Hybrid Models for the simulation and prediction of chromatographic processes for protein capture
- Autores
- Narayanan, Harini; Seidler, Tobias; Luna, Martín Francisco; Sokolov, Michael; Morbidelli, Massimo; Butté, Alessandro
- Año de publicación
- 2021
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- The biopharmaceutical industries are continuously faced with the pressure to reduce the development costs and accelerate development time scales. The traditional approach of heuristic-based or platform process-based optimization is soon getting obsolete, and more generalized tools for process development and optimization are required to keep pace with the emerging trends. Thus, advanced model-based methods that can reduce the can ensure accelerated development of robust processes with minimal experiments are necessary. Though mechanistic models for chromatography are quite popular, their success is limited by the need to have accurate knowledge of adsorption isotherms and mass transfer kinetics. As an alternative, in this work, a hybrid modeling approach is proposed. Thereby, the chromatographic unit behavior is learned by a combination of neural network and mechanistic model while fitting suitable experimental breakthrough curves. Since this approach does not require identifying suitable mechanistic assumptions for all the phenomena, it can be developed with lower effort. Thus, allowing the scientists to concentrate their focus on process development. The performance of the hybrid model is compared with the mechanistic Lumped kinetic Model for in-silico data and experiments conducted on a system of industrial relevance. The flexibility of the hybrid modeling approach results in about three times higher accuracies compared to Lumped Kinetic Model. This is validated for five different isotherm models used to simulate data, with the hybrid model showing about two to three times lower prediction errors in all the cases. Not only in prediction, but we could also show that the hybrid model is more robust in extrapolating across process conditions with about three times lower error than the LKM. Additionally, it could be demonstrated that an appropriately tailored formulation of the hybrid model can be used to generate representations for the underlying principles such as adsorption equilibria and mass transfer kinetics.
Fil: Narayanan, Harini. Institute of Chemical and Bioengineering; Suiza
Fil: Seidler, Tobias. Institute of Chemical and Bioengineering; Suiza
Fil: Luna, Martín Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina. Institute of Chemical and Bioengineering; Suiza
Fil: Sokolov, Michael. No especifíca;
Fil: Morbidelli, Massimo. Politecnico di Milano; Italia
Fil: Butté, Alessandro. No especifíca; - Materia
-
ARTIFICIAL NEURAL NETWORK
CAPTURE
CHROMATOGRAPHY
HYBRID MODELS - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/154013
Ver los metadatos del registro completo
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Hybrid Models for the simulation and prediction of chromatographic processes for protein captureNarayanan, HariniSeidler, TobiasLuna, Martín FranciscoSokolov, MichaelMorbidelli, MassimoButté, AlessandroARTIFICIAL NEURAL NETWORKCAPTURECHROMATOGRAPHYHYBRID MODELShttps://purl.org/becyt/ford/2.9https://purl.org/becyt/ford/2The biopharmaceutical industries are continuously faced with the pressure to reduce the development costs and accelerate development time scales. The traditional approach of heuristic-based or platform process-based optimization is soon getting obsolete, and more generalized tools for process development and optimization are required to keep pace with the emerging trends. Thus, advanced model-based methods that can reduce the can ensure accelerated development of robust processes with minimal experiments are necessary. Though mechanistic models for chromatography are quite popular, their success is limited by the need to have accurate knowledge of adsorption isotherms and mass transfer kinetics. As an alternative, in this work, a hybrid modeling approach is proposed. Thereby, the chromatographic unit behavior is learned by a combination of neural network and mechanistic model while fitting suitable experimental breakthrough curves. Since this approach does not require identifying suitable mechanistic assumptions for all the phenomena, it can be developed with lower effort. Thus, allowing the scientists to concentrate their focus on process development. The performance of the hybrid model is compared with the mechanistic Lumped kinetic Model for in-silico data and experiments conducted on a system of industrial relevance. The