State Estimation and Nonlinear Tracking Control Simulation Approach: Application to a Bioethanol Production System

Autores
Fernández Puchol, María Cecilia; Pantano, Maria Nadia; Rodriguez Aguilar, Leandro Pedro Faustino; Scaglia, Gustavo Juan Eduardo
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Tracking control of specifc variables is key to achieve a proper fermentation. This paper analyzes a fed-batch bioethanol production process. For this system, a controller design based on linear algebra is proposed. Moreover, to achieve a reliable control, on-line monitoring of certain variables is needed. In this sense, for unmeasurable variables, state estimators based on Gaussian processes are designed. Cell, ethanol and glycerol concentrations are predicted with only substrates measurement. Simulation results when the controller and estimators are coupled, are shown. Furthermore, the algorithms were tested with parametric uncertainties and disturbances in the control action, and are compared, in all cases, with neural networks estimators (previous work). Bayesian estimators show a performance improvement, which is refected in a decrease of the total error. Proposed techniques give reliable monitoring and control tools, with a low computational and economic cost, and less mathematical complexity than neural network estimators.
Fil: Fernández Puchol, María Cecilia. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina
Fil: Pantano, Maria Nadia. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina
Fil: Rodriguez Aguilar, Leandro Pedro Faustino. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina
Fil: Scaglia, Gustavo Juan Eduardo. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina
Materia
ON-LINE MONITORING
PROFILES TRACKING CONTROL
FED-BATCH BIOPROCESS
NON-LINEAR AND MULTIVARIABLE SYSTEM
STATE ESTIMATION
GAUSSIAN PROCESS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/152828
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling State Estimation and Nonlinear Tracking Control Simulation Approach: Application to a Bioethanol Production SystemFernández Puchol, María CeciliaPantano, Maria NadiaRodriguez Aguilar, Leandro Pedro FaustinoScaglia, Gustavo Juan EduardoON-LINE MONITORINGPROFILES TRACKING CONTROLFED-BATCH BIOPROCESSNON-LINEAR AND MULTIVARIABLE SYSTEMSTATE ESTIMATIONGAUSSIAN PROCESShttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2Tracking control of specifc variables is key to achieve a proper fermentation. This paper analyzes a fed-batch bioethanol production process. For this system, a controller design based on linear algebra is proposed. Moreover, to achieve a reliable control, on-line monitoring of certain variables is needed. In this sense, for unmeasurable variables, state estimators based on Gaussian processes are designed. Cell, ethanol and glycerol concentrations are predicted with only substrates measurement. Simulation results when the controller and estimators are coupled, are shown. Furthermore, the algorithms were tested with parametric uncertainties and disturbances in the control action, and are compared, in all cases, with neural networks estimators (previous work). Bayesian estimators show a performance improvement, which is refected in a decrease of the total error. Proposed techniques give reliable monitoring and control tools, with a low computational and economic cost, and less mathematical complexity than neural network estimators.Fil: Fernández Puchol, María Cecilia. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; ArgentinaFil: Pantano, Maria Nadia. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; ArgentinaFil: Rodriguez Aguilar, Leandro Pedro Faustino. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; ArgentinaFil: Scaglia, Gustavo Juan Eduardo. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; ArgentinaSpringer2021-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/152828Fernández Puchol, María Cecilia; Pantano, Maria Nadia; Rodriguez Aguilar, Leandro Pedro Faustino; Scaglia, Gustavo Juan Eduardo; State Estimation and Nonlinear Tracking Control Simulation Approach: Application to a Bioethanol Production System; Springer; Bioprocess And Biosystems Engineering; 44; 8-2021; 1755-17681615-75911615-7605CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007/s00449-021-02558-yinfo:eu-repo/semantics/altIdentifier/doi/10.1007/s00449-021-02558-yinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-29T12:51:39Zoai:ri.conicet.gov.ar:11336/152828instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-29 12:51:40.201CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv State Estimation and Nonlinear Tracking Control Simulation Approach: Application to a Bioethanol Production System
title State Estimation and Nonlinear Tracking Control Simulation Approach: Application to a Bioethanol Production System
spellingShingle State Estimation and Nonlinear Tracking Control Simulation Approach: Application to a Bioethanol Production System
Fernández Puchol, María Cecilia
ON-LINE MONITORING
PROFILES TRACKING CONTROL
FED-BATCH BIOPROCESS
NON-LINEAR AND MULTIVARIABLE SYSTEM
