Applied Science, Faculty of
Mining Engineering, Keevil Institute of
DSpace
UBCV
Zhang, Yi Ran
Master of Applied Science - MASc
University of British Columbia
Dual Energy X-ray Transmission (DE-XRT) technology has been widely used for hard rock ore sorting as well as for coal beneficiation purposes. Although there is an abundance of publications on research conducted using the DE-XRT system, there is a very limited number of publications discussing how the results are generated from the raw X-ray signal responses. Since the relative density values generated from the raw X-ray images could potentially alter the final DE-XRT result, it is important to study the image processing methods used to produce the relative density from raw X-ray images. In this thesis, the two known methodologies, the single curve method and dual material decomposition method (the name is used interchangeably with “dual curve method” in this study) were discussed and compared in two studies: a sulphide classification and sortability study (case 1) and a coal washability study (case 2). This thesis also attempts to use a more mathematical approach that takes into account all pixel values of the studied samples. In both case studies, the calibration curves used in single and dual curve methods are generated by evaluating the high and low pixel intensities on the H-L graph. The two sets of relative density results generated from the single curve and dual curve method are used to compare with the equivalent gold assay grades to classify rocks and perform sortability analysis in case 1. In case 2 they are used to compare with ash content and specific gravity (S.G.) plots to develop washability correlations. The results for case 1 show nearly identical performance on sulphide classification and sortability studies between the single curve and dual curve methods with the dual curve method slightly outperforming the single curve method. However, the DE-XRT results generated from both methods were not able to perform effectively “equivalent gold-grade” prediction. In case 2, although the dual material decomposition method outperformed the single curve method on all washability-related analyses, both methods were relatively effective at predicting ash content and S.G. and then could be effectively used for the prediction of washability curves.
ASSESSMENT OF DUAL-ENERGY X-RAY TRANSMISSION IMAGE ANALYSIS PROCESS ON SULPHIDE ORE AND COAL CASES by Yi Ran Zhang B.A.Sc., The University of British Columbia, 2016 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Mining Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April 2023 © Yi Ran Zhang, 2023 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: Assessment of Dual-Energy X-Ray Transmission Image Analysis Process on Sulphide Ore and Coal Cases submitted by Yi Ran Zhang in partial fulfilment of the requirements for the degree of Masters of Applied Science in Mining Engineering Examining Committee: Dr. Maria E. Holuszko, Associate Professor, Mining Engineering, UBC Supervisor Dr. Bern Klein, Professor, Mining Engineering, UBC Supervisory Committee Member Dr. Marek Pawlik, Professor, Mining Engineering, UBC Supervisory Committee Member iii Abstract Dual Energy X-ray Transmission (DE-XRT) technology has been widely used for hard rock ore sorting as well as for coal beneficiation purposes. Although there is an abundance of publications on research conducted using the DE-XRT system, there is a very limited number of publications discussing how the results are generated from the raw X-ray signal responses. Since the relative density values generated from the raw X-ray images could potentially alter the final DE-XRT result, it is important to study the image processing methods used to produce the relative density from raw X-ray images. In this thesis, the two known methodologies, the single curve method and dual material decomposition method (the name is used interchangeably with “dual curve method” in this study) were discussed and compared in two studies: a sulphide classification and sortability study (case 1) and a coal washability study (case 2). This thesis also attempts to use a more mathematical approach that takes into account all pixel values of the studied samples. In both case studies, the calibration curves used in single and dual curve methods are generated by evaluating the high and low pixel intensities on the H-L graph. The two sets of relative density results generated from the single curve and dual curve method are used to compare with the equivalent gold assay grades to classify rocks and perform sortability analysis in case 1. In case 2 they are used to compare with ash content and specific gravity (S.G.) plots to develop washability correlations. iv The results for case 1 show nearly identical performance on sulphide classification and sortability studies between the single curve and dual curve methods with the dual curve method slightly outperforming the single curve method. However, the DE-XRT results generated from both methods were not able to perform effectively “equivalent gold-grade” prediction. In case 2, although the dual material decomposition method outperformed the single curve method on all washability-related analyses, both methods were relatively effective at predicting ash content and S.G. and then could be effectively used for the prediction of washability curves. v Lay Summary The Dual Energy X-ray Transmission (DE-XRT) system is similar to the X-ray scans used for airport security and is commonly used to sort waste from the mined materials from the ground at different mining operations. When mined materials are separated by a sorting machine, the decision is heavily dependent on how accurate the information provided by the sensor in the machine is. This is why it is important to understand how this information is generated. In this study, we explore and compare the two known methods used for converting X-ray raw images into useful density readings that could be used for sorting out rocks and for coal analysis such as washability in plant operation scenarios. vi Preface The data in this thesis is obtained from experiments that have been designed and conducted by me. The data and results used and presented in case 1 and case 2 have been previously presented in two conferences. These data and results were later used in two papers published in refereed journals. Data collection and analysis as well as experimental plan and manuscript preparation has been done by me. Dr. Maria Holuszko and the mining team in Sacre Davey Engineering have provided the samples and some technical inputs regarding data analysis. Published papers in refereed journals: Zhang, Y., Yoon, N., & Holuszko, M. E. (2021). Assessment of sortability using a dual-energy x-ray transmission system for studied sulphide ore. Minerals, 11(5). https://doi.org/10.3390/min11050490 I was responsible for the literature review, sample preparation, test work, data analysis and manuscript composition. Mr. Yoon (co-author) assisted with the test work and insights on data analysis. Dr. Holuszko reviewed the manuscript and assisted with manuscript proofreading. Zhang, Y. R., Yoon, N., & Holuszko, M. E. (2021). Assessment of coal sortability and washability using dual-energy X-ray transmission system. International Journal of Coal Preparation and Utilization, 42(10): 1–13. https://doi.org/10.1080/19392699.2021.1914024. I was responsible for the literature review, sample preparation, test work, data analysis and manuscript composition. Mr. Yoon assisted with data analysis. Dr. Holuszko reviewed the manuscript and assisted with manuscript proofreading. vii Conference Papers Zhang, Y., Holuszko, M. E., & Alexander, J. (2020). Ore Characterization using Dual Energy X-ray Transmission System for the Development of Sensor-Based Sorting Application. IMPC 2020: 30th International Mineral Processing Congress, October, 18–22. I was responsible for sample collection, test work, data analysis and conference papers. Dr. Holuszko and Mr. Alexander reviewed the paper. Zhang, Y. R., Hilscher, B., Holuszko, M., & Yoon, N. (2019). Changing project economics by the application of ore sorting technology. IMPC 2018 - 29th International Mineral Processing Congress, 931–938. I was the keynote speaker and was responsible for sample collection, test work, data analysis and preparation of the paper and presentation. Mr. Hilscher and Mr. Yoon assisted with the data analysis and format of the paper. Dr. Holuszko reviewed the paper and the presentation. viii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ....................................................................................................................... viii List of Tables ............................................................................................................................... xii List of Figures ............................................................................................................................. xiii List of Symbols ........................................................................................................................... xvi List of Abbreviations ............................................................................................................... xviii Acknowledgements ......................................................................................................................xx Dedication ................................................................................................................................... xxi Chapter 1: Introduction ................................................................................................................1 1.1 Dual Energy X-Ray Transmission Technology .............................................................. 1 1.1.1 Dual Energy X-Ray Transmission Sensor Principles ................................................. 2 1.1.2 DE-XRT Lab Unit....................................................................................................... 6 1.1.3 DE-XRT Image Reconstruction .................................................................................. 7 ix 1.1.4 The H-L Curve ............................................................................................................ 8 1.1.5 Single Curve Method ................................................................................................ 10 1.1.6 Dual Material Decomposition Method (Dual Curve Method) .................................. 12 1.2 DE-XRT Sorting for Hard Rock Operations ................................................................ 13 1.2.1 Elements of Sorting................................................................................................... 16 1.2.2 Hard Rock Sortability ............................................................................................... 17 1.3 DE-XRT for Coal Beneficiation and Washability Analysis ......................................... 18 1.3.1 Coal Sorting and Beneficiation ................................................................................. 18 1.3.2 Coal Washability ....................................................................................................... 19 1.4 Problem Statement ........................................................................................................ 22 1.5 Research Questions: ...................................................................................................... 