On-line process monitoring using a robust statistics based methodology

Autores
Llanos, Claudia Elizabeth; Chávez Galletti, Roberto Javier; Sanchez, Mabel Cristina; Maronna, Ricardo Antonio
Año de publicación
2019
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Robust Data Reconciliation strategies provide unbiasedvariable estimates in the presence of a moderate quantity of measurement grosserrors. Systematic errors which persist in time, as biases or drifts, overcome thisquantity causing the deterioration of the estimates. This also occurs due tothe presence of process leaks. The fast detection of those faults avoids theuse of biased solutions of the data reconciliation procedure, and allows toperform quick corrective actions. In this work, a methodology for leakdetection is incorporated into a robust data reconciliation procedure thatdetects and classifies systematic observation errors. The strategy makes use ofthe Robust Measurement Test, to detect outliers and leaks, and the RobustLinear Regression of the data contained in a moving window to distinguish betweenbiases and drifts. The methodology is applied for two benchmarks extracted fromthe literature. Results highlight the performance of the proposed strategy.
Fil: Llanos, Claudia Elizabeth. Universidad Nacional del Sur. Departamento de Ingeniería Química; Argentina. 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
Fil: Chávez Galletti, Roberto Javier. Universidad Nacional del Sur. Departamento de Ingeniería Química; Argentina. 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
Fil: Sanchez, Mabel Cristina. Universidad Nacional del Sur. Departamento de Ingeniería Química; Argentina. 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
Fil: Maronna, Ricardo Antonio. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Matemáticas; Argentina
Materia
MEASUREMENT ERRORS
LEAK DETECTION
ROBUST DATA RECONCILIATION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/108895
Acceder
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network_name_str CONICET Digital (CONICET)
spelling On-line process monitoring using a robust statistics based methodologyLlanos, Claudia ElizabethChávez Galletti, Roberto JavierSanchez, Mabel CristinaMaronna, Ricardo AntonioMEASUREMENT ERRORSLEAK DETECTIONROBUST DATA RECONCILIATIONhttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2Robust Data Reconciliation strategies provide unbiasedvariable estimates in the presence of a moderate quantity of measurement grosserrors. Systematic errors which persist in time, as biases or drifts, overcome thisquantity causing the deterioration of the estimates. This also occurs due tothe presence of process leaks. The fast detection of those faults avoids theuse of biased solutions of the data reconciliation procedure, and allows toperform quick corrective actions. In this work, a methodology for leakdetection is incorporated into a robust data reconciliation procedure thatdetects and classifies systematic observation errors. The strategy makes use ofthe Robust Measurement Test, to detect outliers and leaks, and the RobustLinear Regression of the data contained in a moving window to distinguish betweenbiases and drifts. The methodology is applied for two benchmarks extracted fromthe literature. Results highlight the performance of the proposed strategy.Fil: Llanos, Claudia Elizabeth. Universidad Nacional del Sur. Departamento de Ingeniería Química; Argentina. 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; ArgentinaFil: Chávez Galletti, Roberto Javier. Universidad Nacional del Sur. Departamento de Ingeniería Química; Argentina. 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; ArgentinaFil: Sanchez, Mabel Cristina. Universidad Nacional del Sur. Departamento de Ingeniería Química; Argentina. 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; ArgentinaFil: Maronna, Ricardo Antonio. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Matemáticas; ArgentinaPlanta Piloto de Ingeniería Química2019-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/108895Llanos, Claudia Elizabeth; Chávez Galletti, Roberto Javier; Sanchez, Mabel Cristina; Maronna, Ricardo Antonio; On-line process monitoring using a robust statistics based methodology; Planta Piloto de Ingeniería Química; Latin American Applied Research; 49; 2; 8-2019; 111-1160327-07931851-8796CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://laar.plapiqui.edu.ar/OJS/index.php/laar/article/view/47info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:20:22Zoai:ri.conicet.gov.ar:11336/108895instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 14:20:22.656CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv On-line process monitoring using a robust statistics based methodology
title On-line process monitoring using a robust statistics based methodology
spellingShingle On-line process monitoring using a robust statistics based methodology
