170  32.2  67.8  9.22  10.58  1.97  6.44  71.4  46.7  28.6  53.3  190  21.7  78.3  8.93  14.20  2.84  5.39  46.5  42.1  53.5  57.9  200  14.6  85.4  5.49  12.72  3.94  6.37  19.3  25.5  80.7  74.5  250  5.0  95.0  11.92  19.88  3.76  6.64  14.3  13.6  85.7  86.4  Calculated Heat Grade : 4.16% Pb and 7.30% Zn  198  13.00  100.0 95.0 90.0 85.0 80.0 75.0 70.0 65.0 60.0 55.0 50.0 45.0 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0  12.00 11.00 10.00  Pb grade in fraction, %  9.00 8.00 7.00 6.00 5.00 4.00 3.00 2.00 1.00 0.00 20  40  60  80  100  120  140  160  180  200  220  240  260  Pb recovery in fraction, % & % mass of fraction  Segregation of Pb in Cold and Hot fractions of -19+13.2 mm size on Average temperature under 15s Microwave radioation (Calibrated)  280  Separation limit (average temperature), ℃ Pb grade in cold % mass in hot Pb recovery in hot Pb recovery in cold  Pb grade in hot % mass in cold  22.50  100.0 95.0 90.0 85.0 80.0 75.0 70.0 65.0 60.0 55.0 50.0 45.0 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0  20.00 17.50  Zn grade in fraction, %  15.00 12.50 10.00 7.50 5.00 2.50 0.00 20  40  60  Zn grade in hot % mass in cold  80  100  120  140  160  180  200  220  240  260  Zn recovery in fraction, % & % mass of fraction  Segregation of Zn in Cold and Hot fractions of -19+13.2 mm size on Average temperature under 15s Microwave radioation (Calibrated)  280  Separation limit (average temperature), ℃ Zn grade in cold % mass in hot Zn recovery in hot Zn recovery in cold  199  -19+13.2 mm MW/IR Sorting Results – Individual- 10s -45 rocks (Calibrated XRF Surface Readings) Wt. % in Hot Fraction Concentrate Grades, % Pb Zn  Hot Fraction  Cold Fraction  Conc. %  Waste %  28  91.3  8.7  4.88  29  83.4  16.6  30  76.2  31  Separation Temperature, °C  Wt. % in Cold Fraction  % in Hot Fraction  % in Cold Fraction  Waste Grades, %  Metal Recovery, %  Metal Recovery, %  Pb  Zn  Pb  Zn  Pb  Zn  7.71  0.02  0.88  100.0  98.9  0.0  1.1  5.33  8.36  0.04  0.88  99.8  98.0  0.2  2.0  23.8  5.83  9.07  0.04  0.87  99.8  97.1  0.2  2.9  69.2  30.8  6.43  9.80  0.04  1.11  99.7  95.2  0.3  4.8  40  63.6  36.4  6.99  10.58  0.04  1.09  99.7  94.4  0.3  5.6  60  61.7  38.3  7.43  11.19  0.04  1.07  99.7  94.0  0.3  6.0  70  58.3  41.7  7.62  11.07  0.04  1.59  99.7  90.7  0.3  9.3  100  53.7  46.3  8.26  11.34  0.05  2.22  99.5  85.5  0.5  14.5  105  42.4  57.6  8.15  11.34  1.74  4.01  77.5  67.5  22.5  32.5  125  25.3  74.7  9.14  12.55  2.87  5.28  51.9  44.6  48.1  55.4  130  18.2  81.8  9.49  12.64  3.34  5.89  38.7  32.2  61.3  67.8  155  9.3  90.7  13.24  16.10  3.56  6.20  27.6  21.0  72.4  79.0  Calculated Heat Grade : 4.46% Pb and 7.12% Zn  200  14.00  100.0 95.0 90.0 85.0 80.0 75.0 70.0 65.0 60.0 55.0 50.0 45.0 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0  13.00 12.00 