Monitoring of metallurgical reactors by the use of topographic mapping of process data

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dc.contributor.authorAldrich C.
dc.contributor.authorReuter M.A.
dc.date.accessioned2011-05-15T15:53:57Z
dc.date.available2011-05-15T15:53:57Z
dc.date.issued1999
dc.description.abstractAlthough principal component analysis has been applied widely for monitoring plant performance in a broad range of industrial processes, it is a linear technique that tends to break down when processes exhibit significant non-linear behaviour. In this paper a non-linear multivariate fault diagnostic system is proposed for metallurgical reactors, based on the use of hidden target mapping neural network to project the data to a three-dimensional subspace that can be visualized by a human operator. As is shown by way of a case study, the normal operating region can be defined by means of historic data confined by a convex hull. Subsequent process faults or novel data not projected to the normal operating region are automatically detected and visualized, while a sensitivity analysis of the data can aid the operator in locating the source of the disturbance.
dc.description.versionArticle
dc.identifier.citationMinerals Engineering
dc.identifier.citation12
dc.identifier.citation11
dc.identifier.issn8926875
dc.identifier.other10.1016/S0892-6875(99)00118-1
dc.identifier.urihttp://hdl.handle.net/10019.1/8909
dc.titleMonitoring of metallurgical reactors by the use of topographic mapping of process data
dc.typeArticle
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