Modeling of bioprocesses via MINLP-based symbolic regression of S-system formalisms.
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| Title: | Modeling of bioprocesses via MINLP-based symbolic regression of S-system formalisms. |
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| Authors: | Forster, Tim1 (AUTHOR), Vázquez, Daniel1 (AUTHOR), Cruz-Bournazou, Mariano Nicolas2,3 (AUTHOR), Butté, Alessandro2 (AUTHOR), Guillén-Gosálbez, Gonzalo1 (AUTHOR) gonzalo.guillen.gosalbez@chem.ethz.ch |
| Source: | Computers & Chemical Engineering. Feb2023, Vol. 170, pN.PAG-N.PAG. 1p. |
| Subjects: | Artificial neural networks, Ordinary differential equations, Nonlinear programming, Product quality, Petri nets |
| Abstract: | • A method for building models based on an S-system formalism is proposed. • ODE integration is avoided for model training by a two-stage incremental approach. • Models tailorable in complexity by the maximum number of parameters. • Trained closed-form expressions easily interpretable. Mathematical modeling helps guide experiments more effectively, support process monitoring and control tasks, stabilize product quality, increase consumer safety, or ease specific decision-making tasks for subject matter experts. However, constructing accurate process models can be challenging, especially with bioprocesses, due to complex metabolic mechanisms and data scarcity. This work proposes a method for building models combining a mass balance backbone with a canonical kinetic representation, i.e., the S-system formalism. The model structure and parameters that best describe the studied system are automatically identified by solving a mixed-integer nonlinear programming (MINLP) problem. Following an incremental approach, the integration of ordinary differential equations is avoided. Numerical examples show that our method performs similarly to models based on artificial neural networks, outperforming them in some cases while providing an analytical, closed-form model. Such expressions can be more easily interpreted and optimized in existing algebraic modeling systems. [ABSTRACT FROM AUTHOR] |
| Copyright of Computers & Chemical Engineering is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 161553592 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Modeling of bioprocesses via MINLP-based symbolic regression of S-system formalisms. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Forster%2C+Tim%22">Forster, Tim</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Vázquez%2C+Daniel%22">Vázquez, Daniel</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cruz-Bournazou%2C+Mariano+Nicolas%22">Cruz-Bournazou, Mariano Nicolas</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Butté%2C+Alessandro%22">Butté, Alessandro</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Guillén-Gosálbez%2C+Gonzalo%22">Guillén-Gosálbez, Gonzalo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> gonzalo.guillen.gosalbez@chem.ethz.ch</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computers+%26+Chemical+Engineering%22">Computers & Chemical Engineering</searchLink>. Feb2023, Vol. 170, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Ordinary+differential+equations%22">Ordinary differential equations</searchLink><br /><searchLink fieldCode="DE" term="%22Nonlinear+programming%22">Nonlinear programming</searchLink><br /><searchLink fieldCode="DE" term="%22Product+quality%22">Product quality</searchLink><br /><searchLink fieldCode="DE" term="%22Petri+nets%22">Petri nets</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: • A method for building models based on an S-system formalism is proposed. • ODE integration is avoided for model training by a two-stage incremental approach. • Models tailorable in complexity by the maximum number of parameters. • Trained closed-form expressions easily interpretable. Mathematical modeling helps guide experiments more effectively, support process monitoring and control tasks, stabilize product quality, increase consumer safety, or ease specific decision-making tasks for subject matter experts. However, constructing accurate process models can be challenging, especially with bioprocesses, due to complex metabolic mechanisms and data scarcity. This work proposes a method for building models combining a mass balance backbone with a canonical kinetic representation, i.e., the S-system formalism. The model structure and parameters that best describe the studied system are automatically identified by solving a mixed-integer nonlinear programming (MINLP) problem. Following an incremental approach, the integration of ordinary differential equations is avoided. Numerical examples show that our method performs similarly to models based on artificial neural networks, outperforming them in some cases while providing an analytical, closed-form model. Such expressions can be more easily interpreted and optimized in existing algebraic modeling systems. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computers & Chemical Engineering is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.compchemeng.2022.108108 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Ordinary differential equations Type: general – SubjectFull: Nonlinear programming Type: general – SubjectFull: Product quality Type: general – SubjectFull: Petri nets Type: general Titles: – TitleFull: Modeling of bioprocesses via MINLP-based symbolic regression of S-system formalisms. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Forster, Tim – PersonEntity: Name: NameFull: Vázquez, Daniel – PersonEntity: Name: NameFull: Cruz-Bournazou, Mariano Nicolas – PersonEntity: Name: NameFull: Butté, Alessandro – PersonEntity: Name: NameFull: Guillén-Gosálbez, Gonzalo IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 00981354 Numbering: – Type: volume Value: 170 Titles: – TitleFull: Computers & Chemical Engineering Type: main |
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