22 1.6 Research Objectives ...................................................................................................... 23 Chapter 2: Case 1: Sulphide Classification and Sortability Analysis .....................................24 2.1 Introduction ................................................................................................................... 24 2.2 Background ................................................................................................................... 24 2.3 Materials and Methods .................................................................................................. 25 2.3.1 Assays ....................................................................................................................... 28 2.4 Results and Discussion ................................................................................................. 28 x 2.4.1 DE-XRT Image Processing....................................................................................... 29 2.4.1.1 Classification..................................................................................................... 35 2.4.2 Sortability Analysis ................................................................................................... 39 2.5 Conclusion .................................................................................................................... 42 Chapter 3: Case 2: Coal Washability Analysis .........................................................................44 3.1 Introduction ................................................................................................................... 44 3.2 Materials and Methods .................................................................................................. 44 3.2.1 Coal Classification – Lithotypes ............................................................................... 45 3.2.2 Specific Gravity ........................................................................................................ 47 3.2.3 Ash Content .............................................................................................................. 48 3.3 Results and Discussion ................................................................................................. 48 3.3.1 DE-XRT Image Processing....................................................................................... 48 3.3.2 Correlation analysis .................................................................................................. 54 3.3.3 Washability Analysis ................................................................................................ 58 3.4 Conclusion .................................................................................................................... 61 Chapter 4: Conclusion .................................................................................................................64 Chapter 5: Limitations and Recommendations ........................................................................67 Bibliography .................................................................................................................................69 xi Appendices ....................................................................................................................................73 Appendix A : Table of Sensor Technologies Available for Sensor-Based Sorting (Robben & Wotruba, 2019) ......................................................................................................................... 73 Appendix B : Single Curve vs Dual Curve Images for Hard Rock (1-50) ............................... 75 Appendix C : Single Curve vs Dual Curve Images for Coal (1-50) ......................................... 80 Appendix D :M-Curve Methodology Using Particle by Particle DE-XRT Results ................. 85 xii List of Tables Table 1.1: Sample material density ranges. .................................................................................. 23 Table 2.1: R-squared value comparison between single curve method and dual material decomposition. .............................................................................................................................. 35 Table 2.2: Confusion matrix of sulphides and non-sulphides....................................................... 35 xiii List of Figures Figure 1.1: DE-XRT configuration. ................................................................................................ 4 Figure 1.2: X-ray image reconstruction process. ............................................................................ 5 Figure 1.3: COMEX's MSX-400-VL-XR system in UBC's Coal and Mineral Processing laboratory. ....................................................................................................................................... 6 Figure 1.4: Low-energy and high-energy X-ray images of wood, PLA, and aluminum. ............... 9 Figure 1.5: Characteristic H-L curves of aluminum, PLA, and wood. ......................................... 10 Figure 1.6: Single curve method. .................................................................................................. 11 Figure 1.7: Dual material decomposition (dual curve) method. ................................................... 12 Figure 1.8: Overview of opportunities for sensor-based material characterization in the mining industry (Chelgani & Neisiani, 2022). .......................................................................................... 15 Figure 1.9: Sorting machine sub-process interaction (J.D.Salter & Wyatt, 1991). ...................... 17 Figure 1.10: Float and sink process flow diagram. ....................................................................... 21 Figure 2.1: Non-sulphides (left) and sulphides (right) rock samples. ........................................... 26 Figure 2.2: Histogram of six different assays based on rock type. ............................................... 27 Figure 2.3:Scatter plot of equivalent Au grade versus ICP sulphur grade.................................... 28 Figure 2.4: DE-XRT H-L curves - sulphide deposit. .................................................................... 30 Figure 2.5: Relative density image and histogram of sample #82. ............................................... 31 xiv Figure 2.6: Relative density image and histogram of sample #43. ............................................... 32 Figure 2.7: Relative density image and histogram of sample #52. ............................................... 33 Figure 2.8: Scatter plot of Au assay versus mean, coefficient of variation, and standard deviation of the relative density values in each particle. .............................................................................. 34 Figure 2.9: Receiver operator characteristic curve comparison.................................................... 38 Figure 2.10: Sorting curve for single curve method. .................................................................... 40 Figure 2.11: Sorting curves for dual material decomposition (dual curve) method. .................... 41 Figure 3.1: Banded bright coal. ..................................................................................................... 45 Figure 3.2: Banded bright coal (Left); dull [rock-like] (right). ..................................................... 45 Figure 3.3:Mass distribution and average ash content based on lithotype classification. ............ 46 Figure 3.4: Density measuring device in UBC. ............................................................................ 47 Figure 3.5: H-L curves of high ash and low ash content materials. ............................................. 49 Figure 3.6: Relative density image and histogram of sample #100. ............................................. 51 Figure 3.7: Relative density image and histogram of sample #20. ............................................... 52 Figure 3.8: Relative density image and histogram of sample #10. ............................................... 53 Figure 3.9: Scatter plot of ash content and specific gravity with linearly fitted line. ................... 55 Figure 3.10: Scatter plot of average relative density and specific gravity with linearly fitted lines........................................................................................................................................................ 56 xv Figure 3.11: Scatter plot of average relative density and ash content with linearly fitted lines. .. 57 Figure 3.12: M-curves generated from DE-XRT output (average relative density) and true (measured) M-curve. ..................................................................................................................... 59 Figure 3.13: S.G. yield curves generated from DE-XRT output (average relative density) for single and dual curve methods and true (measured) S.G. yield curve. ......................................... 60 xvi List of Symbols 𝐴1⃗⃗⃗⃗ : 𝑀𝑎𝑡𝑒𝑟𝑖𝑎𝑙 𝑇𝑦𝑝𝑒 1 𝐴2⃗⃗ ⃗⃗ ∶ 𝑀𝑎𝑡𝑒𝑟𝑖𝑎𝑙 𝑇𝑦𝑝𝑒 2 Ag: Silver grade (g/t) Au: Gold grade(g/t) Ca: Calcium grade (%) Eq. Au: Equivalent gold grade (g/t) g: Gravity (m/𝑠2) ?⃗⃗? ∶ 𝑢𝑛𝑘𝑛𝑜𝑤𝑛 𝑚𝑎𝑡𝑒𝑟𝑖𝑎𝑙 𝑚𝑑: Mass of the object in the air (kg) 𝜇𝐻: Mass attenuation coefficient for High Energy (𝑐𝑚2𝑔) 𝜇𝐿: Mass attenuation coefficient for Low Energy (𝑐𝑚2𝑔) Pb: Lead grade (%) 𝜌𝑤: Density of water (kg/𝑚3) 𝜌𝑜: Density of the object (kg/𝑚3) S: Sulfur content (%) SG: Specific gravity of the object xvii 𝑡: Mass thickness (𝑔𝑐𝑚2) 𝑉𝑜: Volume of the object (𝑐𝑚3) 𝑊𝑑: Dry weight of the object (kg) 𝑊𝑠: Submerged Weight of the object (kg) Zn: Zinc grade (%) xviii List of Abbreviations ASBS: Automated Sensor-based Sorting BB: Banded bright BC: Banded coal BD: Banded dull DC: Dull coal-like DE-XRT: Dual-energy X-ray transmission DR: Dull rock-like ESG: Environmental Social and Governance FN: False negative FP: False positive H-L: High and low energy ICP: Inductively Coupled Plasma KPI: Key performance indicator ROC: Receiver Operator Characteristic ROM: Run-of-mine SBS: Sensor-Based Sorting xix S.G.: Specific gravity TP: True positive TN: True negative TPR: Sensitivity TNR: Specificity xx Acknowledgements I would like to offer my sincere gratitude to Dr. Maria E. Holuszko for her supervision, support and guidance throughout this research. I would also like to thank Dr. Bern Klein and Dr. Marek Pawlik for their kind advice and encouragement. I would like to thank everyone who had worked alongside me in the Coal and Mineral Processing lab at the University of British Columbia, particularly millwright Aaron Hope and lab technician Dr.Libin Tong for their constant support. Also many thanks to Dr.Jacek Kolacz and Artur Szewczuk from COMEX for being generous with sharing their knowledge and going above and beyond to help with training me to use the DE-XRT machine. I would like to acknowledge Sacre-Davey Engineering for acquiring the equipment used for this research and the financial support through the MITACS Accelerate program. I would like to thank my parents and my friends for always being there for me and celebrating every achievement, whether they are big or small. Lastly, a special thank you to Mr. Nawoong Yoon for all of his support over the years. xxi Dedication This thesis is humbly dedicated to those valiant individuals who are relentlessly toiling to rehabilitate and revitalize our society in the aftermath of the COVID-19 pandemic. 