The Experts below are selected from a list of 8550 Experts worldwide ranked by ideXlab platform
Dehua Liu - One of the best experts on this subject based on the ideXlab platform.
-
metabolism in 1 3 propanediol Fed Batch Fermentation by a d lactate deficient mutant of klebsiella pneumoniae
Biotechnology and Bioengineering, 2009Co-Authors: Nini Guo, Zongming Zheng, Hongjuan Liu, Dehua LiuAbstract:Klebsiella pneumoniae HR526, a new isolated 1,3-propanediol (1,3-PD) producer, exhibited great productivity. However, the accumulation of lactate in the late-exponential phase remained an obstacle of 1,3-PD industrial scale production. Hereby, mutants lacking D-lactate pathway were constructed by knocking out the ldhA gene encoding fermentative D-lactate dehydrogenase (LDH) of HR526. The mutant K. pneumoniae LDH526 with the lowest LDH activity was studied in aerobic Fed-Batch Fermentation. In experiments using pure glycerol as feedstock, the 1,3-PD concentrations, conversion, and productivity increased from 95.39 g L(-1), 0.48 and 1.98 g L(-1) h(-1) to 102. 06 g L(-1), 0.52 mol mol(-1) and 2.13 g L(-1) h(-1), respectively. The diol (1,3-PD and 2,3-butanediol) conversion increased from 0.55 mol mol(-1) to a maximum of 0.65 mol mol(-1). Lactate would not accumulate until 1,3-PD exceeded 84 g L(-1), and the final lactate concentration decreased dramatically from more than 40 g L(-1) to <3 g L(-1). Enzymic measurements showed LDH activity decreased by 89-98% during Fed-Batch Fermentation, and other related enzyme activities were not affected. NADH/NAD(+) enhanced more than 50% in the late-exponential phase as the D-lactate pathway was cut off, which might be the main reason for the change of final metabolites concentrations. The ability to utilize crude glycerol from biodiesel process and great genetic stability demonstrated that K. pnemoniae LDH526 was valuable for 1,3-PD industrial production.
-
physiologic mechanisms of sequential products synthesis in 1 3 propanediol Fed Batch Fermentation by klebsiella pneumoniae
Biotechnology and Bioengineering, 2008Co-Authors: Zongming Zheng, Nini Guo, Hongjuan Liu, Zhongzhen Cai, Dehua LiuAbstract:The glycerol Fed-Batch Fermentation by Klebsiella pneumoniae CGMCC 1.6366 exhibited the sequential synthesis of products, including acetate, 1,3-propanediol (1,3-PD), 2,3-butanediol, ethanol, succinate, and lactate. The dominant flux distribution was shifted from acetate formation to 1,3-PD formation in early- exponential growth phase and then to lactate synthesis in late-exponential growth phase. The underlying physiological mechanism of the above observations has been investigated via the related enzymes, nucleotide, and intermediary metabolites analysis. The carbon flow shift is dictated by the intrinsic physiological state and enzymatic activity regulation. Especially, the internal redox state could serve as a rate-controlling factor for 1,3-PD production. The q1,3-PD formation was the combined outcomes of regulations of glycerol dehydratase activity and internal redox balancing. The qethanol/qacetate ratios demonstrated the flexible adaptation mechanism of K. pneumoniae preferring ATP generation in early-exponential growth phase. A low PEP to pyruvate ratio corresponded LDH activity increase, leading to lactate accumulation in stationary phase. Biotechnol. Bioeng. 2008;100: 923–932. © 2008 Wiley Periodicals, Inc.
-
optimization of glycerol Fed Batch Fermentation in different reactor states a variable kinetic parameter approach
Applied Biochemistry and Biotechnology, 2002Co-Authors: Dongming Xie, Dehua Liu, Haoli Zhu, Jianan ZhangAbstract:To optimize the Fed-Batch processes of glycerol Fermentation in different reactor states, typical bioreactors including 500-mL shaking flask, 600-mL and 15-L airlift loop reactor, and 5-L stirred vessel were investigated. It was found that by reestimating the values of only two variable kinetic parameters associated with physical transport phenomena in a reactor, the macrokinetic model of glycerol Fermentation proposed in previous work could describe well the Batch processes in different reactor states. This variable kinetic parameter (VKP) approach was further applied to model-based optimization of discrete-pulse feed (DPF) strategies of both glucose and corn steep slurry for glycerol Fed-Batch Fermentation. The experimental results showed that, compared with the feed strategies determined just by limited experimental optimization in previous work, the DPF strategies with VKPs adjusted could improve glycerol productivity at least by 27% in the scale-down and scale-up reactor states. The approach proposed appeared promising for further modeling and optimization of glycerol Fermentation or the similar bioprocesses in larger scales.
