Unsupervised learning bioreactor regimes.
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| Title: | Unsupervised learning bioreactor regimes. |
|---|---|
| Authors: | Laborda, Víctor Puig I1 (AUTHOR) viclab@dtu.dk, Puiman, Lars2 (AUTHOR), Groves, Teddy1 (AUTHOR), Haringa, Cees2 (AUTHOR), Nielsen, Lars Keld1,3 (AUTHOR) lars.nielsen@uq.edu.au |
| Source: | Computers & Chemical Engineering. Mar2025, Vol. 194, pN.PAG-N.PAG. 1p. |
| Subjects: | Clustering algorithms, Machine learning, Computational fluid dynamics, K-means clustering, Time complexity |
| Abstract: | Efficient operation of bioreactors is crucial for the success of biomanufacturing processes. Traditional Computational Fluid Dynamics (CFD) simulations provide detailed insights but often involve lengthy computation times and complexity, hindering their practicality for real-time applications. This study introduces a novel multivariate unsupervised learning algorithm that clusters bioreactors into physically meaningful regions based on CFD-generated and real-world data. These clusters not only facilitate the determination of internal reactor regimes but also serve as a foundational step for developing compartment models. Our approach utilizes a custom k-means clustering algorithm, which ensures spatial continuity of clusters by incorporating geometric data, and optimizes the number of compartments to maximize physical significance and data retention. This optimization is guided by a Pareto front analysis, balancing the need for clear compartment definition with the preservation of maximum information from the dataset. The effectiveness and versatility of this methodology were verified through case studies involving a 202 m³ Rushton impeller bioreactor (steady state simulation) and an 840 m³ airlift reactor (dynamic simulation). In the airlift reactor, the clustering algorithm accounted for dynamic fluctuations by averaging the simulation results, providing a robust method for incorporating temporal variations into the compartment analysis. The findings highlight the advantages of 3-D compartmentalization in capturing the intricate dynamics of fluid motion and cellular activities, thereby advancing the design of bioreactors and scaling down experiments for more efficient industrial applications. [Display omitted] [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: 182447922 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Unsupervised learning bioreactor regimes. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Laborda%2C+Víctor+Puig+I%22">Laborda, Víctor Puig I</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> viclab@dtu.dk</i><br /><searchLink fieldCode="AR" term="%22Puiman%2C+Lars%22">Puiman, Lars</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Groves%2C+Teddy%22">Groves, Teddy</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Haringa%2C+Cees%22">Haringa, Cees</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Nielsen%2C+Lars+Keld%22">Nielsen, Lars Keld</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<i> lars.nielsen@uq.edu.au</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computers+%26+Chemical+Engineering%22">Computers & Chemical Engineering</searchLink>. Mar2025, Vol. 194, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Clustering+algorithms%22">Clustering algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+fluid+dynamics%22">Computational fluid dynamics</searchLink><br /><searchLink fieldCode="DE" term="%22K-means+clustering%22">K-means clustering</searchLink><br /><searchLink fieldCode="DE" term="%22Time+complexity%22">Time complexity</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Efficient operation of bioreactors is crucial for the success of biomanufacturing processes. Traditional Computational Fluid Dynamics (CFD) simulations provide detailed insights but often involve lengthy computation times and complexity, hindering their practicality for real-time applications. This study introduces a novel multivariate unsupervised learning algorithm that clusters bioreactors into physically meaningful regions based on CFD-generated and real-world data. These clusters not only facilitate the determination of internal reactor regimes but also serve as a foundational step for developing compartment models. Our approach utilizes a custom k-means clustering algorithm, which ensures spatial continuity of clusters by incorporating geometric data, and optimizes the number of compartments to maximize physical significance and data retention. This optimization is guided by a Pareto front analysis, balancing the need for clear compartment definition with the preservation of maximum information from the dataset. The effectiveness and versatility of this methodology were verified through case studies involving a 202 m³ Rushton impeller bioreactor (steady state simulation) and an 840 m³ airlift reactor (dynamic simulation). In the airlift reactor, the clustering algorithm accounted for dynamic fluctuations by averaging the simulation results, providing a robust method for incorporating temporal variations into the compartment analysis. The findings highlight the advantages of 3-D compartmentalization in capturing the intricate dynamics of fluid motion and cellular activities, thereby advancing the design of bioreactors and scaling down experiments for more efficient industrial applications. [Display omitted] [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.2024.108891 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Clustering algorithms Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Computational fluid dynamics Type: general – SubjectFull: K-means clustering Type: general – SubjectFull: Time complexity Type: general Titles: – TitleFull: Unsupervised learning bioreactor regimes. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Laborda, Víctor Puig I – PersonEntity: Name: NameFull: Puiman, Lars – PersonEntity: Name: NameFull: Groves, Teddy – PersonEntity: Name: NameFull: Haringa, Cees – PersonEntity: Name: NameFull: Nielsen, Lars Keld IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 00981354 Numbering: – Type: volume Value: 194 Titles: – TitleFull: Computers & Chemical Engineering Type: main |
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