2021 Annual Meeting

(344c) Development and Validation of an Equation Oriented Model Based on a Laboratorial Alcoholic Fermentation Process from the Wort Saccharum Officinarum

The development of biotechnological processes has been growing, in importance and needed, due to its significance to human life. Thereby, phenomenological and/or empirical models are often developed to understand, and predict, the complete phenomena in a bioreactor and improve the design, optimization, control, and process state estimation. Computer simulation is one of the most commonly used tools for analyzing such a system[1,2]. In this context, nonlinear models have been commonly studied to represent bioprocesses behavior, including under conditions not yet tested. This work focuses on alcoholic fermentation from Saccharum officinarum as the carbon source in 4 L bioreactor through mathematical models to create a customized process block at the AVEVA Simulation Process simulator. For this purpose, a specific database has been developed on AVEVA ThermoData tool, based on components and temperature-dependent properties available in the literature. The kinetics of alcoholic fermentation was made in batch reactor and laboratory scale to evaluate and validate the model, where temperature, total soluble solids (°Brix), pH, alcohol content, biomass concentration (by the dry mass method) [3], and total reducing sugars (3,5-Dinitrosalicylic acid curve) [4] were monitored. Fermentation occurred after inoculation of industrial yeast Saccharomyces cerevisiae (initial cell concentration, Cx0=15 g/L), which was first hydrated to be later added to batch bioreactor with 0.74 L of cultivation media (initial substrate concentration, Cs0 = 169.22 g/L). The behavior of the process was numerically simulated using MATLAB.

The model studied is based on the Modified Monod equation, commonly used to explain the relationship between substrate concentration and specific cell growth velocity. Lineweaver-Burk determined kinetic parameters, and the cell growth concentration rates, ethanol production, substrate consumption were considered in the model, and the stoichiometric yield of biomass and ethanol, respectively, per grams of reacted sugar. Concentration profiles were obtained based on the assumption that sugar concentration in the reactor is inhibiting. The need for such an inhibition consideration by substrate to a good acquire a good model response of the model related to the experimental data was observed. The process occurred with an average temperature of 30.4ºC, and the maximum specific velocity found was 1.713 h-1, 45 g/L for growth and 137 g/L for the inhibition constants. It was observed that sugar was not entirely consumed during the experimental batch, and the simulated model has predicted such a behavior. The rate of sugar degradation is associated with the viability of cell growth [5], and a decrease in pH was observed during the experimental batch. In such a condition, an acidic environment can cause yeast inhibition and, pH changes during fermentation are associated with different yeast metabolisms [6]. Ethanol is another factor that is associated with yeast inhibition [7]. The obtained simulated results have been compared with the experimental batch, presenting an excellent agreement.

References

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[2]Jiang, Y.; Welle, J. E. van der; Rubingh, O.; Eikenhorst, G. van; Bakker, W. A. M. (2019) Kinetic model for adherent Vero cell growth and poliovirus production in batch bioreactors, Process Biochemistry, v. 81, p.156-164. <https://doi.org/10.1016/j.procbio.2019.03.010>.

[3] Triboli, E. P. D. R. (1989) Métodos analíticos para o acompanhamento da fermentação alcoólica. São Caetano do Sul: Laboratório de Engenharia Bioquímica e de Alimentos, Escola de Engenharia de Mauá, Instituto Mauá de Tecnologia, 52p.

[4] Miller, G.L. (1959) Use of dinitrosalicylic acid reagent for determination of reducing sugar. Analytical Chemistry, v.31, n.3, p.426-428. <https://pubs.acs.org/doi/10.1021/ac60147a030&gt;.

[5] LI, H.; JIANG, D.; LIU, W.; YANG, Y. ZHANG, Y.; JIN, C.; SUN, S. (2020). Comparison of fermentation behaviors and properties of raspberry wines by spontaneous and controlled alcoholic fermentations, Food Research International, v. 128, 2020, 108801. <10.1016/j.foodres.2019.108801>.

[6] Lu, Y.; Huang, D.; Lee, P.R.; Liu, S.Q. (2016) Assessment of volatile and non-volatile 487 compounds in durian wines fermented with four commercial non-Saccharomyces yeasts. J. 488 Sci. Journal of the Science of Food and Agriculture. v. 96, p. 1511–1521. <10.1002/jsfa.7253>.

[7] Veloso, I. I. K.; Rodrigues, K. C. S.; Sonegob, J. L. S.; Cruza, A. J. G.; Badino, A. C. (2019) Fed-batch ethanol fermentation at low temperature as a way to obtain highly concentrated alcoholic wines: Modeling and optimization, Biochemical Engineering Journal, v. 141, p. 60-70. <https://doi.org/10.1016/j.bej.2018.10.005>.