Dynamic model of temperature impact on cell viability and major product formation during fed-batch and continuous ethanolic fermentation in Saccharomyces cerevisiae

• Published in: Bioresource technology 2012-08, Vol.117, p.242-250
• Main Authors: Amillastre, Emilie, Aceves-Lara, César-Arturo, Uribelarrea, Jean-Louis, Alfenore, Sandrine, Guillouet, Stéphane E.
• Format: Article
• Language: English
• Subjects: Batch Cell Culture Techniques - methods; Bio-ethanol fermentation; Biomass; Computer Simulation; Confidence Intervals; Dynamic model; Ethanol - metabolism; Fermentation - physiology; Glycerol; Glycerol - metabolism; Kinetics; Life Sciences; Microbial Viability; Models, Biological; Reproducibility of Results; Saccharomyces cerevisiae; Saccharomyces cerevisiae - metabolism; Temperature
• ISSN: 0960-8524; 1873-2976
• DOI: 10.1016/j.biortech.2012.04.013

► This study provides a model for the impact of temperature on ethanolic fermentation. ► The innovation is to take into account the total and viable biomass and glycerol. ► The model was validated under different fermentation modes and temperature profiles. The impact of the temperature on an industrial yeast strain was investigated in very high ethanol performance fermentation fed-batch process within the range of 30–47°C. As previously observed with a lab strain, decoupling between growth and glycerol formation occurred at temperature of 36°C and higher. A dynamic model was proposed to describe the impact of the temperature on the total and viable biomass, ethanol and glycerol production. The model validation was implemented with experimental data sets from independent cultures under different temperatures, temperature variation profiles and cultivation modes. The proposed model fitted accurately the dynamic evolutions for products and biomass concentrations over a wide range of temperature profiles. R2 values were above 0.96 for ethanol and glycerol in most experiments. The best results were obtained at 37°C in fed-batch and chemostat cultures. This dynamic model could be further used for optimizing and monitoring the ethanol fermentation at larger scale.