flexibility of the hybrid modeling approach results in about three times higher accuracies compared to Lumped Kinetic Model. This is validated for five different isotherm models used to simulate data, with the hybrid model showing about two to three times lower prediction errors in all the cases. Not only in prediction, but we could also show that the hybrid model is more robust in extrapolating across process conditions with about three times lower error than the LKM. Additionally, it could be demonstrated that an appropriately tailored formulation of the hybrid model can be used to generate representations for the underlying principles such as adsorption equilibria and mass transfer kinetics.Fil: Narayanan, Harini. Institute of Chemical and Bioengineering; SuizaFil: Seidler, Tobias. Institute of Chemical and Bioengineering; SuizaFil: Luna, Martín Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina. Institute of Chemical and Bioengineering; SuizaFil: Sokolov, Michael. No especifíca;Fil: Morbidelli, Massimo. Politecnico di Milano; ItaliaFil: Butté, Alessandro. No especifíca;Elsevier Science2021-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/154013Narayanan, Harini; Seidler, Tobias; Luna, Martín Francisco; Sokolov, Michael; Morbidelli, Massimo; et al.; Hybrid Models for the simulation and prediction of chromatographic processes for protein capture; Elsevier Science; Journal of Chromatography - A; 1650; 8-2021; 1-120021-96731873-3778CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.chroma.2021.462248info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0021967321003721info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:10:05Zoai:ri.conicet.gov.ar:11336/154013instacron: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:34982025-09-29 10:10:05.277CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Hybrid Models for the simulation and prediction of chromatographic processes for protein capture |
title |
Hybrid Models for the simulation and prediction of chromatographic processes for protein capture |
spellingShingle |
Hybrid Models for the simulation and prediction of chromatographic processes for protein capture Narayanan, Harini ARTIFICIAL NEURAL NETWORK CAPTURE CHROMATOGRAPHY HYBRID MODELS |
title_short |
Hybrid Models for the simulation and prediction of chromatographic processes for protein capture |
title_full |
Hybrid Models for the simulation and prediction of chromatographic processes for protein capture |
title_fullStr |
Hybrid Models for the simulation and prediction of chromatographic processes for protein capture |
title_full_unstemmed |
Hybrid Models for the simulation and prediction of chromatographic processes for protein capture |
title_sort |
Hybrid Models for the simulation and prediction of chromatographic processes for protein capture |
dc.creator.none.fl_str_mv |
Narayanan, Harini Seidler, Tobias Luna, Martín Francisco Sokolov, Michael Morbidelli, Massimo Butté, Alessandro |
author |
Narayanan, Harini |
author_facet |
Narayanan, Harini Seidler, Tobias Luna, Martín Francisco Sokolov, Michael Morbidelli, Massimo Butté, Alessandro |
author_role |
author |
author2 |
Seidler, Tobias Luna, Martín Francisco Sokolov, Michael Morbidelli, Massimo Butté, Alessandro |
author2_role |
author author author author author |
dc.subject.none.fl_str_mv |
ARTIFICIAL NEURAL NETWORK CAPTURE CHROMATOGRAPHY HYBRID MODELS |
topic |
ARTIFICIAL NEURAL NETWORK CAPTURE CHROMATOGRAPHY HYBRID MODELS |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/2.9 https://purl.org/becyt/ford/2 |
dc.description.none.fl_txt_mv |
The biopharmaceutical industries are continuously faced with the pressure to reduce the development costs and accelerate development time scales. The traditional approach of heuristic-based or platform process-based optimization is soon getting obsolete, and more generalized tools for process development and optimization are required to keep pace with the emerging trends. Thus, advanced model-based methods that can reduce the can ensure accelerated development of robust processes with minimal experiments are necessary. Though mechanistic models for chromatography are quite popular, their success is limited by the need to have accurate knowledge of adsorption isotherms and mass transfer kinetics. As an alternative, in this work, a hybrid modeling approach is proposed. Thereby, the chromatographic unit behavior is learned by a combination of neural network and mechanistic model while fitting suitable experimental breakthrough curves. Since this approach does not require identifying suitable mechanistic assumptions for all the phenomena, it can