1 Chapter 1: Introduction 1.1 Dual Energy X-Ray Transmission Technology Global carbon reduction and Environmental Social Governance (ESG) targets have induced momentum for the mining industry to actively look for technologies that could reduce the environmental footprint while maintaining production levels. As a result, sensor-based sorting has been gaining increasing attention in recent years for its ability to pre-concentrate the mined material before it is sent to the processing plant. This technique is able to decrease the amount of energy, and water consumption as well as tailing production per tonnage produced. Furthermore, with this technology, mines can re-evaluate their waste stockpiles and low-grade marginal materials as recoverable materials. One of the most commonly used sensors for sensor-based sorting is the Dual Energy X-ray Transmission (DE-XRT) system. It is an image-based sorting technology inspired by the X-ray detection unit used for airport security inspection and medical X-ray scans. It is capable of evaluating materials based on its X-ray energy attenuation capabilities which are correlated with atomic number (Tong, 2012). DE-XRT is a dry process and its penetrative ability and system robustness has made DE-XRT one of the most utilized sensor system for sensor-based sorting. In addition to hard-rock sorting, DE-XRT has also been widely used for coal beneficiation since it delivers similar separation efficiencies to dense-medium separation in the deshaling of raw coal but requires no water and/or heavy medium to be used in processing. However, the use of the DE-XRT system in coal operations should not be limited to deshaling. Since DE-XRT 2 identifies the density differences of the scanned material, it holds great potential for being used in washability analyses (density-based analytical procedure) for the prediction of coal separation performance in gravity-based processes. Parameters derived from washability analysis can also be used for seam blending at the mine site before its processing stage. 1.1.1 Dual Energy X-Ray Transmission Sensor Principles Why do we use dual energy? Although single-sourced X-rays can identify the energy attenuation differences between materials, the energy attenuation capabilities are affected by both density and thickness. This becomes an issue when both parameters are variables in a sample set. As a result, single energy (monochromatic imaging) is insufficient for recognizing different materials with different thicknesses. There are two ways to resolve the problem as stated above, by use of Computerized Tomography Scan (CAT or CT scan) or Dual Energy X-ray Scan (DE-XRT). The CT scan is a computerized axial tomography scan. It is able to produce a series of X-ray images by scanning the object with a narrow X-ray beam at different angles. These images are later combined to produce a detailed 3D image of the density distribution within the scanned object. This process is appropriate for the detailed analysis of a single rock sample. However, the processing time, complexity and price limit its application (Hu & Zhao, 2016), especially in mining, when throughput and efficiency are two of the most important factors. As an alternative, DE-XRT generates two sets of 2D X-ray images from two energy levels. It is capable of minimizing the effect of thickness changes within the samples when processed under the correct calibration without compromising efficiency and processing time. 3 DE-XRT is generally operated as a line scanner. As shown in Figure 1.1, the X-ray is emitted from the X-ray source which is applied to the object on the conveyor belt moving at a constant speed. The X-ray sensor system is below the conveyor belt and functions similarly to a line camera. The X-ray sensors register the X-rays that have penetrated the scanned material and convert them into digital image data (Von Ketelhodt & Bergmann, 2010). Although DE-XRT implies that there are two levels of X-ray sources, in reality, there is only one X-ray energy source. In Figure 1.1, in the diagram showing the X-ray setup, both the single-energy and dual-energy X-ray sensor setups are shown. With the dual-energy X-ray sensor, the sensor system is able to produce two sets of raw digital X-ray images - one of the high energy level and one of the low energy level shown in the right portion of Figure 1.1. Since the X-ray source produces a spectrum of energy, the energies can be segregated into low or high energy intensity levels by having two sets of sensors stacked on top of each other with a metal filter (0.6 mm Cu sheet) in between (Veras et al., 2020). The pixel intensity of each X-ray image is directly correlated to the energy attenuated by the material in that area. 4 Figure 1.1: DE-XRT configuration. Once the raw high and low-energy X-ray images are acquired, the image processing software then combines the pixel intensities from both raw images and reconstructs an image that reflects the density distribution of the scanned materials shown in Figure 1.2. One of the main focuses of this research is to study the X-ray image transformation methods used in the mining industry. Details of the theories and methodologies are discussed in the following sections. 5 Figure 1.2: X-ray image reconstruction process. 6 1.1.2 DE-XRT Lab Unit Laboratory experiments in case 1 and case 2 studies were conducted using the COMEX's MSX-400-VL-XR system installed at the University of British Columbia (UBC) shown in Figure 1.3. This equipment has an X-ray source of a maximum voltage of 160Kv and X-ray sensors of 1.5mm resolution. The machine’s belt width is 51 cm, travelling at a speed of 0.5 m/s. To minimize the errors from equipment malfunctions, the machine calibration is maintained regularly and necessary repairs would need to be completed to avoid cases of inaccurate calibration (Triche et al., 2019). Figure 1.3: COMEX's MSX-400-VL-XR system in UBC's Coal and Mineral Processing laboratory. 7 1.1.3 DE-XRT Image Reconstruction In the previous section, it was described how the raw high and low-energy X-ray images are generated from the DE-XRT setup and that one of the key focuses of this research is to study the image processing methods which are currently practiced in the mining industry. Since the objective of the utilization of DE-XRT is to achieve material recognition, the image processing method must be able to eliminate the effect of thickness as much as possible and correctly display the density distribution corresponding to the atomic numbers of elements present in the studied material. Early studies on DE-XRT image reconstruction have mostly been carried out in the medical or security inspection domain. In 1976, Robert E. Alvarez and Albert A. Macovski’s proposed the reconstruction method involving solving non-linear equation sets to determine some elementary tissue properties (Alvarez & Macovski, 1976). In 1987, a method that uses iso-transmission lines and a lookup table was introduced to obtain aluminum and plastic equivalent images of the scanned material (Keh-Shih Chuang H.k. Huang, 1987). In the 2000s, Jeffrey A. Fessler (date) described a statistical (maximum-likelihood) method based on a polychromatic projection model that could accommodate various types of potential noises and problems with DE-XRT. Although these algorithms have been proven to be milestones for X-ray imaging in the medical industry, they are considered impractical for mining due to their computational costs. Evolving from the above-mentioned techniques, a dual energy curve method (also known as the H-L curve method) was introduced in the 2000s. This method provides a more accurate and less 8 time-consuming option for X-ray image processing. De Jong (2006) and H. Harbeck (2004) implemented the same concept in mining scenarios, eventually forming the foundation of DE-XRT sorting (Harbeck, 2004; Jong & T.P.R., 2006). 1.1.4 The H-L Curve The Dual-Energy X-ray Transmission (DE-XRT) technology measures the X-ray attenuation (inverse of transparency) according to Beer's Law for monochromatic narrow X-ray beams (Zhang et al., 2006), which is expressed in the equation below. The final µ coefficient will be a function of radiation energy in addition to the properties like material density and thickness (Kolacz, 2012). 𝐻 = 𝑒−𝜇𝐻𝑡 (1) 𝐿 = 𝑒−𝜇𝐿𝑡 (2) 𝝁𝑯: Mass attenuation coefficient for High Energy (𝑐𝑚2𝑔) 𝝁𝑳: Mass attenuation coefficient for Low Energy (𝑐𝑚2𝑔) 𝒕: Mass thickness (𝑔𝑐𝑚2) The relationship between the high- and low-energy X-ray attenuation can be expressed by rearranging Equations ((1, (2), which is the basis of H-L curve generation: 𝐻 = 𝐿𝜇𝐻𝜇𝐿 (3) As a result, different materials can be distinguished from each other by comparing and plotting the pixel value (this represents the level of energy attenuation) at two different energy levels 9 which generate specific transmission curves for different material types (Harbeck, 2004; Jong & T.P.R., 2006). To validate this concept, three staircase-shaped scanning samples were made with wood, polylactic acid (PLA) and aluminum, which are all different in terms of atomic number makeup and density (Figure 1.4). First, the samples were scanned and the raw high and low-energy images of the samples were obtained (Figure 1.4). Following that, the pixel intensities which is equivalent to the energy attenuation level of each height of each material (area A1 to A4, B1 to B4 and C1 to C4) were plotted on the dual energy curve which is also known as the H-L curve (Figure 1.4). Figure 1.4: Low-energy and high-energy X-ray images of wood, PLA, and aluminum. 10 Figure 1.5: Characteristic H-L curves of aluminum, PLA, and wood. Figure 1.5 shows that each type of material has its own specific dual energy curve plotted by the pixel values obtained at different thicknesses of each sample. It is observed that the thickness of the material increases moving up along each material's associated H-L curve. Also, the density of the material increases as the H-L lines are closer to the bottom of the plot (Figure 1.5). 1.1.5 Single Curve Method Although the fundamentals of the DE-XRT curve are well understood, there is a very limited number of publications on image analysis and sorting algorithms for mining applications. One algorithm that has been briefly recorded is the calibration curve method in the Mikrosort® 11 simulation package cited in Tong (2017). This algorithm was first described and used in Strydom’s (2010) thesis for the coal sortability study. The algorithm involves a calibration curve that would be used to translate each pixel from the raw high and low-energy images to high or low-density relative density (Strydom, 2010; Tong, 2012). Based on the sorting criteria, (rejection ratio and acceptable quality of reject material), a calibration curve is generated to classify all pixels within a sample into either low-and/or high-density ranges. As shown in Figure 1.6, the area above the calibration curve is the low-density zone, and the area below the calibration curve is the high-density zone. After the pixels from the raw high and low-density images have been plotted onto the H-L curve, the pixels that are in the low-density zone will be classified as low-density and assigned the colour blue (or any equivalent colour based on the software), and any plotted pixels that are in the high-density zone will be classified as high-density and assigned the colour yellow. This methodology produces a binary output that is proven to be sufficient for the sorting study that was conducted in Strydom’s thesis (Strydom, 2010). Figure 1.6: Single curve method. 