A predictive model for a Wet-high-intensity-magnetic-separator (WHIMS) using artificial neural networks
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Date
2018Author
Reichel, C.R.M.
Van der Merwe, A.F.
Cronje, J.
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—Materials can be classified into three major categories
based on the magnetic susceptibility thereof; the property
governing the response of the material subjected to a magnetic
field. Chromium has many uses in the industry, with stainless
steel production being the most important. Chromite ore in South
Africa is mainly mined in the Bushveld complex situated in the
central western region of the Highveld. Magnetic separation is the
physical separation of discrete particles. Wet high-intensity
magnetic separation (WHIMS) is commonly used in the gold,
uranium, iron and chromite recovery industries. The WHIMS
system is based on the imbalance of forces on particles. These
forces are magnetic, gravitational, centrifugal, frictional or
inertial, and attractive or repulsive forces in favour of the
magnetic forces, all due to the production of a magnetic field.
During the experimental procedure, single stage separation was
used for the aim of this project. Operational parameters such as
magnetic intensity (flux), wash water flow rate, feed flow rate and
feed density along with particle size are varied. The primary
objectives in this study were to obtain experimental data from a
laboratory scale WHIMS and to use this data to construct an
artificial neural network (ANN) able accurately predict grade,
yield and recovery. Sampling and analysis were used to determine
the recoveries, grades and yields for the varied operating
conditions. The material used during this study is chromite ore.
The ANN’s predicted the grade, recovery and yield with high
accuracy. The data from experimentation suggest that the
WHIMS system recovers best at smaller particle sizes
URI
http://hdl.handle.net/10394/34217https://www.eares.org/siteadmin/upload/7592EAP1118253.pdf
https://doi.org/10.17758/EARES4.EAP1118253