Enhancement of ethanol production efficiency in repeated-batch fermentation from sweet sorghum stem juice: effect of initial sugar, nitrogen and aeration

BACKGROUND: Ethanol concentration (PE), ethanol productivity (QP) and sugar consumption (SC) are important values in industrial ethanol production. In this study, initial sugar and nitrogen (urea) concentrations in sweet sorghum stem juice (SSJ) were optimized for high PE (≥10%, v/v), QP, (≥2.5 g/L·h) and SC (≥90%) by Saccharomyces cerevisiae SSJKKU01. Then, repeated-batch fermentations under normal gravity (NG) and high gravity (HG) conditions were studied. RESULTS: The initial sugar at 208 g/L and urea at 2.75 g/L were the optimum values to meet the criteria. At the initial yeast cell concentration of ~1 × 108 cells/mL, the PE, QP and SC were 97.06 g/L, 3.24 g/L·h and 95.43%, respectively. Repeated-batch fermentations showed that the ethanol production efficiency of eight successive cycles with and without aeration were not significantly different when the initial sugar of cycles 2 to 8 was under NG conditions (~140 g/L). Positive effects of aeration were observed when the initial sugar from cycle 2 was under HG conditions (180­200 g/L). The PE and QP under no aeration were consecutively lower from cycle 1 to cycle 6. Additionally, aeration affected ergosterol formation in yeast cell membrane at high ethanol concentrations, whereas trehalose content under all conditions was not different. CONCLUSION: Initial sugar, sufficient nitrogen and appropriated aeration are necessary for promoting yeast growth and ethanol fermentation. The SSJ was successfully used as an ethanol production medium for a high level of ethanol production. Aeration was not essential for repeated-batch fermentation under NG conditions, but it was beneficial under HG conditions.

On-line monitoring of multiple component parameters during ethanol fermentation by near-infrared spectroscopy / 生物工程学报

The quantity of biomass, glucose concentration and ethanol concentration are important parameters in ethanol fermentation. Traditional methods are usually based on samples for off-line measurement, which not only requires multiple instruments for test and analysis but also consumes notable time and effort, and therefore is inconvenient for real-time process control and optimization. In this study, an in-situ detection method based on the near-infrared (NIR) spectroscopy is proposed for measuring the above process parameters in real time. The in-situ measurement is carried out by using an immersion type NIR spectroscopy. A multi-output prediction model for simultaneously estimating the quantity of glucose, biomass and ethanol is established based on a multi-output least-squares support vector regression algorithm. The experimental results show that the proposed method can precisely measure the quantity of glucose, biomass and ethanol during the ethanol fermentation process. Compared to the existing partial-least-squares method for modeling and prediction of individual components, the proposed method could evidently improve the measurement accuracy and reliability.