The use of neural network analysis of diagnostic leaching data in gold liberation modelling

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dc.contributor.authorPetersen K.R.P.
dc.contributor.authorLorenzen L.
dc.contributor.authorAmandale D.J.
dc.date.accessioned2011-05-15T15:59:48Z
dc.date.available2011-05-15T15:59:48Z
dc.date.issued2003
dc.description.abstractThe interrelationship between mineral liberation and leaching behaviour of a gold ore is ill defined, mainly due to the complexity of both leaching and mineral liberation. This study presents a neural network approach to modelling the liberation of gold bearing ores. A complete mineralogical analysis of unmilled and milled ores, including gold deportment and gangue content are used as inputs to a self-organizing neural net, which generates order preserving topological maps. The arrangement and shapes of these clusters are coupled to unmilled free gold data to predict gold liberation in milled ores (absolute error: 8.1%). Moreover, the self-organizing maps were diagnostic of the quality of data used, indicating that the relationship between particle size and gangue material content requires further investigation.
dc.description.versionArticle
dc.identifier.citationJournal of The South African Institute of Mining and Metallurgy
dc.identifier.citation103
dc.identifier.citation2
dc.identifier.issn0038223X
dc.identifier.urihttp://hdl.handle.net/10019.1/11369
dc.subjectLeaching
dc.subjectNeural networks
dc.subjectParticle size analysis
dc.subjectTopology
dc.subjectGold liberation modelling
dc.subjectGold ore treatment
dc.titleThe use of neural network analysis of diagnostic leaching data in gold liberation modelling
dc.typeArticle
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