12 1.1.6 Dual Material Decomposition Method (Dual Curve Method) The software used in the COMEX DE-XRT machine in the UBC lab is called the COMEX's MSX-400-VL-XR system. The software uses an image analysis method called the Dual Material Decomposition method, also referred to as the “Dual-Curve method” in this study. Similar to the single curve mentioned in the section above, it involved two calibration curves (shown in Figure 1.7): a high-density curve ( 𝐴1⃗⃗⃗⃗ ⃗) which represents the threshold for high-density materials (material 1) and a low-density curve (𝐴2⃗⃗ ⃗⃗ ) which represents the threshold of low-density materials (material 2). Any pixels that are above the low-density line will be classified as low-density and assigned the blue colour and any pixels that are below the high-density curve will be classified as high-density and assigned the yellow colour. Any pixels that are in between the two lines are represented as the vector sum of the two curves (see equation below) as the colour will be different shades of green based on the composition of materials 1 and 2. Figure 1.7: Dual material decomposition (dual curve) method. 13 ?⃗⃗? = 𝐴1⃗⃗⃗⃗ + 𝐴2⃗⃗⃗⃗ 𝑤ℎ𝑒𝑟𝑒 ?⃗⃗? 𝑖𝑠 𝑢𝑛𝑘𝑛𝑜𝑤𝑛 𝑚𝑎𝑡𝑒𝑟𝑖𝑎𝑙 𝐴1⃗⃗⃗⃗ 𝑖𝑠 𝑀𝑎𝑡𝑒𝑟𝑖𝑎𝑙 𝑇𝑦𝑝𝑒 1 𝐴2⃗⃗ ⃗⃗ 𝑖𝑠 𝑀𝑎𝑡𝑒𝑟𝑖𝑎𝑙 𝑇𝑦𝑝𝑒 2 In this study, the relative density number for each pixel is determined by finding the magnitude of each vector from 𝐴1⃗⃗⃗⃗ ( material 1) and 𝐴2⃗⃗ ⃗⃗ (material 2). The relative density value, in this case, represents the percentage of materials 1 and 2, which is expressed in the equation below 𝑅𝑒𝑙𝑎𝑡𝑖𝑣𝑒 𝐷𝑒𝑛𝑠𝑖𝑡𝑦 =𝑀𝑎𝑔𝑛𝑖𝑡𝑢𝑑𝑒 𝑜𝑓 𝐴1⃗⃗⃗⃗ 𝑀𝑎𝑔𝑛𝑖𝑡𝑢𝑑𝑒 𝑜𝑓 𝐴1⃗⃗⃗⃗ + 𝑀𝑎𝑔𝑛𝑖𝑡𝑢𝑑𝑒 𝑜𝑓 𝐴2⃗⃗⃗⃗ 𝐴1⃗⃗⃗⃗ ⃗ = 𝑀𝑎𝑡𝑒𝑟𝑖𝑎𝑙 𝑇𝑦𝑝𝑒 1 𝐴2⃗⃗ ⃗⃗ = 𝑀𝑎𝑡𝑒𝑟𝑖𝑎𝑙 𝑇𝑦𝑝𝑒 2 1.2 DE-XRT Sorting for Hard Rock Operations The sensor-based ore sorting technology has been gaining increasing attention and popularity in the mining industry over the past decade. As a pre-concentration process, ore sorting has shown as being an effective process in reducing dilution in the mill feed which poses a potential solution to reduce milling waste, help to decrease water usage and minimize environmental impact during the mine life cycle as well as increasing the head grade of the feed into the mill. The term "sensor-based (ore)sorting" (SBS), also known as “automatic sensor-based sorting” (ASBS), automated-sorting, or electronic sorting was first introduced as “an umbrella term” for all applications where particles are detected by one type of sensor or combinations of sensors and 14 ejected by an amplified mechanical, hydraulic, or pneumatic process (Wotruba & Harbeck, 2010). In SBS, the coarse particles ( roughly 10-350mm) are sorted based on their physical and/or chemical characteristics (Wotruba & Harbeck, 2010). This technology has evolved from its early form of the pre-concentration method of hand sorting (Wills & Finch, 2015) which was patented in 1928 (Andrew T Sweet, 1926; J.D.Salter & Wyatt, 1991) and has been applied to the mining industry since 1950 (Robben & Wotruba, 2019; Sivamohan & Forssberg, 1991). Figure 1.8 shows various ways SBS techniques can be applied in different stages in the mine-to-mill processing routes. These stages include drill core scanning for grade control in blast holes, mine face/muck pile classification, shovel/truckload classification and classification before the material enters the mill. The SBS techniques can be incorporated individually and/or in combination into mineral processing circuits to eliminate gangue coarse particles from being processed by the traditional processing treatment (Kolacz, 2012; Lessard et al., 2014). In brownfield projects, this can improve the efficiency of the mill by increasing head grade, and in greenfield projects, this can provide value by lowering processing capital costs (Bergmann, 2019). 15 Figure 1.8: Overview of opportunities for sensor-based material characterization in the mining industry (Chelgani & Neisiani, 2022). To capture the vast range of material properties of different orebodies, the sorting industries have been actively exploring various types of sensors. Some of the commercially available sensors with their associated material properties of detection and examples of industrial applications are shown in APPENDIX A. Among these sensors, the X-ray technology was one of the first to be formally used in the mineral processing industry (Kolacz, 2019). Because it is a dry process, it does not need water and as a result, it will be favorable for operations that lack access to water sources. Moreover, its material penetrative ability and quick processing time have been gaining increasing attention from the mining industry, making it one of the most commonly utilized sensors for mineral processing. 16 1.2.1 Elements of Sorting The four key elements important in the sorting process as per the description made by R.A Wyman in "Selective Electronic Mineral Sorting to 1972" still hold to this day which is the following (R. A. Wyman, 1973): 1. Feed presentation 2. Material differentiation 3. Information comprehension 4. Material separation The flowchart in Figure 1.9 shows how the involved sub-processes interact with each other. The term "particle" is rather referred to as "material" in this case to better represent the two applications of SBS which can be either particle sorting or bulk sorting. In particle sorting, the material is presented without overlaps and sorting is performed on a piece-by-piece basis by a series of mechanical air nozzles that shoots out the particles to achieve classification. Bulk sorting, on the other hand, is a similar process but rather applied to a group of particles and classification is achieved by either diverting the conveyor belt of material for a specific time interval or by shovels. 17 Figure 1.9: Sorting machine sub-process interaction (J.D.Salter & Wyatt, 1991). 1.2.2 Hard Rock Sortability Ore sortability studies generally focus on assessing the amenability of an ore to sorting and providing system designs for the specific operation (Seiler, 2017). The SBS description in the previous sections is generally related to hard rock applications. In this case, the sortability depends on four parameters. 1. Heterogeneity of rock material 2. Sensor response evaluation 3. Sorting analysis 4. Feasibility In case 1 of this thesis, the focus will be on using relative density information generated from DE-XRT sensor response for sortability analysis. The Receiver Operator Characteristic (ROC) curve for classification performance is an evaluation metric for binary classification problems 18 and this is studied with the sortability analysis graph to compare the performance of the two image reconstruction methods (single and dual curve). 1.3 DE-XRT for Coal Beneficiation and Washability Analysis In pursuit of enhancing their ESG performance, coal operations have been actively seeking methods to minimize their environmental impact and increase the safety standard of the operations. Additionally, water scarcity and pollution have been continuous issues in coal processing plants. This is why DE-XRT technology has been widely adopted for coal beneficiation in coal operations. DE-XRT is known to have similar performance efficiencies as dense-medium separation for coal particles ranging from 12mm to 120mm without using water or any type of dense medium. Since it is a dry process, the process produces no slurry that could potentially cause groundwater pollution and acid rock drainage. However, its applicability should not stop at coal deshaling or just beneficiation. Since DE-XRT response is correlated to the density of each area pixel on a scanned material, it could potentially be used to perform online washability analysis for coal processing and/or blending purposes. The scanned material could be either particle or bulk since the goal is to analyze the washability of the scanned material not mechanically separating or sorting them. 1.3.1 Coal Sorting and Beneficiation Coal sorting has attracted a great deal of attention in recent years. The advancement in sorting technologies has allowed both optical cameras and DE-XRT to be used simultaneously (Kolacz 2014). Also, the separation of coal and torbanite using the DE-XRT system has been 19 demonstrated in some of South Africa's coal mines (von Ketelhodt, 2010). Coal sorting continues to be an attractive topic in pre-concentrating coal before it is processed (beneficiated) at the preparation plant (Orene et al., 2019; Bergmann, 2019). Coal beneficiation is a process of separating the inorganic impurities from the ROM coal. The cleaning processes are designed to explore physical differences between the organic (i.e., coal) and inorganic (i.e. minerals aka ash) components. During coal beneficiation, the aim is to produce clean coal with the desired ash content at the end of the processing stage. Coal samples from both drill core and run-of-mine seams are taken for feasibility testing to determine the treatment process and its efficiency for specific coal types. In many cases, DE-XRT is used to sort out the high ash and inorganic (mineral matter) from the clean coal of coarser sizes to minimize downstream processes. 1.3.2 Coal Washability At the coal preparation plant, coal is treated in various size ranges and gravity separation circuits are employed for coarse and intermediate sizes, while fine coal is usually treated by flotation. Washability analysis is used to predict the ease of washing of coal treated by gravity circuits and washability is assessed from a series of float-and-sink tests. In traditional float-and-sink tests, the underlying principle for separation is the density difference between different coal particles hence the coal particles are grouped into a range of specific gravities from each round of float-and-sink tests (Leonard, 1979; Laskowski and Walters, 1987). This process can be time-consuming for operations where they require a quick turnaround of multiple washability results in a given day. Since the DE-XRT is able to analyze particle by particle, one should be able to 20 predict the ash content and the density of a given particle and as a result, it should also be possible to estimate the washability of any given coal sample. If successfully validated, it can also be used in routine washability tests at the plant. Washability derived from float and sink tests is expressed by several curves (plotted correlations) that visually represent the relationship between ash content and the masses of “float” and “sink” produced at any particular S.G. of separation thus helping the operation to establish the maximum separation performance criteria for a given coal feed (Wurst et al., 2013). The float-and-sink analysis is conducted under the ideal condition to model the separation performance of clean coal from high ash coal and mineral matter, in organic liquids of different densities (process shown in Figure 1.10). The heavy-density liquids used in the float-and-sink process are usually toxic and the process is laborious and time-consuming. Therefore, one of the objectives of using a DE-XRT sorter is to use it for washability analysis to replace the float-and-sink procedure using organic and toxic liquids. 