Conference Proceedings
Iron Ore 2017
Conference Proceedings
Iron Ore 2017
Influence of shear rate on separation of iron ore fines using the REFLUXTM Classifier
The REFLUX Classifier is used to perform particle separations on the basis of density utilising a set of parallel inclined channels positioned above a vertical fluidised section. In continuous mode, feed enters part way up the vertical section, while fluidisation water is introduced from below. Lower density particles are conveyed upwards and collected in an overflow stream, while high density particles segregate onto the inclined surfaces, settle to the base of the system and empty via an underflow stream. Pressure transducers are used to measure the suspension density in the vertical section. This suspension density is maintained by a pneumatically controlled underflow valve to maintain a target suspension density or set point'.Previously, Galvin et al (2010) demonstrated the significant improvement achieved via a new laminar-shear separation mechanism, achieved by utilising closely spaced inclined channels in the REFLUX Classifier. For the separation of low-density coal particles from mineral matter, a channel spacing of 6 mm proved effective; however, for the separation of gangue minerals from much higher density minerals such as iron ore, the shear-induced lift needs to be even higher. In this paper, we examine the influence of two different channel widths on the continuous processing of an industrial iron ore feed in the REFLUX Classifier. It was found that when moving from 6.0 mm to 3.0 mm channels, the ability of the system to reject coarser gangue material and retain finer high density material across a particle size range from 0.125 to 1.0 mm was increased, resulting in a higher grade product.CITATION:Hunter, D M, Zhou, J, Iveson, S M and Galvin, K P, 2017. Influence of shear rate on separation of iron ore fines using the REFLUXTM Classifier, in Proceedings Iron Ore 2017, pp 205-210 (The Australasian Institute of Mining and Metallurgy: Melbourne).
Contributor(s):
D M Hunter, J Zhou, S M Iveson, K P Galvin
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- Published: 2017
- PDF Size: 0.182 Mb.
- Unique ID: P201703031