Modeling of the poultry carcass immersion chilling system using artificial neural networks - DOI: 10.4025/actascitechnol.v31i2.3358
Autor(a) principal: | |
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Data de Publicação: | 2009 |
Outros Autores: | , , |
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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Acta scientiarum. Technology (Online) |
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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 info:eu-repo/semantics/publishedVersion Modelagem |
format |
article |
status_str |
publishedVersion |
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 |
dc.relation.none.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/3358/3358 |
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) instacron:UEM |
instname_str |
Universidade Estadual de Maringá (UEM) |
instacron_str |
UEM |
institution |
UEM |
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) |
repository.mail.fl_str_mv |
||actatech@uem.br |
_version_ |
1799315333153030144 |