Data-efficient hybrid parameter scaling for accurate microbial bioreactor scale-up.
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| Title: | Data-efficient hybrid parameter scaling for accurate microbial bioreactor scale-up. |
|---|---|
| Authors: | Atabaev, Otabek1 (AUTHOR), Babaa, Moulay Rachid1,2 (AUTHOR) m.babaa@newuu.uz |
| Source: | Bioprocess & Biosystems Engineering. May2026, Vol. 49 Issue 5, p1263-1274. 12p. |
| Subjects: | Scaling laws (Statistical physics), Extrapolation, Biotechnology, Lipases, Fermentation, Dynamic models |
| Abstract: | Accurately predicting microbial fermentation performance at an industrial scale is challenging due to hydrodynamic and oxygen-transfer limitations, which disrupt geometric similarity and cause nonlinear changes in apparent production kinetics. The goal of this work is to develop a data-efficient hybrid scaling law that extrapolates time-course model parameters of relative lipase concentration across bioreactor volumes using minimal experimental scales. Sigmoidal lipase accumulation curves from 10 L, 100 L, 4 m³, and 100 m³ bioreactors were extracted from the study of Geraats (1994) and fitted using Logistic, Gompertz, and Baranyi–Roberts (BR) models. Kinetic parameters (Lmax, k or µ, tmid or λ) obtained from small-scale bioreactors (10 L and 100 L) were used to construct two minimal two-point relations: power-law and logarithmic. As these relations showed systematic overshoot and undershoot during extrapolation, a hybrid convex-weighting scheme was developed and calibrated at the 4 m³ pilot scale. When applied to the 100 m³ industrial dataset, the hybrid method significantly improved prediction accuracy compared to either scaling law alone, reducing RMSE by more than 60%. The Baranyi–Roberts model combined with hybrid scaling achieved the highest overall accuracy (RMSE = 2.651). Requiring only three experimental scales, this approach is computationally efficient, mechanistically interpretable, and suitable for industrial contexts where extensive pilot campaigns or computational fluid dynamics simulations are not feasible. The hybrid scaling framework thus offers a practical and data-efficient solution for reliable bioreactor scale-up. [ABSTRACT FROM AUTHOR] |
| Copyright of Bioprocess & Biosystems Engineering is the property of Springer Nature 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: 194545363 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Data-efficient hybrid parameter scaling for accurate microbial bioreactor scale-up. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Atabaev%2C+Otabek%22">Atabaev, Otabek</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Babaa%2C+Moulay+Rachid%22">Babaa, Moulay Rachid</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> m.babaa@newuu.uz</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Bioprocess+%26+Biosystems+Engineering%22">Bioprocess & Biosystems Engineering</searchLink>. May2026, Vol. 49 Issue 5, p1263-1274. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Scaling+laws+%28Statistical+physics%29%22">Scaling laws (Statistical physics)</searchLink><br /><searchLink fieldCode="DE" term="%22Extrapolation%22">Extrapolation</searchLink><br /><searchLink fieldCode="DE" term="%22Biotechnology%22">Biotechnology</searchLink><br /><searchLink fieldCode="DE" term="%22Lipases%22">Lipases</searchLink><br /><searchLink fieldCode="DE" term="%22Fermentation%22">Fermentation</searchLink><br /><searchLink fieldCode="DE" term="%22Dynamic+models%22">Dynamic models</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Accurately predicting microbial fermentation performance at an industrial scale is challenging due to hydrodynamic and oxygen-transfer limitations, which disrupt geometric similarity and cause nonlinear changes in apparent production kinetics. The goal of this work is to develop a data-efficient hybrid scaling law that extrapolates time-course model parameters of relative lipase concentration across bioreactor volumes using minimal experimental scales. Sigmoidal lipase accumulation curves from 10 L, 100 L, 4 m³, and 100 m³ bioreactors were extracted from the study of Geraats (1994) and fitted using Logistic, Gompertz, and Baranyi–Roberts (BR) models. Kinetic parameters (Lmax, k or µ, tmid or λ) obtained from small-scale bioreactors (10 L and 100 L) were used to construct two minimal two-point relations: power-law and logarithmic. As these relations showed systematic overshoot and undershoot during extrapolation, a hybrid convex-weighting scheme was developed and calibrated at the 4 m³ pilot scale. When applied to the 100 m³ industrial dataset, the hybrid method significantly improved prediction accuracy compared to either scaling law alone, reducing RMSE by more than 60%. The Baranyi–Roberts model combined with hybrid scaling achieved the highest overall accuracy (RMSE = 2.651). Requiring only three experimental scales, this approach is computationally efficient, mechanistically interpretable, and suitable for industrial contexts where extensive pilot campaigns or computational fluid dynamics simulations are not feasible. The hybrid scaling framework thus offers a practical and data-efficient solution for reliable bioreactor scale-up. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Bioprocess & Biosystems Engineering is the property of Springer Nature 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: 10.1007/s00449-026-03314-w Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 1263 Subjects: – SubjectFull: Scaling laws (Statistical physics) Type: general – SubjectFull: Extrapolation Type: general – SubjectFull: Biotechnology Type: general – SubjectFull: Lipases Type: general – SubjectFull: Fermentation Type: general – SubjectFull: Dynamic models Type: general Titles: – TitleFull: Data-efficient hybrid parameter scaling for accurate microbial bioreactor scale-up. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Atabaev, Otabek – PersonEntity: Name: NameFull: Babaa, Moulay Rachid IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 16157591 Numbering: – Type: volume Value: 49 – Type: issue Value: 5 Titles: – TitleFull: Bioprocess & Biosystems Engineering Type: main |
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