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dc.contributor.author
Luna, Martin F.
dc.contributor.author
Ochsner, Andrea M.
dc.contributor.author
Amstutz, Véronique
dc.contributor.author
von Blarer, Damian
dc.contributor.author
Sokolov, Michael
dc.contributor.author
Arosio, Paolo
dc.contributor.author
Zinn, Manfred
dc.date.accessioned
2021-09-30T07:43:25Z
dc.date.available
2021-09-14T02:45:05Z
dc.date.available
2021-09-30T07:43:25Z
dc.date.issued
2021-09
dc.identifier.other
10.3390/pr9091560
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/505413
dc.identifier.doi
10.3929/ethz-b-000505413
dc.description.abstract
Polyhydroxyalkanoates (PHA) are renewable alternatives to traditional oil-derived poly-mers. PHA can be produced by different microorganisms in continuous culture under specific media composition, which makes the production process both promising and challenging. In order to achieve large productivities while maintaining high yield and efficiency, the continuous culture needs to be operated in the so-called dual nutrient limitation condition, where both the nitrogen and carbon sources are kept at very low concentrations. Mathematical models can greatly assist both design and operation of the bioprocess, but are challenged by the complexity of the system, in particular by the dual nutrient-limited growth phenomenon, where the cells undergo a metabolic shift that abruptly changes their behavior. Traditional, non-structured mechanistic models based on Monod uptake kinetics can be used to describe the bioreactor operation under specific process conditions. However, in the absence of a model description of the metabolic phenomena inside the cell, the extrapolation to a broader operation domain (e.g., different feeding concentrations and dilution rates) may present mismatches between the predictions and the actual process outcomes. Such detailed models may require almost perfect knowledge of the cell metabolism and omic-level measurements, hampering their development. On the other hand, purely data-driven models that learn correlations from experimental data do not require any prior knowledge of the process and are therefore unbiased and flexible. However, many more data are required for their development and their extrapolation ability is limited to conditions that are similar to the ones used for training. An attractive alternative is the combination of the extrapolation power of first principles knowledge with the flexibility of machine learning methods. This approach results in a hybrid model for the growth and uptake rates that can be used to predict the dynamic operation of the bioreactor. Here we develop a hybrid model to describe the continuous production of PHA by Pseudomonas putida GPo1 culture. After training, the model with experimental data gained under different dilution rates and medium compositions, we demonstrate how the model can describe the process in a wide range of operating conditions, including both single and dual nutrient-limited growth.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
MDPI
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
artificial intelligence
en_US
dc.subject
bioprocess modelling
en_US
dc.subject
hybrid models
en_US
dc.subject
machine learning
en_US
dc.subject
PHA production
en_US
dc.title
Modeling of Continuous PHA Production by a Hybrid Approach Based on First Principles and Machine Learning
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2021-09-01
ethz.journal.title
Processes
ethz.journal.volume
9
en_US
ethz.journal.issue
9
en_US
ethz.pages.start
1560
en_US
ethz.size
15 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Basel
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02020 - Dep. Chemie und Angewandte Biowiss. / Dep. of Chemistry and Applied Biosc.::02516 - Inst. f. Chemie- und Bioingenieurwiss. / Inst. Chemical and Bioengineering::09572 - Arosio, Paolo / Arosio, Paolo
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02020 - Dep. Chemie und Angewandte Biowiss. / Dep. of Chemistry and Applied Biosc.::02516 - Inst. f. Chemie- und Bioingenieurwiss. / Inst. Chemical and Bioengineering::09572 - Arosio, Paolo / Arosio, Paolo
ethz.date.deposited
2021-09-14T02:45:17Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-09-30T07:43:42Z
ethz.rosetta.lastUpdated
2021-09-30T07:43:42Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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