STATE ESTIMATION AND TRAJECTORY TRACKING CONTROL FOR A NONLINEAR AND MULTIVARIABLE BIOETHANOL PRODUCTION SYSTEM
Main Author: | |
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Publication Date: | 2019 |
Other Authors: | , , , |
Format: | Article |
Language: | eng |
Source: | Brazilian Journal of Chemical Engineering |
Download full: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322019000100421 |
Summary: | ABSTRACT In this paper a controller is proposed based on linear algebra for a fed-batch bioethanol production process. It involves finding feed rate profiles (control actions obtained as a solution of a linear equations system) in order to make the system follow predefined concentration profiles. A neural network states estimation is designed in order to know those variables that cannot be measured. The controller is tuned using a Monte Carlo experiment for which a cost function that penalizes tracking errors is defined. Moreover, several tests (adding parametric uncertainty and perturbations in the control action) are carried out so as to evaluate the controller performance. A comparison with another controller is made. The demonstration of the error convergence, as well as the stability analysis of the neural network, are included. |
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Brazilian Journal of Chemical Engineering |
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STATE ESTIMATION AND TRAJECTORY TRACKING CONTROL FOR A NONLINEAR AND MULTIVARIABLE BIOETHANOL PRODUCTION SYSTEMFed-batch bioprocessNonlinear and multivariable systemProfiles tracking controlNumerical methods/linear algebraState estimationABSTRACT In this paper a controller is proposed based on linear algebra for a fed-batch bioethanol production process. It involves finding feed rate profiles (control actions obtained as a solution of a linear equations system) in order to make the system follow predefined concentration profiles. A neural network states estimation is designed in order to know those variables that cannot be measured. The controller is tuned using a Monte Carlo experiment for which a cost function that penalizes tracking errors is defined. Moreover, several tests (adding parametric uncertainty and perturbations in the control action) are carried out so as to evaluate the controller performance. A comparison with another controller is made. The demonstration of the error convergence, as well as the stability analysis of the neural network, are included.Brazilian Society of Chemical Engineering2019-03-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322019000100421Brazilian Journal of Chemical Engineering v.36 n.1 2019reponame:Brazilian Journal of Chemical Engineeringinstname:Associação Brasileira de Engenharia Química (ABEQ)instacron:ABEQ10.1590/0104-6632.20190361s20170379info:eu-repo/semantics/openAccessCecilia Fernández,M.Nadia Pantano,M.Rossomando,Francisco G.Alberto Ortiz,O.Scaglia,Gustavo J. E.eng2019-07-10T00:00:00Zoai:scielo:S0104-66322019000100421Revistahttps://www.scielo.br/j/bjce/https://old.scielo.br/oai/scielo-oai.phprgiudici@usp.br||rgiudici@usp.br1678-43830104-6632opendoar:2019-07-10T00:00Brazilian Journal of Chemical Engineering - Associação Brasileira de Engenharia Química (ABEQ)false |
dc.title.none.fl_str_mv |
STATE ESTIMATION AND TRAJECTORY TRACKING CONTROL FOR A NONLINEAR AND MULTIVARIABLE BIOETHANOL PRODUCTION SYSTEM |
title |
STATE ESTIMATION AND TRAJECTORY TRACKING CONTROL FOR A NONLINEAR AND MULTIVARIABLE BIOETHANOL PRODUCTION SYSTEM |
spellingShingle |
STATE ESTIMATION AND TRAJECTORY TRACKING CONTROL FOR A NONLINEAR AND MULTIVARIABLE BIOETHANOL PRODUCTION SYSTEM Cecilia Fernández,M. Fed-batch bioprocess Nonlinear and multivariable system Profiles tracking control Numerical methods/linear algebra State estimation |
title_short |
STATE ESTIMATION AND TRAJECTORY TRACKING CONTROL FOR A NONLINEAR AND MULTIVARIABLE BIOETHANOL PRODUCTION SYSTEM |
title_full |
STATE ESTIMATION AND TRAJECTORY TRACKING CONTROL FOR A NONLINEAR AND MULTIVARIABLE BIOETHANOL PRODUCTION SYSTEM |
title_fullStr |
STATE ESTIMATION AND TRAJECTORY TRACKING CONTROL FOR A NONLINEAR AND MULTIVARIABLE BIOETHANOL PRODUCTION SYSTEM |
title_full_unstemmed |
STATE ESTIMATION AND TRAJECTORY TRACKING CONTROL FOR A NONLINEAR AND MULTIVARIABLE BIOETHANOL PRODUCTION SYSTEM |
title_sort |
STATE ESTIMATION AND TRAJECTORY TRACKING CONTROL FOR A NONLINEAR AND MULTIVARIABLE BIOETHANOL PRODUCTION SYSTEM |
author |
Cecilia Fernández,M. |
author_facet |
Cecilia Fernández,M. Nadia Pantano,M. Rossomando,Francisco G. Alberto Ortiz,O. Scaglia,Gustavo J. E. |
author_role |
author |
author2 |
Nadia Pantano,M. Rossomando,Francisco G. Alberto Ortiz,O. Scaglia,Gustavo J. E. |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Cecilia Fernández,M. Nadia Pantano,M. Rossomando,Francisco G. Alberto Ortiz,O. Scaglia,Gustavo J. E. |
dc.subject.por.fl_str_mv |
Fed-batch bioprocess Nonlinear and multivariable system Profiles tracking control Numerical methods/linear algebra State estimation |
topic |
Fed-batch bioprocess Nonlinear and multivariable system Profiles tracking control Numerical methods/linear algebra State estimation |
description |
ABSTRACT In this paper a controller is proposed based on linear algebra for a fed-batch bioethanol production process. It involves finding feed rate profiles (control actions obtained as a solution of a linear equations system) in order to make the system follow predefined concentration profiles. A neural network states estimation is designed in order to know those variables that cannot be measured. The controller is tuned using a Monte Carlo experiment for which a cost function that penalizes tracking errors is defined. Moreover, several tests (adding parametric uncertainty and perturbations in the control action) are carried out so as to evaluate the controller performance. A comparison with another controller is made. The demonstration of the error convergence, as well as the stability analysis of the neural network, are included. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-03-01 |
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=S0104-66322019000100421 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322019000100421 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0104-6632.20190361s20170379 |
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.36 n.1 2019 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 |
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1754213176351653888 |