Model Adaptation for Real-Time Optimization in Energy Systems
- Autores
- Serralunga, Fernán José; Mussati, Miguel Ceferino; Aguirre, Pio Antonio
- Año de publicación
- 2013
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Real-time optimization (RTO) is widely used in industry to operate processes close to their maximum performance. The models used for RTO need to be adapted using real-time data to ensure feasibility of the model optimal inputs and convergence to the real plant optimal point. Heat and power systems are suitable for being optimized in real-time because of their fast dynamics and the benefits achievable by reacting to changes in power prices and steam demand. This work proposes a modifier adaptation strategy that exploits the structure of certain problems to make the adaptation faster and more reliable, which is proven to be particularly useful for heat and power systems. The adaptation is performed in the equations that predict efficiencies or performance of unit operations. By identifying the variables that modify each performance factor, the number of data sets needed for gradient correction is reduced. This makes the proposed strategy suitable for real-time optimization of processes with a large number of inputs. Two alternatives are proposed to implement the approach: gradient calculation by finite differences and quadratic regression using current and past data. The features and behavior of this approach are shown through two case studies: (i) a simple model with three processes, and (ii) a heat and power system of a sugar and ethanol plant. A comparison with other existent approaches shows a better performance in terms of operating cost and sensitivity to noise.
Fil: Serralunga, Fernán José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - CONICET - Santa Fe. Instituto de Desarrollo y Diseño (i); Argentina;
Fil: Mussati, Miguel Ceferino. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - CONICET - Santa Fe. Instituto de Desarrollo y Diseño (i); Argentina;
Fil: Aguirre, Pio Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - CONICET - Santa Fe. Instituto de Desarrollo y Diseño (i); Argentina; - Materia
-
Real Time Optimization
Model Adaptation
Energy Systems - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/1987
Ver los metadatos del registro completo
id |
CONICETDig_d7587936ea5f6ec956983eefbd694269 |
---|---|
oai_identifier_str |
oai:ri.conicet.gov.ar:11336/1987 |
network_acronym_str |
CONICETDig |
repository_id_str |
3498 |
network_name_str |
CONICET Digital (CONICET) |
spelling |
Model Adaptation for Real-Time Optimization in Energy SystemsSerralunga, Fernán JoséMussati, Miguel CeferinoAguirre, Pio AntonioReal Time OptimizationModel AdaptationEnergy Systemshttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2https://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2Real-time optimization (RTO) is widely used in industry to operate processes close to their maximum performance. The models used for RTO need to be adapted using real-time data to ensure feasibility of the model optimal inputs and convergence to the real plant optimal point. Heat and power systems are suitable for being optimized in real-time because of their fast dynamics and the benefits achievable by reacting to changes in power prices and steam demand. This work proposes a modifier adaptation strategy that exploits the structure of certain problems to make the adaptation faster and more reliable, which is proven to be particularly useful for heat and power systems. The adaptation is performed in the equations that predict efficiencies or performance of unit operations. By identifying the variables that modify each performance factor, the number of data sets needed for gradient correction is reduced. This makes the proposed strategy suitable for real-time optimization of processes with a large number of inputs. Two alternatives are proposed to implement the approach: gradient calculation by finite differences and quadratic regression using current and past data. The features and behavior of this approach are shown through two case studies: (i) a simple model with three processes, and (ii) a heat and power system of a sugar and ethanol plant. A comparison with other existent approaches shows a better performance in terms of operating cost and sensitivity to noise.Fil: Serralunga, Fernán José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - CONICET - Santa Fe. Instituto de Desarrollo y Diseño (i); Argentina;Fil: Mussati, Miguel Ceferino. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - CONICET - Santa Fe. Instituto de Desarrollo y Diseño (i); Argentina;Fil: Aguirre, Pio Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - CONICET - Santa Fe. Instituto de Desarrollo y Diseño (i); Argentina;American Chemical Society2013-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/1987Serralunga, Fernán José; Mussati, Miguel Ceferino; Aguirre, Pio Antonio; Model Adaptation for Real-Time Optimization in Energy Systems; American Chemical Society; Industrial & Engineering Chemical Research; 52; 47; 11-2013; 16795-168100888-5885http://dx.doi.org/DOI:10.1021/ie303621jenginfo:eu-repo/semantics/altIdentifier/url/http://pubs.acs.org/doi/abs/10.1021/ie303621jinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:49:49Zoai:ri.conicet.gov.ar:11336/1987instacron: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 09:49:50.1CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Model Adaptation for Real-Time Optimization in Energy Systems |
title |
Model Adaptation for Real-Time Optimization in Energy Systems |
spellingShingle |
