The application of neural nets in the metallurgical industry

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dc.contributor.authorAldrich C.
dc.contributor.authorVan Deventer J.S.J.
dc.contributor.authorReuter M.A.
dc.date.accessioned2011-05-15T15:53:59Z
dc.date.available2011-05-15T15:53:59Z
dc.date.issued1994
dc.description.abstractAlthough the potential of new techniques for the construction of accurate plant models, such as those based on connectionist methods, is generally acknowledged, little on their practical application can be found in the chemical and metallurgical engineering literature. In this paper the use of neural nets to model gold losses on a reduction plant and the consumption of an additive on a leach plant, as well as the pyrometallurgical processing of zinc and aluminium is discussed. The gold and leach plant models performed better than the multilinear regression models used on the plants, even where relatively few data were available. The neural networks used to model the recovery of lead and zinc from industrial flue dusts, process synthesis of zinc recovery plants and the processing of secondary aluminium in a rotary salt flux furnace produced realistic results that could be used by plant personnel to optimize their operations. © 1994.
dc.description.versionArticle
dc.identifier.citationMinerals Engineering
dc.identifier.citation7
dc.identifier.citation06-May
dc.identifier.issn8926875
dc.identifier.urihttp://hdl.handle.net/10019.1/8932
dc.titleThe application of neural nets in the metallurgical industry
dc.typeArticle
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