21 Figure 1.10: Float and sink process flow diagram. Generally, all data for the construction of the washability curves can be derived from the weight percentages and ash contents of the S.G. fractions. For the purpose of this study, the float and sink test is replaced with the pycnometer test which will be explained in case study 2 (see case 2 for details). In this research, the M-curve and Specific Gravity Yield curves are used to demonstrate the relationship between DE-XRT results and ash content as well as the S.G. of the samples. The M-curve (abbreviation for Mittlewertkuive in German or the Mayer-Curve) (F.W. Mayer, 1950, 1953, 1957; S. Venkatesan, 1975; A. Salama, 1989) is a well-known curve used to display the 22 ash content of different mass yield (A. I. A. Salama, 1998), and the specific gravity curve is plotted to give the mass yield of specific S.G.’s. 1.4 Problem Statement Dual-Xray Transmission (DE-XRT) sensor is a popular choice for material classification technology in mining based on its robustness against surface contamination of the scanned object, which minimizes the process of sample pre-treatment such as washing of the samples. However, the decision-making process in mining applications is not as simple as it is in other fields that utilize DE-XRT. The main reason is due to the complex nature of mineral compositions in various ore deposits and the lack of published resources on the algorithms that could be used in such applications. Presently, there has not been any publication that compares the known types of image reconstruction methods which are used in the industry. Furthermore, the known techniques do not follow any rigorous mathematical procedure but are rather an approximated approach. 1.5 Research Questions: The research questions posed in this thesis are as follows: 1. How are the raw images from DE-XRT transformed and analyzed using single curve and dual curve methods? 2. Could there be a mathematical approach to generate the calibration curves? 3. Do the methods studied here perform better in a specific application (e.g., ore classification and sortability and/or coal washability)? 23 1.6 Research Objectives This thesis aims to demonstrate how relative density information is generated from raw X-ray images by using single curve and dual curve methods and attempts to provide a mathematical approach to the calibration process to generate the relative density values. Furthermore, this thesis will compare and discuss the advantages and disadvantages of both single and dual curve methods by applying them to ore classification and sortability analysis (case 1), as well as to coal washability analysis (case 2) respectively. The two case studies are designed to assess the full extent of DE-XRT’s capabilities on a variety of materials. Two groups of samples are chosen to cover a wide range of material densities (Table 1.1) and DE-XRT applications. The density values in case 1 of Table 1.1 are taken from Illinois State Geological Survey (Illinois State Geological Survey, 2023) and the density values for case 2 are measured SG of samples from case 2. Table 1.1: Sample material density ranges. Case Study Minerals / Coal types SG Range Material TypesGelena 7.4-7.6Pyrite 6.0-6.5Arsenopyrite 5.5-6.0Sphalerite 3.9-4.1Mica 2.7-3.2Quartz 2.7Calcite 2.7Dull Rock-like 1.7-2.0 RockBanded BrightBanded CoalBanded DullDull Coal-like Case 1SulphidesHost RocksCase 21.3-1.7 Coal 24 Chapter 2: Case 1: Sulphide Classification and Sortability Analysis 2.1 Introduction The dual-material decomposition and single curve methods are used to classify the sulphide material from the waste material. This study aims to assess the effectiveness of DE-XRT to sort the sulphides from the non-sulphides and compare the dual material decomposition method against the single curve classification method. Density measurements generated from the DE-XRT were used to determine average atomic density by measuring the X-ray attenuation for each specimen used in the study. Both silver and gold assays are determined by the fire assay method, and the rest of the elements are determined by the Inductively Coupled Plasma Method (ICP). In this study, the equivalent gold grade is used to assess the value of the sample since it is a polymetallic ore deposit. The equivalent gold grade is calculated by summarizing the dollar value of gold, silver, zinc, and lead, divided by the price of gold. The commodity price used in this study to calculate equivalent gold grade is 1300 $/oz of Au, 17.5 $/oz of Ag, 5 $/lb of Mo, 1.25 $/lb of Zn and 1.05 $/lb of Pb. 2.2 Background The samples were collected from a Lead-Zinc-Gold-Silver mine. The deposit contained replacement-style massive sulphide orebodies hosted by marble and interlayered within a sequence of gneiss. The main sulphide minerals were galena, sphalerite, pyrite, and arsenopyrite. Gold was known to occur primarily in solid solutions with arsenopyrite and pyrite. The host rock 25 types were marble and gneiss which were composed of carbonate minerals, quartz, feldspars, mica and other lower S.G. minerals. 2.3 Materials and Methods Three main parameters were recorded for each rock specimen in this experiment: rock classification, DE-XRT output, and assays. It is critical to ensure that the rock classification and DE-XRT tests are conducted for each sample before the samples are crushed for assaying. Five hundred rock specimens were used in this study, and the particle size ranged between 50 mm to 100 mm. The initial sample consisted of run-of-mine (ROM) material, which included fines and coarse particles not suitable for particle sorting. The ROM material was screened to the desired particle size between 50 mm and 100 mm and washed where the five hundred representative samples were selected. The average grade of the entire sample was 13.47 g/t gold, 80.32 g/t silver, 2.58% lead, and 2.66% zinc. Figure 2.1 show the distribution of the valuable metals in terms of value based on the metal prices mentioned in the introduction. Figure 2.1: Distribution of the valuable metal 76.0%6.1%8.1%9.9%Metal Value DistributionAu Ag Pb Zn 26 Individual rock specimens were classified into two classes based on visual observations of mineralization. When classifying rock types, two factors were considered for this ore deposit. Initially, visual parameters were considered where rock specimens were observed showing mineralization; those with silver or golden tints were considered sulphides, while the dull and whitish-looking rocks were considered non-sulphides (mostly carbonates in this case). Also, the density of the samples aided in the classification, where the heavier sample of the same rock size was classified as a sulphide. Examples of sulphide and non-sulphide specimens are illustrated in Figure 2.2. Figure 2.2: Non-sulphides (left) and sulphides (right) rock samples. Figure 2.3 illustrates the distribution of six different assays for all samples coloured by the rock type. The histograms show that sulphide materials tend to have a much higher grade for gold, silver, zinc, and lead while non-sulphide materials tend to have higher calcium. This is what would be expected given the mineralogy and the known association of gold with the sulphide minerals. 27 Figure 2.3: Histogram of six different assays based on rock type. Histogram of Various Assays based on Rock Type 28 2.3.1 Assays Upon completion of the DE-XRT tests, the rocks were then sent for assaying. The fire assay determined the amount of gold present in every rock using 30 g of a sample. Additionally, 33-element ICP-ES was conducted on the individual samples including silver, lead and zinc. 2.4 Results and Discussion In this sample, the ore and waste have been determined by the amount of particular mineralization visible macroscopically (by the naked eye). Of the 500 rock specimens, 218 (43.6%) samples were visually classified as non-sulphides and the remaining 282 (46.4%) samples are sulphides. Figure 2.4:Scatter plot of equivalent Au grade versus ICP sulphur grade. 29 As illustrated in Figure 2.4 there are two distinct sets of material. Sulphides (orange dots) have high equivalent gold grades as well as high sulphur content while the non-sulphides have low equivalent gold grades and low sulphur grades. This illustrates the valuable metal association with sulphide minerals and also shows that the visual classification method (into sulphides and non-sulphides) is promising. 2.4.1 DE-XRT Image Processing In this study, the two types of rocks were chosen according to the amount of equivalent gold content in the specimen. The rocks with an equivalent gold grade of the top 5 percentile (Sulphides) and the bottom 5 percentile (Non-Sulphides) are chosen to generate the characteristic function in the H-L plot as shown in Figure 2.5. The top 5 percentile rocks can have gold grades as high as 60 g/t, silver grade of 200 g/t, lead grade of 7%, or zinc grade of 4%. In this H-L plot, the blue markers represent the non-sulphide material and the red markers represent the sulphide material. The gray and black dotted lines are best-fit curves based on the red markers and blue markers respectively. The green dotted curve is the line that is approximated between the gray and the black dotted lines. 30 Figure 2.5: DE-XRT H-L curves - sulphide deposit. To generate the DE-XRT output, the gray and black dotted curves are used for dual material decomposition and the green dotted curve is used for single curve classification. The dual material decomposition method produces an image with pixel intensities ranging from 0 to 200 where 0 represents low density and 200 represents high density. The single curve classification will produce an image with two distinct pixel values 0 and 200. Three samples in Figure 2.6, Figure 2.7, and Figure 2.8 are selected to illustrate the difference between high, medium and low sulphur content samples where yellow represents low density and blue represents high density. The three figures also compare the two different methods where the left side represents the dual material decomposition, and the right represents the single 31 curve classification method. In each figure, the top half is the pixel-by-pixel information displayed as an image and the bottom half of the figure illustrates the histogram of the pixel values in the image. The relative density range is from 0 to 200 and the count record is the number of pixels in each density bin. Figure 2.6: Relative density image and histogram of sample #82. In Figure 2.6, Figure 2.7, and Figure 2.8, when the two methods are compared, the dual material decomposition method has better resolution and information across the rock. In this thesis, all pixels are averaged to obtain a single relative density for each sample. However, there is an 32 opportunity to use the higher resolution data to capture any localized group of pixels within a desired relative density threshold. Figure 2.7: Relative density image and histogram of sample #43. 33 Figure 2.8: Relative density image and histogram of sample #52. The histograms shown above reflect a clear distinction of density distribution between sulphide and non-sulphide rocks. This suggests that taking an average value of the relative density of an entire sample can be effective in separating the sulphides from the non-sulphide rocks. 