Model Adaptation for Real-Time Optimization in Energy Systems Serralunga, Fernán José Real Time Optimization Model Adaptation Energy Systems |
title_short |
Model Adaptation for Real-Time Optimization in Energy Systems |
title_full |
Model Adaptation for Real-Time Optimization in Energy Systems |
title_fullStr |
Model Adaptation for Real-Time Optimization in Energy Systems |
title_full_unstemmed |
Model Adaptation for Real-Time Optimization in Energy Systems |
title_sort |
Model Adaptation for Real-Time Optimization in Energy Systems |
dc.creator.none.fl_str_mv |
Serralunga, Fernán José Mussati, Miguel Ceferino Aguirre, Pio Antonio |
author |
Serralunga, Fernán José |
author_facet |
Serralunga, Fernán José Mussati, Miguel Ceferino Aguirre, Pio Antonio |
author_role |
author |
author2 |
Mussati, Miguel Ceferino Aguirre, Pio Antonio |
author2_role |
author author |
dc.subject.none.fl_str_mv |
Real Time Optimization Model Adaptation Energy Systems |
topic |
Real Time Optimization Model Adaptation Energy Systems |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/2.4 https://purl.org/becyt/ford/2 https://purl.org/becyt/ford/2.4 https://purl.org/becyt/ford/2 |
dc.description.none.fl_txt_mv |
Real-time optimization (RTO) is widely used in industry to operate processes close to their maximum performance. The models used for RTO need to be adapted using real-time data to ensure feasibility of the model optimal inputs and convergence to the real plant optimal point. Heat and power systems are suitable for being optimized in real-time because of their fast dynamics and the benefits achievable by reacting to changes in power prices and steam demand. This work proposes a modifier adaptation strategy that exploits the structure of certain problems to make the adaptation faster and more reliable, which is proven to be particularly useful for heat and power systems. The adaptation is performed in the equations that predict efficiencies or performance of unit operations. By identifying the variables that modify each performance factor, the number of data sets needed for gradient correction is reduced. This makes the proposed strategy suitable for real-time optimization of processes with a large number of inputs. Two alternatives are proposed to implement the approach: gradient calculation by finite differences and quadratic regression using current and past data. The features and behavior of this approach are shown through two case studies: (i) a simple model with three processes, and (ii) a heat and power system of a sugar and ethanol plant. A comparison with other existent approaches shows a better performance in terms of operating cost and sensitivity to noise. Fil: Serralunga, Fernán José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - CONICET - Santa Fe. Instituto de Desarrollo y Diseño (i); Argentina; Fil: Mussati, Miguel Ceferino. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - CONICET - Santa Fe. Instituto de Desarrollo y Diseño (i); Argentina; Fil: Aguirre, Pio Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - CONICET - Santa Fe. Instituto de Desarrollo y Diseño (i); Argentina; |
description |
Real-time optimization (RTO) is widely used in industry to operate processes close to their maximum performance. The models used for RTO need to be adapted using real-time data to ensure feasibility of the model optimal inputs and convergence to the real plant optimal point. Heat and power systems are suitable for being optimized in real-time because of their fast dynamics and the benefits achievable by reacting to changes in power prices and steam demand. This work proposes a modifier adaptation strategy that exploits the structure of certain problems to make the adaptation faster and more reliable, which is proven to be particularly useful for heat and power systems. The adaptation is performed in the equations that predict efficiencies or performance of unit operations. By identifying the variables that modify each performance factor, the number of data sets needed for gradient correction is reduced. This makes the proposed strategy suitable for real-time optimization of processes with a large number of inputs. Two alternatives are proposed to implement the approach: gradient calculation by finite differences and quadratic regression using current and past data. The features and behavior of this approach are shown through two case studies: (i) a simple model with three processes, and (ii) a heat and power system of a sugar and ethanol plant. A comparison with other existent approaches shows a better performance in terms of operating cost and sensitivity to noise. |
publishDate |
2013 |
dc.date.none.fl_str_mv |
2013-11 |
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/11336/1987 Serralunga, Fernán José; Mussati, Miguel Ceferino; Aguirre, Pio Antonio; Model Adaptation for Real-Time Optimization in Energy Systems; American Chemical Society; Industrial & Engineering Chemical Research; 52; 47; 11-2013; 16795-16810 0888-5885 http://dx.doi.org/DOI:10.1021/ie303621j |
url |
http://hdl.handle.net/11336/1987 http://dx.doi.org/DOI:10.1021/ie303621j |
identifier_str_mv |
Serralunga, Fernán José; Mussati, Miguel Ceferino; Aguirre, Pio Antonio; Model Adaptation for Real-Time Optimization in Energy Systems; American Chemical Society; Industrial & Engineering Chemical Research; 52; 47; 11-2013; 16795-16810 0888-5885 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/http://pubs.acs.org/doi/abs/10.1021/ie303621j |
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 |
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 |
_version_ |
1842268997079269376 |
score |
13.13397 |