A simultaneous optimization approach for off-line blending and scheduling of oil-refinery operations

Autores
Mendez, Carlos Alberto; Grossmann, Ignacio E.; Harjunkoski, I.; Kaboré, P.
Año de publicación
2006
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This paper presents a novel MILP-based method that addresses the simultaneous optimization of the off-line blending and the short-term scheduling problem in oil-refinery applications. Depending on the problem characteristics as well as the required flexibility in the solution, the model can be based on either a discrete or a continuous time domain representation. In order to preserve the model’s linearity, an iterative procedure is proposed to effectively deal with non-linear gasoline properties and variable recipes for different product grades. Thus, the solution of a very complex MINLP formulation is replaced by a sequential MILP approximation. Instead of predefining fixed component concentrations for products, preferred blend recipes can be forced to apply whenever it is possible. Also, different alternatives for coping with infeasible problems are presented. Sufficient conditions for convergence for the proposed approach are presented as well as a comparison with NLP and MINLP solvers to demonstrate that the method provides an effective integrated solution method for the blending and scheduling of large-scale problems. The new method is illustrated with several real world problems requiring very low computational requirements.
Fil: Mendez, Carlos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
Fil: Grossmann, Ignacio E.. University Of Carnegie Mellon; Estados Unidos
Fil: Harjunkoski, I.. ABB Corporate Research Center; Alemania
Fil: Kaboré, P.. ABB Corporate Research Center; Alemania
Materia
Scheduling And Planning;
Blending
Refinery Operations
Mixed-Integer Programming
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:11336/21457
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/21457
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling A simultaneous optimization approach for off-line blending and scheduling of oil-refinery operationsMendez, Carlos AlbertoGrossmann, Ignacio E.Harjunkoski, I.Kaboré, P.Scheduling And Planning;BlendingRefinery OperationsMixed-Integer Programminghttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2This paper presents a novel MILP-based method that addresses the simultaneous optimization of the off-line blending and the short-term scheduling problem in oil-refinery applications. Depending on the problem characteristics as well as the required flexibility in the solution, the model can be based on either a discrete or a continuous time domain representation. In order to preserve the model’s linearity, an iterative procedure is proposed to effectively deal with non-linear gasoline properties and variable recipes for different product grades. Thus, the solution of a very complex MINLP formulation is replaced by a sequential MILP approximation. Instead of predefining fixed component concentrations for products, preferred blend recipes can be forced to apply whenever it is possible. Also, different alternatives for coping with infeasible problems are presented. Sufficient conditions for convergence for the proposed approach are presented as well as a comparison with NLP and MINLP solvers to demonstrate that the method provides an effective integrated solution method for the blending and scheduling of large-scale problems. The new method is illustrated with several real world problems requiring very low computational requirements.Fil: Mendez, Carlos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Grossmann, Ignacio E.. University Of Carnegie Mellon; Estados UnidosFil: Harjunkoski, I.. ABB Corporate Research Center; AlemaniaFil: Kaboré, P.. ABB Corporate Research Center; AlemaniaElsevier2006-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/21457Mendez, Carlos Alberto; Grossmann, Ignacio E.; Harjunkoski, I.; Kaboré, P.; A simultaneous optimization approach for off-line blending and scheduling of oil-refinery operations; Elsevier; Computers and Chemical Engineering; 30; 4; 12-2006; 614-6340098-1354CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.compchemeng.2005.11.004info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0098135405002966?via%3Dihubinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:50:03Zoai:ri.conicet.gov.ar:11336/21457instacron: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 09:50:04.266CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A simultaneous optimization approach for off-line blending and scheduling of oil-refinery operations
title A simultaneous optimization approach for off-line blending and scheduling of oil-refinery operations
spellingShingle A simultaneous optimization approach for off-line blending and scheduling of oil-refinery operations
Mendez, Carlos Alberto
Scheduling And Planning;
Blending
Refinery Operations
Mixed-Integer Programming
title_short A simultaneous optimization approach for off-line blending and scheduling of oil-refinery operations
title_full A simultaneous optimization approach for off-line blending and scheduling of oil-refinery operations
title_fullStr A simultaneous optimization approach for off-line blending and scheduling of oil-refinery operations
title_full_unstemmed A simultaneous optimization approach for off-line blending and scheduling of oil-refinery operations
title_sort A simultaneous optimization approach for off-line blending and scheduling of oil-refinery operations
dc.creator.none.fl_str_mv Mendez, Carlos Alberto
Grossmann, Ignacio E.
