Show simple item record

dc.contributor.author Sanchez, Mabel Cristina
dc.contributor.author Alvarez Medina, Carlos Rodrigo
dc.contributor.author Brandolin, Adriana
dc.date.available 2018-10-16T15:33:33Z
dc.date.issued 2008-08
dc.identifier.citation Sanchez, 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.identifier.issn 0001-1541
dc.identifier.uri http://hdl.handle.net/11336/62418
dc.description.abstract In 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.format application/pdf
dc.language.iso eng
dc.publisher John Wiley & Sons Inc
dc.rights info:eu-repo/semantics/restrictedAccess
dc.rights.uri https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject DATA RECONCILIATION
dc.subject STATISTICAL ANALYSIS
dc.subject.classification Otras Ingeniería Química
dc.subject.classification Ingeniería Química
dc.subject.classification INGENIERÍAS Y TECNOLOGÍAS
dc.title A multivariate statistical process control procedure for BIAS identification in steady-state processes
dc.type info:eu-repo/semantics/article
dc.type info:ar-repo/semantics/artículo
dc.type info:eu-repo/semantics/publishedVersion
dc.date.updated 2018-10-12T14:46:44Z
dc.journal.volume 54
dc.journal.number 8
dc.journal.pagination 2082-2088
dc.journal.pais Estados Unidos
dc.journal.ciudad Nueva York
dc.description.fil Fil: 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.description.fil Fil: 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.description.fil Fil: 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.journal.title Aiche Journal
dc.relation.alternativeid info:eu-repo/semantics/altIdentifier/doi/https://dx.doi.org/10.1002/aic.11547
dc.relation.alternativeid info:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/abs/10.1002/aic.11547
dc.conicet.fuente individual


Archivos asociados

Icon
Blocked Acceso no disponible

This item appears in the following Collection(s)

Show simple item record

info:eu-repo/semantics/restrictedAccess Excepto donde se diga explícitamente, este item se publica bajo la siguiente descripción: Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Unported (CC BY-NC-SA 2.5)