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dc.contributor.authorReichel, C.R.M.
dc.contributor.authorVan der Merwe, A.F.
dc.contributor.authorCronje, J.
dc.date.accessioned2020-02-27T12:30:04Z
dc.date.available2020-02-27T12:30:04Z
dc.date.issued2018
dc.identifier.citationReichel, C.R.M. et al. 2018. A predictive model for a Wet-high-intensity-magnetic-separator (WHIMS) using artificial neural networks. 10th International Conference on Advances in Science, Engineering, Technology & Healthcare (ASETH-18), 19-20 Nov 2018, Cape Town, South Africa: 216-220. [https://doi.org/10.17758/EARES4.EAP1118253]en_US
dc.identifier.isbn978-81-938365-2-1
dc.identifier.urihttp://hdl.handle.net/10394/34217
dc.identifier.urihttps://www.eares.org/siteadmin/upload/7592EAP1118253.pdf
dc.identifier.urihttps://doi.org/10.17758/EARES4.EAP1118253
dc.description.abstract—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 sizesen_US
dc.language.isoenen_US
dc.publisherEARETen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectModellingen_US
dc.subjectPredictive Modelen_US
dc.subjectWet High Intensity Magnetic Separatoren_US
dc.subjectWHIMSen_US
dc.titleA predictive model for a Wet-high-intensity-magnetic-separator (WHIMS) using artificial neural networksen_US
dc.typePresentationen_US
dc.contributor.researchID24936618 - Reichel, Coenraad Robbert Marks
dc.contributor.researchID10212361 - Van der Merwe, Abraham Frederik


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