11.00  9.00 8.00 7.00 6.00 5.00 4.00 3.00 2.00 1.00 0.00 20  30  40  50  60  Pb grade in hot % mass in cold  Zn grade in fraction, %  70  80  90  100 110 120 130 140 150 160 170  Separation limit (average temperature), ℃ Pb grade in cold % mass in hot Pb recovery in hot Pb recovery in cold  Segregation of Zn in Cold and Hot fractions of -19+13.2 mm size ore on Average temperature under 10s Microwave radioation (45 rocks)-Calibrated  17.00 16.00 15.00 14.00 13.00 12.00 11.00 10.00 9.00 8.00 7.00 6.00 5.00 4.00 3.00 2.00 1.00 0.00  100.0 95.0 90.0 85.0 80.0 75.0 70.0 65.0 60.0 55.0 50.0 45.0 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0  20  30  40  Zn grade in hot % mass in cold  50  60  Zn recovery in fraction, % & % mass of fraction  Pb grade in fraction, %  10.00  Pb recovery in fraction, % & % mass of fraction  Segregation of Pb in Cold and Hot fractions of -19+13.2 mm size on Average temperature under 10s Microwave radioation (45 rocks)-Calibrated  70 80 90 100 110 120 130 140 150 160 170 Separation limit (average temperature), ℃ Zn grade in cold % mass in hot Zn recovery in hot Zn recovery in cold  201  -19+13.2 mm MW/IR Sorting Results – Group in 9- 10s -45 rocks (Calibrated XRF Surface Readings) Wt. % in Hot Fraction  Wt. % in Cold Fraction Waste Grades, %  % in Hot Fraction  % in Cold Fraction  Metal Recovery, %  Metal Recovery, %  Hot Fraction  Cold Fraction  Conc. %  Waste %  Pb  Zn  Pb  Zn  Pb  Zn  Pb  Zn  26  92.5  7.5  4.81  7.53  0.06  2.01  99.9  97.9  0.1  2.1  27  79.2  20.8  5.62  8.66  0.04  1.26  99.8  96.3  0.2  3.7  28  68.9  31.1  6.45  9.82  0.04  1.14  99.7  95.0  0.3  5.0  30  63.6  36.4  6.99  10.58  0.04  1.09  99.7  94.4  0.3  5.6  50  57.2  42.8  7.87  11.42  0.04  1.55  99.6  90.5  0.4  9.5  70  48.6  51.4  8.54  11.15  0.60  3.30  93.1  76.1  6.9  23.9  80  39.3  60.7  8.63  12.61  1.75  3.56  76.1  69.6  23.9  30.4  85  29.2  70.8  10.74  14.71  1.86  3.98  70.5  60.4  29.5  39.6  90  23.5  76.5  9.49  17.42  2.91  3.95  50.1  57.6  49.9  42.4  100  9.9  90.1  15.20  16.62  3.27  6.07  33.8  23.1  66.2  76.9  120  5.5  94.5  11.92  19.88  4.03  6.38  14.6  15.2  85.4  84.8  Separation Temperature, °C  Concentrate Grades, %  Calculated Heat Grade : 4.46% Pb and 7.12% Zn  202  16.00 15.00 14.00 13.00 12.00 11.00 10.00 9.00 8.00 7.00 6.00 5.00 4.00 3.00 2.00 1.00 0.00  100.0 95.0 90.0 85.0 80.0 75.0 70.0 65.0 60.0 55.0 50.0 45.0 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0 20  30  40  50  60  70  80  90  100  110  120  Pb recovery in fraction, % & % mass of fraction  Pb grade in fraction, %  Segregation of Pb in Cold and Hot fractions of -19+13.2 mm size on Average temperature under 10s Microwave radioation grouped in 9 (45 rocks) - Calibrated  130  Separation limit (average temperature), ℃ Pb grade in hot % mass in cold  Pb grade in cold Pb recovery