Modeling of the poultry carcass immersion chilling system using artificial neural networks - DOI: 10.4025/actascitechnol.v31i2.3358

Detalhes bibliográficos
Autor(a) principal: Klassen, Túlio
Data de Publicação: 2009
Outros Autores: Martins, Tiago Dias, Cardozo Filho, Lucio, Silva, Edson Antonio da
Tipo de documento: Artigo
Idioma: por
Título da fonte: Acta scientiarum. Technology (Online)
Texto Completo: http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/3358
Resumo: The process of cooling chicken carcasses by immersing them in mixture of cold water and ice (chillers) is complex. It is very difficult to represent it by a transport phenomenon model. In this work, artificial neural networks were used with an intermediary layer in the description and modeling of the cooling process of chickens. Different architectures of the neural network were tested, altering the numbers of input and hidden units. Data supplied by the Sadia-Toledo Company were used for training and validation of the neural networks. The input variables selected for the model were the following: carcass weight, initial temperature, propylene glycol temperature with external circulation, water flow rate of water in each tank, renewal water cooling time and temperature, and as output variable the temperature of the chicken when exiting the chiller. The results obtained showed that the network with 8 neurons in the input layer and 24 in the hidden layer best represented the investigated system.
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spelling Modeling of the poultry carcass immersion chilling system using artificial neural networks - DOI: 10.4025/actascitechnol.v31i2.3358Modelagem do sistema de resfriamento por imersão de carcaças de frangos utilizando redes neurais artificiais - DOI: 10.4025/actascitechnol.v31i2.3358coolingchillersneural networkschickenresfriamentochillersredes neuraisfrangoProcessos Industriais de Engenharia QuímicaThe process of cooling chicken carcasses by immersing them in mixture of cold water and ice (chillers) is complex. It is very difficult to represent it by a transport phenomenon model. In this work, artificial neural networks were used with an intermediary layer in the description and modeling of the cooling process of chickens. Different architectures of the neural network were tested, altering the numbers of input and hidden units. Data supplied by the Sadia-Toledo Company were used for training and validation of the neural networks. The input variables selected for the model were the following: carcass weight, initial temperature, propylene glycol temperature with external circulation, water flow rate of water in each tank, renewal water cooling time and temperature, and as output variable the temperature of the chicken when exiting the chiller. The results obtained showed that the network with 8 neurons in the input layer and 24 in the hidden layer best represented the investigated system.A modelagem matemática fenomenológica do processo de resfriamento de carcaças de frango em chillers é complexa pela quantidade de variáveis que interferem no processo, além de tratar de um problema que envolve transferência de calor e de massa em regime transeniente. Uma alternativa para modelar este tipo de sistema é o emprego de Redes Neurais Artificiais. Neste trabalho foram investigadas diversas estruturas de redes com uma camada intermediária para modelar o processo de resfriamento de frangos. Foram testadas diferentes arquiteturas alterando os números de neurônios das camadas de entrada e intermediária. Foram utilizados dados coletados na empresa Sadia–Toledo, Estado do Paraná, para treinamento e validação das redes. As variáveis de entrada da rede eram: massa da carcaça, temperatura antes do resfriamento, temperatura da camisa de propilenoglicol, vazão de água em cada módulo, tempo de residência e temperatura da água de renovação; a temperatura do frango na saída do último tanque de resfriamento era a variável de saída. Os resultados obtidos mostraram que as redes representam apropriadamente o processo e que a rede com estrutura 8-24-1 foi a que melhor modelou o sistema investigado.Universidade Estadual De Maringá2009-06-17info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionModelagemapplication/pdfhttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/335810.4025/actascitechnol.v31i2.3358Acta Scientiarum. Technology; Vol 31 No 2 (2009); 201-205Acta Scientiarum. Technology; v. 31 n. 2 (2009); 201-2051806-25631807-8664reponame:Acta scientiarum. Technology (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMporhttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/3358/3358Klassen, TúlioMartins, Tiago DiasCardozo Filho, LucioSilva, Edson Antonio dainfo:eu-repo/semantics/openAccess2024-05-17T13:03:01Zoai:periodicos.uem.br/ojs:article/3358Revistahttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/indexPUBhttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/oai||actatech@uem.br1807-86641806-2563opendoar:2024-05-17T13:03:01Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM)false