Llanos, Claudia Elizabeth
MEASUREMENT ERRORS
LEAK DETECTION
ROBUST DATA RECONCILIATION
title_short On-line process monitoring using a robust statistics based methodology
title_full On-line process monitoring using a robust statistics based methodology
title_fullStr On-line process monitoring using a robust statistics based methodology
title_full_unstemmed On-line process monitoring using a robust statistics based methodology
title_sort On-line process monitoring using a robust statistics based methodology
dc.creator.none.fl_str_mv Llanos, Claudia Elizabeth
Chávez Galletti, Roberto Javier
Sanchez, Mabel Cristina
Maronna, Ricardo Antonio
author Llanos, Claudia Elizabeth
author_facet Llanos, Claudia Elizabeth
Chávez Galletti, Roberto Javier
Sanchez, Mabel Cristina
Maronna, Ricardo Antonio
author_role author
author2 Chávez Galletti, Roberto Javier
Sanchez, Mabel Cristina
Maronna, Ricardo Antonio
author2_role author
author
author
dc.subject.none.fl_str_mv MEASUREMENT ERRORS
LEAK DETECTION
ROBUST DATA RECONCILIATION
topic MEASUREMENT ERRORS
LEAK DETECTION
ROBUST DATA RECONCILIATION
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Robust Data Reconciliation strategies provide unbiasedvariable estimates in the presence of a moderate quantity of measurement grosserrors. Systematic errors which persist in time, as biases or drifts, overcome thisquantity causing the deterioration of the estimates. This also occurs due tothe presence of process leaks. The fast detection of those faults avoids theuse of biased solutions of the data reconciliation procedure, and allows toperform quick corrective actions. In this work, a methodology for leakdetection is incorporated into a robust data reconciliation procedure thatdetects and classifies systematic observation errors. The strategy makes use ofthe Robust Measurement Test, to detect outliers and leaks, and the RobustLinear Regression of the data contained in a moving window to distinguish betweenbiases and drifts. The methodology is applied for two benchmarks extracted fromthe literature. Results highlight the performance of the proposed strategy.
Fil: Llanos, Claudia Elizabeth. Universidad Nacional del Sur. Departamento de Ingeniería Química; Argentina. 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
Fil: Chávez Galletti, Roberto Javier. Universidad Nacional del Sur. Departamento de Ingeniería Química; Argentina. 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
Fil: Sanchez, Mabel Cristina. Universidad Nacional del Sur. Departamento de Ingeniería Química; Argentina. 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
Fil: Maronna, Ricardo Antonio. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Matemáticas; Argentina
description Robust Data Reconciliation strategies provide unbiasedvariable estimates in the presence of a moderate quantity of measurement grosserrors. Systematic errors which persist in time, as biases or drifts, overcome thisquantity causing the deterioration of the estimates. This also occurs due tothe presence of process leaks. The fast detection of those faults avoids theuse of biased solutions of the data reconciliation procedure, and allows toperform quick corrective actions. In this work, a methodology for leakdetection is incorporated into a robust data reconciliation procedure thatdetects and classifies systematic observation errors. The strategy makes use ofthe Robust Measurement Test, to detect outliers and leaks, and the RobustLinear Regression of the data contained in a moving window to distinguish betweenbiases and drifts. The methodology is applied for two benchmarks extracted fromthe literature. Results highlight the performance of the proposed strategy.
publishDate 2019
dc.date.none.fl_str_mv 2019-08
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/108895
Llanos, Claudia Elizabeth; Chávez Galletti, Roberto Javier; Sanchez, Mabel Cristina; Maronna, Ricardo Antonio; On-line process monitoring using a robust statistics based methodology; Planta Piloto de Ingeniería Química; Latin American Applied Research; 49; 2; 8-2019; 111-116
0327-0793
1851-8796
CONICET Digital
CONICET
url http://hdl.handle.net/11336/108895
identifier_str_mv Llanos, Claudia Elizabeth; Chávez Galletti, Roberto Javier; Sanchez, Mabel Cristina; Maronna, Ricardo Antonio; On-line process monitoring using a robust statistics based methodology; Planta Piloto de Ingeniería Química; Latin American Applied Research; 49; 2; 8-2019; 111-116
0327-0793
1851-8796
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://laar.plapiqui.edu.ar/OJS/index.php/laar/article/view/47
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Planta Piloto de Ingeniería Química
publisher.none.fl_str_mv Planta Piloto de Ingeniería Química
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1846082577900765184
score 13.22299

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