in hot  % mass in hot Pb recovery in cold  22.00  100.0 95.0 90.0 85.0 80.0 75.0 70.0 65.0 60.0 55.0 50.0 45.0 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0  20.00 18.00  Zn grade in fraction, %  16.00 14.00 12.00 10.00 8.00 6.00 4.00 2.00 0.00 20  30  40  Zn grade in hot % mass in cold  50  60  70  80  90  100  110  120  Zn recovery in fraction, % & % mass of fraction  Segregation of Zn in Cold and Hot fractions of -19+13.2 mm size on Average temperature under 10s Microwave radioation grouped in 9 (45 rocks) - Calibrated  130  Separation limit (average temperature), ℃ Zn grade in cold % mass in hot Zn recovery in hot Zn recovery in cold  203  -19+13.2 mm MW/IR Sorting Results – Group in 25- 10s -45 rocks (Calibrated XRF Surface Readings) Wt. % in Hot Fraction Concentrate Grades, % Pb Zn  Hot Fraction  Cold Fraction  Conc. %  Waste %  25  90.4  9.6  4.93  26  64.9  35.1  35  58.3  50  Separation Temperature, °C  Wt. % in Cold Fraction  % in Hot Fraction  % in Cold Fraction  Waste Grades,%  Metal Recovery, %  Metal Recovery, %  Pb  Zn  Pb  Zn  Pb  Zn  7.69  0.05  1.73  99.9  97.7  0.1  2.3  6.84  10.37  0.04  1.10  99.7  94.6  0.3  5.4  41.7  7.62  11.07  0.04  1.59  99.7  90.7  0.3  9.3  43.4  56.6  7.36  11.51  2.24  3.75  71.3  73.0  28.7  27.0  60  28.9  71.1  6.43  13.95  3.65  4.34  41.7  56.7  58.3  43.3  70  16.0  84.0  9.37  19.42  3.52  4.78  33.6  43.6  66.4  56.4  80  8.3  91.7  13.60  23.01  3.62  5.67  25.2  29.0  74.8  71.0  90  2.9  97.1  32.84  26.78  3.60  6.53  21.5  11.0  78.5  89.0  Calculated Heat Grade : 4.46% Pb and 7.12% Zn  204  40.00 38.00 36.00 34.00 32.00 30.00 28.00 26.00 24.00 22.00 20.00 18.00 16.00 14.00 12.00 10.00 8.00 6.00 4.00 2.00 0.00  100.0 95.0 90.0 85.0 80.0 75.0 70.0 65.0 60.0 55.0 50.0 45.0 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0 20  30  40  Pb grade in hot % mass in cold  Pb recovery in fraction, % & % mass of fraction  Pb grade in fraction, %  Segregation of Pb in Cold and Hot fractions of -19+13.2 mm size on Average temperature under 10s Microwave radioation grouped in 25 (45 rocks) - Calibrated  50 60 70 80 90 100 Separation limit (average temperature), ℃ Pb grade in cold % mass in hot Pb recovery in hot Pb recovery in cold  40.00 38.00 36.00 34.00 32.00 30.00 28.00 26.00 24.00 22.00 20.00 18.00 16.00 14.00 12.00 10.00 8.00 6.00 4.00 2.00 0.00  100.0 95.0 90.0 85.0 80.0 75.0 70.0 65.0 60.0 55.0 50.0 45.0 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0 20  30  Zn grade in hot % mass in cold  40  Zn recovery in fraction, % & % mass of fraction  Zn grade in fraction, %  Segregation of Zn in Cold and Hot fractions of -19+13.2 mm size on Average temperature under 10s Microwave radioation grouped in 25 (45 rocks) - Calibrated  50 60 70 80 90 100 Separation limit (average temperature), ℃ Zn grade in cold % mass in hot