Mineralogical characterisation using QEMSCAN of the Nechalacho heavy rare earth metal deposit, Northwest Territories, Canada

Grammatikopoulos, Tassos; Mercer, William E.; Gunning, C

doi:10.1179/1879139513y.0000000090

Cited by 23 publications

(12 citation statements)

References 8 publications

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“…QEMSCAN has become a standard tool to obtain fundamental data for all four aspects (Grammatikopoulos et al, 2013); it can quantify a diversity of mineralogical attributes: liberation, exposure, grain size, etc. Electron microprobe analysis and laser ablation ICP-MS are used determine the mineral chemistry that is critical with respect to metals of interest.…”

Section: Conditional Geometallurgical Modellingmentioning

confidence: 99%

“…As described in the following section, the compositional and morphological features, which can be observed with the QEMSCAN provide insight to determine which combination of blending, milling and concentration parameters might constitute operational modes that are economically viable (Grammatikopoulos et al, 2013;Jordens et al, 2016;Little et al, 2016). Different sources of ore can be regarded as distinct geometallurgical units, depending on whether or not they exhibit categorically different responses to the viable operational modes (Navarra et al, 2017a).…”

Section: Introductionmentioning

confidence: 99%

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Incorporation of geometallurgical modelling into long-term production planning

Navarra

Grammatikopoulos²,

Waters

2018

Minerals Engineering

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Strategic decisions to develop a mineral deposit are subject to geological uncertainty, due to the sparsity of drill core samples. The selection of metallurgical equipment is especially critical, since it restricts the processing options that are available to different ore blocks, even as the nature of the deposit is still highly uncertain. Current approaches for long-term mine planning are successful at addressing geological uncertainty, but do not adequately represent alternate modes of operation for the mineral processing plant, nor do they provide sufficient guidance for developing processing options. Nonetheless, recent developments in stochastic optimization and computer data structures have resulted in a framework that can integrate operational modes into strategic mine planning algorithms. A logical next step is to incorporate geometallurgical models that relate mineralogical features to plant performance, as described in this paper.

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Section: Conditional Geometallurgical Modellingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Incorporation of geometallurgical modelling into long-term production planning

Navarra

Grammatikopoulos²,

Waters

2018

Minerals Engineering

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“…Most samples used are standard polished 30 micron thin sections of carbonatite rare earth element ores 54 . The barite rock sample was from Ward’s Science (Rochester NY).…”

Section: Methodsmentioning

confidence: 99%

Direct mineralogical imaging of economic ore and rock samples with multi-modal nonlinear optical microscopy

Kao¹,

Pegoraro

Kingston

et al. 2018

Sci Rep

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Multi-modal nonlinear optical (NLO) microscopy, including stimulated Raman scattering (SRS) and second harmonic generation (SHG), was used to directly image mineralogical features of economic ore and rock samples. In SRS/SHG imaging, ore samples generally require minimal preparation and may be rapidly imaged, even in their wet state. 3D structural details, at submicron resolution, are revealed tens of microns deep within samples. Standard mineral imaging based on scanning electron microscopy (SEM), with elemental analysis via energy dispersive X-Ray spectroscopy, was used to independently validate the mineral composition of the samples. Spatially-resolved SRS from dominant Raman-resonant bands precisely maps the locations of specific minerals contained within the samples. SHG imaging reveals locally non-centrosymmetric structures, such as quartz grains. Competing absorption and nonlinear scattering processes, however, can reduce contrast in SRS imaging. Importantly, the correlation between standard electron microscopy and multi-modal NLO optical microscopy shows that the latter offers rapid image contrast based on the mineral content of the sample.

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“…The compositions of the REM were determined using the wavelength-dispersive spectrometer (WDS) mode on the JEOL JXA-8900L electron microprobe at McGill University (Grammatikopoulos et al, 2013).…”

Section: Electron Probe Micro Analysis (Epma)mentioning

confidence: 99%

Beneficiation of the Nechalacho rare earth deposit. Part 2: Characterisation of products from gravity and magnetic separation

Jordens

Marion

Langlois

et al. 2016

Minerals Engineering

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show abstract

Mineralogical characterisation using QEMSCAN of the Nechalacho heavy rare earth metal deposit, Northwest Territories, Canada

Cited by 23 publications

References 8 publications

Incorporation of geometallurgical modelling into long-term production planning

Incorporation of geometallurgical modelling into long-term production planning

Direct mineralogical imaging of economic ore and rock samples with multi-modal nonlinear optical microscopy

Beneficiation of the Nechalacho rare earth deposit. Part 2: Characterisation of products from gravity and magnetic separation

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