Behavior comparison for biomass observers in batch processes

Autores
Amicarelli, Adriana Natacha; Quintero, Olga; Di Sciascio, Fernando Agustín
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
On-line estimation of biomass concentration in batch biotechnological processes is an active area of research because normally, the biomass is the desired process product output, and also because it is necessary for control purposes to replace the unavailable biomass concentration measurements with reliable and robust on-line estimations. This work presents five different alternatives to face the problem of biomass estimation in a particular batch bioprocess (δ-endotoxins production of Bacillus thuringiensis), namely: a phenomenological estimator based on dissolved oxygen balance, an extended Kalman filter estimator, a Gaussian process regression-based estimator, an artificial neural networks-based estimator, and finally, an estimator based on information fusion by a decentralized Kalman filter. Each proposed biomass estimation method has its own advantages and drawbacks according to their ability to take into account the model uncertainties and the measurement errors. First, the design techniques of these five biomass estimators are exposed, and finally, the behavior of each estimation method is compared. The availability of efficient biomass estimators is of great importance for engineers because, on the one hand, it allows developing new control strategies for other bioprocess variables such as for instance: the growth rate of the microorganism, the dissolved oxygen concentration, and so on. On the other hand, it is also important to improve the performance of the bioprocess optimization procedure. This work also aims to show the evolution on biomass estimation techniques from classical to more contemporary approaches, such as the design based on neural networks and Gaussian processes regression.
Fil: Amicarelli, Adriana Natacha. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Quintero, Olga. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Di Sciascio, Fernando Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Materia
BATCH PROCESSES
BIOMASS ESTIMATION
BIOPROCESS MODEL
GAUSSIAN PROCESS
STATE OBSERVERS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/86257
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Behavior comparison for biomass observers in batch processesAmicarelli, Adriana NatachaQuintero, OlgaDi Sciascio, Fernando AgustínBATCH PROCESSESBIOMASS ESTIMATIONBIOPROCESS MODELGAUSSIAN PROCESSSTATE OBSERVERShttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2On-line estimation of biomass concentration in batch biotechnological processes is an active area of research because normally, the biomass is the desired process product output, and also because it is necessary for control purposes to replace the unavailable biomass concentration measurements with reliable and robust on-line estimations. This work presents five different alternatives to face the problem of biomass estimation in a particular batch bioprocess (δ-endotoxins production of Bacillus thuringiensis), namely: a phenomenological estimator based on dissolved oxygen balance, an extended Kalman filter estimator, a Gaussian process regression-based estimator, an artificial neural networks-based estimator, and finally, an estimator based on information fusion by a decentralized Kalman filter. Each proposed biomass estimation method has its own advantages and drawbacks according to their ability to take into account the model uncertainties and the measurement errors. First, the design techniques of these five biomass estimators are exposed, and finally, the behavior of each estimation method is compared. The availability of efficient biomass estimators is of great importance for engineers because, on the one hand, it allows developing new control strategies for other bioprocess variables such as for instance: the growth rate of the microorganism, the dissolved oxygen concentration, and so on. On the other hand, it is also important to improve the performance of the bioprocess optimization procedure. This work also aims to show the evolution on biomass estimation techniques from classical to more contemporary approaches, such as the design based on neural networks and Gaussian processes regression.Fil: Amicarelli, Adriana Natacha. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Quintero, Olga. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Di Sciascio, Fernando Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaJohn Wiley & Sons Inc2014-01info: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/11336/86257Amicarelli, Adriana Natacha; Quintero, Olga; Di Sciascio, Fernando Agustín; Behavior comparison for biomass observers in batch processes; John Wiley & Sons Inc; Asia-Pacific Journal of Chemical Engineering; 9; 1; 1-2014; 81-921932-2143CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1002/apj.1748info:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/abs/10.1002/apj.1748info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-17T10:45:23Zoai:ri.conicet.gov.ar:11336/86257instacron: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:34982025-09-17 10:45:23.627CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Behavior comparison for biomass observers in batch processes
title Behavior comparison for biomass observers in batch processes
spellingShingle Behavior comparison for biomass observers in batch processes
Amicarelli, Adriana Natacha
BATCH PROCESSES
BIOMASS ESTIMATION
BIOPROCESS MODEL
GAUSSIAN PROCESS
STATE OBSERVERS
title_short Behavior comparison for biomass observers in batch processes
