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

Effective application of immobilized second generation industrial Saccharomyces cerevisiae strain on consolidated bioprocessing

Integrated bioprocessing strategies can facilitate ethanol production from both cellulose and hemicellulose fractions of lignocellulosic biomass. Consolidated bioprocessing (CBP) is an approach that combines enzyme production, biomass hydrolysis and sugar fermentation in a single step. However, technologies that propose the use of microorganisms together with solid biomass present the difficulty of the recovery and reuse of the biocatalyst, which can be overcome by cell immobilization. In this regard, this work applied immobilized cells of AC14 yeast, a recombinant yeast that secretes 7 hydrolytic enzymes, in the CBP process in a successful proof-of-concept for the enzyme access to the substrate polymers. The most appropriate cell load for CBP under the conditions studied with immobilized cells was selected among three optical densities (OD) 10, 55 and 100. These experiments were performed with free cells to ensure that the results were not biased by mass limitations effects. OD 10 achieved 100% of the sugar consumption and the higher specific production of enzymes, being selected for further studies. Diffusional effects were observed with immobilized cells under static conditions. However, mass transfer limitations were mitigated under agitation, with an 18.5% increase in substrate consumption rate (from 2.7 to 3.5 g/L/h), reaching the same substrate uptake rates as free cells. In addition, immobilized cells achieved 100% hydrolysis and consumption of all substrates offered within only 12 h. Overall, this is the first report of a successful application of immobilized yeast cells in CBP processes for bioethanol production, a promising technology that can be extended to other biorefinery bioproducts.

Cell Immobilization Using Alginate-Based Beads as a Protective Technique against Stressful Conditions of Hydrolysates for 2G Ethanol Production

The development of biorefineries brings the necessity of an efficient consumption of all sugars released from biomasses, including xylose. In addition, the presence of inhibitors in biomass hydrolysates is one of the main challenges in bioprocess feasibility. In this study, the application of Ca-alginate hybrid gels in the immobilization of xylose-consuming recombinant yeast was explored with the aim of improving the tolerance of inhibitors. The recombinant yeast Saccharomyces cerevisiae GSE16-T18SI.1 (T18) was immobilized in Ca-alginate and Ca-alginate–chitosan hybrid beads, and its performance on xylose fermentation was evaluated in terms of tolerance to different acetic acid concentrations (0–12 g/L) and repeated batches of crude sugarcane bagasse hemicellulose hydrolysate. The use of the hybrid gel improved yeast performance in the presence of 12 g/L of acetic acid, achieving 1.13 g/L/h of productivity and reaching 75% of the theoretical ethanol yield, with an improvement of 32% in the xylose consumption rate (1:1 V_beads/V_medium, 35 °C, 150 rpm and pH 5.2). The use of hybrid alginate–chitosan gel also led to better yeast performance at crude hydrolysate, yielding one more batch than the pure-alginate beads. These results demonstrate the potential of a hybrid gel as an approach that could increase 2G ethanol productivity and allow cell recycling for a longer period.

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

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.