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Prediction of Acid Mine Drainage Formation
Published in Geoffrey S. Simate, Sehliselo Ndlovu, Acid Mine Drainage, 2021
James Manchisi, Sehliselo Ndlovu
Some of the recent developments in AMD prediction include the acid rock drainage index (ARDI) tool that was proposed by Parbhakar-Fox et al (2011). This tool addresses some limitations in the waste characterisation criteria using an integrated geochemistry-mineralogy-texture (GMT) approach to generate detailed and accurate prediction data at a relatively low cost. Other new tests have been developed such as the computed acid rock drainage (CARD) risk protocol that uses the automated mineralogy data to calculate surface area of minerals that form or neutralise acid. In addition, the existing testing tools are being validated, for example, the paste pH method by ASTM has been found to be the best paste pH procedure on drill cores (Lottermoser, 2015).
Automated mineralogy: analysis of REE samples using micro-XRF
Published in Applied Earth Science, 2019
A. H. Menzies, G. Gloy, S. Scheller, E. Álvarez, R. Tagle
Automated mineralogy has been successfully used in the mining industry since the 1970s, with one common aspect being that they are all based on an electron beam (e-beam) system [e.g. scanning electron microscope (SEM) with energy dispersive spectrometers (EDS)]. It is now possible to focus X-rays to a small spot size using a polycapillary lens, and accordingly a micro-X-ray fluorescence (XRF) system can be operated using similar parameters as an e-beam system and thus yield results compatible with traditional automated mineralogical analysis. Micro-XRF systems have distinct advantages over e-beam based systems in that the sample preparation is simpler (sample cut to have a surface plane parallel to chamber table and no carbon-coating required) and that larger samples can be analysed (33 × 17 × 12 cm in dimension, with the maximal analysis area being 20 × 16 cm). In addition, the X-ray source yields a spectral excitation with significantly lower limits of detection. However, e-beam based systems are able to obtain a smaller beam size. As with traditional automated mineralogical systems, there are fundamental parameters that impact on the mineralogical classification and analytical time; this includes (but is not limited too) X-ray beam excitation (kV and µA), detector active areas (mm2), pixel spacing (µm) and dwell time (ms).
Abstracts from the 2017–2018 Mineral Deposits Studies Group meeting
Published in Applied Earth Science, 2018
L. Santoro, St. Tshipeng Yav, E. Pirard, A. Kaniki, G. Arfè, N. Mondillo, M. Boni, M. Joachimski, G. Balassone, A. Mormone, A. Cauceglia, N. Mondillo, G. Balassone, M. Boni, W. Robb, T. L. Smith, David Currie, Finlay Stuart, John Faithfull, Adrian Boyce, N. Mondillo, C. Chelle-Michou, M. Boni, S. Cretella, G. Scognamiglio, M. Tarallo, G. Arfè, F. Putzolu, M. Boni, N. Mondillo, F. Pirajno, N. Mondillo, C. Chelle-Michou, M. Boni, S. Cretella, G. Scognamiglio, M. Tarallo, G. Arfè, Saltanat Aitbaeva, Marina Mizernaya, Boris Dyachkov, Andrew J Martin, Iain McDonald, Christopher J MacLeod, Katie McFall, Hazel M Prichard, Gawen R T Jenkin, B. Kennedy, I. McDonald, D. Tanner, L. Longridge, A. M. Borst, A. A. Finch, H. Friis, N. J. Horsburgh, P. N. Gamaletsos, J. Goettlicher, R. Steininger, K. Geraki, Jonathan Cloutier, Stephen J. Piercey, Connor Allen, Craig Storey, James Darling, Stephanie Lasalle, A. Dobrzanski, L. Kirstein, R. Walcott, I. Butler, B. Ngwenya, Andrew Dobrzanski, Simon Howard, Lore Troalen, Peter Davidson, Rachel Walcott, Drew Drummond, Jonathan Cloutier, Drew Drummond, Adrian Boyce, Robert Blakeman, John Ashton, Eva Marquis, Kathryn Goodenough, Guillaume Estrade, Martin Smith, E. Zygouri, S. P. Kilias, T. Zack, I. Pitcairn, E. Chi Fru, P. Nomikou, A. Argyraki, M. Ivarsson, Adrian A. Finch, Anouk M. Borst, William Hutchison, Nicola J. Horsburgh, Tom Andersen, Siri Simonsen, Hamidullah Waizy, Norman Moles, Martin Smith, Steven P. Hollis, Julian F. Menuge, Aileen L. Doran, Paul Dennis, Brett Davidheiser-Kroll, Alina Marca, Jamie Wilkinson, Adrian Boyce, John Güven, Steven P. Hollis, Julian F. Menuge, Aileen L. Doran, Stephen J. Piercey, Mark R. Cooper, J. Stephen Daly, Oakley Turner, Brian McConnell, Hannah S. R. Hughes, Hannah S. R. Hughes, Magdalena M. Matusiak-Małek, Iain McDonald, Ben Williamson, James Williams, Guy Dishaw, Harri Rees, Roger Key, Simon Bate, Andy Moore, Katie McFall, Iain McDonald, Dominque Tanner, Manuel Keith, Karsten M. Haase, Daniel J. Smith, Reiner Klemd, Ulrich Schwarz-Schampera, Wolfgang Bach, Sam J Walding, Gawen RT Jenkin, Daniel James, David Clark, Lisa Hart-Madigan, Robin Armstrong, Jamie Wilkinson, Gawen RT Jenkin, Hugh Graham, Daniel J Smith, Andrew P Abbott, David A Holwell, Eva Zygouri, Robert C Harris, Christopher J Stanley, Hannah L.J. Grant, Mark D. Hannington, Sven Petersen, Matthias Frische, Fei Zhang, Ben J. Williamson, Hannah Hughes, Joshua Smiles, Manuel Keith, Daniel J. Smith, Chetan Nathwani, Robert Sievwright, Jamie Wilkinson, Matthew Loader, Daryl E. Blanks, David A. Holwell, W.D. Smith, J.R. Darling, D.S. Bullen, R.C. Scrivener, Aileen L. Doran, Steven P. Hollis, Julian F. Menuge, John Güven, Adrian J. Boyce, Oakley Turner, Sam Broom-Fendley, Aoife E Brady, Karen Hudson-Edwards, Oakley Turner, Steve Hollis, Sean McClenaghan, Aileen Doran, John Güven, Emily K. Fallon, Richard Brooker, Thomas Scott
