Strategies for the simultaneous optimization of the structure and the process variables of a protein production plant

Autores
Asenjo, Juan Carlos; Montagna, Jorge Marcelo; Vecchietti, Aldo; Iribarren, Oscar Alberto; Pinto, Jose M.
Año de publicación
2000
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Process performance models for a multiproduct batch protein plant are used to exploit alternative strategies in the optimization of both the process variables and the structure of the plant. Simple process performance models are used to describe the unit operations, which renders explicit expressions for the size and time factor model in the design of batch plants. In the proposed approach the process variables are optimized regardless the plant structure constraints, which are left as a posterior decision. This optimization is done in a single product-free intermediate storage (SP-FIS) scenario, unbiased with any plant structure. The approach is compared to the case of recipe values for the process variables and to the best optimal solution for the nonconvex mixed integer nonlinear program (MINLP), which arises when simultaneously optimizing the structure and the process variables. This last optimization model is hard to solve and its global solution remains as an open problem. The proposed approach generates solutions very close to the ones obtained from nonconvex MINLP and is quite superior than simply resorting to recipes. We also study the role of process variables in this approach. It is found that they behave as in continuous processes by trading off cost components, with a smooth dependence on the overall cost. Moreover, for feasible designs that include the size and time constraints that correspond to the plant structure, the process variables accommodate the size and time factors to reduce idle times and equipment under-occupancy.
Fil: Asenjo, Juan Carlos. Universidad de Chile; Chile
Fil: Montagna, Jorge Marcelo. 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
Fil: Vecchietti, Aldo. 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
Fil: Iribarren, Oscar Alberto. 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
Fil: Pinto, Jose M.. Universidade de Sao Paulo; Brasil
Materia
PROCESS VARIABLES
PROTEIN PRODUCTION PLANT
SIMULTANEOUS OPTIMIZATION
STRUCTURE
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:11336/98759
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Strategies for the simultaneous optimization of the structure and the process variables of a protein production plantAsenjo, Juan CarlosMontagna, Jorge MarceloVecchietti, AldoIribarren, Oscar AlbertoPinto, Jose M.PROCESS VARIABLESPROTEIN PRODUCTION PLANTSIMULTANEOUS OPTIMIZATIONSTRUCTUREhttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2Process performance models for a multiproduct batch protein plant are used to exploit alternative strategies in the optimization of both the process variables and the structure of the plant. Simple process performance models are used to describe the unit operations, which renders explicit expressions for the size and time factor model in the design of batch plants. In the proposed approach the process variables are optimized regardless the plant structure constraints, which are left as a posterior decision. This optimization is done in a single product-free intermediate storage (SP-FIS) scenario, unbiased with any plant structure. The approach is compared to the case of recipe values for the process variables and to the best optimal solution for the nonconvex mixed integer nonlinear program (MINLP), which arises when simultaneously optimizing the structure and the process variables. This last optimization model is hard to solve and its global solution remains as an open problem. The proposed approach generates solutions very close to the ones obtained from nonconvex MINLP and is quite superior than simply resorting to recipes. We also study the role of process variables in this approach. It is found that they behave as in continuous processes by trading off cost components, with a smooth dependence on the overall cost. Moreover, for feasible designs that include the size and time constraints that correspond to the plant structure, the process variables accommodate the size and time factors to reduce idle times and equipment under-occupancy.Fil: Asenjo, Juan Carlos. Universidad de Chile; ChileFil: Montagna, Jorge Marcelo. 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; ArgentinaFil: Vecchietti, Aldo. 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; ArgentinaFil: Iribarren, Oscar Alberto. 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; ArgentinaFil: Pinto, Jose M.. Universidade de Sao Paulo; BrasilPergamon-Elsevier Science Ltd2000-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/98759Asenjo, Juan Carlos; Montagna, Jorge Marcelo; Vecchietti, Aldo; Iribarren, Oscar Alberto; Pinto, Jose M.; Strategies for the simultaneous optimization of the structure and the process variables of a protein production plant; Pergamon-Elsevier Science Ltd; Computers and Chemical Engineering; 24; 9-10; 10-2000; 2277-22900098-1354CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/S0098-1354(00)00572-Xinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:59:52Zoai:ri.conicet.gov.ar:11336/98759instacron: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:34982025-09-03 09:59:53.298CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Strategies for the simultaneous optimization of the structure and the process variables of a protein production plant
title Strategies for the simultaneous optimization of the structure and the process variables of a protein production plant
spellingShingle Strategies for the simultaneous optimization of the structure and the process variables of a protein production plant
Asenjo, Juan Carlos
PROCESS VARIABLES
PROTEIN PRODUCTION PLANT
SIMULTANEOUS OPTIMIZATION
STRUCTURE
title_short Strategies for the simultaneous optimization of the structure and the process variables of a protein production plant
title_full Strategies for the simultaneous optimization of the structure and the process variables of a protein production plant
title_fullStr Strategies for the simultaneous optimization of the structure and the process variables of a protein production plant
title_full_unstemmed Strategies for the simultaneous optimization of the structure and the process variables of a protein production plant
title_sort Strategies for the simultaneous optimization of the structure and the process variables of a protein production plant
dc.creator.none.fl_str_mv Asenjo, Juan Carlos
Montagna, Jorge Marcelo
Vecchietti, Aldo
Iribarren, Oscar Alberto
Pinto, Jose M.
