Machine learning applied for metabolic flux‐based control of micro‐aerated fermentations in bioreactors
Thiago José Barbosa Mesquita, Gilson Campani, Roberto C. Giordano, Teresa Cristina Zangirolami, Antônio Carlos Luperni Horta
Abstract
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 g ethanol .g substrate −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 g ethanol ·g substrate −1 , respectively). Due to its modular character, the MF‐ANN strategy could be adapted to other micro‐aerated bioprocesses.