Dynamic modelling of a carbon-in-leach process with the regression network

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dc.contributor.authorVan Deventer J.S.J.
dc.contributor.authorKam K.M.
dc.contributor.authorVan Der Walt T.J.
dc.date.accessioned2011-05-15T15:59:00Z
dc.date.available2011-05-15T15:59:00Z
dc.date.issued2004
dc.description.abstractThe regression network provides a connectionist framework in which both parametric and non-parametric modelling can be implemented. It is shown how mechanistic knowledge can be built directly within the connectionist structure that results in a semi-empirical network model. In doing so the inherent freedom of a specific model is restricted so that the generalisation performance of such a model improves accordingly. It is described how a semi-empirical regression network kinetic model is developed for the dynamic modelling of the carbon-in-leach (CIL) process for gold recovery. By providing for mechanistic knowledge in the connectionist structure and catering for poorly understood aspects of the process by use of non-parametric regions within the structure of the semi-empirical regression network, the regression network kinetic model displayed significant superiority in generalisation properties over other non-parametric regression models if evaluated during dynamic simulation runs. © 2004 Elsevier Ltd. All rights reserved.
dc.description.versionArticle
dc.identifier.citationChemical Engineering Science
dc.identifier.citation59
dc.identifier.citation21
dc.identifier.issn92509
dc.identifier.other10.1016/j.ces.2004.06.020
dc.identifier.urihttp://hdl.handle.net/10019.1/10952
dc.subjectLeaching
dc.subjectMathematical models
dc.subjectRegression analysis
dc.subjectCarbon-in-leach process
dc.subjectNon-parametric modelling
dc.subjectRegression networks
dc.subjectCarbon
dc.subjectleaching
dc.titleDynamic modelling of a carbon-in-leach process with the regression network
dc.typeArticle
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