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Conference Proceedings

Preconcentration Digital Conference 2020

Conference Proceedings

Preconcentration Digital Conference 2020

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Bulk sensing of trucks, in-pit equipment and the mine bench for preconcentration

The CSIRO has pioneered the development and use of certain classes of magnetic resonance (MR) for mining applications. The MR technique is a radio frequency-based spectroscopy that provides highly quantitative measurement of selected ore minerals. MR has many inherent advantages for bulk sensing in mining applications, including deep sensing penetration into most bulk ores with the use of non-contact sensors that are robust and tolerant of demanding mine conditions. The highly discriminating nature of the technique also provides the very desirable attribute of robust and simple calibration, that can often be performed prior to sensor installation. Rapid sensing (within seconds) at very low grade (few hundred ppm Cu) has been demonstrated, as well as sensing at high tonnage rate.
Besides sorting applications, MR may also be applied to preconcentration applications involving very large volume sensing including:
(i) haul truck sensing using portal arrangements, where truck destination assignment may be dynamically determined based on the truck grade,
(ii) in-pit gantries used for loading trucks that may similarly be equipped with sensors for truck assignment and
(iii) deep mine bench sensing, recently progressed through a CRC ORE project, where local bench grade is determined at the few-metre lateral scale and at depth prior to blasting, to aid selective blasting and ore extraction.
MR sensor prototypes have been developed for each of these applications and have been tested in laboratory or limited field conditions. To inform the value proposition for truck or bench sensing, initial analysis of truck assignment improvement based on an analysis of in-situ mine bench grade has been performed for a porphyry style deposit. The prototype sensors, preliminary results and future technology development and objectives will be described.
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  • Published: 2020
  • Pages: 8
  • PDF Size: 0.57 Mb.
  • Unique ID: P-01475-S2M7P1

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