dc.title.none.fl_str_mv Modeling of the poultry carcass immersion chilling system using artificial neural networks - DOI: 10.4025/actascitechnol.v31i2.3358
Modelagem do sistema de resfriamento por imersão de carcaças de frangos utilizando redes neurais artificiais - DOI: 10.4025/actascitechnol.v31i2.3358
title Modeling of the poultry carcass immersion chilling system using artificial neural networks - DOI: 10.4025/actascitechnol.v31i2.3358
spellingShingle Modeling of the poultry carcass immersion chilling system using artificial neural networks - DOI: 10.4025/actascitechnol.v31i2.3358
Klassen, Túlio
cooling
chillers
neural networks
chicken
resfriamento
chillers
redes neurais
frango
Processos Industriais de Engenharia Química
title_short Modeling of the poultry carcass immersion chilling system using artificial neural networks - DOI: 10.4025/actascitechnol.v31i2.3358
title_full Modeling of the poultry carcass immersion chilling system using artificial neural networks - DOI: 10.4025/actascitechnol.v31i2.3358
title_fullStr Modeling of the poultry carcass immersion chilling system using artificial neural networks - DOI: 10.4025/actascitechnol.v31i2.3358
title_full_unstemmed Modeling of the poultry carcass immersion chilling system using artificial neural networks - DOI: 10.4025/actascitechnol.v31i2.3358
title_sort Modeling of the poultry carcass immersion chilling system using artificial neural networks - DOI: 10.4025/actascitechnol.v31i2.3358
author Klassen, Túlio
author_facet Klassen, Túlio
Martins, Tiago Dias
Cardozo Filho, Lucio
Silva, Edson Antonio da
author_role author
author2 Martins, Tiago Dias
Cardozo Filho, Lucio
Silva, Edson Antonio da
author2_role author
author
author
dc.contributor.author.fl_str_mv Klassen, Túlio
Martins, Tiago Dias
Cardozo Filho, Lucio
Silva, Edson Antonio da
dc.subject.por.fl_str_mv cooling
chillers
neural networks
chicken
resfriamento
chillers
redes neurais
frango
Processos Industriais de Engenharia Química
topic cooling
chillers
neural networks
chicken
resfriamento
chillers
redes neurais
frango
Processos Industriais de Engenharia Química
description The process of cooling chicken carcasses by immersing them in mixture of cold water and ice (chillers) is complex. It is very difficult to represent it by a transport phenomenon model. In this work, artificial neural networks were used with an intermediary layer in the description and modeling of the cooling process of chickens. Different architectures of the neural network were tested, altering the numbers of input and hidden units. Data supplied by the Sadia-Toledo Company were used for training and validation of the neural networks. The input variables selected for the model were the following: carcass weight, initial temperature, propylene glycol temperature with external circulation, water flow rate of water in each tank, renewal water cooling time and temperature, and as output variable the temperature of the chicken when exiting the chiller. The results obtained showed that the network with 8 neurons in the input layer and 24 in the hidden layer best represented the investigated system.
publishDate 2009
dc.date.none.fl_str_mv 2009-06-17
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.uri.fl_str_mv http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/3358
10.4025/actascitechnol.v31i2.3358
url http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/3358
identifier_str_mv 10.4025/actascitechnol.v31i2.3358
dc.language.iso.fl_str_mv por
language por
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dc.rights.driver.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 Universidade Estadual De Maringá
publisher.none.fl_str_mv Universidade Estadual De Maringá
dc.source.none.fl_str_mv Acta Scientiarum. Technology; Vol 31 No 2 (2009); 201-205
Acta Scientiarum. Technology; v. 31 n. 2 (2009); 201-205
1806-2563
1807-8664
reponame:Acta scientiarum. Technology (Online)
instname:Universidade Estadual de Maringá (UEM)
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instname_str Universidade Estadual de Maringá (UEM)
instacron_str UEM
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reponame_str Acta scientiarum. Technology (Online)
collection Acta scientiarum. Technology (Online)
repository.name.fl_str_mv Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM)
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