by HV Shinde · 2014 · Cited by 1 — It was observed that the preliminary air classifier design showed more promise than the air jig in terms of control over mineral recovery and preconcentrating
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Michigan Technological University Michigan Technological University Digital Commons @ Michigan T ech Digital Commons @ Michigan T ech Dissertations, Master’s Theses and Master ‘s Reports – Open Dissertations, Master’s Theses and Master ‘s Reports 2014 INVESTIGATION OF AIR JIGGING AND AIR CL ASSIFICATION TO INVESTIGATION OF AIR JIGGING AND AIR CL ASSIFICATION TO RECOVER METALLIC PARTICLES FROM AN ALYTICAL SAMPLES RECOVER METALLIC PARTICLES FROM AN ALYTICAL SAMPLES Hrishikesh Vilas Shinde Michigan Technological University Follow this and additional works at: https://digitalcommons.mtu.edu/etds Part of the Chemical Engineering Commons Copyright 2014 Hrishikesh Vilas Shinde Recommended Citation Recommended Citation Shinde, Hrishikesh Vilas, “INVESTIGATION OF AIR JIGGING AND AIR CL ASSIFICATION TO RECOVER METALLIC PARTICLES FROM ANALYTICAL SAMPLES”, Master’s Thesis, Michigan T echnological University, 2014. https://digitalcommons.mtu.edu/etds/874 Follow this and additional works at: https://digitalcommons.mtu.edu/etds Part of the Chemical Engineering Commons COREMetadata, citation and similar papers at core.ac.ukProvided by Michigan Technological University
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INVESTIGATION OF AIR JIGGING AND AIR CLASSIFICATION TO RECOVER METALLIC PARTICLES FROM ANALYTICAL SAMPLES By Hrishikesh Vilas Shinde A THESIS Submitted in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE In Chemical Engineering MICHIGAN TECHNOLOGICAL UNIVERSITY 2014 © Hrishikesh Vilas Shinde 2014
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This thesis has been approved in partial fulfillment of the requirements for the Degree of MASTER OF SCIENCE in Chemical Engineering. Department of Chemical Engineering Thesis Advisor: Dr. Surendra K Kawatra Committee Member: Dr. Julia King Committee Member: Dr. Gowtham Shankara Department Chair: Dr. Surendra K Kawatra
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Table of Contents List of Figures 5 List of Tables . 6 Acknowledgem ents . 7 Abstract . . 8 1. Introduction .. 9 1.1 Current Gold Sampling Scenario .. 10 1.2 Research Scope .. 11 2. Research Objective: 13 3. Preliminary Designs .. 14 3.1 Air Jig- Preliminary Design 14 3.1.1 Introduction 14 3.1.1.1 Air Jigging Theory .. 15 3.1.2 Materials . 17 3.1.3 Experimental Setup 18 3.1.4 Working Principle .. 20 3.1.4.1 Air jig testing procedure .. 20 3.1.5 Results and Discussions .. 21 3.2 Air Classifier РPreliminary Design 23 3.2.1 Introduction 23 3.2.2 Materials . 26 3.2.3 Experimental Setup 27 3.2.4 Working Principle .. 28 3.2.5 Results and Discussions .. 30 4. Comparison between Preliminary Air Ji g and Air Classifier Designs . 32 5. Final Design ΠAir Classifier . 34 5.1 Materials .. 36 5.2 Experimental Setup: .. 37 5.3 Working Principle: . 38 5.4 Results and Discussions .. 40 5.4.1 Percent tungsten recovered in the bottom fraction .. 40 3
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List of Figures Figure 1: Current gold sampling scenario 10 Figure 2: Mechanism of Air jigging. Figure adapted from (Gupta and Yang 2006) .. 15 Figure 3: Size distribution of Newmont sa mples vs silica crushed at Michigan Tech. .. 17 Figure 4: Air jigging preliminary design 18 Figure 5: Air jigging laboratory set up and speaker top view . 19 Figure 6: Force balance on a particle in motion under drag force .. 24 Figure 7: Calculated settling velocities of spherical particles of various specific gravities as a function of particle diameter .. 25 Figure 8: Air classifier -preliminary design . 27 Figure 9: Magnetite-Silica mixture run results .. 30 Figure 10: Cyclone design and cyclone made at Michigan Technological University. . 35 Figure 11: Air Classifier schematic .. 37 Figure 12: Air classifier laboratory set-up 38 Figure 13: Tungsten recovery into the fire assay concentrate 40 Figure 14: Weight collected at the bottom of the air classifier (direct fire assay sample) . 42 Figure 15: Tungsten recovered vs preconcentrate sample size . 43 Figure 16: Total material recovery ( fines fraction plus heavy fraction) .. 44 Figure 17: Cyclone efficiency . 46 Figure 18: Mineral recovery comparison w ith change in density. Tungsten and iron particles sizes were both above 75 microns . 48 5
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Acknowledgements I would like to thank my thesis adviser Dr. S.Komar Kawatra, for his skilled guidance throughout the course of this research. I would like to thank Joe Halt and Jacob McD onald, graduate student s in the department of chemical engineering for conducting prelimin ary studies on the project; sparkling ideas from men like these make the future of the min eral processing industry. I appreciate the trust that they showed in me while letting me conduct further investig ations on their start- up research project. I would like to specifically thank Joe Halt for his time in reading my work and providing valuable feedback. He has been of great help to me during my master™s research. I would like to thank Howard Haselhuhn for his exemplary display of mentorship skill towards helping me in every way possible while my time at Michigan Tech. I would like to take this opportunity to thank him for every single time that he has vouched for me in our industry meetings. I would like to acknowledge Advanced Sust ainable Iron and Steel Making Center (ASISC) and Newmont Mining Corpor ation for their project support. Last but not the least, I would like to thank my family for their constant support and encouragement. 7
