Quantification of Particle Size Distribution with Different Analytical and Statistical Techniques

Hamidreza Sam

Research output: ThesisMaster's Thesis

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Abstract

Global trends of increasing ore complexity, growing demand for mineral resources, and rising social, environmental, and health issues awareness of mining have led the mining sector, as the primary sector of many national economies, to implement sustainable and resource-efficient strategies. Geological and geometallurgical block models of the resources and reserves play a crucial role in resource efficiency, along with the management of mines and tailings (dumps). Data must flow into such models throughout all stages, from designing and planning and operation stages from exploration to reclamation and rehabilitation. In this regard, early knowledge of ore and waste characteristics is necessary for, e.g., early-stage constraints on the mine system's geotechnical stability or the physical-chemical behavior of dumps and tailings, and of course, for defining mine products and concentrate qualities. An analytical technique to characterize modal mineralogy, mineral association, and grain size distribution of ore and waste samples is scanning electron microscope based automated mineralogy (such as mineral liberation analysis, MLA, or Zeiss Mineralogical) which provides detailed information on many single particles, albeit on the polished section of the sample and not concerning the whole volume. In this study, a method to reconstruct the true 3D size (with uncertainty) from such 2D sections will be developed using Bayes' Theorem. This study constructs particle size distributions from a combination of 2D individual-particle measurements and bulk particle size distribution measured by sieving and laser diffraction. Verification of the prediction of the statistical model for the 3D sizes were done with data from X-ray computed tomography (XCT).
Translated title of the contributionQuantification of Particle Size Distribution with Different Analytical and Statistical Techniques
Original languageEnglish
QualificationMSc
Awarding Institution
  • Montanuniversität
Supervisors/Advisors
  • Moser, Peter, Supervisor (internal)
Award date25 Jun 2021
Publication statusPublished - 2021

Bibliographical note

embargoed until null

Keywords

  • Particle size distribution
  • MLA
  • Laser diffraction
  • X-ray computed tomography
  • Bayes Theorem
  • Q-values

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