Modeling techniques and processes control application based on Neural Networks with on-line adjustment using Genetic Algorithms
Autor(a) principal: | |
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Data de Publicação: | 2009 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Brazilian Journal of Chemical Engineering |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322009000100011 |
Resumo: | In this work a strategy is presented for the temperature control of the polymerization reaction of styrene in suspension in batch. A three-layer feed forward Artificial Neural Network was trained in an off-line way starting from a removed group of patterns of the experimental system and applied in the recurrent form (RNN) to a Predictive Controller based on a Nonlinear Model (NMPC). This controller presented very superior results to the classic controller PID in the maintenance of the temperature. Still to improve the performance of the model used by NMPC (RNN) that can present differences in relation to the system due to the dead time involved in the control actions, nonlinear characteristic of the system and variable dynamics; an on-line adjustment methodology of the parameters of the exit layer of the Network is implemented, presenting superior results and treating the difficulties satisfactorily in the temperature control. All the presented results are obtained for a real system. |
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Brazilian Journal of Chemical Engineering |
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Modeling techniques and processes control application based on Neural Networks with on-line adjustment using Genetic AlgorithmsPredictive ControlNeural NetworksGenetic AlgorithmsPolystyreneArtificial IntelligenceIn this work a strategy is presented for the temperature control of the polymerization reaction of styrene in suspension in batch. A three-layer feed forward Artificial Neural Network was trained in an off-line way starting from a removed group of patterns of the experimental system and applied in the recurrent form (RNN) to a Predictive Controller based on a Nonlinear Model (NMPC). This controller presented very superior results to the classic controller PID in the maintenance of the temperature. Still to improve the performance of the model used by NMPC (RNN) that can present differences in relation to the system due to the dead time involved in the control actions, nonlinear characteristic of the system and variable dynamics; an on-line adjustment methodology of the parameters of the exit layer of the Network is implemented, presenting superior results and treating the difficulties satisfactorily in the temperature control. All the presented results are obtained for a real system.Brazilian Society of Chemical Engineering2009-03-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322009000100011Brazilian Journal of Chemical Engineering v.26 n.1 2009reponame:Brazilian Journal of Chemical Engineeringinstname:Associação Brasileira de Engenharia Química (ABEQ)instacron:ABEQ10.1590/S0104-66322009000100011info:eu-repo/semantics/openAccessMarcolla,R. F.Machado,R. A. F.Cancelier,A.Claumann,C. A.Bolzan,A.eng2009-03-10T00:00:00Zoai:scielo:S0104-66322009000100011Revistahttps://www.scielo.br/j/bjce/https://old.scielo.br/oai/scielo-oai.phprgiudici@usp.br||rgiudici@usp.br1678-43830104-6632opendoar:2009-03-10T00:00Brazilian Journal of Chemical Engineering - Associação Brasileira de Engenharia Química (ABEQ)false |
dc.title.none.fl_str_mv |
Modeling techniques and processes control application based on Neural Networks with on-line adjustment using Genetic Algorithms |
title |
Modeling techniques and processes control application based on Neural Networks with on-line adjustment using Genetic Algorithms |
spellingShingle |
Modeling techniques and processes control application based on Neural Networks with on-line adjustment using Genetic Algorithms Marcolla,R. F. Predictive Control Neural Networks Genetic Algorithms Polystyrene Artificial Intelligence |
title_short |
Modeling techniques and processes control application based on Neural Networks with on-line adjustment using Genetic Algorithms |
title_full |
Modeling techniques and processes control application based on Neural Networks with on-line adjustment using Genetic Algorithms |
title_fullStr |
Modeling techniques and processes control application based on Neural Networks with on-line adjustment using Genetic Algorithms |
title_full_unstemmed |
Modeling techniques and processes control application based on Neural Networks with on-line adjustment using Genetic Algorithms |
title_sort |
Modeling techniques and processes control application based on Neural Networks with on-line adjustment using Genetic Algorithms |
author |
Marcolla,R. F. |
author_facet |
Marcolla,R. F. Machado,R. A. F. Cancelier,A. Claumann,C. A. Bolzan,A. |
author_role |
author |
author2 |
Machado,R. A. F. Cancelier,A. Claumann,C. A. Bolzan,A. |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Marcolla,R. F. Machado,R. A. F. Cancelier,A. Claumann,C. A. Bolzan,A. |
dc.subject.por.fl_str_mv |
Predictive Control Neural Networks Genetic Algorithms Polystyrene Artificial Intelligence |
topic |
Predictive Control Neural Networks Genetic Algorithms Polystyrene Artificial Intelligence |
description |
In this work a strategy is presented for the temperature control of the polymerization reaction of styrene in suspension in batch. A three-layer feed forward Artificial Neural Network was trained in an off-line way starting from a removed group of patterns of the experimental system and applied in the recurrent form (RNN) to a Predictive Controller based on a Nonlinear Model (NMPC). This controller presented very superior results to the classic controller PID in the maintenance of the temperature. Still to improve the performance of the model used by NMPC (RNN) that can present differences in relation to the system due to the dead time involved in the control actions, nonlinear characteristic of the system and variable dynamics; an on-line adjustment methodology of the parameters of the exit layer of the Network is implemented, presenting superior results and treating the difficulties satisfactorily in the temperature control. All the presented results are obtained for a real system. |
publishDate |
2009 |
dc.date.none.fl_str_mv |
2009-03-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322009000100011 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322009000100011 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/S0104-66322009000100011 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Brazilian Society of Chemical Engineering |
publisher.none.fl_str_mv |
Brazilian Society of Chemical Engineering |
dc.source.none.fl_str_mv |
Brazilian Journal of Chemical Engineering v.26 n.1 2009 reponame:Brazilian Journal of Chemical Engineering instname:Associação Brasileira de Engenharia Química (ABEQ) instacron:ABEQ |
instname_str |
Associação Brasileira de Engenharia Química (ABEQ) |
instacron_str |
ABEQ |
institution |
ABEQ |
reponame_str |
Brazilian Journal of Chemical Engineering |
collection |
Brazilian Journal of Chemical Engineering |
repository.name.fl_str_mv |
Brazilian Journal of Chemical Engineering - Associação Brasileira de Engenharia Química (ABEQ) |
repository.mail.fl_str_mv |
rgiudici@usp.br||rgiudici@usp.br |
_version_ |
1754213172730920960 |