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Investigation of Air Jigging and Air Classification to Recover Metallic Particles from Analytical Samples Abstract Analyzing finuggetyfl gold samples commonly produces erratic fire assay results, due to random inclusion or exclusion of coarse gold in analytical samples. Preconcentrating gold samples might allow the nuggets to be concentr ated and fire assayed separately. In this investigation synthetic gold samples were made using similar density tungsten powder and silica, and were preconcentrated using two approaches: an air jig and an air classifier. Current analytical gold sampling method is time and labor intensiv e and our aim is to design a set-up for rapid testing. It was observed that the preliminary air classifier design showed more promise than the air jig in terms of control over mineral recovery and preconcentrating bulk ore sub-sa mples. Hence the air classifier was modified with the goal of producing 10-30 grams sa mples aiming to capture all of the high density metallic particles, tungsten in this case. Effects of air velocity and feed rate on the recovery of tungsten from synthetic tungsten-silica mixtures were studied. The air classifier achieved optimal high density metal recovery of 97.7% at an air velocity of 0.72 m/s and feed rate of 160 g/min. Effects of density on classificati on were investigated by using iron as the dense metal instead of tungsten and the recovery was seen to drop from 96.13% to 20.82%. Preliminary investigations suggest that preconcentration of gold samples is feasible using the laboratory designed air classifier. 8
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1. Introduction Determining the gold content of low-grade ores (< 5 g/t) is a labor- and time- intensive process. Furthermore, the presence of the fiNugget Effectfl (where the bulk of the gold content is found in a few sporadic nuggets) can cause erratic fire assay results. When analytical samples are prepared, small amounts (10 grams, Clifton et al. 1969) of samples are randomly chosen to be analyzed by fire assay or by atomic adsorption spectroscopy to estimate the gold content. This sample is assumed to be a representative of the analytical sample but the presence or abse nce of gold nugget in this randomly chosen sample from the analytical sample cannot be assured. The inclusion or exclusion of these nuggets in a particular analytical sample can have an effect of over-estimating or under- estimating the total gold content respectively. Hence a method to preconcentrate all the gold particles and nuggets from a bulk sample into a sample small enough to be analyzed directly by fire-assay / atomic adsorpti on spectroscopy needs to be formulated. Current approach towards gold sampling is screening. This is a tedious time-and-labor intensive approach. Can this a pproach be addressed by deve loping an alternative rapid testing approach that takes a dvantage of high density gold particles in the gold ore? The potential of a Knelson concentrator was recently investigated as a means to preconcentrate bulk gold samples. Typically op erated on a wet basis, it was suggested to convert a Knelson concentrator to a dry basi s by using air instead of water (Greenwood 2013). Tungsten recovery was seen to range be tween 70-80% for particles in a size range of -300 to +38 microns at an air pressure of 2 PSI, but the tungsten grade was very poor. A wind tunnel designed to classify granular material particles of approximately similar density with varying particle size has been pa tented as granular mate rial separating device (Vickery 1991); this device consists of a s cattering assembly whic h facilitates effective dispersion of the granular feed material pr ior to classification. Th e granular material separating device can be more effectively used as a gold separation tool rather than an analytical gold sampling tool. Gold pans w ith water delivery cups were designed and patented to separate gold particles from blac k sand (Krenzler 1999). 9
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Figure 1: Current gold sampling scenario 1.1 Current Gold Sampling Scenario The causes and nature of nugget effect have been studied (Carrasco 2010). Gold sampling leads to erratic results due non-uniform distri bution and nuggety nature of gold ore. The current gold sampling scenario is demonstrated in Figure 1. To determine the potential of gold mining on a desired site, bulk ore samples are crushed until the mineral liberation size. The crushed sample is rotary split to sub-samples weighing approximately 200 grams and from these samples, random scoops weighting between 10-30 grams are selected and analyzed for their gold content by using techniques like atomic adsorption spectroscopy or fire assaying. The underlying assumption is that the random scoop is a representative of the rota ry split sub- sample. In case of nuggety gold sample the inclusion or exclusion of the gold nugget from the scoop (10-30 grams) will lead to erratic spectroscopy or fire assa y results due to non-uniform gold distribution. 10
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