title_full Behavior comparison for biomass observers in batch processes
title_fullStr Behavior comparison for biomass observers in batch processes
title_full_unstemmed Behavior comparison for biomass observers in batch processes
title_sort Behavior comparison for biomass observers in batch processes
dc.creator.none.fl_str_mv Amicarelli, Adriana Natacha
Quintero, Olga
Di Sciascio, Fernando Agustín
author Amicarelli, Adriana Natacha
author_facet Amicarelli, Adriana Natacha
Quintero, Olga
Di Sciascio, Fernando Agustín
author_role author
author2 Quintero, Olga
Di Sciascio, Fernando Agustín
author2_role author
author
dc.subject.none.fl_str_mv BATCH PROCESSES
BIOMASS ESTIMATION
BIOPROCESS MODEL
GAUSSIAN PROCESS
STATE OBSERVERS
topic BATCH PROCESSES
BIOMASS ESTIMATION
BIOPROCESS MODEL
GAUSSIAN PROCESS
STATE OBSERVERS
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv On-line estimation of biomass concentration in batch biotechnological processes is an active area of research because normally, the biomass is the desired process product output, and also because it is necessary for control purposes to replace the unavailable biomass concentration measurements with reliable and robust on-line estimations. This work presents five different alternatives to face the problem of biomass estimation in a particular batch bioprocess (δ-endotoxins production of Bacillus thuringiensis), namely: a phenomenological estimator based on dissolved oxygen balance, an extended Kalman filter estimator, a Gaussian process regression-based estimator, an artificial neural networks-based estimator, and finally, an estimator based on information fusion by a decentralized Kalman filter. Each proposed biomass estimation method has its own advantages and drawbacks according to their ability to take into account the model uncertainties and the measurement errors. First, the design techniques of these five biomass estimators are exposed, and finally, the behavior of each estimation method is compared. The availability of efficient biomass estimators is of great importance for engineers because, on the one hand, it allows developing new control strategies for other bioprocess variables such as for instance: the growth rate of the microorganism, the dissolved oxygen concentration, and so on. On the other hand, it is also important to improve the performance of the bioprocess optimization procedure. This work also aims to show the evolution on biomass estimation techniques from classical to more contemporary approaches, such as the design based on neural networks and Gaussian processes regression.
Fil: Amicarelli, Adriana Natacha. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Quintero, Olga. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Di Sciascio, Fernando Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
description On-line estimation of biomass concentration in batch biotechnological processes is an active area of research because normally, the biomass is the desired process product output, and also because it is necessary for control purposes to replace the unavailable biomass concentration measurements with reliable and robust on-line estimations. This work presents five different alternatives to face the problem of biomass estimation in a particular batch bioprocess (δ-endotoxins production of Bacillus thuringiensis), namely: a phenomenological estimator based on dissolved oxygen balance, an extended Kalman filter estimator, a Gaussian process regression-based estimator, an artificial neural networks-based estimator, and finally, an estimator based on information fusion by a decentralized Kalman filter. Each proposed biomass estimation method has its own advantages and drawbacks according to their ability to take into account the model uncertainties and the measurement errors. First, the design techniques of these five biomass estimators are exposed, and finally, the behavior of each estimation method is compared. The availability of efficient biomass estimators is of great importance for engineers because, on the one hand, it allows developing new control strategies for other bioprocess variables such as for instance: the growth rate of the microorganism, the dissolved oxygen concentration, and so on. On the other hand, it is also important to improve the performance of the bioprocess optimization procedure. This work also aims to show the evolution on biomass estimation techniques from classical to more contemporary approaches, such as the design based on neural networks and Gaussian processes regression.
publishDate 2014
dc.date.none.fl_str_mv 2014-01
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/11336/86257
Amicarelli, Adriana Natacha; Quintero, Olga; Di Sciascio, Fernando Agustín; Behavior comparison for biomass observers in batch processes; John Wiley & Sons Inc; Asia-Pacific Journal of Chemical Engineering; 9; 1; 1-2014; 81-92
1932-2143
CONICET Digital
CONICET
url http://hdl.handle.net/11336/86257
identifier_str_mv Amicarelli, Adriana Natacha; Quintero, Olga; Di Sciascio, Fernando Agustín; Behavior comparison for biomass observers in batch processes; John Wiley & Sons Inc; Asia-Pacific Journal of Chemical Engineering; 9; 1; 1-2014; 81-92
1932-2143
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1002/apj.1748
info:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/abs/10.1002/apj.1748
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv John Wiley & Sons Inc
publisher.none.fl_str_mv John Wiley & Sons Inc
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 13.001348

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