Zn recovery in hot Zn recovery in cold  205  -19+13.2 mm Size Fraction Sorting Results Summary Test Condition  Separation Limit, °C  Mass % of Cold  5s  27  5s  Pb in Hot Fraction, %  Zn in Hot Fraction, %  Grade  Recovery  Grade  Recovery  21.4  5.28  99.8  9.04  97.3  28  35.6  6.44  99.7  10.73  94.7  10s  31  29.7  5.90  99.7  9.92  95.6  10s  40  36.8  6.56  99.7  10.93  94.6  15s  36  30.6  5.98  99.7  10.02  95.3  15s  50(40)  36.8  6.56  99.7  10.93  94.6  Calculated Heat Grade : 4.16% Pb and 7.30% Zn Test Condition  Separation Limit, °C  Mass % of Cold  Individual  31  Individual  40  Group in 9  50 rocks being tested  Pb in Hot Fraction, % Grade  Recovery  30.8  6.43  36.4  6.99  28  31.1  Group in 9  30  Group in 25  26  Zn in Hot Fraction, % Grade  Recovery  99.7  9.80  95.2  99.7  10.58  94.4  6.45  99.7  9.82  95.0  36.4  6.99  99.7  10.58  94.4  35.1  6.84  99.7  10.37  94.6  Calculated Heat Grade : 4.46% Pb and 7.12% Zn  45 rocks being tested  206  APPENDIX E  IMPACT EVALUATION DATA  Full Bond Ball Mill Work Index Test Bond Test #:  POM BW  Aperture Test Sieve:  180  microns  Project:  MASc Research Thesis  3.60  g/cc  Date:  2-Mar-12  12.70  %  Performed by: Ore Type Sample Source:  Yan Tong  Test Feed Density: Undersize in the Test Feed: Mill Solid Load: Ideal Potential Product: Ideal Circulating Load:  1524.7  g  435.6  g  1089.1  g  Lead-Zinc massive sulfide Pend Oreille Mine  Weight of Undersize Net Net / Rec Product 424.1 4.24  Cycle  Test Feed Added  Number of Revs.  Weight of Oversize  Feed  Discharge  1  1524.7  100  907.0  193.6  617.7  2  617.7  84  1161.6  78.4  363.1  284.7  3.38  320  3  363.1  115  1073.3  46.1  451.4  405.3  3.52  238  4  451.4  108  1099.3  57.3  425.4  368.1  3.42  258  5  425.4  112  1090.0  54.0  434.7  380.7  3.41  251  6  434.7  111  1089.0  55.2  435.7  380.5  3.41  250  7  435.7  111  1089.0  55.3  435.7  380.4  3.41  250  Circulating Load Ratio 147  BOND'S WORK INDEX FORMULA  Wi = 44.5 / (Pi^.23 x Gpb^.82 x (10/√P - 10/√F)) Pi = Sieve size tested  180  microns  Gpb = Net Undersize produced per revolution of mill  3.41  grams  P = 80% passing size of test prodcut  136  microns  F = 80% passing size of test feed  2338  microns  WORK INDEX (Wi) 7.57 8.33  kw-hr/ton kw-hr/tonne  NB: Gbp = Average of last 3 Net/Rev Cycles  207  Bond Ball Mill Grindability Test Size Analysis  Sieve  Size  Weight  [mesh]  [microns]  [g]  7 10 14 18 25 35 45 60 80 120 170 230 325  2800 2000 1400 1000 710 500 355 250 180 125 90 63 45 -45 Total mass  0.0 76.6 41.3 22.7 16.5 12.5 9.3 8.0 6.2 6.1 5.0 4.9 3.8 8.3 221.2  Cum. Passing [%] 100.0 65.4 46.7 36.4 29.0 23.3 19.1 15.5 12.7 9.9 7.7 5.5 3.8 0.0  Product Cum. Weight Passing [g] [%] Linear Linear Semi-log Semi-log Log-log Log-log  0.0 212.8 175.5 121.3 118.0 223.8 851.4  100.0 75.0 54.4 40.1 26.3 0.0  10000 