Self-Optimizing Control Structures with Minimum Number of Process-Dependent Controlled Variables

Autores
Marchetti, Alejandro Gabriel; Zumoffen, David Alejandro Ramon
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In order to operate continuous processes near the economically optimal steady-state operating point, selfoptimizing control schemes reformulate the optimization problem as a process control problem. The objective is to find controlled variables and constant set points such that the controller leads to optimal adjustments of the inputs in the presence of stable disturbances. In particular, the null space approach consists in selecting the self-optimizing controlled variables as linear combinations of the inactive output variables, based on the first-order variation of the necessary conditions of optimality. In the self-optimizing control structures proposed in the literature, the number of controlled variables required is typically equal to the number of degrees of freedom (inputs) that are available after all the equality and active inequality constrained variables are controlled. In this paper, we propose new self-optimizing control structures based on the null space approach, where depending on the number of disturbances, the number of active constraints, and the number of inputs, it is possible to decrease the number of process-dependent controlled variables by fixing linear combinations of the inputs. The effectiveness of the proposed selfoptimizing control structures with minimum number of process-dependent controlled variables is demonstrated in simulation by means of a continuous stirred tank reactor and an evaporator
Fil: Marchetti, Alejandro Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina
Fil: Zumoffen, David Alejandro Ramon. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina
Materia
Self-Optimizing Control
Null-Space Method
Minimum Number of Control Loops
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:11336/29714
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/29714
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Self-Optimizing Control Structures with Minimum Number of Process-Dependent Controlled VariablesMarchetti, Alejandro GabrielZumoffen, David Alejandro RamonSelf-Optimizing ControlNull-Space MethodMinimum Number of Control Loopshttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2https://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2In order to operate continuous processes near the economically optimal steady-state operating point, selfoptimizing control schemes reformulate the optimization problem as a process control problem. The objective is to find controlled variables and constant set points such that the controller leads to optimal adjustments of the inputs in the presence of stable disturbances. In particular, the null space approach consists in selecting the self-optimizing controlled variables as linear combinations of the inactive output variables, based on the first-order variation of the necessary conditions of optimality. In the self-optimizing control structures proposed in the literature, the number of controlled variables required is typically equal to the number of degrees of freedom (inputs) that are available after all the equality and active inequality constrained variables are controlled. In this paper, we propose new self-optimizing control structures based on the null space approach, where depending on the number of disturbances, the number of active constraints, and the number of inputs, it is possible to decrease the number of process-dependent controlled variables by fixing linear combinations of the inputs. The effectiveness of the proposed selfoptimizing control structures with minimum number of process-dependent controlled variables is demonstrated in simulation by means of a continuous stirred tank reactor and an evaporatorFil: Marchetti, Alejandro Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; ArgentinaFil: Zumoffen, David Alejandro Ramon. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; ArgentinaAmerican Chemical Society2014-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/29714Marchetti, Alejandro Gabriel; Zumoffen, David Alejandro Ramon; Self-Optimizing Control Structures with Minimum Number of Process-Dependent Controlled Variables; American Chemical Society; Industrial & Engineering Chemical Research; 153; 4-2014; 10177-101930888-5885CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1021/ie5010509info:eu-repo/semantics/altIdentifier/url/http://pubs.acs.org/doi/10.1021/ie5010509info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:53:41Zoai:ri.conicet.gov.ar:11336/29714instacron: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:34982025-10-22 11:53:41.322CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Self-Optimizing Control Structures with Minimum Number of Process-Dependent Controlled Variables
title Self-Optimizing Control Structures with Minimum Number of Process-Dependent Controlled Variables
spellingShingle Self-Optimizing Control Structures with Minimum Number of Process-Dependent Controlled Variables
Marchetti, Alejandro Gabriel
Self-Optimizing Control
Null-Space Method
