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dc.contributor.authorBradford, Eric
dc.contributor.authorSchweidtmann, Artur M.
dc.contributor.authorZhang, Dongda
dc.contributor.authorJing, Keju
dc.contributor.authordel Rio-Chanona, Ehecatl Antonio
dc.date.accessioned2018-12-17T10:02:55Z
dc.date.available2018-12-17T10:02:55Z
dc.date.created2018-10-20T10:04:09Z
dc.date.issued2018
dc.identifier.citationComputers and Chemical Engineering. 2018, 118 143-158.nb_NO
dc.identifier.issn0098-1354
dc.identifier.urihttp://hdl.handle.net/11250/2577889
dc.description.abstractDynamic modeling is an important tool to gain better understanding of complex bioprocesses and to determine optimal operating conditions for process control. Currently, two modeling methodologies have been applied to biosystems: kinetic modeling, which necessitates deep mechanistic knowledge, and artificial neural networks (ANN), which in most cases cannot incorporate process uncertainty. The goal of this study is to introduce an alternative modeling strategy, namely Gaussian processes (GP), which incorporates uncertainty but does not require complicated kinetic information. To test the performance of this strategy, GPs were applied to model microalgae growth and lutein production based on existing experimental datasets and compared against the results of previous ANNs. Furthermore, a dynamic optimization under uncertainty is performed, avoiding over-optimistic optimization outside of the model’s validity. The results show that GPs possess comparable prediction capabilities to ANNs for long-term dynamic bioprocess modeling, while accounting for model uncertainty. This strongly suggests their potential applications in bioprocess systems engineering.nb_NO
dc.language.isoengnb_NO
dc.publisherElseviernb_NO
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.titleDynamic modeling and optimization of sustainable algal production with uncertainty using multivariate Gaussian processesnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionacceptedVersionnb_NO
dc.source.pagenumber143-158nb_NO
dc.source.volume118nb_NO
dc.source.journalComputers and Chemical Engineeringnb_NO
dc.identifier.doi10.1016/j.compchemeng.2018.07.015
dc.identifier.cristin1621886
dc.description.localcode© 2018. This is the authors’ accepted and refereed manuscript to the article. Locked until 2.8.2020 due to copyright restrictions. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/nb_NO
cristin.unitcode194,63,25,0
cristin.unitnameInstitutt for teknisk kybernetikk
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode2


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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
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