Linear 80 1000  Semi-log 80 Log-log 80 Linear 50  100  Semi-log 50 Log-log 50  10  50 80 50 80 50 80  Interpolations Feed P50= 1506.1 2338.0 P80= P50= 1491.1 2305.5 P80= P50= 1505.1 2346.7 P80=  Product F50= 81.7 F80= 136.0 P50= 80.6 P80= 134.4 P50= 81.5 P80= 135.6  100 90 80 Cum. percent passing, %  Feed  70 60 50 40 30 20  Feed  10  Product  0 10  100  1000  10000  Particle size, microns  208  Feed size analysis of XRF Feed, XRF Concentrates and XRF Waste Bond Ball Mill Grindability Test Size Analysis  Sieve  Size  [mesh]  [microns]  XRF Feed Cum. Weight Passing [g] [%]  XRF Conc. Cum. Weight Passing [g] [%]  XRF Conc.-2 Cum. Weight Passing [g] [%]  XRF Waste Cum. Weight Passing [g] [%]  7  2800  0.0  100.0  0.0  100.0  0.0  100.0  0.0  100.0  10  2000  76.6  65.4  62.2  71.9  52.1  63.5  59.5  63.2  14  1400  41.3  46.7  38.1  54.7  25.0  45.9  31.0  44.0  18  1000  22.7  36.4  24.1  43.8  13.6  36.4  16.1  34.0  25  710  16.5  29.0  18.3  35.5  10.4  29.1  11.9  26.7  35  500  12.5  23.3  13.9  29.3  7.7  23.7  8.5  21.4  45  355  9.3  19.1  10.5  24.5  6.0  19.5  6.5  17.4  60  250  8.0  15.5  8.9  20.5  4.8  16.1  5.5  14.0  80  180  6.2  12.7  6.8  17.4  5.6  12.2  4.3  11.3  120  125  6.1  9.9  7.1  14.2  3.4  9.8  4.1  8.8  170  90  5.0  7.7  5.4  11.8  2.5  8.0  3.1  6.9  230  63  4.9  5.5  6.5  8.9  1.8  6.8  3.0  5.0  45  3.8  3.8  5.6  6.3  2.8  4.8  2.1  3.7  -45  8.3  0.0  14.0  0.0  6.9  0.0  6.0  0.0  325  Total mass  221.2  221.4  142.6  161.6  100 90  Cum. percent passing, %  80 70 60 50 40 30 XRF Feed  20  XRF Conc. 10  XRF Conc.-2 XRF Waste  0 10  100  1000  10000  Particle size, microns  209  Product size analysis of XRF Feed, XRF Concentrates and XRF Waste Bond Ball Mill Grindability Test Product Size Analysis  Sieve  Size  [mesh]  [microns]  XRF Feed Cum. Weight Passing [g] [%]  XRF Conc. Cum. Weight Passing [g] [%]  XRF Conc.-2 Cum. Weight Passing [g] [%]  XRF Waste Cum. Weight Passing [g] [%]  7  2800  0.0  100.0  0.0  100.0  0.0  100.0  0.0  100.0  14  1400  10.2  97.6  8.0  98.1  10.7  97.5  10.5  97.5  25  710  7.4  95.9  7.3  96.4  8.7  95.4  8.6  95.5  45  355  8.0  94.0  8.0  94.5  8.9  93.3  9.8  93.2  80  180  15.8  90.3  10.3  92.1  9.5  91.0  12.6  90.3  120  125  22.7  84.9  8.0  90.2  10.7  88.5  23.2  84.8  170  90  27.9  78.3  26.5  84.0  24.8  82.6  29.5  77.9  230  63  35.8  69.9  36.5  75.4  35.8  74.0  47.2  66.8  45  71.4  53.1  84.0  55.7  80.2  55.0  84.2  47.1  -45  225.5  0.0  237.0  0.0  231.1  0.0  200.6  0.0  325 Total mass  424.7  425.6  420.4  426.2  100 90  Cum. percent passing, %  80 70 60 50 40 30 XRF Feed 20 XRF Conc. 10  XRF Conc.