Optimization Approaches for Efficient Crude Blending in Large Oil Refineries

Autores
Cerda, Jaime; Pautasso, Pedro Carlos; Cafaro, Diego Carlos
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
To increase profit margin, refiners usually upgrade low cost crude oils by mixing them with light crudes to obtain blends of higher value. In recent years, this trend is favored by a shifting in the market demand from gasoline toward diesel fuels that makes it more attractive to process crude blends with higher diesel yields. Using in-line blending stations, feedstocks for crude distillation units (CDUs) with the desired properties are obtained by mixing flows of different types of crude oils using the right blending recipe. In large oil refineries, several CDUs are available to process a wide variety of crude oils stored in many dedicated tanks. The scheduler must not only select the cluster of tanks allocated to each CDU but also determine the scheduling of the blending operations providing the best qualified feedstocks for every distillation unit. Trace element compositions and the temperature boiling point (TBP) curve are the properties normally controlled to set the feedstock quality. In this work, two alternative approaches are proposed to solve this challenging scheduling problem: (a) an exact mixed-integer nonlinear (MINLP) formulation that simultaneously considers tank allocation and operations scheduling decisions; (b) an efficient sequential approach based on a pair of MINLP subproblems making the tank allocation at the upper level and the scheduling decisions at the lower one. After validation, the sequential approach is successfully applied to new nine case studies involving up to four CDUs, 60 charging tanks, and 14 types of crude oil.
Fil: Cerda, Jaime. 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: Pautasso, Pedro Carlos. 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: Cafaro, Diego Carlos. 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
Materia
Crude Oil
Blending
Optimization
TBP Curve
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:11336/86892
Acceder
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spelling Optimization Approaches for Efficient Crude Blending in Large Oil RefineriesCerda, JaimePautasso, Pedro CarlosCafaro, Diego CarlosCrude OilBlendingOptimizationTBP Curvehttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2To increase profit margin, refiners usually upgrade low cost crude oils by mixing them with light crudes to obtain blends of higher value. In recent years, this trend is favored by a shifting in the market demand from gasoline toward diesel fuels that makes it more attractive to process crude blends with higher diesel yields. Using in-line blending stations, feedstocks for crude distillation units (CDUs) with the desired properties are obtained by mixing flows of different types of crude oils using the right blending recipe. In large oil refineries, several CDUs are available to process a wide variety of crude oils stored in many dedicated tanks. The scheduler must not only select the cluster of tanks allocated to each CDU but also determine the scheduling of the blending operations providing the best qualified feedstocks for every distillation unit. Trace element compositions and the temperature boiling point (TBP) curve are the properties normally controlled to set the feedstock quality. In this work, two alternative approaches are proposed to solve this challenging scheduling problem: (a) an exact mixed-integer nonlinear (MINLP) formulation that simultaneously considers tank allocation and operations scheduling decisions; (b) an efficient sequential approach based on a pair of MINLP subproblems making the tank allocation at the upper level and the scheduling decisions at the lower one. After validation, the sequential approach is successfully applied to new nine case studies involving up to four CDUs, 60 charging tanks, and 14 types of crude oil.Fil: Cerda, Jaime. 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: Pautasso, Pedro Carlos. 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: Cafaro, Diego Carlos. 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; ArgentinaAmerican Chemical Society2018-06info: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/11336/86892Cerda, Jaime; Pautasso, Pedro Carlos; Cafaro, Diego Carlos; Optimization Approaches for Efficient Crude Blending in Large Oil Refineries; American Chemical Society; Industrial & Engineering Chemical Research; 57; 25; 6-2018; 8484-85010888-5885CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.8b01008info:eu-repo/semantics/altIdentifier/doi/10.1021/acs.iecr.8b01008info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:51:22Zoai:ri.conicet.gov.ar:11336/86892instacron: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 09:51:23.251CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Optimization Approaches for Efficient Crude Blending in Large Oil Refineries
title Optimization Approaches for Efficient Crude Blending in Large Oil Refineries
spellingShingle Optimization Approaches for Efficient Crude Blending in Large Oil Refineries
Cerda, Jaime
Crude Oil
Blending
Optimization
TBP Curve
