Evaluation of robust functions for data reconciliation in thermal systems
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Acta scientiarum. Technology (Online) |
Texto Completo: | http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/28188 |
Resumo: | Process variables regularly control and evaluate industrial processes. Information with gross errors may in some cases not be attenuated by function reconciliation and change the calculation of process balance, leading optimization results towards non-feasible regions or to optimal sites. A promising alternative for reconciling functions is the use of robust functions. Current paper considers the above scenario and evaluates the fitness of some robust functions in solving in steady state chemical processes data reconciliation problems represented by linear and nonlinear systems in the presence of gross errors. Traditional Cauchy, Fair, Contaminated Normal and Logistic robust functions are used in the reconciliation problem where their estimates are compared to those obtained with the use of the latest features, such as New Target and Alarm. Rates for gross errors in tests were limited between 4 and 10σ of the measured current and elaborated a region of outliers. Results showed that New Target and Alarm functions are different from the others as the magnitude of the gross error increases, tending towards true rates specified by set point. |
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Evaluation of robust functions for data reconciliation in thermal systemsEvaluation of robust functions for data reconciliation in thermal systemsset measuresrobustnessefficiencynonlinear systemsReconciliação de Dadosset measuresrobustnessefficiencynonlinear systems.Reconciliação de dadosProcess variables regularly control and evaluate industrial processes. Information with gross errors may in some cases not be attenuated by function reconciliation and change the calculation of process balance, leading optimization results towards non-feasible regions or to optimal sites. A promising alternative for reconciling functions is the use of robust functions. Current paper considers the above scenario and evaluates the fitness of some robust functions in solving in steady state chemical processes data reconciliation problems represented by linear and nonlinear systems in the presence of gross errors. Traditional Cauchy, Fair, Contaminated Normal and Logistic robust functions are used in the reconciliation problem where their estimates are compared to those obtained with the use of the latest features, such as New Target and Alarm. Rates for gross errors in tests were limited between 4 and 10σ of the measured current and elaborated a region of outliers. Results showed that New Target and Alarm functions are different from the others as the magnitude of the gross error increases, tending towards true rates specified by set point.Process variables regularly control and evaluate industrial processes. Information with gross errors may in some cases not be attenuated by function reconciliation and change the calculation of process balance, leading optimization results towards non-feasible regions or to optimal sites. A promising alternative for reconciling functions is the use of robust functions. Current paper considers the above scenario and evaluates the fitness of some robust functions in solving in steady state chemical processes data reconciliation problems represented by linear and nonlinear systems in the presence of gross errors. Traditional Cauchy, Fair, Contaminated Normal and Logistic robust functions are used in the reconciliation problem where their estimates are compared to those obtained with the use of the latest features, such as New Target and Alarm. Rates for gross errors in tests were limited between 4 and 10σ of the measured current and elaborated a region of outliers. Results showed that New Target and Alarm functions are different from the others as the magnitude of the gross error increases, tending towards true rates specified by set point.Universidade Estadual De Maringá2016-04-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionMétodoMétodoapplication/pdfhttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/2818810.4025/actascitechnol.v38i2.28188Acta Scientiarum. Technology; Vol 38 No 2 (2016); 185-191Acta Scientiarum. Technology; v. 38 n. 2 (2016); 185-1911806-25631807-8664reponame:Acta scientiarum. Technology (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMenghttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/28188/pdf_145França, Regina Luana Santos deOliveira Júnior, Antonio MartinsSouza, Domingos Fabiano Santanainfo:eu-repo/semantics/openAccess2016-04-12T14:40:38Zoai:periodicos.uem.br/ojs:article/28188Revistahttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/indexPUBhttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/oai||actatech@uem.br1807-86641806-2563opendoar:2016-04-12T14:40:38Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM)false |
dc.title.none.fl_str_mv |
Evaluation of robust functions for data reconciliation in thermal systems Evaluation of robust functions for data reconciliation in thermal systems |
title |
Evaluation of robust functions for data reconciliation in thermal systems |
spellingShingle |
Evaluation of robust functions for data reconciliation in thermal systems França, Regina Luana Santos de set measures robustness efficiency nonlinear systems Reconciliação de Dados set measures robustness efficiency nonlinear systems. Reconciliação de dados |
title_short |
Evaluation of robust functions for data reconciliation in thermal systems |
title_full |
Evaluation of robust functions for data reconciliation in thermal systems |
title_fullStr |
Evaluation of robust functions for data reconciliation in thermal systems |
title_full_unstemmed |
Evaluation of robust functions for data reconciliation in thermal systems |
title_sort |
Evaluation of robust functions for data reconciliation in thermal systems |
author |
França, Regina Luana Santos de |
author_facet |
França, Regina Luana Santos de Oliveira Júnior, Antonio Martins Souza, Domingos Fabiano Santana |
author_role |
author |
author2 |
Oliveira Júnior, Antonio Martins Souza, Domingos Fabiano Santana |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
França, Regina Luana Santos de Oliveira Júnior, Antonio Martins Souza, Domingos Fabiano Santana |
dc.subject.por.fl_str_mv |
set measures robustness efficiency nonlinear systems Reconciliação de Dados set measures robustness efficiency nonlinear systems. Reconciliação de dados |
topic |
set measures robustness efficiency nonlinear systems Reconciliação de Dados set measures robustness efficiency nonlinear systems. Reconciliação de dados |
description |
Process variables regularly control and evaluate industrial processes. Information with gross errors may in some cases not be attenuated by function reconciliation and change the calculation of process balance, leading optimization results towards non-feasible regions or to optimal sites. A promising alternative for reconciling functions is the use of robust functions. Current paper considers the above scenario and evaluates the fitness of some robust functions in solving in steady state chemical processes data reconciliation problems represented by linear and nonlinear systems in the presence of gross errors. Traditional Cauchy, Fair, Contaminated Normal and Logistic robust functions are used in the reconciliation problem where their estimates are compared to those obtained with the use of the latest features, such as New Target and Alarm. Rates for gross errors in tests were limited between 4 and 10σ of the measured current and elaborated a region of outliers. Results showed that New Target and Alarm functions are different from the others as the magnitude of the gross error increases, tending towards true rates specified by set point. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-04-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Método Método |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/28188 10.4025/actascitechnol.v38i2.28188 |
url |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/28188 |
identifier_str_mv |
10.4025/actascitechnol.v38i2.28188 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/28188/pdf_145 |
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 38 No 2 (2016); 185-191 Acta Scientiarum. Technology; v. 38 n. 2 (2016); 185-191 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_ |
1799315335880376320 |