Harjunkoski, I.
Kaboré, P.
author Mendez, Carlos Alberto
author_facet Mendez, Carlos Alberto
Grossmann, Ignacio E.
Harjunkoski, I.
Kaboré, P.
author_role author
author2 Grossmann, Ignacio E.
Harjunkoski, I.
Kaboré, P.
author2_role author
author
author
dc.subject.none.fl_str_mv Scheduling And Planning;
Blending
Refinery Operations
Mixed-Integer Programming
topic Scheduling And Planning;
Blending
Refinery Operations
Mixed-Integer Programming
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv This paper presents a novel MILP-based method that addresses the simultaneous optimization of the off-line blending and the short-term scheduling problem in oil-refinery applications. Depending on the problem characteristics as well as the required flexibility in the solution, the model can be based on either a discrete or a continuous time domain representation. In order to preserve the model’s linearity, an iterative procedure is proposed to effectively deal with non-linear gasoline properties and variable recipes for different product grades. Thus, the solution of a very complex MINLP formulation is replaced by a sequential MILP approximation. Instead of predefining fixed component concentrations for products, preferred blend recipes can be forced to apply whenever it is possible. Also, different alternatives for coping with infeasible problems are presented. Sufficient conditions for convergence for the proposed approach are presented as well as a comparison with NLP and MINLP solvers to demonstrate that the method provides an effective integrated solution method for the blending and scheduling of large-scale problems. The new method is illustrated with several real world problems requiring very low computational requirements.
Fil: Mendez, Carlos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
Fil: Grossmann, Ignacio E.. University Of Carnegie Mellon; Estados Unidos
Fil: Harjunkoski, I.. ABB Corporate Research Center; Alemania
Fil: Kaboré, P.. ABB Corporate Research Center; Alemania
description This paper presents a novel MILP-based method that addresses the simultaneous optimization of the off-line blending and the short-term scheduling problem in oil-refinery applications. Depending on the problem characteristics as well as the required flexibility in the solution, the model can be based on either a discrete or a continuous time domain representation. In order to preserve the model’s linearity, an iterative procedure is proposed to effectively deal with non-linear gasoline properties and variable recipes for different product grades. Thus, the solution of a very complex MINLP formulation is replaced by a sequential MILP approximation. Instead of predefining fixed component concentrations for products, preferred blend recipes can be forced to apply whenever it is possible. Also, different alternatives for coping with infeasible problems are presented. Sufficient conditions for convergence for the proposed approach are presented as well as a comparison with NLP and MINLP solvers to demonstrate that the method provides an effective integrated solution method for the blending and scheduling of large-scale problems. The new method is illustrated with several real world problems requiring very low computational requirements.
publishDate 2006
dc.date.none.fl_str_mv 2006-12
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/11336/21457
Mendez, Carlos Alberto; Grossmann, Ignacio E.; Harjunkoski, I.; Kaboré, P.; A simultaneous optimization approach for off-line blending and scheduling of oil-refinery operations; Elsevier; Computers and Chemical Engineering; 30; 4; 12-2006; 614-634
0098-1354
CONICET Digital
CONICET
url http://hdl.handle.net/11336/21457
identifier_str_mv Mendez, Carlos Alberto; Grossmann, Ignacio E.; Harjunkoski, I.; Kaboré, P.; A simultaneous optimization approach for off-line blending and scheduling of oil-refinery operations; Elsevier; Computers and Chemical Engineering; 30; 4; 12-2006; 614-634
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/j.compchemeng.2005.11.004
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0098135405002966?via%3Dihub
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 Elsevier
publisher.none.fl_str_mv Elsevier
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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