Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/49116
Title: Machine learning applied for metabolic flux-based control of micro-aerated fermentations in bioreactors
Keywords: Machine learning
Saccharomyces cerevisiae
Micro-aerobic fermentation strategy
Issue Date: May-2021
Publisher: Wiley
Citation: MESQUITA, T. J. B. et al. Machine learning applied for metabolic flux-based control of micro-aerated fermentations in bioreactors. Biotechnology and Bioengineering, [S.l.], v. 118, n. 5, p. 2076-2091, May 2021.
Abstract: Various 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.
URI: https://onlinelibrary.wiley.com/doi/abs/10.1002/bit.27721
http://repositorio.ufla.br/jspui/handle/1/49116
Appears in Collections:DEG - Artigos publicados em periódicos

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