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dc.provenanceCONICET-
dc.creatorSanchez, Mabel Cristina-
dc.creatorAlvarez Medina, Carlos Rodrigo-
dc.creatorBrandolin, Adriana-
dc.date2018-10-17T17:46:53Z-
dc.date2018-10-17T17:46:53Z-
dc.date2008-08-
dc.date2018-10-12T14:46:30Z-
dc.date.accessioned2019-04-29T15:25:57Z-
dc.date.available2019-04-29T15:25:57Z-
dc.date.issued2018-10-17T17:46:53Z-
dc.date.issued2018-10-17T17:46:53Z-
dc.date.issued2008-08-
dc.date.issued2018-10-12T14:46:30Z-
dc.identifierSanchez, Mabel Cristina; Alvarez Medina, Carlos Rodrigo; Brandolin, Adriana; A multivariate statistical process control procedure for BIAS identification in steady-state processes; John Wiley & Sons Inc; Aiche Journal; 54; 8; 8-2008; 2082-2088-
dc.identifier0001-1541-
dc.identifierhttp://hdl.handle.net/11336/62576-
dc.identifierCONICET Digital-
dc.identifierCONICET-
dc.identifier.urihttp://rodna.bn.gov.ar:8080/jspui/handle/bnmm/294110-
dc.descriptionIn this article, a multivariate statistical process control (MSPC) strategy, devoted to bias identification and estimation for processes operating under steady-state conditions, is presented. The technique makes use of the D statistic to detect the presence of biases. Besides, it uses a new decomposition of this statistic to identify the faulty sensors. The strategy is based only on historical process data. Neither process modeling nor assumptions about the probability distribution of measurement errors are required. In contrast to methods based on fundamental models, both redundant and nonredundant measurements can be examined to identify the presence of biases. The performance of the proposed technique is evaluated using data-reconciliation benchmarks. Results indicate that the technique succeeds in identifying single and multiple biases and fulfills three paramount issues to practical implementation in commercial software: robustness, uncertainty, and efficiency. © 2008 American Institute of Chemical Engineers.-
dc.descriptionFil: Sanchez, Mabel Cristina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina-
dc.descriptionFil: Alvarez Medina, Carlos Rodrigo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina-
dc.descriptionFil: Brandolin, Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina-
dc.formatapplication/pdf-
dc.formatapplication/pdf-
dc.languageeng-
dc.publisherJohn Wiley & Sons Inc-
dc.relationinfo:eu-repo/semantics/altIdentifier/doi/https://dx.doi.org/10.1002/aic.11547-
dc.relationinfo:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/abs/10.1002/aic.11547-
dc.rightsinfo:eu-repo/semantics/restrictedAccess-
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/-
dc.sourcereponame:CONICET Digital (CONICET)-
dc.sourceinstname:Consejo Nacional de Investigaciones Científicas y Técnicas-
dc.sourceinstacron:CONICET-
dc.source.urihttp://hdl.handle.net/11336/62576-
dc.subjectDATA RECONCILIATION-
dc.subjectSTATISTICAL ANALYSIS-
dc.subjectOtras Ingeniería Química-
dc.subjectIngeniería Química-
dc.subjectINGENIERÍAS Y TECNOLOGÍAS-
dc.titleA multivariate statistical process control procedure for BIAS identification in steady-state processes-
dc.typeinfo:eu-repo/semantics/article-
dc.typeinfo:eu-repo/semantics/publishedVersion-
dc.typeinfo:ar-repo/semantics/articulo-
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