A grey-box Neural Network Composite Model for an Industrial Heating Furnace
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10174/27622 |
Resumo: | Industrial furnaces consume large amounts of energy and their operating points have a major influence on the quality of the final product. Design- ing a tool that analyzes the combustion process, fluid mechanics and heat transfer and assists the energy audit work is then of the most importance. This work proposes a hybrid composite model for such a tool, having, as its base, two white-box models, namely a detailed Computational Fluid Dynamics (CFD) model and a simplified Reduced-Order (RO) model, plus a black-box model developed using Artificial Neural Networks. The preliminary results presented in this paper show that this composite model is able to improve the accuracy of the RO model without having the high computational load of the CFD model. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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A grey-box Neural Network Composite Model for an Industrial Heating Furnacefurnaceneural networkcomposite modelindustrialIndustrial furnaces consume large amounts of energy and their operating points have a major influence on the quality of the final product. Design- ing a tool that analyzes the combustion process, fluid mechanics and heat transfer and assists the energy audit work is then of the most importance. This work proposes a hybrid composite model for such a tool, having, as its base, two white-box models, namely a detailed Computational Fluid Dynamics (CFD) model and a simplified Reduced-Order (RO) model, plus a black-box model developed using Artificial Neural Networks. The preliminary results presented in this paper show that this composite model is able to improve the accuracy of the RO model without having the high computational load of the CFD model.APRP2020-03-02T16:34:54Z2020-03-022019-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/27622http://hdl.handle.net/10174/27622engDaniel Santos, Sérgio Costa, Luís Rato, Teresa Gonçalves, Isabel Malico, Paulo Canhoto, Frederico Alvarez, Miguel Barão, A grey-box Neural Network Composite Model for an Industrial Heating Furnace, 25th Portuguese Conference on Pattern Recognition RECPAD2019, pp 83-85, October, 2019.http://www.di.uevora.pt/~lmr/recpad2019.pdfndndlmr@uevora.pttcg@uevora.ptimbm@uevora.ptcanhoto@uevora.ptndmjsb@uevora.pt498Santos, DanielCosta, SérgioRato, LuísGonçalves, TeresaMalico, IsabelCanhoto, PauloAlvarez, FredericoBarão, Miguelinfo: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:RCAAP2024-01-03T19:23:14Zoai:dspace.uevora.pt:10174/27622Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:17:39.564363Repositório 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 |
A grey-box Neural Network Composite Model for an Industrial Heating Furnace |
title |
A grey-box Neural Network Composite Model for an Industrial Heating Furnace |
spellingShingle |
A grey-box Neural Network Composite Model for an Industrial Heating Furnace Santos, Daniel furnace neural network composite model industrial |
title_short |
A grey-box Neural Network Composite Model for an Industrial Heating Furnace |
title_full |
A grey-box Neural Network Composite Model for an Industrial Heating Furnace |
title_fullStr |
A grey-box Neural Network Composite Model for an Industrial Heating Furnace |
title_full_unstemmed |
A grey-box Neural Network Composite Model for an Industrial Heating Furnace |
title_sort |
A grey-box Neural Network Composite Model for an Industrial Heating Furnace |
author |
Santos, Daniel |
author_facet |
Santos, Daniel Costa, Sérgio Rato, Luís Gonçalves, Teresa Malico, Isabel Canhoto, Paulo Alvarez, Frederico Barão, Miguel |
author_role |
author |
author2 |
Costa, Sérgio Rato, Luís Gonçalves, Teresa Malico, Isabel Canhoto, Paulo Alvarez, Frederico Barão, Miguel |
author2_role |
author author author author author author author |
dc.contributor.author.fl_str_mv |
Santos, Daniel Costa, Sérgio Rato, Luís Gonçalves, Teresa Malico, Isabel Canhoto, Paulo Alvarez, Frederico Barão, Miguel |
dc.subject.por.fl_str_mv |
furnace neural network composite model industrial |
topic |
furnace neural network composite model industrial |
description |
Industrial furnaces consume large amounts of energy and their operating points have a major influence on the quality of the final product. Design- ing a tool that analyzes the combustion process, fluid mechanics and heat transfer and assists the energy audit work is then of the most importance. This work proposes a hybrid composite model for such a tool, having, as its base, two white-box models, namely a detailed Computational Fluid Dynamics (CFD) model and a simplified Reduced-Order (RO) model, plus a black-box model developed using Artificial Neural Networks. The preliminary results presented in this paper show that this composite model is able to improve the accuracy of the RO model without having the high computational load of the CFD model. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-01-01T00:00:00Z 2020-03-02T16:34:54Z 2020-03-02 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10174/27622 http://hdl.handle.net/10174/27622 |
url |
http://hdl.handle.net/10174/27622 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Daniel Santos, Sérgio Costa, Luís Rato, Teresa Gonçalves, Isabel Malico, Paulo Canhoto, Frederico Alvarez, Miguel Barão, A grey-box Neural Network Composite Model for an Industrial Heating Furnace, 25th Portuguese Conference on Pattern Recognition RECPAD2019, pp 83-85, October, 2019. http://www.di.uevora.pt/~lmr/recpad2019.pdf nd nd lmr@uevora.pt tcg@uevora.pt imbm@uevora.pt canhoto@uevora.pt nd mjsb@uevora.pt 498 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
APRP |
publisher.none.fl_str_mv |
APRP |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799136658594988032 |