34 Figure 2.9: Scatter plot of Au assay versus mean, coefficient of variation, and standard deviation of the relative density values in each particle. In Figure 2.9, every point in the graph represents a sample. The graphs compare the mean, of each pixel from each sample. The graph on the left is generated from the single curve method while the graph on the right is generated from the dual curve method. The two plots illustrate that below 140 relative density value, samples have significantly lower Au assay values. Also, there are two very distinct clusters of points which are distinguished by the rock type. Comparing the mean value between the two methods, the dual material decomposition method has an r-squared value of 0.572 while the single curve method had 0.547. When sulphides and non-sulphides are isolated, the dual material decomposition method has a higher r-squared value (0.371) for sulphides while the single curve method has a higher r-squared value (0.306) for non-sulphides as shown in Table 2.1 35 Table 2.1: R-squared value comparison between single curve method and dual material decomposition. R-squared Value Single Curve Dual Material Decomposition Sulphide 0.316 0.371 Non-Sulphide 0.306 0.273 All Samples 0.547 0.572 Both methods have a poor linear correlation between the gold grade versus the DE-XRT results. The dual material decomposition method did have a more linear relationship compared to the single curve method which is illustrated in the top two plots in Figure 2.9. However, for classification, the two approaches have similar results. In the next section, classification performance will be discussed in more detail. 2.4.1.1 Classification To perform classification, the confusion matrix and various Key Performance Indicators (KPIs) such as accuracy, precision, sensitivity, and specificity can be compared. The confusion matrix is defined as a table used to describe the performance of a classification model on a set of test data for which the true values are known. Table 2.2 illustrates the confusion matrix of the current classification problem. Table 2.2: Confusion matrix of sulphides and non-sulphides. Actual Values Sulphide Non-Sulphide Predicted Values Sulphide True Positive (TP) False Positive (FP) Non-Sulphide False Negative (FN) True Negative (TN) 36 Accuracy measures how often the classifier correctly predicts which can be defined as the ratio of the number of correct predictions and the total number of predictions described by the formula below: 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =𝑇𝑃 + 𝑇𝑁𝑇𝑃 + 𝑇𝑁 + 𝐹𝑃 + 𝐹𝑁 Precision explains how many of the correctly predicted cases turned out to be positive as described by the formula below: 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 =𝑇𝑃𝑇𝑃 + 𝐹𝑃 Sensitivity also known as True Positive Rate (TPR) tells us what proportion of the positive class got correctly classified as described by the formula below: 𝑆𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦 (𝑇𝑃𝑅) =𝑇𝑃𝑇𝑃 + 𝐹𝑁 Specificity also known as True Negative Rate (TNR) tells us what proportion of the negative class got correctly classified as described by the formula below: 𝑆𝑝𝑒𝑐𝑖𝑓𝑖𝑐𝑖𝑡𝑦 (𝑇𝑁𝑅) =𝑇𝑁𝑇𝑁 + 𝐹𝑃 False Positive Rate (FPR) tells us what proportion of the negative class was incorrectly classified by the classifier as described by the formula below: 𝐹𝑃𝑅 = 1 − 𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑐𝑖𝑡𝑦 (𝑇𝑁𝑅) = 𝐹𝑃𝑇𝑁 + 𝐹𝑃 37 The KPIs are compared by ranking the mean relative density value for each sample from highest to lowest, then taking the cumulative counts of the confusion matrix (TP, TN, FP, FN) to calculate the four classifications KPIs. The region where the accuracy is at its peak and the precision and sensitivity remain high is quite close for both methods. The dual curve method does perform slightly better where the peak accuracy is at approximately 82% and 10% specificity at 50% cumulative weight% while the single curve has the highest accuracy of 85% and 22% specificity at 60% cumulative weight. This indicates that although the single curve method was able to classify sulphides slightly more accurately compared to the dual curve, twice as many non-sulphides were misclassified as sulphides. It also demonstrates much higher precision at low cumulative weights regions. The KPIs described above are used to generate the receiver operator characteristic (ROC) curve. It is a probability curve that plots the TPR against FPR at various threshold values and essentially separates the ‘signal’ from the ‘noise’. The Area Under the Curve (AUC) is the measure of the ability of a classifier to distinguish between classes and is used as a summary of the ROC curve. 38 Figure 2.10: Receiver operator characteristic curve comparison. As illustrated in Figure 2.10, the black line represents the dual material decomposition method and the red line represents the single curve classification method. The dual material decomposition method has better performance up to the point where we can achieve 80% TPR and 10% FPR, but beyond the cross point, both methods had very similar performance. The AUC of the single curve method and dual curve method is 0.6659 and 0.6744 respectively. However, this performance evaluation is subject to how the sorting plant operates. 39 2.4.2 Sortability Analysis To assess the sortability, the average relative density value for each rock is put in order from the highest value to the lowest value. Then the metal recovery can be calculated at a given mass yield. To plot a sortability curve derived from DE-XRT, each specimen was ranked by the highest average relative density and the lowest relative density. Then, the recovery curve is generated using the modelled metal grade of each rock specimen as illustrated in Figure 2.11. The line graphs represent the cumulative mass yield and cumulative metal recovery (y-axis on the right side) while the bar graph represents the individual equivalent gold grade based on the descending order of average relative density for each particle. 40 Figure 2.11: Sorting curve for single curve method. As shown in Figure 2.11, 90% of the sulphides can be recovered in approximately 55% of the mass. Furthermore, more than 95% of equivalent gold can be recovered in the same amount of mass. It is also important to note that at 55% mass yield, the rate of calcium recovery starts to increase more than double. This means that the carbonates start to be recovered when the 55% mass of the rocks are collected into the concentrate. 41 Figure 2.12: Sorting curves for dual material decomposition (dual curve) method. The dual material decomposition had similar results to the single curve method where 90% of the sulphides can be recovered in approximately 55% of the mass and more than 95% of equivalent gold can be recovered in the same amount of mass. However, when the cumulative weight is below 40%, the Ca ICP recovery for the dual material decomposition method is approximately 5% lower than the single curve method. This indicates that the dual material decomposition is able to remove the waste material better in the lower mass pull scenario. 42 2.5 Conclusion In this study, the effectiveness of DE-XRT in characterizing material type for sensor-based sorting was explored with the aim of a mathematical approach to DE-XRT image analysis by implementing the dual material decomposition method. With DE-XRT sorting technology, 90% of the sulphides can be recovered with 55% of mass yield. Although the DE-XRT was unable to accurately predict the grade of the rock based on the average relative density of the sample, it was able to separate sulphides from non-sulphides effectively. Both dual material decomposition and single curve methods had similar sortability results. However, the dual material decomposition methods demonstrated a more linear relationship between the DE-XRT response and Au grade compared to the single curve method. In this thesis, the dual material decomposition method has been discussed using the average relative density per sample. However, since there is more data available, with hundreds of pixels per sample, clustering methods can be used to evaluate for nuggety mineralization. The clustering method can identify a cluster of pixels which share similar characteristics. For example, a quartz vein in a quartz vein deposit. The amount of vein present can become a measure of the grade or material type. Furthermore, machine learning applications such as supervised learning can be used to train the classification of rock types based on a much larger set of samples. In summary, the DE-XRT is an effective sensor that can be used to differentiate sulphides from non-sulphide waste material. Although the two image reconstruction methods produced similar 43 results in the classification and sortability analysis, the dual material decomposition method had a slightly better performance than the single curve method. 44 Chapter 3: Case 2: Coal Washability Analysis 3.1 Introduction The objective of this case study was to assess the effectiveness of DE-XRT for deriving washability data for the coarse coal fraction and using this washability data for setting the parameters for coal sorting, while also comparing the single curve method and dual material decomposition method for DE-XRT image processing. Since coal is very heterogeneous, lithotypes (with different physical properties including density) were identified to account for the petrographic composition of various pieces of coal. The test work was designed to assess each piece of coal representing different coal lithotypes (aka rock type). Density measurements using DE-XRT and pycnometer tests were used to determine the average atomic density and the specific gravity of each coal specimen used in the study, respectively. The ash content was determined by the standard method (ASTM D7582) for each studied piece of coal. 3.2 Materials and Methods Four main parameters were recorded for each coal specimen in this experiment, which included coal lithotype classifications, specific gravity determined by pycnometer, DE-XRT relative density output, and ash content. It is critical to ensure that the same sample is measured for each parameter to obtain correct data for correlation analysis. A total of 100 coal specimens were used in this study and the particle size ranged between +50 mm -100 mm. In this experiment, similar to Case 1 as discussed in Chapter 2: both dual material decomposition and single curve methods were used to process the DE-XRT image. 45 3.2.1 Coal Classification – Lithotypes Individual coal specimens were classified based on lithotypes. By observing the ratio of the bright and dull-looking coal (Figure 3.1) in the sample, each particle was classified into its respective lithotype category. The five lithotypes found in the sample set include Banded Bright Coal, Banded Coal, Banded Dull Coal, Dull (Coal-like), and Dull (Rock-like). (Diessel, 1965, Rusnak 2018) Figure 3.1: Banded bright coal. Figure 3.2: Banded bright coal (left); dull [rock-like] (right). 46 Figure 3.3 illustrates the mass distribution of each lithotype (black) and each lithotype's average ash content (red). The mass distribution diagram complemented with the ash analysis of each lithotype indicates that 13% of the mass in this sample is represented by Dull-Rock (DR) coal particles with an ash content of 42% which could indicate the highest ash particles due to the significant presence of mineral matter in this particular coal sample. Banded Bright (BB) and Banded Coal (BC) lithotypes accounted for 27% by weight with an ash content of 8% which indicates relatively clean coal, while Banded Dull (BD) and Dull (D) lithotypes accounted for 60% of mass with their cumulative ash of 13.5%. indicating the middling type of materials in this sample. Figure 3.3:Mass distribution and average ash content based on lithotype classification. 