Biochemical Engineering and Pilot Plant Research and Development Unit, King Mongkut's University of Technology Thonburi, 49 Soi Tientalay 25, Thakhum, Bangkhuntien, Bangkok 10150, Thailand.
Biotechnol Prog. 2012 Sep-Oct;28(5):1119-25. doi: 10.1002/btpr.1595. Epub 2012 Jul 31.
Optimization of culture parameters for achieving the most efficient ethanol fermentation is challenging due to multiple variables involved. Here we presented a rationalized methodology for multi-variables optimization through the design of experiments DoE approach. Three critical parameters, pH, temperature, and agitation speed, affecting ethanol fermentation in S. stipitis was investigated. A predictive model showed that agitation speed significantly affected ethanol synthesis. Reducing pH and temperature also improved ethanol production. The model identified the optimum culture conditions for the most efficient ethanol production with the yield and productivity of 0.46 g/g and 0.28 g/l h, respectively, which is consistent with experimental observation. The results also indicated the scalability of the model from shake flask to bioreactor. Thus, DoE is a promising tool permitting the rapid establishment of culture conditions for the most efficient ethanol fermentation in S. stipitis. The approach could be useful to reduce process development time in lignocellulosic ethanol industry.
由于涉及多个变量，优化达到最高效率乙醇发酵的培养参数具有挑战性。在这里，我们通过实验设计（DoE）方法提出了一种多变量优化的合理方法。研究了影响酿酒酵母乙醇发酵的三个关键参数，即 pH 值、温度和搅拌速度。预测模型表明，搅拌速度显著影响乙醇合成。降低 pH 值和温度也能提高乙醇的产量。该模型确定了最有利于高效乙醇生产的最佳培养条件，产率和生产效率分别为 0.46 g/g 和 0.28 g/l/h，与实验观察结果一致。结果还表明该模型从摇瓶到生物反应器具有可扩展性。因此，DoE 是一种很有前途的工具，可以快速建立酿酒酵母中最高效率乙醇发酵的培养条件。该方法可用于减少木质纤维素乙醇产业的工艺开发时间。
