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Conference Proceedings

Iron Ore 2017

Conference Proceedings

Iron Ore 2017

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Real-time slurry elemental analyser for iron ore beneficiation plant control

Iron ore beneficiation plants are facing economic pressures to treat more challenging ore types, improve efficiency and improve product quality. Better control and stability of the plant process in the face of increasing feed ore variability has been a goal for many of the operations facing these challenges.Prompt gamma neutron activation analysis (PGNAA) technology has proven to be a successful measurement technique within an iron ore beneficiation plant. PGNAA was first developed in the early 1980s for the measurement of the elemental composition of bulk materials. Today, PGNAA technology is applied for elemental analysis within the iron ore industry on both bulk materials and slurries.Traditional composite sampling throughout the wet iron ore beneficiation process provides a delayed measurement of average properties but does not capture the true variability of the process in real-time. As such this is a limiting factor towards stabilisation and optimisation of the process. A PGNAA analyser provides real-time slurry elemental analysis of feed, concentrate and tails streams within the processing plant. The analytical data from the analyser can be exploited with a control strategy to correct and stabilise the plant on a much shorter time scale, delivering significant economic benefits and greater operational flexibility. The economic benefits include more stable product quality and a reduction in composite sampling and laboratory costs.Some example results will be presented from the Vale Vargem Grande iron ore beneficiation plants, which are utilising a six stream multiplexed Thermo Scientific PGNAA Slurry Analyser.CITATION:Noble, G, Stabile, A, Portes, E and Roberto, J, 2017. Real-time slurry elemental analyser for iron ore beneficiation plant control, in Proceedings Iron Ore 2017, pp 481-486 (The Australasian Institute of Mining and Metallurgy: Melbourne).
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  • Published: 2016
  • PDF Size: 0.802 Mb.
  • Unique ID: P201703067

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