Implementation of RTO in a large hydrogen network considering uncertainty

Producción Científica

Detalles Bibliográficos
Autores: Galán, Aníbal, Prada Moraga, César de, Gutiérrez Rodríguez, Gloria, Sarabia, Daniel, Grossmann, Ignacio, González, Rafael
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2019
País:España
Institución:Universidad de Valladolid
Repositorio:UVaDOC. Repositorio Documental de la Universidad de Valladolid
OAI Identifier:oai:uvadoc.uva.es:10324/65041
Acceso en línea:https://doi.org/10.1007/s11081-019-09444-3
https://uvadoc.uva.es/handle/10324/65041
Access Level:acceso abierto
Palabra clave:Process optimization
Hydrogen networks
Real-time optimization
Two-stage stochastic programming
CVaR
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spelling Implementation of RTO in a large hydrogen network considering uncertaintyGalán, AníbalPrada Moraga, César deGutiérrez Rodríguez, GloriaSarabia, DanielGrossmann, IgnacioGonzález, RafaelProcess optimizationHydrogen networksReal-time optimizationTwo-stage stochastic programmingCVaRProducción CientíficaThis paper describes the problems associated with the implementation of a real-time optimization (RTO) decision support tool, for the operation of a large scale hydrogen network of an oil refinery. In addition, a formulation which takes into account the stochastic uncertainty of hydrogen demand, due to hydrocarbons quality change, is described and further studied, focusing on its utility in the decision-making process of operators. An integrated robust data reconciliation, and economic optimization, considering plant-wide uncertain parameters is presented and discussed. Moreover, stochastic uncertainty in hydrogen demand is assessed for its inclusion within the RTO framework. A novel approach of the decisions stages at hydrogen producers and consumers is proposed, which supports the formulation of the problem as a two-stage stochastic non-linear program. Representative results are presented and discussed, aimed at assessing the potential impact in the hydrogen management policies. For this purpose, the value of the stochastic solution, perfect information, and expectation of the expected value are analyzed. Complementarily, a risk-averse formulation is presented (value-at-risk and conditional-value-at-risk) and its results compared against the formulation without risk considerations. Finally, some attention is given to future directions of this decision support tool, based on these work contributions, including the importance of the decision makers’ participation in the analysis of the potential impact of risk-averse results.Este trabajo forma parte del proyecto de investigación CYCIT: Integrated plant wide control and optimization for Industry4.0 (InCO4IN)Springer2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://doi.org/10.1007/s11081-019-09444-3https://uvadoc.uva.es/handle/10324/65041reponame:UVaDOC. Repositorio Documental de la Universidad de Valladolidinstname:Universidad de ValladolidIngléshttps://link.springer.com/article/10.1007/s11081-019-09444-3info:eu-repo/semantics/openAccessoai:uvadoc.uva.es:10324/650412026-06-13T12:44:47Z
dc.title.none.fl_str_mv Implementation of RTO in a large hydrogen network considering uncertainty
title Implementation of RTO in a large hydrogen network considering uncertainty
spellingShingle Implementation of RTO in a large hydrogen network considering uncertainty
Galán, Aníbal
Process optimization
Hydrogen networks
Real-time optimization
Two-stage stochastic programming
CVaR
title_short Implementation of RTO in a large hydrogen network considering uncertainty
title_full Implementation of RTO in a large hydrogen network considering uncertainty
title_fullStr Implementation of RTO in a large hydrogen network considering uncertainty
title_full_unstemmed Implementation of RTO in a large hydrogen network considering uncertainty
title_sort Implementation of RTO in a large hydrogen network considering uncertainty
dc.creator.none.fl_str_mv Galán, Aníbal
Prada Moraga, César de
Gutiérrez Rodríguez, Gloria
Sarabia, Daniel
Grossmann, Ignacio
González, Rafael
author Galán, Aníbal
author_facet Galán, Aníbal
Prada Moraga, César de
Gutiérrez Rodríguez, Gloria
Sarabia, Daniel
Grossmann, Ignacio
González, Rafael
author_role author
author2 Prada Moraga, César de
Gutiérrez Rodríguez, Gloria
Sarabia, Daniel
Grossmann, Ignacio
González, Rafael
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Process optimization
Hydrogen networks
Real-time optimization
Two-stage stochastic programming
CVaR
topic Process optimization
Hydrogen networks
Real-time optimization
Two-stage stochastic programming
CVaR
description Producción Científica
publishDate 2019
dc.date.none.fl_str_mv 2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.1007/s11081-019-09444-3
https://uvadoc.uva.es/handle/10324/65041
url https://doi.org/10.1007/s11081-019-09444-3
https://uvadoc.uva.es/handle/10324/65041
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://link.springer.com/article/10.1007/s11081-019-09444-3
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:UVaDOC. Repositorio Documental de la Universidad de Valladolid
instname:Universidad de Valladolid
instname_str Universidad de Valladolid
reponame_str UVaDOC. Repositorio Documental de la Universidad de Valladolid
collection UVaDOC. Repositorio Documental de la Universidad de Valladolid
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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