A High-Throughput Approach for Modeling and Simulation of Homofermentative Microorganisms Applied to Ethanol Fermentation by <i>S. cerevisiae</i>

Mesquita, Thiago José Barbosa; Sandri, Juliana P.; Giordano, Roberto de Campos; Horta, Antonio Carlos Luperni; Zangirolami, Teresa Cristina

doi:10.1089/ind.2020.0034

Cited by 11 publications

(15 citation statements)

References 48 publications

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“…A fixed air flow rate of 0.36 L.min −1 was used, without any control action regarding the gas-phase composition. The selection of the air flow rate value was based on the average air flow rate of the two previous Cases (A and B) (Mesquita et al, 2021). A similar strategy was investigated in the literature (Silva et al, 2011) to improve ethanol production in batch fermentations of Pichia stipitis.…”

Section: Mol O2mentioning

confidence: 99%

“…In this case study, a standard fermentation, with no gas flow or control system, was carried out as a reference condition. It mimicked the Melle-Boinot process commonly used in Brazilian ethanol production facilities (Amorim et al, 2011;Basso et al, 2011;Lopes et al, 2016;Mesquita et al, 2021).…”

Section: Mol O2mentioning

confidence: 99%

“…The experiments were performed using the FT858L industrial yeast strain, kindly donated by Fermentec Ltda (FERMENTEC, 2018; Mesquita et al, 2021). The lyophilized cells were hydrated and activated to prepare the inoculum used in the bioreactor cultures, as described elsewhere (Mesquita et al, 2021). All the bioreactor experiments used defined minimal medium (5.0 g…”

Section: Micro-organism and Culture Mediummentioning

confidence: 99%

“…urea, 3.0 ppm Kamoran antibiotic, and 1 ml•L −1 polypropylene glycol as a defoamer) (Mesquita et al, 2021;Milessi et al, 2020a). The antibiotic was kindly donated by Química Real Ltda.…”

Section: Micro-organism and Culture Mediummentioning

confidence: 99%

“…The strategies studied to produce ethanol could be evaluated considering several connected aspects, starting from the micro-aeration approaches applied, which triggered different cell responses and influenced the performance indexes (Table 2). The concentration profiles for cases HRC, PID-RQ, Qfix-VL, MF-ANN, and MF-ANN-PI are available in the Supporting Information Material, or elsewhere for cases Qfix-L and BBP (Mesquita et al, 2021). 6) can meet the desired setpoint.…”

Section: Performance Of the Cultivation Strategiesmentioning

confidence: 99%

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

Mesquita

Campani

Giordano

et al. 2021

Biotech & Bioengineering

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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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Section: Mol O2mentioning

confidence: 99%

Section: Mol O2mentioning

confidence: 99%

Section: Micro-organism and Culture Mediummentioning

confidence: 99%

Section: Micro-organism and Culture Mediummentioning

confidence: 99%

Section: Performance Of the Cultivation Strategiesmentioning

confidence: 99%

See 3 more Smart Citations

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

Mesquita

Campani

Giordano

et al. 2021

Biotech & Bioengineering

View full text Add to dashboard Cite

show abstract

Screening of yeast co‐culture using crude hydrolysate for co‐fermentation of pentose and hexose

Sandri

Milessi

Zangirolami

et al. 2023

Biofuels Bioprod Bioref

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The use of all sugars from biomass fractionation – that is, hexoses and pentoses – is essential to develop feasible processes in a biorefinery context. Hexoses are converted into different products by most microorganisms. The main issue is the conversion of pentoses, especially when mixed with hexoses. Improving sugar conversion in co‐fermentation is a major challenge, which was explored in this work. Co‐culture using Saccharomyces cerevisiae and evolved Kluyveromyces marxianus strains was evaluated by screening using 24 deep‐well plates. Glucose depletion and 83% of xylose conversion were achieved with supplemented hemicellulosic hydrolysate medium (medium volume corresponding to 2/5 of deep‐well volume), and initial cell concentrations of 2.5 g L‐1 for each yeast, incubated at 150 rpm. The screening strategy provided reliable data that confirmed the co‐culture as a promising approach for the co‐assimilation of biomass‐derived sugars. The importance of aeration control for effective xylose assimilation was also pointed out.

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Evaluation of Consolidated Bioprocessing of Sugarcane Biomass by a Multiple Hydrolytic Enzyme Producer Saccharomyces Yeast

et al. 2023

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A High-Throughput Approach for Modeling and Simulation of Homofermentative Microorganisms Applied to Ethanol Fermentation by S. cerevisiae

Cited by 11 publications

References 48 publications

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

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

Screening of yeast co‐culture using crude hydrolysate for co‐fermentation of pentose and hexose

Evaluation of Consolidated Bioprocessing of Sugarcane Biomass by a Multiple Hydrolytic Enzyme Producer Saccharomyces Yeast

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