Cobalt in the Congolese Copperbelt mines is commonly recovered from Co-oxi-hydroxides (i.e. heterogenite, asbolane) by acid-leaching under reducing conditions. However, most operations face a limit in the leaching yields of cobalt, which usually do not exceed 80%. The main aim of this work was to investigate the causes of the poor recovery, in order to reconcile the Co recovery with processing techniques. Several concentrate samples from different mine plants of Katanga Copperbelt (Kalukuluku, Mutanda, Mabaya, Kamwali and Fungurume) were selected and subjected to a full mineralogical characterisation by Optical Microscopy (OM), X-Ray Diffraction (XRD), automated mineralogy and Scanning Electron Microscopy by Energy Dispersive Spectroscopy (SEM-EDS) prior and after leaching tests. OM and XRD results were used as background information to build a mineral list for mineral identification during automated mineralogy analyses by Mineralogic Mining System (Zeiss ltd.). Automated mineralogy allowed obtaining mineral maps, modal mineralogy, chemical assays and Co deportment for each specimen prior and after leaching. Mineral maps of the leached samples were useful to observe the occurrences of poorly leached Co-bearing particles which were further investigated by SEM-EDS and X-mapping. The results showed that heterogenite (rarely associated with asbolane) is the main cobalt mineral in Katanga. Mineralogic Mining System was able to discriminate between pure heterogenite, and Si-Al-K-bearing heterogenite, asbolane/heterogenite, Heterogenite+Fe-oxi-hydroxide and Co-bearing mixed phases, which resulted more refractory to leaching. The comparison between modal mineralogy of pre- and post-leached samples indicates a decrease, but not a full leaching of these Co phases: chemical assays and Co-deportment, in fact, still reveal the presence of low Co% within Co phases listed above (Table 1). SEM-EDS and X-mapping on single particles of some specimens corroborated the results obtained by Mineralogic.
Investigation of Copper Recovery from a New Copper Deposit (Nussir) in Northern Norway
Published in Mineral Processing and Extractive Metallurgy Review, 2019
Priyanka Dhar, Maria Thornhill, Hanumantha Rao Kota
Process mineralogy is an integral part of mineral processing especially in the copper, gold, or silver mining industries where the process of beneficiation determines the difference between economic and uneconomic ventures. Process mineralogy comprises information and evaluation of mineral composition, size, and shape of mineral grains as well as mineral association, locking, and liberation data. The development of SEM and automated mineralogy techniques has been widely employed to find and classify copper mineralogy, finely disseminated minerals and associated sulfides–silicate phases, etc. In process mineralogy study, besides mineral characterization (Donskoi et al. 2007; Celik et al. 2010), detailed quantitative data are produced and interpreted with flotation operating conditions (Bahrami et al. 2019). The data are very useful particularly in plant design (Sant’agostino et al. 2001), flow sheet development (Nice and Brown 1995; Morizot et al. 1997), performance evaluation, and optimization studies (Frew and Davey 1993; Young, Pease, Johnson, and Munro 1997). The metallurgical results are usually assessed with the chemical analysis, which is relatively quick and straightforward. However, the data are not sufficient to keep the recovery/grade of the flotation stabilized and control the flotation performance. The metallurgical performance of a flotation process can be altered due to mineralogy, texture changes, and degree of liberation of the minerals in the ore. Young et al. (1997) suggested a size-by-size mineralogical approach to identify and solve the problem related to ore quality and flotation performance in long term. In the literature, the information about the mineralization, texture, and liberation statements of the minerals are equally important for better recovery of skarn ores (Pangum et al. 2001), platinum group minerals (PGM), and gold with sulfide minerals (Cabri et al. 2005) and complex sulfide ores (Ecrola and Paloaari 1995; Ekmekc¸i et al. 2005; Lastra 2007; Gharai and Venugopal 2016). While implementing Isa Mills, besides circuit simplification, 20% of increase for zinc recovery, 5% of increase for lead recovery, and 7% of increase in liberation was achieved. There are many available examples, where mineralogic studies led to increase in production. The Cu-Pb selectivity was very low due to significant amounts of sphalerite and pyrite to the Cu-Pb bulk concentrate in a concentrator (Lastra 2007). It was found that reducing the feed size led to increase in liberation; but the gangue minerals sphalerite and pyrite were found in Cu-Pb concentrate in liberated form. Thus, the liberation analysis confirmed that the inability of the selective depression of sphalerite and pyrite was the major problem, but not the liberation.