Robust optimization framework for process parameter and tolerance design

Detalhes bibliográficos
Autor(a) principal: Bernardo, Fernando P.
Data de Publicação: 1998
Outros Autores: Saraiva, Pedro M.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10316/8195
https://doi.org/10.1002/aic.690440908
Resumo: This article introduces a framework for including different uncertainties at the chemical plant design stage. Through an integrated robust optimization approach and problem formulation, equipment, operating, control, and quality costs are simultaneously taken into account, leading to system, parameter, and tolerance design. Rather than using single pointwise solutions in the decision space, operating windows leading to overall best performance are identified and defined. Such windows and their width allow us to point out control needs and goals at a very early stage of plant design. Two small-scale case studies (for a CSTR and a batch distillation column) provide enough evidence to support the practicality of the optimization framework: the robust solutions found are different and much better than the corresponding solutions obtained with the fully deterministic optimization paradigms.
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spelling Robust optimization framework for process parameter and tolerance designThis article introduces a framework for including different uncertainties at the chemical plant design stage. Through an integrated robust optimization approach and problem formulation, equipment, operating, control, and quality costs are simultaneously taken into account, leading to system, parameter, and tolerance design. Rather than using single pointwise solutions in the decision space, operating windows leading to overall best performance are identified and defined. Such windows and their width allow us to point out control needs and goals at a very early stage of plant design. Two small-scale case studies (for a CSTR and a batch distillation column) provide enough evidence to support the practicality of the optimization framework: the robust solutions found are different and much better than the corresponding solutions obtained with the fully deterministic optimization paradigms.1998info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/8195http://hdl.handle.net/10316/8195https://doi.org/10.1002/aic.690440908engAIChE Journal. 44:9 (1998) 2007-2017Bernardo, Fernando P.Saraiva, Pedro M.info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2020-03-30T10:39:42Zoai:estudogeral.uc.pt:10316/8195Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:59:17.857527Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Robust optimization framework for process parameter and tolerance design
title Robust optimization framework for process parameter and tolerance design
spellingShingle Robust optimization framework for process parameter and tolerance design
Bernardo, Fernando P.
title_short Robust optimization framework for process parameter and tolerance design
title_full Robust optimization framework for process parameter and tolerance design
title_fullStr Robust optimization framework for process parameter and tolerance design
title_full_unstemmed Robust optimization framework for process parameter and tolerance design
title_sort Robust optimization framework for process parameter and tolerance design
author Bernardo, Fernando P.
author_facet Bernardo, Fernando P.
Saraiva, Pedro M.
author_role author
author2 Saraiva, Pedro M.
author2_role author
dc.contributor.author.fl_str_mv Bernardo, Fernando P.
Saraiva, Pedro M.
description This article introduces a framework for including different uncertainties at the chemical plant design stage. Through an integrated robust optimization approach and problem formulation, equipment, operating, control, and quality costs are simultaneously taken into account, leading to system, parameter, and tolerance design. Rather than using single pointwise solutions in the decision space, operating windows leading to overall best performance are identified and defined. Such windows and their width allow us to point out control needs and goals at a very early stage of plant design. Two small-scale case studies (for a CSTR and a batch distillation column) provide enough evidence to support the practicality of the optimization framework: the robust solutions found are different and much better than the corresponding solutions obtained with the fully deterministic optimization paradigms.
publishDate 1998
dc.date.none.fl_str_mv 1998
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dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.uri.fl_str_mv http://hdl.handle.net/10316/8195
http://hdl.handle.net/10316/8195
https://doi.org/10.1002/aic.690440908
url http://hdl.handle.net/10316/8195
https://doi.org/10.1002/aic.690440908
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv AIChE Journal. 44:9 (1998) 2007-2017
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