Optimization of Solid-State Ethanol Fermentation with the Soluble Carbohydrate in Sweet Sorghum Stalk Using Response Surface Methodology

被引:2
|
作者
Mei, Xiaoyan [1 ,2 ]
Liu, Ronghou [1 ]
Shen, Fei [1 ]
Cao, Weixing [1 ]
Wu, Hanjing [1 ]
Liu, Shiyu [3 ]
机构
[1] Shanghai Jiao Tong Univ, Sch Agr & Biol, Biomass Energy Engn Res Ctr, Shanghai 200240, Peoples R China
[2] Liaoning Inst Managerial Staff, Shenyang 110161, Peoples R China
[3] Shanghai Normal Univ, Coll Life & Environm Sci, Shanghai 201418, Peoples R China
关键词
Sweet Sorghum; Solid-State Fermentation; Ethanol; Response Surface Methodology; BIOETHANOL; JUICE;
D O I
10.1166/jbmb.2011.1169
中图分类号
O69 [应用化学];
学科分类号
081704 ;
摘要
In order to improve the ethanol yield efficiency through solid-state fermentation with sweet sorghum stalk, the Plackett-Burman design was applied for screening the impact of fermentation temperature, initial pH, yeast inoculation ratio, supplement dose of (NH4)(2)SO4, K2HPO4, and MgSO4 center dot 7H(2)O on ethanol yield efficiency. Meanwhile, according to the screened results,the Box-Behnken design was employed for the optimization of the main factors. The results of Plackett-Burman analysis indicated that the importance sequence of the mentioned 6 factors was the supplement dose of K2HPO4 > the supplement dose of (NH4)(2)SO4 > initial pH > yeast inoculation ratio > fermentation temperature. Three significant factors, including temperature, supplement dose of (NH4)(2)SO4 and supplement dose of K2HPO4, were chosen as the independent variables. Box-Behnken design was conducted to obtain the multivariate quadratic regression model, which was used to predict ethanol yield efficiency. The optimum conditions for solid-state ethanol fermentation of sweet sorghum stalk by response surface methodology were determined as follows: fermentation temperature 30.72 degrees C, (NH4)(2)SO4 supplement dose 0.202%, K2HPO4 supplement dose 0.127%. The maximum predictive ethanol yield efficiency of 93.0% was obtained at the optimized conditions. Ethanol yield efficiency of the verification experiment was 93.4 +/- 0.7%, suggesting that the multivariate quadratic regression model was valid for the prediction.
引用
收藏
页码:532 / 538
页数:7
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