author Asenjo, Juan Carlos
author_facet Asenjo, Juan Carlos
Montagna, Jorge Marcelo
Vecchietti, Aldo
Iribarren, Oscar Alberto
Pinto, Jose M.
author_role author
author2 Montagna, Jorge Marcelo
Vecchietti, Aldo
Iribarren, Oscar Alberto
Pinto, Jose M.
author2_role author
author
author
author
dc.subject.none.fl_str_mv PROCESS VARIABLES
PROTEIN PRODUCTION PLANT
SIMULTANEOUS OPTIMIZATION
STRUCTURE
topic PROCESS VARIABLES
PROTEIN PRODUCTION PLANT
SIMULTANEOUS OPTIMIZATION
STRUCTURE
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Process performance models for a multiproduct batch protein plant are used to exploit alternative strategies in the optimization of both the process variables and the structure of the plant. Simple process performance models are used to describe the unit operations, which renders explicit expressions for the size and time factor model in the design of batch plants. In the proposed approach the process variables are optimized regardless the plant structure constraints, which are left as a posterior decision. This optimization is done in a single product-free intermediate storage (SP-FIS) scenario, unbiased with any plant structure. The approach is compared to the case of recipe values for the process variables and to the best optimal solution for the nonconvex mixed integer nonlinear program (MINLP), which arises when simultaneously optimizing the structure and the process variables. This last optimization model is hard to solve and its global solution remains as an open problem. The proposed approach generates solutions very close to the ones obtained from nonconvex MINLP and is quite superior than simply resorting to recipes. We also study the role of process variables in this approach. It is found that they behave as in continuous processes by trading off cost components, with a smooth dependence on the overall cost. Moreover, for feasible designs that include the size and time constraints that correspond to the plant structure, the process variables accommodate the size and time factors to reduce idle times and equipment under-occupancy.
Fil: Asenjo, Juan Carlos. Universidad de Chile; Chile
Fil: Montagna, Jorge Marcelo. 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
Fil: Vecchietti, Aldo. 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
Fil: Iribarren, Oscar Alberto. 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
Fil: Pinto, Jose M.. Universidade de Sao Paulo; Brasil
description Process performance models for a multiproduct batch protein plant are used to exploit alternative strategies in the optimization of both the process variables and the structure of the plant. Simple process performance models are used to describe the unit operations, which renders explicit expressions for the size and time factor model in the design of batch plants. In the proposed approach the process variables are optimized regardless the plant structure constraints, which are left as a posterior decision. This optimization is done in a single product-free intermediate storage (SP-FIS) scenario, unbiased with any plant structure. The approach is compared to the case of recipe values for the process variables and to the best optimal solution for the nonconvex mixed integer nonlinear program (MINLP), which arises when simultaneously optimizing the structure and the process variables. This last optimization model is hard to solve and its global solution remains as an open problem. The proposed approach generates solutions very close to the ones obtained from nonconvex MINLP and is quite superior than simply resorting to recipes. We also study the role of process variables in this approach. It is found that they behave as in continuous processes by trading off cost components, with a smooth dependence on the overall cost. Moreover, for feasible designs that include the size and time constraints that correspond to the plant structure, the process variables accommodate the size and time factors to reduce idle times and equipment under-occupancy.
publishDate 2000
dc.date.none.fl_str_mv 2000-10
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/11336/98759
Asenjo, Juan Carlos; Montagna, Jorge Marcelo; Vecchietti, Aldo; Iribarren, Oscar Alberto; Pinto, Jose M.; Strategies for the simultaneous optimization of the structure and the process variables of a protein production plant; Pergamon-Elsevier Science Ltd; Computers and Chemical Engineering; 24; 9-10; 10-2000; 2277-2290
0098-1354
CONICET Digital
CONICET
url http://hdl.handle.net/11336/98759
identifier_str_mv Asenjo, Juan Carlos; Montagna, Jorge Marcelo; Vecchietti, Aldo; Iribarren, Oscar Alberto; Pinto, Jose M.; Strategies for the simultaneous optimization of the structure and the process variables of a protein production plant; Pergamon-Elsevier Science Ltd; Computers and Chemical Engineering; 24; 9-10; 10-2000; 2277-2290
0098-1354
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/S0098-1354(00)00572-X
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 Pergamon-Elsevier Science Ltd
publisher.none.fl_str_mv Pergamon-Elsevier Science Ltd
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 13.13397

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