Machine learning applied for metabolic flux-based control of micro-aerated fermentations in bioreactors

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.creatorMesquita, Thiago J. B.-
Autor(es): dc.creatorCampani, Gilson-
Autor(es): dc.creatorGiordano, Roberto C.-
Autor(es): dc.creatorZangirolami, Teresa C.-
Autor(es): dc.creatorHorta, Antonio C. L.-
Data de aceite: dc.date.accessioned2026-02-09T12:02:34Z-
Data de disponibilização: dc.date.available2026-02-09T12:02:34Z-
Data de envio: dc.date.issued2022-01-31-
Data de envio: dc.date.issued2022-01-31-
Data de envio: dc.date.issued2021-05-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/49116-
Fonte completa do material: dc.identifierhttps://onlinelibrary.wiley.com/doi/abs/10.1002/bit.27721-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1153061-
Descrição: dc.descriptionVarious bio-based processes depend on controlled micro-aerobic conditions to achieve a satisfactory product yield. However, the limiting oxygen concentration varies according to the micro-organism employed, while for industrial applications, there is no cost-effective way of measuring it at low levels. This study proposes a machine learning procedure within a metabolic flux-based control strategy (SUPERSYS_MCU) to address this issue. The control strategy used simulations of a genome-scale metabolic model to generate a surrogate model in the form of an artificial neural network, to be used in a micro-aerobic fermentation strategy (MF-ANN). The meta-model provided setpoints to the controller, allowing adjustment of the inlet air flow to control the oxygen uptake rate. The strategy was evaluated in micro-aerobic batch cultures employing industrial Saccharomyces cerevisiae yeast, with defined medium and glucose as the carbon source, as a case study. The performance of the proposed control scheme was compared with a conventional fermentation and with three previously reported micro-aeration strategies, including respiratory quotient-based control and constant air flow rate. Due to maintenance of the oxidative balance at the anaerobiosis threshold, the MF-ANN provided volumetric ethanol productivity of 4.16 g·L−1·h−1 and a yield of 0.48 gethanol.gsubstrate−1, which were higher than the values achieved for the other conditions studied (maximum of 3.4 g·L−1·h−1 and 0.35–0.40 gethanol·gsubstrate−1, respectively). Due to its modular character, the MF-ANN strategy could be adapted to other micro-aerated bioprocesses.-
Idioma: dc.languageen-
Publicador: dc.publisherWiley-
Direitos: dc.rightsrestrictAccess-
???dc.source???: dc.sourceBiotechnology and Bioengineering-
Palavras-chave: dc.subjectMachine learning-
Palavras-chave: dc.subjectSaccharomyces cerevisiae-
Palavras-chave: dc.subjectMicro-aerobic fermentation strategy-
Título: dc.titleMachine learning applied for metabolic flux-based control of micro-aerated fermentations in bioreactors-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

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