为给今后复合发酵添加剂的开发提供新的途径,研究了纤维素分解菌与乳酸菌复合发酵情况下二者数量均达到最优值的发酵剂量。采用响应曲面设计法,设计乳酸菌(LAB)和真菌的数量为响应值,求出响应值均为最大时的自变量值。自变量为同、异型...为给今后复合发酵添加剂的开发提供新的途径,研究了纤维素分解菌与乳酸菌复合发酵情况下二者数量均达到最优值的发酵剂量。采用响应曲面设计法,设计乳酸菌(LAB)和真菌的数量为响应值,求出响应值均为最大时的自变量值。自变量为同、异型发酵LAB和纤维素分解菌添加比例及添加剂量,其中同型[植物乳杆菌(Lactobacillus plantarum)+戊糖片球菌(Pediococcus acidilactici)]、异型[布氏乳杆菌(Lactobacillus buchneri)]发酵LAB的添加量均为1×105、3×105和5×105cfu·g^(-1)(即5、5.48、5.70 lg cfu·g^(-1)),纤维素分解菌的比例为黑曲霉(Aspergillus niger)∶绿色木霉菌(Trichoderma viride)∶枯草芽孢杆菌(Bacillus subtilis)=1∶1∶2、1∶2∶1和2∶1∶1,纤维素分解菌的添加剂量为0.1%、0.2%和0.3%。LAB和真菌的数量的测定采用传统菌种计数法计数。结果表明:建立的LAB和真菌数量的二次多项式模型显著性分别为P<0.01和P=0.03,R2分别为0.95和0.74。其中同、异型发酵LAB的添加量及其交互作用对LAB数量的曲面效应影响显著(P<0.05)。真菌的添加量以及同、异型发酵LAB的交互作用对真菌数量的曲面效应影响显著(P<0.05)。最终优化结果为同型发酵LAB添加量为5×105cfu·g^(-1)(5.70 lg cfu·g^(-1)),异型发酵LAB添加量为4.7×105cfu·g^(-1)(5.67 lg cfu·g^(-1)),纤维素分解菌比例为2∶1∶1,添加量为0.3%。展开更多
Reliable production of biofuels and specifically bioethanol has attracted a significant amount of re-search recently.Within this context,this study deals with dynamic simulation of bioethanol production processes and ...Reliable production of biofuels and specifically bioethanol has attracted a significant amount of re-search recently.Within this context,this study deals with dynamic simulation of bioethanol production processes and in particular aims at developing a mathematical model for describing simultaneous saccharification and co-fermentation （SSCF） of C6 and C5 sugars.The model is constructed by combining existing mathematical mod-els for enzymatic hydrolysis and co-fermentation.An inhibition of ethanol on cellulose conversion is introduced in order to increase the reliability.The mathematical model for the SSCF is verified by comparing the model predic-tions with experimental data obtained from the ethanol production based on kraft paper mill sludge.When fitting the model to the data,only the yield coefficients for glucose and xylose metabolism were fine-tuned,which were found to be 0.43 g·g-1 （ethanol/glucose） and 0.35 g·g-1 （ethanol/xylose） respectively.These promising validation results encourage further model application to evaluate different process configurations for lignocellulosic bioetha-nol technology.展开更多
文摘为给今后复合发酵添加剂的开发提供新的途径,研究了纤维素分解菌与乳酸菌复合发酵情况下二者数量均达到最优值的发酵剂量。采用响应曲面设计法,设计乳酸菌(LAB)和真菌的数量为响应值,求出响应值均为最大时的自变量值。自变量为同、异型发酵LAB和纤维素分解菌添加比例及添加剂量,其中同型[植物乳杆菌(Lactobacillus plantarum)+戊糖片球菌(Pediococcus acidilactici)]、异型[布氏乳杆菌(Lactobacillus buchneri)]发酵LAB的添加量均为1×105、3×105和5×105cfu·g^(-1)(即5、5.48、5.70 lg cfu·g^(-1)),纤维素分解菌的比例为黑曲霉(Aspergillus niger)∶绿色木霉菌(Trichoderma viride)∶枯草芽孢杆菌(Bacillus subtilis)=1∶1∶2、1∶2∶1和2∶1∶1,纤维素分解菌的添加剂量为0.1%、0.2%和0.3%。LAB和真菌的数量的测定采用传统菌种计数法计数。结果表明:建立的LAB和真菌数量的二次多项式模型显著性分别为P<0.01和P=0.03,R2分别为0.95和0.74。其中同、异型发酵LAB的添加量及其交互作用对LAB数量的曲面效应影响显著(P<0.05)。真菌的添加量以及同、异型发酵LAB的交互作用对真菌数量的曲面效应影响显著(P<0.05)。最终优化结果为同型发酵LAB添加量为5×105cfu·g^(-1)(5.70 lg cfu·g^(-1)),异型发酵LAB添加量为4.7×105cfu·g^(-1)(5.67 lg cfu·g^(-1)),纤维素分解菌比例为2∶1∶1,添加量为0.3%。
基金Supported by the Mexican National Council for Science and Technology (CONACyT# 118903)the Danish Research Council for Technology and Production Sciences (FTP# 274-07-0339)
文摘Reliable production of biofuels and specifically bioethanol has attracted a significant amount of re-search recently.Within this context,this study deals with dynamic simulation of bioethanol production processes and in particular aims at developing a mathematical model for describing simultaneous saccharification and co-fermentation （SSCF） of C6 and C5 sugars.The model is constructed by combining existing mathematical mod-els for enzymatic hydrolysis and co-fermentation.An inhibition of ethanol on cellulose conversion is introduced in order to increase the reliability.The mathematical model for the SSCF is verified by comparing the model predic-tions with experimental data obtained from the ethanol production based on kraft paper mill sludge.When fitting the model to the data,only the yield coefficients for glucose and xylose metabolism were fine-tuned,which were found to be 0.43 g·g-1 （ethanol/glucose） and 0.35 g·g-1 （ethanol/xylose） respectively.These promising validation results encourage further model application to evaluate different process configurations for lignocellulosic bioetha-nol technology.