8.0%7%7.9%20%12.8%42%15.2%18%42.0%13%Mass Distribution and Average Ash Content Based on Lithotype ClassificationBanded BrightBanded CoalBanded DullDull Coal-likeDull Rock-like 47 3.2.2 Specific Gravity The specific gravity of each coal piece is derived from the dry weight of each sample measured by a scale and the submerged weight measured from the submerged weight measuring device in Figure 3.4 using the following two equations from the buoyancy principle shown below. 𝑊𝑑 − 𝑊𝑠 = 𝜌𝑤𝑉𝑜𝑔 (1) 𝜌𝑜 =𝑚𝑑𝑉0 (2) 𝑾𝒅: Dry weight of the object 𝑾𝒔: Submerged Weight of the object 𝑽𝒐: Volume of the object g: Gravity 𝝆𝒘: Density of water 𝝆𝒐: Density of the object 𝒎𝒅: mass of the object in the air Figure 3.4: Density measuring device in UBC. Based on the calculated density of each particle, the following equation is used to calculate the SG of each particle. 𝑆𝐺 =𝜌𝑤𝜌𝑜 𝝆𝒐: Density of the object 𝝆𝒘: Density of water SG: Specific gravity of the object 48 3.2.3 Ash Content The measurement of ash content is one of the most important parameters to generate the washability curve. Individual coal specimens’ ash content has been measured following the ASTM (D7582) standard method. 3.3 Results and Discussion To imitate coal washability resulting from float-and-sink tests, pycnometry in combination with DE-XRT analysis was used to reconstitute washability data. To obtain a washability data set, a linear model was developed to predict the ash content and specific gravity based on the DE-XRT response from dual material decomposition and single curve method. Then various correlations were compared before generating the washability curves to predict the ash content as well as specific gravity cut points for desired clean coal products. 3.3.1 DE-XRT Image Processing The dual curve method and single curve methods were compared in this case study to determine the relative density of each pixel in each tested coal sample. In this trial, the two coal types were chosen according to the amount of ash content in the specimen (dirtiest and cleanest). The coal specimen with specific gravity values of the top 10 percentile (Dull-Rock) and the bottom 10 percentile (Banded Bright) were chosen to generate the characteristic function in the H-L plot. 49 Figure 3.5: H-L curves of high ash and low ash content materials. Figure 3.5 illustrates the high (DR) and low (BB) ash samples representing these two types of lithotypes on the H-L plot. The high ash coal (DR) is plotted as red and the low ash coal (BB) is plotted as blue. The high ash specimens have a higher specific gravity value and are plotted below the low ash specimens on the HL curve graph. The two types of coal can be clearly distinguished in the graph above. The high-energy and low-energy pixel values obtained from the high and low-energy raw images are used as input values to derive the relative density values. The new images of each sample with relative density values can be used to investigate various relationships. 50 Figure 3.6 shows the DE-XRT results of sample #100 which is a high-ash (high-density) dull rock-like specimen. In comparison Figure 3.7 shows the DE-XRT results of sample #20 which is a low-ash (low-density) banded coal specimen. The two images above the histogram are the pixel-by-pixel information of the relative density displayed as images. The histograms on the bottom illustrate the pixel values and relative densities of the samples. The information on the left side is generated by the dual curve method, whereas the information on the right side is generated by the single curve method. There is a significant difference between low-ash coal and high-ash coal from both processed image and histogram results. From the first observation, the difference between the single curve and dual curve method results is insignificant for samples at super-high or super-low ash/density ranges, which means that both methods have a similar capability at differentiating super-high and super-low density materials. 51 Figure 3.6: Relative density image and histogram of sample #100. 52 Figure 3.7: Relative density image and histogram of sample #20. 53 Figure 3.8 show the comparison of the single curve method vs dual material decomposition method results of sample #10, a mid-range density/ash, banded coal sample. In this case, the result from the dual curve method was able to display more details and a wider range of relative density information. Figure 3.8: Relative density image and histogram of sample #10. 54 3.3.2 Correlation analysis Three main correlations are explored in this case study which includes the following: 1. Specific Gravity (S.G) versus ash content 2. Specific gravity (S.G) versus DE-XRT average relative (atomic) density 3. Ash content versus DE-XRT average relative (atomic) density The first correlation is explored to ensure that the data collected are not erroneous and demonstrate a reasonable correlation between S.G. and ash content. The second correlation is investigated since the DE-XRT signal is related to atomic density, and it may correlate with S.G. Then finally the third correlation is compared since S.G. and ash content in coal usually correlate well. When comparing the DE-XRT response with S.G. and ash content, the relative density values generated from each pixel have been averaged for each specimen. In all the plots shown in this section, each point will represent a particle where the relative density value will be the average relative density value of every pixel in that specimen. There was a total of 100 coal specimens compared in this analysis. 55 Figure 3.9: Scatter plot of ash content and specific gravity with linearly fitted line. In Figure 3.9, as expected, a strong correlation (illustrated by a black dashed line) can be observed between the ash content and specific gravity with an r-squared value of 0.961. This provides confidence in the ash content and specific gravity measurements of the 100 coal samples. 56 Figure 3.10: Scatter plot of average relative density and specific gravity with linearly fitted lines. In Figure 3.10, the DE-XRT response from both dual material decomposition and single curve method is plotted against the specific gravity. The red dashed line (single curve method) has an r-squared value of 0.902 while the green dashed line (dual material decomposition) has an r-squared value of 0.911 57 Figure 3.11: Scatter plot of average relative density and ash content with linearly fitted lines. In Figure 3.11, the same comparison is made for ash content rather than specific gravity. In this case, the r-squared value for the single curve is 0.844 and dual material decomposition is 0.852. This is lower than the previous comparison, however, there is still a strong correlation between average relative density and ash content. From the correlation between the average relative density and specific gravity as well as ash content, the DE-XRT system can capture the linear relationship between specific gravity and ash content. 58 3.3.3 Washability Analysis To develop the true M-curves and S.G. yield curves and replicate the float-and-sink test using each individual particle’s S.G., the coal specimens are put in ascending order by true S.G. as measured from the pycnometer test. The black dashed line represented in Figure 3.12 is the true M-curves and S.G. yield curves. Then to plot the M-curves and S.G. yield curves derived from DE-XRT, each specimen was ranked by the lowest average relative density to the highest relative density which is linearly correlated with the S.G. Then, the M-curve is generated using the modelled ash content of each rock specimen as illustrated in Figure 3.12. The procedure to construct M-curve is detailed in Appendix D. 59 Figure 3.12: M-curves generated from DE-XRT output (average relative density) and true (measured) M-curve. To estimate the ash content, the following linear formula derived from the previous DE-XRT average relative density vs. Ash content graph was used for the two different methods: 𝑃𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑 𝑎𝑠ℎ 𝑐𝑜𝑛𝑡𝑒𝑛𝑡 𝑓𝑟𝑜𝑚 𝑆𝑖𝑛𝑔𝑙𝑒 𝐶𝑢𝑟𝑣𝑒 𝑀𝑒𝑡ℎ𝑜𝑑 = 0.001561 ∗ (𝑆𝑖𝑛𝑔𝑙𝑒 𝐶𝑢𝑟𝑣𝑒 𝑀𝑒𝑡ℎ𝑜𝑑 𝑅𝑒𝑙𝑎𝑡𝑖𝑣𝑒 𝐷𝑒𝑛𝑠𝑖𝑡𝑦) + 0.102137 60 𝑃𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑 𝑎𝑠ℎ 𝑐𝑜𝑛𝑡𝑒𝑛𝑡 𝑓𝑟𝑜𝑚 𝐷𝑢𝑎𝑙 𝑀𝑎𝑡𝑒𝑟𝑖𝑎𝑙 𝐷𝑒𝑐𝑜𝑚𝑝𝑜𝑠𝑖𝑡𝑖𝑜𝑛 = 0.001691 ∗ (𝐷𝑢𝑎𝑙 𝑀𝑎𝑡𝑒𝑟𝑖𝑎𝑙 𝐷𝑒𝑐𝑜𝑚𝑝𝑜𝑠𝑖𝑡𝑖𝑜𝑛 𝑅𝑒𝑙𝑎𝑡𝑖𝑣𝑒 𝐷𝑒𝑛𝑠𝑖𝑡𝑦)+ 0.081679 From this analysis, using average relative density, the washability curve can be modelled similarly to the washability curve as derived from the standard float-and-sink analysis. This also emphasizes the importance of classifying the different types of lithotypes to model ash content, since the lithotypes correlate well with the ash content. Figure 3.13: S.G. yield curves generated from DE-XRT output (average relative density) for single and dual curve methods and true (measured) S.G. yield curve. 61 Like the M-curve, the S.G. curve also uses the linear equation from correlation analysis to predict the S.G. To estimate the S.G. the following linear formula derived from the previous DE-XRT average relative density vs. Specific Gravity graph was used for the two different methods: 𝑆𝑝𝑒𝑐𝑖𝑓𝑖𝑐 𝐺𝑟𝑎𝑣𝑖𝑡𝑦 𝑓𝑟𝑜𝑚 𝑆𝑖𝑛𝑔𝑙𝑒 𝐶𝑢𝑟𝑣𝑒 𝑀𝑒𝑡ℎ𝑜𝑑 = 0.001830 ∗ (𝑆𝑖𝑛𝑔𝑙𝑒 𝐶𝑢𝑟𝑣𝑒 𝑀𝑒𝑡ℎ𝑜𝑑 𝑅𝑒𝑙𝑎𝑡𝑖𝑣𝑒 𝐷𝑒𝑛𝑠𝑖𝑡𝑦)+ 1.350837 𝑆𝑝𝑒𝑐𝑖𝑓𝑖𝑐 𝐺𝑟𝑎𝑣𝑖𝑡𝑦 𝑓𝑟𝑜𝑚 𝐷𝑢𝑎𝑙 𝑀𝑎𝑡𝑒𝑟𝑖𝑎𝑙 𝐷𝑒𝑐𝑜𝑚𝑝𝑜𝑠𝑖𝑡𝑖𝑜𝑛 = 0.001981 ∗ (𝐷𝑢𝑎𝑙 𝑀𝑎𝑡𝑒𝑟𝑖𝑎𝑙 𝐷𝑒𝑐𝑜𝑚𝑝𝑜𝑠𝑖𝑡𝑖𝑜𝑛 𝑅𝑒𝑙𝑎𝑡𝑖𝑣𝑒 𝐷𝑒𝑛𝑠𝑖𝑡𝑦)+ 1.326877 The solid line in Figure 3.13 represents the float-and-sink results. DE-XRT's relative density can model the lower specific gravity ranging from 1.3 to 1.65 but the S.G. predictions become unreliable for specific gravity above 1.65. Once again, the dual material decomposition method has outperformed the single curve method 3.4 Conclusion In this case study, the effectiveness of DE-XRT to derive the washability data for the coarse coal was explored to devise a method to derive sorting parameters for coal separation by DE-XRT. Various lithotypes were identified and each piece of coal was analyzed. Density measurements using DE-XRT and pycnometer tests were used to determine the average atomic density and the specific gravity of each coal specimen used in the study, respectively. The ash content was determined on each piece of coal. 62 Coal ash content has a direct correlation with specific gravity. Since the DE-XRT outputs correlate relatively well with S.G., it is also possible to observe DE-XRT output correlating with ash content. Although the DE-XRT results can predict ash content and S.G. to a certain extent, it has difficulty efficiently distinguishing all the lithotypes. However, it was able to distinguish the Banded Bright (cleanest coal) coal from Dull Rock-like (dirtiest coal). This information can provide very quick input to XRT to remove very high ash coal particles while reducing the mass of coal to be sent to the plant and at the same time reduce the feed ash for the coal entering the plant for further processing. This can be also applied as a preconcentration method, deshaling by removing high ash particles from thin seams and avoiding their contamination by bulk mining. The coal washability curves derived from dual material decomposition modelled the true washability