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Conference Proceedings

Seventh International Conference & Exhibition on Mass Mining (MassMin 2016)

Conference Proceedings

Seventh International Conference & Exhibition on Mass Mining (MassMin 2016)

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Investigation into the Implementation of Sensor-based OreSorting Systems at a Block Caving Operation

A cave-to-mill study being conducted at the New Afton block caving operation demonstrated the potential for sensor-based ore sorting equipment to reject waste and improve the grade of feed to the mill. The lack of selectivity and potential for dilution entry associated with the block cave mining method results in many caving operations having to mine and process material that is below cut-off grade at certain stages of production. Online monitoring and bulk sorting systems have the potential to alleviate this problem and improve the productivity of caving operations while reducing energy and water requirements for each tonne of concentrate produced.As part of the cave-to-mill study, the suitability of a range of sensors, such as X-ray fluorescence and induction-type sensors, to sort mine production at the New Afton mine was investigated. Results showed that the discrimination of ore and waste can potentially be carried out by implementing surficial sensors into the material handling system. The study also showed that a combination of sensor outputs could be used to identify a certain ore type so that pre-emptive changes can be made in the mill to improve beneficiation performance. Overall, the outcomes of the study on implementing sensor systems at block cave mines are considered to be of significant relevance to a number of existing and future caving operations.CITATION:Nadolski, S, Klein, B, Elmo, D, Scoble, M, Liu, Y and Scholar, J, 2016. Investigation into the implementation of sensor-based ore sorting systems at a block caving operation, in Proceedings Seventh International Conference and Exhibition on Mass Mining (MassMin 2016), pp 393-400 (The Australasian Institute of Mining and Metallurgy: Melbourne).
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  • Published: 2016
  • PDF Size: 1.286 Mb.
  • Unique ID: P201602043

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