title_short Optimization Approaches for Efficient Crude Blending in Large Oil Refineries
title_full Optimization Approaches for Efficient Crude Blending in Large Oil Refineries
title_fullStr Optimization Approaches for Efficient Crude Blending in Large Oil Refineries
title_full_unstemmed Optimization Approaches for Efficient Crude Blending in Large Oil Refineries
title_sort Optimization Approaches for Efficient Crude Blending in Large Oil Refineries
dc.creator.none.fl_str_mv Cerda, Jaime
Pautasso, Pedro Carlos
Cafaro, Diego Carlos
author Cerda, Jaime
author_facet Cerda, Jaime
Pautasso, Pedro Carlos
Cafaro, Diego Carlos
author_role author
author2 Pautasso, Pedro Carlos
Cafaro, Diego Carlos
author2_role author
author
dc.subject.none.fl_str_mv Crude Oil
Blending
Optimization
TBP Curve
topic Crude Oil
Blending
Optimization
TBP Curve
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv To increase profit margin, refiners usually upgrade low cost crude oils by mixing them with light crudes to obtain blends of higher value. In recent years, this trend is favored by a shifting in the market demand from gasoline toward diesel fuels that makes it more attractive to process crude blends with higher diesel yields. Using in-line blending stations, feedstocks for crude distillation units (CDUs) with the desired properties are obtained by mixing flows of different types of crude oils using the right blending recipe. In large oil refineries, several CDUs are available to process a wide variety of crude oils stored in many dedicated tanks. The scheduler must not only select the cluster of tanks allocated to each CDU but also determine the scheduling of the blending operations providing the best qualified feedstocks for every distillation unit. Trace element compositions and the temperature boiling point (TBP) curve are the properties normally controlled to set the feedstock quality. In this work, two alternative approaches are proposed to solve this challenging scheduling problem: (a) an exact mixed-integer nonlinear (MINLP) formulation that simultaneously considers tank allocation and operations scheduling decisions; (b) an efficient sequential approach based on a pair of MINLP subproblems making the tank allocation at the upper level and the scheduling decisions at the lower one. After validation, the sequential approach is successfully applied to new nine case studies involving up to four CDUs, 60 charging tanks, and 14 types of crude oil.
Fil: Cerda, Jaime. 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: Pautasso, Pedro Carlos. 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: Cafaro, Diego Carlos. 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
description To increase profit margin, refiners usually upgrade low cost crude oils by mixing them with light crudes to obtain blends of higher value. In recent years, this trend is favored by a shifting in the market demand from gasoline toward diesel fuels that makes it more attractive to process crude blends with higher diesel yields. Using in-line blending stations, feedstocks for crude distillation units (CDUs) with the desired properties are obtained by mixing flows of different types of crude oils using the right blending recipe. In large oil refineries, several CDUs are available to process a wide variety of crude oils stored in many dedicated tanks. The scheduler must not only select the cluster of tanks allocated to each CDU but also determine the scheduling of the blending operations providing the best qualified feedstocks for every distillation unit. Trace element compositions and the temperature boiling point (TBP) curve are the properties normally controlled to set the feedstock quality. In this work, two alternative approaches are proposed to solve this challenging scheduling problem: (a) an exact mixed-integer nonlinear (MINLP) formulation that simultaneously considers tank allocation and operations scheduling decisions; (b) an efficient sequential approach based on a pair of MINLP subproblems making the tank allocation at the upper level and the scheduling decisions at the lower one. After validation, the sequential approach is successfully applied to new nine case studies involving up to four CDUs, 60 charging tanks, and 14 types of crude oil.
publishDate 2018
dc.date.none.fl_str_mv 2018-06
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/11336/86892
Cerda, Jaime; Pautasso, Pedro Carlos; Cafaro, Diego Carlos; Optimization Approaches for Efficient Crude Blending in Large Oil Refineries; American Chemical Society; Industrial & Engineering Chemical Research; 57; 25; 6-2018; 8484-8501
0888-5885
CONICET Digital
CONICET
url http://hdl.handle.net/11336/86892
identifier_str_mv Cerda, Jaime; Pautasso, Pedro Carlos; Cafaro, Diego Carlos; Optimization Approaches for Efficient Crude Blending in Large Oil Refineries; American Chemical Society; Industrial & Engineering Chemical Research; 57; 25; 6-2018; 8484-8501
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/pdf/10.1021/acs.iecr.8b01008
info:eu-repo/semantics/altIdentifier/doi/10.1021/acs.iecr.8b01008
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 13.13397

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