Modeling of bioprocesses via MINLP-based symbolic regression of S-system formalisms

dc.contributor.authorForster, Tim
dc.contributor.authorVázquez, Daniel
dc.contributor.authorCruz-Bournazou, Mariano Nicolas
dc.contributor.authorButté, Alessandro
dc.contributor.authorGuillén-Gosálbez, Gonzalo
dc.date.accessioned2023-03-29T11:26:32Z
dc.date.available2023-03-29T11:26:32Z
dc.date.issued2022-12-17
dc.description.abstractMathematical 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.en
dc.identifier.eissn1873-4375
dc.identifier.issn0098-1354
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/18700
dc.identifier.urihttps://doi.org/10.14279/depositonce-17508
dc.language.isoen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject.ddc600 Technik, Medizin, angewandte Wissenschaften::660 Chemische Verfahrenstechnik::660 Chemische Verfahrenstechnik
dc.subject.otheroptimizationen
dc.subject.otherMINLPen
dc.subject.otherArtificial neural networken
dc.subject.otherlow sized dataseten
dc.subject.otherbioprocessen
dc.titleModeling of bioprocesses via MINLP-based symbolic regression of S-system formalisms
dc.typeArticle
dc.type.versionpublishedVersion
dcterms.bibliographicCitation.articlenumber108108
dcterms.bibliographicCitation.doi10.1016/j.compchemeng.2022.108108
dcterms.bibliographicCitation.journaltitleComputers & Chemical Engineering
dcterms.bibliographicCitation.originalpublishernameElsevier
dcterms.bibliographicCitation.originalpublisherplaceAmsterdam
dcterms.bibliographicCitation.volume170
dcterms.rightsHolder.referenceCreative-Commons-Lizenz
tub.accessrights.dnbfree
tub.affiliationFak. 3 Prozesswissenschaften::Inst. Biotechnologie::FG Bioverfahrenstechnik
tub.publisher.universityorinstitutionTechnische Universität Berlin

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