Minimum Number of Control Loops
title_short Self-Optimizing Control Structures with Minimum Number of Process-Dependent Controlled Variables
title_full Self-Optimizing Control Structures with Minimum Number of Process-Dependent Controlled Variables
title_fullStr Self-Optimizing Control Structures with Minimum Number of Process-Dependent Controlled Variables
title_full_unstemmed Self-Optimizing Control Structures with Minimum Number of Process-Dependent Controlled Variables
title_sort Self-Optimizing Control Structures with Minimum Number of Process-Dependent Controlled Variables
dc.creator.none.fl_str_mv Marchetti, Alejandro Gabriel
Zumoffen, David Alejandro Ramon
author Marchetti, Alejandro Gabriel
author_facet Marchetti, Alejandro Gabriel
Zumoffen, David Alejandro Ramon
author_role author
author2 Zumoffen, David Alejandro Ramon
author2_role author
dc.subject.none.fl_str_mv Self-Optimizing Control
Null-Space Method
Minimum Number of Control Loops
topic Self-Optimizing Control
Null-Space Method
Minimum Number of Control Loops
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv In order to operate continuous processes near the economically optimal steady-state operating point, selfoptimizing control schemes reformulate the optimization problem as a process control problem. The objective is to find controlled variables and constant set points such that the controller leads to optimal adjustments of the inputs in the presence of stable disturbances. In particular, the null space approach consists in selecting the self-optimizing controlled variables as linear combinations of the inactive output variables, based on the first-order variation of the necessary conditions of optimality. In the self-optimizing control structures proposed in the literature, the number of controlled variables required is typically equal to the number of degrees of freedom (inputs) that are available after all the equality and active inequality constrained variables are controlled. In this paper, we propose new self-optimizing control structures based on the null space approach, where depending on the number of disturbances, the number of active constraints, and the number of inputs, it is possible to decrease the number of process-dependent controlled variables by fixing linear combinations of the inputs. The effectiveness of the proposed selfoptimizing control structures with minimum number of process-dependent controlled variables is demonstrated in simulation by means of a continuous stirred tank reactor and an evaporator
Fil: Marchetti, Alejandro Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina
Fil: Zumoffen, David Alejandro Ramon. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina
description In order to operate continuous processes near the economically optimal steady-state operating point, selfoptimizing control schemes reformulate the optimization problem as a process control problem. The objective is to find controlled variables and constant set points such that the controller leads to optimal adjustments of the inputs in the presence of stable disturbances. In particular, the null space approach consists in selecting the self-optimizing controlled variables as linear combinations of the inactive output variables, based on the first-order variation of the necessary conditions of optimality. In the self-optimizing control structures proposed in the literature, the number of controlled variables required is typically equal to the number of degrees of freedom (inputs) that are available after all the equality and active inequality constrained variables are controlled. In this paper, we propose new self-optimizing control structures based on the null space approach, where depending on the number of disturbances, the number of active constraints, and the number of inputs, it is possible to decrease the number of process-dependent controlled variables by fixing linear combinations of the inputs. The effectiveness of the proposed selfoptimizing control structures with minimum number of process-dependent controlled variables is demonstrated in simulation by means of a continuous stirred tank reactor and an evaporator
publishDate 2014
dc.date.none.fl_str_mv 2014-04
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/11336/29714
Marchetti, Alejandro Gabriel; Zumoffen, David Alejandro Ramon; Self-Optimizing Control Structures with Minimum Number of Process-Dependent Controlled Variables; American Chemical Society; Industrial & Engineering Chemical Research; 153; 4-2014; 10177-10193
0888-5885
CONICET Digital
CONICET
url http://hdl.handle.net/11336/29714
identifier_str_mv Marchetti, Alejandro Gabriel; Zumoffen, David Alejandro Ramon; Self-Optimizing Control Structures with Minimum Number of Process-Dependent Controlled Variables; American Chemical Society; Industrial & Engineering Chemical Research; 153; 4-2014; 10177-10193
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/doi/10.1021/ie5010509
info:eu-repo/semantics/altIdentifier/url/http://pubs.acs.org/doi/10.1021/ie5010509
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
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 12.982451

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