from the sink and float test much more closely than the single curve method. Since the r-squared value between the DE-XRT response from dual material decomposition and S.G. as well as ash content is 0.911 and 0.852 respectively, it seems that it can be used to fit the washability data with relatively high confidence. With the ability to model the washability data from DE-XRT outputs, one would be able to set an SG cut point for efficient sorting with the ability to predict more accurately the yield of the sorted product, middlings and quality of the rejects. In future work, testing more material from the same coal deposit or coal seam using the same equations to predict the ash content and S.G. should be conducted to validate the robustness of the model predictions. This study demonstrates that distinguishing only between the BB (lowest ash) and Dull Rock (high ash) coal particles was possible. In the studied sample, BB lithotype accounted only for 63 7%, while Dull Rock (DR) lithotype accounted for 13% with an ash content of 42% and if this material could be removed it would result in a reduction of ash from 15.72% to 11.79% in the sample after sorting was performed. However, to properly investigate sorting parameters such as specific gravity (relative atomic density) and predict the yield of clean coal for the desired product, washability data obtained from XRT analysis is needed. In general, on the S.G. curve, the low-density region of the DE-XRT curve is modelled comparably better with the -float-and-sink test derived from pycnometry. However, as the S.G. surpasses 1.45 the prediction becomes less reliable for the true specific gravity. This could be a result of the X-ray hardening effect for high-density minerals present in coal. (Triche et al., 2019) In summary, the washability can be modelled much more closely with the dual material decomposition method compared to the single curve method. Both methods are able to distinguish the banded bright and dull coal-like samples efficiently. 64 Chapter 4: Conclusion The purpose of this research was to demonstrate how the relative density information is generated from the single curve and dual curve (also referred to as the dual material decomposition method). X-ray image processing methods are discussed showing the advantages and disadvantages of each method for classification and sortability performance evaluation in hard rock application and washability prediction in coal application. The details of analytical techniques of raw X-ray image processing are not widely discussed in the mining industry. Also, there have been no publications found that attempt to model washability using the results from processed DE-XRT images. DE-XRT images are processed based on the calibration curves (i.e. density curves) for both single curve and dual curve methods. In this research, the generation of calibration curves was mathematically generated using the following principles. For a mathematical approach, based on the rank of relative gold content in case 1 and ash content in case 2, the top and bottom 5 percentiles of the pixel values on the H-L graph were selected for case 1; and the top and bottom 10 percentiles were selected for case 2. In each case, the high and low-density curves used in the dual curve method were then generated by plotting the best-fit lines for the top and bottom percentiles of the pixel values. Similarly, the single calibration used in the single curve method was generated by plotting a centre line between the top and bottom percentiles of the pixel values. The dual curve method generates relative density pixel values ranging from 0 to 200 while the single curve method generates binary relative density pixel values. Both mathematical 65 methodologies were used in both case studies and demonstrated that it was possible to obtain intuitive images. In the first case when it was used for the rock types classification application, both single curve and dual material decomposition techniques performed similarly. Both methods were able to achieve over 90% equivalent gold recovery within 55% mass. However, the dual material decomposition methods did show a more linear relationship between the DE-XRT response and gold grade with an R-squared value of 0.572. In the second case for the coal washability study, in both DE-XRT average density vs S.G. and DE-XRT average density vs ash content graphs, both DE-XRT average density results showed linear relationships with the true S.G. and ash content with dual curve method scoring a higher r-square value than the single curve method. Using this information, the washability curves are generated. The visual comparison of the true M-curve derived from the measurements of true S.G.and ash content of each sample versus the M-curves derived from the DE-XRT response illustrated that in the coal products with lower ash contents, the dual material decomposition method was closer to the true washability curve. Moreover, the single curve method had a lower r-squared value for both ash and S.G. correlations with respect to the DE-XRT response. The single curve method had an r-squared value of 0.902 for the S.G.yield curve and 0.844 for the M-curve while the dual material decomposition had an r-squared value of 0.911 for the S.G. yield curve and 0.852 for M-curve which seems to be very similar. Although it is difficult to conclude that the dual material decomposition performed significantly better than the single curve method, the DE-XRT response from dual material decomposition 66 does have a more linear relationship with sulphides, S.G. and ash content in both case studies as discussed in this thesis. The methods discussed in this thesis take all of the relative density values from each pixel and average all pixel values to a single relative density value for each sample. This value is used to build relationships with other variables and all analyses are conducted based on this fundamental aggregation of the pixels which presents the limitation of this study. Even with the current limitations, the dual material decomposition method can be used to classify the sulphide material from non-sulphides and estimate the washability characteristic of the coarse coal in the industrial setting. It would be beneficial to conduct more tests similar to the methods discussed in this thesis to increase confidence in the dual material decomposition method. 67 Chapter 5: Limitations and Recommendations The goal of this research was to start the conversation and discussion of the image reconstruction methods used for DE-XRT image processing in the hope to set a basis for future research using DE-XRT results to consider the validity and accuracy of their data. There are a variety of studies that could potentially improve and enhance the quality of the results in this research. A list of limitations and recommendations is shown below: • In this study, image reconstruction and analysis were performed on two case studies; hard rock sulphide ore and coal samples. For future work, other types of deposits and mineralization should be investigated to understand the impact of the nature of samples on the DE-XRT analytical techniques. • In this study, the mean value of the histogram results was used for sortability and washability analysis. Other features from the pixels can be derived to conduct other types of analyses. Instead of using the average of all pixel values, thresholding can be applied to the pixels to obtain a certain feature. For example, the number of pixels above 150 relative density value can be one of the features to obtain any nugget effect within the rock. Furthermore, values such as the standard deviation of the relative density can be used as additional parameters for correlation analysis. • In this study, the number of samples was limited to 500 for the sulphide case and 100 coal case. 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Exact Reconstruction for Dual Energy Computed Tomography Using an H-L Curve Method. 2006 IEEE Nuclear Science Symposium Conference Record. 73 Appendices Appendix A : Table of Sensor Technologies Available for Sensor-Based Sorting (Robben & Wotruba, 2019) Technology Material property Applications Radiometric (RM) Natural gamma radiation Radioactive ores Prompt gamma neutron activation analysis (PGNAA) Absorption and emission of prompt gamma rays Ferrous metals X-ray transmission (XRT) Energy attenuation which is correlated to atomic density Base metal ores Precious metal ores Coal Tungsten Industrial minerals Diamonds Scrap metals Gold ores Fluorite X-ray fluorescence (XRF) Compositional analysis Diamonds Base sulphide ores Precious metal ores Chromite Manganese ores X-ray luminescence (XRL) Visual fluorescence Diamond Colour Camera Detection (CCD) Reflection Base metal ores Absorption Precious metal ores Transmission Industrial minerals Brightness Diamonds Transparency Gemstones Visual Spectrometry (VIS) Reflection or Absorption of visible radiation Industrial mineral ores Gemstones 74 Technology Material property Applications Photometric (PM) Monochromatic reflection Precious metal ores Absorption Industrial minerals Diamonds Gemstones Near infrared spectroscopy (NIR) Reflection Base metal ores Absorption Industrial minerals Talc Limestone Lithium ores Kimberlite Infrared camera (IR) Heat conductivity Base metal ores Heat dissipation Industrial minerals Precious metal ores Graphite Coal Thermal infrared (TR) Microwave excitation and thermal infrared detection Base metal ores Precious metal ores Electromagnetic (EM) Conductivity Base metal ores Permeability Scrap metals Microwave infrared (MW-IR) Microwave absorption Base metal ores Heat conductivity Carbonaceous materials Laser triangulation (LT) Hull detection (shape and form) Base metal ores Ferrous metals Precious metal ores Laser-induced fluorescence (LIF) Absorption of laser light and spontaneous light emission Industrial minerals Laser-induced breakdown spectrometry (LIBS) Evaporation of matter Industrial minerals 75 Appendix B : Single Curve vs Dual Curve Images for Hard Rock (1-50) 76 77 78 79 80 Appendix C : Single Curve vs Dual Curve Images for Coal (1-50) 81 82 83 84 85 Appendix D : M-Curve Methodology Using Particle by Particle DE-XRT Results To generate the M-Curve in case study 2, two relationships were first established. The first one is the relationship between specific gravity and DE-XRT responses, and the second one is the relationship between ash content and DE-XRT response. In this thesis, it was found that both specific gravity and ash content obtained a linear relationship with DE-XRT response. To estimate the specific gravity and ash content of individual samples, the DE-XRT responses serve as the input for the linear function derived from the correlations mentioned above. The resulting predictions are then used to rank specific gravity values from the lowest to highest to generate the M-Curve. The process of generating the M-Curve remains unchanged from the traditional method, with cumulative ash content plotted on the x-axis and mass yield plotted on the y-axis. Notably, the y-axis displays a decreasing trend from 100% mass yield to 0% mass yield as it progresses in the positive direction.
Thesis/Dissertation
eng
Mining Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Holuszko, M. E.
Assessment of dual-energy X-ray transmission image analysis process on sulphide ore and coal cases
Text
Still Image