Background Fine-tuning the aeration for cultivations when oxygen-limited conditions are demanded (such as the production of vaccines, isobutanol, 2–3 butanediol, acetone, and bioethanol) is still a challenge in the area of bioreactor automation and advanced control. In this work, an innovative control strategy based on metabolic fluxes was implemented and evaluated in a case study: micro-aerated ethanol fermentation. Results The experiments were carried out in fed-batch mode, using commercial Saccharomyces cerevisiae , defined medium, and glucose as carbon source. Simulations of a genome-scale metabolic model for Saccharomyces cerevisiae were used to identify the range of oxygen and substrate fluxes that would maximize ethanol fluxes. Oxygen supply and feed flow rate were manipulated to control oxygen and substrate fluxes, as well as the respiratory quotient (RQ). The performance of the controlled cultivation was compared to two other fermentation strategies: a conventional “Brazilian fuel-ethanol plant” fermentation and a strictly anaerobic fermentation (with ultra-pure nitrogen used as the inlet gas). The cultivation carried out under the proposed control strategy showed the best average volumetric ethanol productivity (7.0 g L −1 h −1 ), with a final ethanol concentration of 87 g L −1 and yield of 0.46 g ethanol g substrate −1 . The other fermentation strategies showed lower yields (close to 0.40 g ethanol g substrate −1 ) and ethanol productivity around 4.0 g L −1 h −1 . Conclusion The control system based on fluxes was successfully implemented. The proposed approach could also be adapted to control several bioprocesses that require restrict aeration.
RESUMO -A preciosa (Aniba canelilla) é uma espécie produtora de óleo essencial aromático e possível fonte de metabólitos secundários antioxidantes, compostos de grande interesse para a indústria de cosméticos. Tais metabólitos podem ser usados na produção de matérias primas ou em formulações que contenham elementos fitoquímicos benéficos para a saúde. Neste estudo foi realizada uma triagem fitoquímica para conhecer os constituintes da preciosa e avaliar seu potencial como fonte de novos antioxidantes. Os testes fitoquímicos qualitativos foram positivos para fenóis, taninos, flavonóides e saponinas. A atividade antioxidante, avaliada pelo métdo do DPPH, foi mais expressiva no extrato etanólico de folhas, seguida do extrato proveniente dos galhos. Observou-se uma queda significativa da atividade antioxidante nos extratos obtidos após a extração de óleo essencial. Verifica-se que A. canelilla apresenta metabólitos capazes de inibir os processos de oxidação, através do sequestro de radicais livres, apresentando potencial como fonte de antioxidantes. INTRODUÇÃOA oxidação ocorre no corpo humano como consequência da combustão biológica, que está envolvida no processo de respiração. A oxidação incompleta provoca a produção de espécies reativas de oxigênio (EROs) ou radicais livres (RL), danosos ao organismo, gerando o estresse oxidativo, responsável pelo envelhecimento e até o surgimento de câncer (Dröge, 2002;Valko et al., 2007). A quantidade de RL nos seres vivos é controlada por antioxidantes, que podem ser produzidos pelo próprio ser vivo ou adquiridos pela dieta, podendo ser ingeridos compostos sintéticos ou naturais. Atualmente, a busca por antioxidantes naturais tem aumentado, pois além de serem mais facilmente aceitos na sociedade, sua capacidade de inibir a formação ou a propagação de RL apresenta pouco efeito colateral quando comparados aos seus equivalentes sintéticos (De La Roche et al., 2010;Russo et al., 2012).
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.
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