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Identification of gross errors in material balance measurements by means of neural nets

dc.contributor.authorAldrich C.
dc.contributor.authorVan Deventer J.S.J.
dc.date.accessioned2011-05-15T15:59:01Z
dc.date.available2011-05-15T15:59:01Z
dc.date.issued1994
dc.identifier.citationChemical Engineering Science
dc.identifier.citation49
dc.identifier.citation9
dc.identifier.issn92509
dc.identifier.urihttp://hdl.handle.net/10019.1/10960
dc.description.abstractReliable sets of steady-state component and total flow rate data form the cornerstone for the monitoring of plant performance. The detection and isolation of gross errors in these data constitute an essential part of the process of reconciliation of the measurement data, which are generally inconsistent with process constraints. By using a neural net to classify measurement or constraint residuals, gross errors in the data can be identified accurately and efficiently. Gross error detection and isolation with artificial neural nets do not require explicit knowledge of the distribution of random errors in measurement values and can be applied to processes with arbitrary constraints. © 1994.
dc.subjectClassification (of information)
dc.subjectConstraint theory
dc.subjectIndustrial plants
dc.subjectLinear algebra
dc.subjectMatrix algebra
dc.subjectMeasurement errors
dc.subjectMonitoring
dc.subjectNeural networks
dc.subjectVectors
dc.subjectError isolation
dc.subjectGross errors
dc.subjectMaterial balance measurement
dc.subjectProcess constraints
dc.subjectError detection
dc.titleIdentification of gross errors in material balance measurements by means of neural nets
dc.typeArticle
dc.description.versionArticle


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