Chemical Engineering Laboratory, Faculty of Sciences, University of La Coruña, Rúa da Fraga 10, 15008 La Coruña, Spain.
Bioresour Technol. 2012 Jun;114:518-22. doi: 10.1016/j.biortech.2012.03.027. Epub 2012 Mar 21.
A two-level full factorial design was carried out in order to investigate the effect of four factors on the bioconversion of carbon monoxide to ethanol and acetic acid by Clostridium autoethanogenum: initial pH (4.75-5.75), initial total pressure (0.8-1.6 bar), cysteine-HCl·H(2)O concentration (0.5-1.2 g/L) and yeast extract concentration (0.6-1.6 g/L). The maximum ethanol production was enhanced up to 200% when lowering the pH and amount yeast extract from 5.75 to 4.75 g/L and 1.6 to 0.6 g/L, respectively. The regression coefficient, regression model and analysis of variance (ANOVA) were obtained using MINITAB 16 software for ethanol, acetic acid and biomass. For ethanol, it was observed that all the main effects and the interaction effects were found statistically significant (p<0.05). The comparison between the experimental and the predicted values was found to be very satisfactory, indicating the suitability of the predicted model.
为了研究四个因素对产酸克雷伯氏菌（Clostridium autoethanogenum）将一氧化碳生物转化为乙醇和乙酸的影响，进行了两级完全因子设计：初始 pH 值（4.75-5.75）、初始总压力（0.8-1.6 巴）、半胱氨酸盐酸盐·H₂O 浓度（0.5-1.2 g/L）和酵母提取物浓度（0.6-1.6 g/L）。当将 pH 值从 5.75 降低到 4.75 g/L，酵母提取物的量从 1.6 降低到 0.6 g/L 时，乙醇的最大产量提高了 200%。使用 MINITAB 16 软件获得了乙醇、乙酸和生物量的回归系数、回归模型和方差分析（ANOVA）。对于乙醇，观察到所有的主要影响和交互作用都具有统计学意义（p<0.05）。实验值和预测值之间的比较非常令人满意，表明预测模型的适用性。