STATE ESTIMATION
GAUSSIAN PROCESS
title_short State Estimation and Nonlinear Tracking Control Simulation Approach: Application to a Bioethanol Production System
title_full State Estimation and Nonlinear Tracking Control Simulation Approach: Application to a Bioethanol Production System
title_fullStr State Estimation and Nonlinear Tracking Control Simulation Approach: Application to a Bioethanol Production System
title_full_unstemmed State Estimation and Nonlinear Tracking Control Simulation Approach: Application to a Bioethanol Production System
title_sort State Estimation and Nonlinear Tracking Control Simulation Approach: Application to a Bioethanol Production System
dc.creator.none.fl_str_mv Fernández Puchol, María Cecilia
Pantano, Maria Nadia
Rodriguez Aguilar, Leandro Pedro Faustino
Scaglia, Gustavo Juan Eduardo
author Fernández Puchol, María Cecilia
author_facet Fernández Puchol, María Cecilia
Pantano, Maria Nadia
Rodriguez Aguilar, Leandro Pedro Faustino
Scaglia, Gustavo Juan Eduardo
author_role author
author2 Pantano, Maria Nadia
Rodriguez Aguilar, Leandro Pedro Faustino
Scaglia, Gustavo Juan Eduardo
author2_role author
author
author
dc.subject.none.fl_str_mv ON-LINE MONITORING
PROFILES TRACKING CONTROL
FED-BATCH BIOPROCESS
NON-LINEAR AND MULTIVARIABLE SYSTEM
STATE ESTIMATION
GAUSSIAN PROCESS
topic ON-LINE MONITORING
PROFILES TRACKING CONTROL
FED-BATCH BIOPROCESS
NON-LINEAR AND MULTIVARIABLE SYSTEM
STATE ESTIMATION
GAUSSIAN PROCESS
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Tracking control of specifc variables is key to achieve a proper fermentation. This paper analyzes a fed-batch bioethanol production process. For this system, a controller design based on linear algebra is proposed. Moreover, to achieve a reliable control, on-line monitoring of certain variables is needed. In this sense, for unmeasurable variables, state estimators based on Gaussian processes are designed. Cell, ethanol and glycerol concentrations are predicted with only substrates measurement. Simulation results when the controller and estimators are coupled, are shown. Furthermore, the algorithms were tested with parametric uncertainties and disturbances in the control action, and are compared, in all cases, with neural networks estimators (previous work). Bayesian estimators show a performance improvement, which is refected in a decrease of the total error. Proposed techniques give reliable monitoring and control tools, with a low computational and economic cost, and less mathematical complexity than neural network estimators.
Fil: Fernández Puchol, María Cecilia. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina
Fil: Pantano, Maria Nadia. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina
Fil: Rodriguez Aguilar, Leandro Pedro Faustino. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina
Fil: Scaglia, Gustavo Juan Eduardo. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina
description Tracking control of specifc variables is key to achieve a proper fermentation. This paper analyzes a fed-batch bioethanol production process. For this system, a controller design based on linear algebra is proposed. Moreover, to achieve a reliable control, on-line monitoring of certain variables is needed. In this sense, for unmeasurable variables, state estimators based on Gaussian processes are designed. Cell, ethanol and glycerol concentrations are predicted with only substrates measurement. Simulation results when the controller and estimators are coupled, are shown. Furthermore, the algorithms were tested with parametric uncertainties and disturbances in the control action, and are compared, in all cases, with neural networks estimators (previous work). Bayesian estimators show a performance improvement, which is refected in a decrease of the total error. Proposed techniques give reliable monitoring and control tools, with a low computational and economic cost, and less mathematical complexity than neural network estimators.
publishDate 2021
dc.date.none.fl_str_mv 2021-08
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/152828
Fernández Puchol, María Cecilia; Pantano, Maria Nadia; Rodriguez Aguilar, Leandro Pedro Faustino; Scaglia, Gustavo Juan Eduardo; State Estimation and Nonlinear Tracking Control Simulation Approach: Application to a Bioethanol Production System; Springer; Bioprocess And Biosystems Engineering; 44; 8-2021; 1755-1768
1615-7591
1615-7605
CONICET Digital
CONICET
url http://hdl.handle.net/11336/152828
identifier_str_mv Fernández Puchol, María Cecilia; Pantano, Maria Nadia; Rodriguez Aguilar, Leandro Pedro Faustino; Scaglia, Gustavo Juan Eduardo; State Estimation and Nonlinear Tracking Control Simulation Approach: Application to a Bioethanol Production System; Springer; Bioprocess And Biosystems Engineering; 44; 8-2021; 1755-1768
1615-7591
1615-7605
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007/s00449-021-02558-y
info:eu-repo/semantics/altIdentifier/doi/10.1007/s00449-021-02558-y
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.10058

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