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

Autor(es)

Mesquita, Thiago J. B. ^[1] ; Campani, Gilson ^[2] ; Giordano, Roberto C. ^[1] ; Zangirolami, Teresa C. ^[1] ; Horta, Antonio C. L. ^[1]

Afiliação do(s) autor(es)

^[1] Fed Univ Sao Carlos PPGEQ UFSCar, Grad Program Chem Engn, Rodovia Washington Luis, Km 235, BR-13565905 Sao Carlos, SP - Brazil

^[2] Univ Fed Lavras, Dept Engn, Lavras, MG - Brazil

• Tipo de documento: Artigo Científico
• Fonte: Biotechnology and Bioengineering; v. 118, n. 5 MAR 2021.
• Citações Web of Science: 1

Resumo

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 center dot L-1 center dot h(-1) and a yield of 0.48 g(ethanol).g(substrate)(-1), which were higher than the values achieved for the other conditions studied (maximum of 3.4 g center dot L-1 center dot h(-1) and 0.35-0.40 g(ethanol)center dot g(substrate)(-1), respectively). Due to its modular character, the MF-ANN strategy could be adapted to other micro-aerated bioprocesses. (AU)

• Processo FAPESP: 16/10636-8 - Da fábrica celular à biorrefinaria integrada Biodiesel-Bioetanol: uma abordagem sistêmica aplicada a problemas complexos em micro e macroescalas
• Beneficiário: Roberto de Campos Giordano
• Modalidade de apoio: Auxílio à Pesquisa - Programa BIOEN - Temático