be developed with lower effort. Thus, allowing the scientists to concentrate their focus on process development. The performance of the hybrid model is compared with the mechanistic Lumped kinetic Model for in-silico data and experiments conducted on a system of industrial relevance. The flexibility of the hybrid modeling approach results in about three times higher accuracies compared to Lumped Kinetic Model. This is validated for five different isotherm models used to simulate data, with the hybrid model showing about two to three times lower prediction errors in all the cases. Not only in prediction, but we could also show that the hybrid model is more robust in extrapolating across process conditions with about three times lower error than the LKM. Additionally, it could be demonstrated that an appropriately tailored formulation of the hybrid model can be used to generate representations for the underlying principles such as adsorption equilibria and mass transfer kinetics. Fil: Narayanan, Harini. Institute of Chemical and Bioengineering; Suiza Fil: Seidler, Tobias. Institute of Chemical and Bioengineering; Suiza Fil: Luna, Martín Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina. Institute of Chemical and Bioengineering; Suiza Fil: Sokolov, Michael. No especifíca; Fil: Morbidelli, Massimo. Politecnico di Milano; Italia Fil: Butté, Alessandro. No especifíca; |
description |
The biopharmaceutical industries are continuously faced with the pressure to reduce the development costs and accelerate development time scales. The traditional approach of heuristic-based or platform process-based optimization is soon getting obsolete, and more generalized tools for process development and optimization are required to keep pace with the emerging trends. Thus, advanced model-based methods that can reduce the can ensure accelerated development of robust processes with minimal experiments are necessary. Though mechanistic models for chromatography are quite popular, their success is limited by the need to have accurate knowledge of adsorption isotherms and mass transfer kinetics. As an alternative, in this work, a hybrid modeling approach is proposed. Thereby, the chromatographic unit behavior is learned by a combination of neural network and mechanistic model while fitting suitable experimental breakthrough curves. Since this approach does not require identifying suitable mechanistic assumptions for all the phenomena, it can be developed with lower effort. Thus, allowing the scientists to concentrate their focus on process development. The performance of the hybrid model is compared with the mechanistic Lumped kinetic Model for in-silico data and experiments conducted on a system of industrial relevance. The flexibility of the hybrid modeling approach results in about three times higher accuracies compared to Lumped Kinetic Model. This is validated for five different isotherm models used to simulate data, with the hybrid model showing about two to three times lower prediction errors in all the cases. Not only in prediction, but we could also show that the hybrid model is more robust in extrapolating across process conditions with about three times lower error than the LKM. Additionally, it could be demonstrated that an appropriately tailored formulation of the hybrid model can be used to generate representations for the underlying principles such as adsorption equilibria and mass transfer kinetics. |
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/11336/154013 Narayanan, Harini; Seidler, Tobias; Luna, Martín Francisco; Sokolov, Michael; Morbidelli, Massimo; et al.; Hybrid Models for the simulation and prediction of chromatographic processes for protein capture; Elsevier Science; Journal of Chromatography - A; 1650; 8-2021; 1-12 0021-9673 1873-3778 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/154013 |
identifier_str_mv |
Narayanan, Harini; Seidler, Tobias; Luna, Martín Francisco; Sokolov, Michael; Morbidelli, Massimo; et al.; Hybrid Models for the simulation and prediction of chromatographic processes for protein capture; Elsevier Science; Journal of Chromatography - A; 1650; 8-2021; 1-12 0021-9673 1873-3778 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.chroma.2021.462248 info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0021967321003721 |
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 application/pdf |
dc.publisher.none.fl_str_mv |
Elsevier Science |
publisher.none.fl_str_mv |
Elsevier Science |
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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1844613986217099264 |
score |
13.070432 |