Dataset Open Access

Data from a pilot plant experiment for the processing of a complex tin skarn ore - 19.11.2018

Schach, Edgar; Padula, Flavio; Buchmann, Markus; Möckel, Robert; Ebert, Doreen; Pereira, Lucas; Kern, Marius; Leißner, Thomas; Pashkevich, Dzimitry; Sousa, Rui; Bremerstein, Irina; Breuer, Ben; Oliver, Karen; Seltmann, Reimar; Reimer, Wolfgang; Wotruba, Hermann; Filippov, Lev; Peuker, Urs; Rudolph, Martin; Broadbent, Chris; Roscher, Marco; van den Boogaart, Karl Gerald

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    <subfield code="a">&lt;p&gt;This data set derives from a pilot plant campaign for the beneficiation of a complex tin bearing skarn ore, including different separation and classification steps. The aim of the pilot plant test work was to prove a flowsheet that had been developed based on detailed geometallurgical analysis and results from the research projects AFK (Aufbereitung feink&amp;ouml;rniger Komplexerze, BMBF grant number 033R128) and FAME (European Union grant 641650) to produce a cassiterite concentrate for tin production, and further preconcentrates for iron, zinc, copper, indium, and arsenic. The tin mineralization is partially well localized in cassiterite, but also partially finely disseminated and thus unrecoverable as minor components in other minerals. The iron is located in magnetic and nonmagnetic iron oxides sometimes intergrown with cassiterite. Therefore, iron concentrates are recovered at larger grain sizes but need a further tin recovery step not implemented in the reported experiment. The other elements are mainly deported in sulfides, which are bulk recovered in a flotation step. A subsequent selective flotation is needed to recover them individually. This selective flotation is, however, not part of the reported experiment. The two tin concentrates recovered from the shaking table should be considered as preconcentrates, that can be enriched further e.g. through multi-stage gravity separation.&lt;/p&gt;

&lt;p&gt;The motivation for this data set is to provide a consistent basis for the application of new particle based geometallurgical methods enabled by automated mineralogy (e.g. Buchmann et al. 2018; Schach et al. 2019; Buchmann et al. 2020; Pereira et al. 2020).&lt;/p&gt;

&lt;p&gt;In addition, it should also allow for the comparison and evaluation of different analytical methods, which were used during the pilot plant experiments to generate a validated data set for the whole plant and to correlate different result from various methods. This is the basis for further investigations enabling the application of various analyzing methods in a synergetic way. Those synergies can help in the future to compensate drawbacks of certain methods by an adequate combination of multiple approaches.&lt;/p&gt;

&lt;p&gt;This repository includes raw data and processed data from November 19, 2018. The following data is included:&lt;/p&gt;

&lt;ul&gt;
	&lt;li&gt;X-ray fluorescence spectroscopy (XRF)&lt;/li&gt;
	&lt;li&gt;X-ray diffraction (XRD)&lt;/li&gt;
	&lt;li&gt;Automated Mineralogy (MLA)&lt;/li&gt;
	&lt;li&gt;The balanced mass flows and element/mineral grades for the XRF- and the MLA data&lt;/li&gt;
	&lt;li&gt;External certified analysis including different inductive coupled plasma (ICP) and XRF methods from ALS&lt;/li&gt;
	&lt;li&gt;R scripts for the mass balance&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Please find further information in the&lt;em&gt; &amp;quot;supplementary information&amp;quot; &lt;/em&gt;file&lt;/p&gt;</subfield>
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Publication date:
January 6, 2021
DOI:
10.14278/rodare.715

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10.14278/rodare.715

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  • FAME - Flexible and Mobile Economic Processing Technologies (641650)
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