Nonlinear predictive control of an industrial slurry reactor
Autor(a) principal: | |
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Data de Publicação: | 2008 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Sba: Controle & Automação Sociedade Brasileira de Automatica |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-17592008000400005 |
Resumo: | A nonlinear model predictive control (NMPC) is applied to a slurry polymerization stirred tank reactor for the production of high-density polyethylene. Its performance is examined to reach the required mean molecular weight and comonomer composition, together with the temperature setpoint. A complete phenomenological model including the microscale, the mesoscale and the macroscale levels was developed to represent the plant. The control algorithm comprises a neural dynamic model that uses a neural network structure with a feedforward topology. The algorithm implementation considers the optimization problem, the manipulated and controlled variables adopted and presents results for the regulatory and servo problems, including the possibility of dead time and multi-rate sampling in the controlled variables. The simulation results show the high performance of the NMPC algorithm based in a model for one-step ahead prediction only, and, at the same time, attests the strong difficulty to control polymer properties with dead time in their measurements. |
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Nonlinear predictive control of an industrial slurry reactorOlefin PolymerizationPredictive controlneural networksA nonlinear model predictive control (NMPC) is applied to a slurry polymerization stirred tank reactor for the production of high-density polyethylene. Its performance is examined to reach the required mean molecular weight and comonomer composition, together with the temperature setpoint. A complete phenomenological model including the microscale, the mesoscale and the macroscale levels was developed to represent the plant. The control algorithm comprises a neural dynamic model that uses a neural network structure with a feedforward topology. The algorithm implementation considers the optimization problem, the manipulated and controlled variables adopted and presents results for the regulatory and servo problems, including the possibility of dead time and multi-rate sampling in the controlled variables. The simulation results show the high performance of the NMPC algorithm based in a model for one-step ahead prediction only, and, at the same time, attests the strong difficulty to control polymer properties with dead time in their measurements.Sociedade Brasileira de Automática2008-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-17592008000400005Sba: Controle & Automação Sociedade Brasileira de Automatica v.19 n.4 2008reponame:Sba: Controle & Automação Sociedade Brasileira de Automaticainstname:Sociedade Brasileira de Automática (SBA)instacron:SBA10.1590/S0103-17592008000400005info:eu-repo/semantics/openAccessFontes,C. H.Mendes,M. J.eng2009-01-20T00:00:00Zoai:scielo:S0103-17592008000400005Revistahttps://www.sba.org.br/revista/PUBhttps://old.scielo.br/oai/scielo-oai.php||revista_sba@fee.unicamp.br1807-03450103-1759opendoar:2009-01-20T00:00Sba: Controle & Automação Sociedade Brasileira de Automatica - Sociedade Brasileira de Automática (SBA)false |
dc.title.none.fl_str_mv |
Nonlinear predictive control of an industrial slurry reactor |
title |
Nonlinear predictive control of an industrial slurry reactor |
spellingShingle |
Nonlinear predictive control of an industrial slurry reactor Fontes,C. H. Olefin Polymerization Predictive control neural networks |
title_short |
Nonlinear predictive control of an industrial slurry reactor |
title_full |
Nonlinear predictive control of an industrial slurry reactor |
title_fullStr |
Nonlinear predictive control of an industrial slurry reactor |
title_full_unstemmed |
Nonlinear predictive control of an industrial slurry reactor |
title_sort |
Nonlinear predictive control of an industrial slurry reactor |
author |
Fontes,C. H. |
author_facet |
Fontes,C. H. Mendes,M. J. |
author_role |
author |
author2 |
Mendes,M. J. |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Fontes,C. H. Mendes,M. J. |
dc.subject.por.fl_str_mv |
Olefin Polymerization Predictive control neural networks |
topic |
Olefin Polymerization Predictive control neural networks |
description |
A nonlinear model predictive control (NMPC) is applied to a slurry polymerization stirred tank reactor for the production of high-density polyethylene. Its performance is examined to reach the required mean molecular weight and comonomer composition, together with the temperature setpoint. A complete phenomenological model including the microscale, the mesoscale and the macroscale levels was developed to represent the plant. The control algorithm comprises a neural dynamic model that uses a neural network structure with a feedforward topology. The algorithm implementation considers the optimization problem, the manipulated and controlled variables adopted and presents results for the regulatory and servo problems, including the possibility of dead time and multi-rate sampling in the controlled variables. The simulation results show the high performance of the NMPC algorithm based in a model for one-step ahead prediction only, and, at the same time, attests the strong difficulty to control polymer properties with dead time in their measurements. |
publishDate |
2008 |
dc.date.none.fl_str_mv |
2008-12-01 |
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=S0103-17592008000400005 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-17592008000400005 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/S0103-17592008000400005 |
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 |
Sociedade Brasileira de Automática |
publisher.none.fl_str_mv |
Sociedade Brasileira de Automática |
dc.source.none.fl_str_mv |
Sba: Controle & Automação Sociedade Brasileira de Automatica v.19 n.4 2008 reponame:Sba: Controle & Automação Sociedade Brasileira de Automatica instname:Sociedade Brasileira de Automática (SBA) instacron:SBA |
instname_str |
Sociedade Brasileira de Automática (SBA) |
instacron_str |
SBA |
institution |
SBA |
reponame_str |
Sba: Controle & Automação Sociedade Brasileira de Automatica |
collection |
Sba: Controle & Automação Sociedade Brasileira de Automatica |
repository.name.fl_str_mv |
Sba: Controle & Automação Sociedade Brasileira de Automatica - Sociedade Brasileira de Automática (SBA) |
repository.mail.fl_str_mv |
||revista_sba@fee.unicamp.br |
_version_ |
1754824564831944704 |