Statistical Simplex Method for Experimental Design in Process Optimization

Autores
Martínez, Ernesto Carlos
Año de publicación
2005
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Experimental optimization with scarce and noisy process data is a key issue in laboratory automation for faster chemical process research and development, real-time process optimization, and the ability to embed a learning capability into the design of self-calibrating instruments and extremum-seeking controllers. To deal successfully with noise and uncontrollable factors in experimental design for process optimization, a statistical characterization of an optimum using process data is proposed. The Kendall?s tau statistic is used for identifying a minimum (maximum) in a data set as a cluster center of positively (negatively) correlated points. A new simplex search algorithm with a logic that resorts to correlation-based ranking of simplex vertices for reflection, expansion, contraction, and shrinking steps is proposed. The advantage of resorting to a data set that cumulatively provides a global perspective of the output landscape through Kendall?s tau calculations is a novel feature of the statistical simplex method. Encouraging results obtained for Rastringin?s multimodal function and in the optimization of the operating policy for a semibatch reactor are presented.
Fil: Martínez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina
Materia
SIMPLEX METHOD
EXPERIMENTAL OPTIMIZATION
NOISY FUNCTION OPTIMIZATION
PROCESS DEVELOPMENT
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/102574
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/102574
network_acronym_str CONICETDig
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network_name_str CONICET Digital (CONICET)
spelling Statistical Simplex Method for Experimental Design in Process OptimizationMartínez, Ernesto CarlosSIMPLEX METHODEXPERIMENTAL OPTIMIZATIONNOISY FUNCTION OPTIMIZATIONPROCESS DEVELOPMENThttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2Experimental optimization with scarce and noisy process data is a key issue in laboratory automation for faster chemical process research and development, real-time process optimization, and the ability to embed a learning capability into the design of self-calibrating instruments and extremum-seeking controllers. To deal successfully with noise and uncontrollable factors in experimental design for process optimization, a statistical characterization of an optimum using process data is proposed. The Kendall?s tau statistic is used for identifying a minimum (maximum) in a data set as a cluster center of positively (negatively) correlated points. A new simplex search algorithm with a logic that resorts to correlation-based ranking of simplex vertices for reflection, expansion, contraction, and shrinking steps is proposed. The advantage of resorting to a data set that cumulatively provides a global perspective of the output landscape through Kendall?s tau calculations is a novel feature of the statistical simplex method. Encouraging results obtained for Rastringin?s multimodal function and in the optimization of the operating policy for a semibatch reactor are presented.Fil: Martínez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaAmerican Chemical Society2005-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/102574Martínez, Ernesto Carlos; Statistical Simplex Method for Experimental Design in Process Optimization; American Chemical Society; Industrial & Engineering Chemical Research; 44; 23; 11-2005; 8796-88050888-5885CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://pubs.acs.org/doi/10.1021/ie050165minfo:eu-repo/semantics/altIdentifier/doi/10.1021/ie050165minfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-12-23T13:13:54Zoai:ri.conicet.gov.ar:11336/102574instacron: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:34982025-12-23 13:13:54.298CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Statistical Simplex Method for Experimental Design in Process Optimization
title Statistical Simplex Method for Experimental Design in Process Optimization
spellingShingle Statistical Simplex Method for Experimental Design in Process Optimization
Martínez, Ernesto Carlos
SIMPLEX METHOD
EXPERIMENTAL OPTIMIZATION
NOISY FUNCTION OPTIMIZATION
PROCESS DEVELOPMENT
title_short Statistical Simplex Method for Experimental Design in Process Optimization
title_full Statistical Simplex Method for Experimental Design in Process Optimization
title_fullStr Statistical Simplex Method for Experimental Design in Process Optimization
title_full_unstemmed Statistical Simplex Method for Experimental Design in Process Optimization
title_sort Statistical Simplex Method for Experimental Design in Process Optimization
dc.creator.none.fl_str_mv Martínez, Ernesto Carlos
author Martínez, Ernesto Carlos
author_facet Martínez, Ernesto Carlos
author_role author
dc.subject.none.fl_str_mv SIMPLEX METHOD
EXPERIMENTAL OPTIMIZATION
NOISY FUNCTION OPTIMIZATION
PROCESS DEVELOPMENT
topic SIMPLEX METHOD
EXPERIMENTAL OPTIMIZATION
NOISY FUNCTION OPTIMIZATION
PROCESS DEVELOPMENT
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Experimental optimization with scarce and noisy process data is a key issue in laboratory automation for faster chemical process research and development, real-time process optimization, and the ability to embed a learning capability into the design of self-calibrating instruments and extremum-seeking controllers. To deal successfully with noise and uncontrollable factors in experimental design for process optimization, a statistical characterization of an optimum using process data is proposed. The Kendall?s tau statistic is used for identifying a minimum (maximum) in a data set as a cluster center of positively (negatively) correlated points. A new simplex search algorithm with a logic that resorts to correlation-based ranking of simplex vertices for reflection, expansion, contraction, and shrinking steps is proposed. The advantage of resorting to a data set that cumulatively provides a global perspective of the output landscape through Kendall?s tau calculations is a novel feature of the statistical simplex method. Encouraging results obtained for Rastringin?s multimodal function and in the optimization of the operating policy for a semibatch reactor are presented.
Fil: Martínez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina
description Experimental optimization with scarce and noisy process data is a key issue in laboratory automation for faster chemical process research and development, real-time process optimization, and the ability to embed a learning capability into the design of self-calibrating instruments and extremum-seeking controllers. To deal successfully with noise and uncontrollable factors in experimental design for process optimization, a statistical characterization of an optimum using process data is proposed. The Kendall?s tau statistic is used for identifying a minimum (maximum) in a data set as a cluster center of positively (negatively) correlated points. A new simplex search algorithm with a logic that resorts to correlation-based ranking of simplex vertices for reflection, expansion, contraction, and shrinking steps is proposed. The advantage of resorting to a data set that cumulatively provides a global perspective of the output landscape through Kendall?s tau calculations is a novel feature of the statistical simplex method. Encouraging results obtained for Rastringin?s multimodal function and in the optimization of the operating policy for a semibatch reactor are presented.
publishDate 2005
dc.date.none.fl_str_mv 2005-11
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/11336/102574
Martínez, Ernesto Carlos; Statistical Simplex Method for Experimental Design in Process Optimization; American Chemical Society; Industrial & Engineering Chemical Research; 44; 23; 11-2005; 8796-8805
0888-5885
CONICET Digital
CONICET
url http://hdl.handle.net/11336/102574
identifier_str_mv Martínez, Ernesto Carlos; Statistical Simplex Method for Experimental Design in Process Optimization; American Chemical Society; Industrial & Engineering Chemical Research; 44; 23; 11-2005; 8796-8805
0888-5885
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://pubs.acs.org/doi/10.1021/ie050165m
info:eu-repo/semantics/altIdentifier/doi/10.1021/ie050165m
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv American Chemical Society
publisher.none.fl_str_mv American Chemical Society
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
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score 12.441415

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