-2 XRF Waste  0 10  100  1000  10000  Particle size, microns  210  Grindability Test Results P80 of different grinding time 5  98.4  116.2  3  174.5  2  1 1  10  100  1000  Particle size, microns  Product Size Distribution of various grinding time 100 90 Cum. percent passing, %  Grinding time, min  4  80 70 60 50 40 30  Product-3 min  20  Product-4 min  10  Product-5 min  0 10  100  1000  10000  Particle size, microns  211  Rod Mill Grindability Test Size Analysis- 3 minutes Feed Sieve [mesh]  Size  [microns] 12000 1/2 inch 8000 5 4000 7 2800 25 710 35 500 60 250 80 180 120 125 170 90 230 63 325 45 -45 Total mass  Weight [g] 10.3 485.5 400.1  Cum. Passing [%] 100.0 98.9 44.7 0.0  895.9  Product Cum. Weight Passing [g] [%]  3.3 0.7 32.7 55.7 75.6 62.8 61.4 56.4 151.4 500  100.0 99.3 99.2 92.7 81.5 66.4 53.8 41.6 30.3 0.0  Linear Linear Semi-log Semi-log Log-log Log-log  50 80 50 80 50 80  Interpolations Feed P50= 4394.2 6608.6 P80= P50= 4282.8 6286.0 P80= P50= 4414.3 6651.6 P80=  Product F50= 81.6 174.5 F80= P50= 80.5 173.5 P80= P50= 81.3 174.1 P80=  10000 Linear 80 1000  Semi-log 80 Log-log 80 Linear 50  100  100  90  Log-log 50  80 Cum. percent passing, %  Semi-log 50  10  70 60 50 40 30 20 Feed  10  Product  0 10  100  1000  10000  Particle size, microns  212  Rod Mill Grindability Test Size Analysis – 4 minutes Feed Sieve  Size  Weight  [mesh]  [microns] 12000 8000 4000 2800 710 500 250  [g]  1/2 inch 5 7 25 35 60  180 125 90 63 45 -45 Total mass  10.3 485.5 400.1  Product Cum. Weight Passing [g] [%]  Cum. Passing [%] 100.0 98.9 44.7 0.0  0.6 0.1 2.3 14.6  80 120 170 230 325  59.5 91.2 65.5 78.3 187.9 500  895.9  100.0 99.9 99.9 99.4 96.5 84.6 66.3 53.2 37.6 0.0  Linear Linear Semi-log Semi-log Log-log Log-log  50 80 50 80 50 80  Interpolations Feed P50= 4394.2 6608.6 P80= P50= 4282.8 6286.0 P80= P50= 4414.3 6651.6 P80=  Product F50= 59.3 116.2 F80= P50= 58.8 115.1 P80= P50= 59.3 115.9 P80=  10000 Linear 80 1000  Semi-log 80 Log-log 80 Linear 50  100  Semi-log 50 Log-log 50  10  100 90 Cum. percent passing, %  80 70 60 50 40 30 20  Feed  10  Product  0 10  100  1000 Particle size, microns  10000  213  Rod Mill Grindability Test Size Analysis – 5 minutes Feed Sieve  Size  Weight  [mesh]  [microns] 12000 8000 4000 2800 710 500 250  [g]  1/2 inch 5 7 25 35 60  180 125 90 63 45 -45 Total mass  10.3 485.5 400.1  Cum. Passing [%] 100.0 98.9 44.7 0.0  Product Cum. Weight Passing [g] [%]  100.0 99.9 99.9 99.9  0.4 0.1 0.2 2.6  80 120 170 230 325  99.3 93.3 75.8 62.1 43.6 0.0  30.0 87.8 68.3 92.8 217.8 500  895.9  Linear Linear Semi-log Semi-log Log-log Log-log  50 80 50 80 50 80  Interpolations Feed P50= 4394.2 P80= 6608.6 P50= 4282.8 P80= 6286.0 P50= 4414.3 P80= 6651.6  Product F50= 51.2 F80= 98.4 P50= 50.6 P80= 97.4 P50= 51.3 P80= 98.0  10000 Linear 80 1000  Semi-log 80 Log-log 80 Linear 50  100  Semi-log 50 Log-log 50  100 10  90 Cum. percent passing, %  80 70 60 50 40 30 20  Feed  10  Product  0 10  100  1000  10000  Particle size, microns  214  

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