Shangtian Yang - One of the best experts on this subject based on the ideXlab platform.
-
high titer n butanol production by clostridium acetobutylicum jb200 in Fed Batch Fermentation with intermittent gas stripping
Biotechnology and Bioengineering, 2012Co-Authors: Jingbo Zhao, Shangtian Yang, Ching I TangAbstract:Acetone–butanol–ethanol (ABE) Fermentation with a hyper-butanol producing Clostridium acetobutylicum JB200 was studied for its potential to produce a high titer of butanol that can be readily recovered with gas stripping. In Batch Fermentation without gas stripping, a final butanol concentration of 19.1 g/L was produced from 86.4 g/L glucose consumed in 78 h, and butanol productivity and yield were 0.24 g/L h and 0.21 g/g, respectively. In contrast, when gas stripping was applied intermittently in Fed-Batch Fermentation, 172 g/L ABE (113.3 g/L butanol, 49.2 g/L acetone, 9.7 g/L ethanol) were produced from 474.9 g/L glucose in six feeding cycles over 326 h. The overall productivity and yield were 0.53 g/L h and 0.36 g/g for ABE and 0.35 g/L h and 0.24 g/g for butanol, respectively. The higher productivity was attributed to the reduced butanol concentration in the Fermentation broth by gas stripping that alleviated butanol inhibition, whereas the increased butanol yield could be attributed to the reduced acids accumulation as most acids produced in acidogenesis were reassimilated by cells for ABE production. The intermittent gas stripping produced a highly concentrated condensate containing 195.9 g/L ABE or 150.5 g/L butanol that far exceeded butanol solubility in water. After liquid–liquid demixing or phase separation, a final product containing ∼610 g/L butanol, ∼40 g/L acetone, ∼10 g/L ethanol, and no acids was obtained. Compared to conventional ABE Fermentation, the Fed-Batch Fermentation with intermittent gas stripping has the potential to reduce at least 90% of energy consumption and water usage in n-butanol production from glucose. Biotechnol. Bioeng. 2012; 109: 2746–2756. © 2012 Wiley Periodicals, Inc.
-
Fed Batch Fermentation for n butanol production from cassava bagasse hydrolysate in a fibrous bed bioreactor with continuous gas stripping
Bioresource Technology, 2012Co-Authors: Congcong Lu, Jingbo Zhao, Shangtian YangAbstract:Concentrated cassava bagasse hydrolysate (CBH) containing 584.4 g/L glucose was studied for acetone–butanol–ethanol (ABE) Fermentation with a hyper-butanol-producing Clostridium acetobutylicum strain in a fibrous bed bioreactor with gas stripping for continuous butanol recovery. With periodical nutrient supplementation, stable production of n-butanol from glucose in the CBH was maintained in the Fed-Batch Fermentation over 263 h with an average sugar consumption rate of 1.28 g/L h and butanol productivity of 0.32 ± 0.03 g/L h. A total of 108.5 g/L ABE (butanol: 76.4 g/L, acetone: 27.0 g/L, ethanol: 5.1 g/L) was produced, with an overall yield of 0.32 ± 0.03 g/g glucose for ABE and 0.23 ± 0.01 g/g glucose for butanol. The gas stripping process generated a product containing 10–16% (w/v) of butanol, ∼4% (w/v) of acetone, a small amount of ethanol (<0.8%) and almost no acids, resulting in a highly concentrated butanol solution of ∼64% (w/v) after phase separation.
-
Fed Batch Fermentation for n butanol production from cassava bagasse hydrolysate in a fibrous bed bioreactor with continuous gas stripping
Bioresource Technology, 2012Co-Authors: Jingbo Zhao, Shangtian Yang, Dong WeiAbstract:Abstract Concentrated cassava bagasse hydrolysate (CBH) containing 584.4 g/L glucose was studied for acetone–butanol–ethanol (ABE) Fermentation with a hyper-butanol-producing Clostridium acetobutylicum strain in a fibrous bed bioreactor with gas stripping for continuous butanol recovery. With periodical nutrient supplementation, stable production of n-butanol from glucose in the CBH was maintained in the Fed-Batch Fermentation over 263 h with an average sugar consumption rate of 1.28 g/L h and butanol productivity of 0.32 ± 0.03 g/L h. A total of 108.5 g/L ABE (butanol: 76.4 g/L, acetone: 27.0 g/L, ethanol: 5.1 g/L) was produced, with an overall yield of 0.32 ± 0.03 g/g glucose for ABE and 0.23 ± 0.01 g/g glucose for butanol. The gas stripping process generated a product containing 10–16% (w/v) of butanol, ∼4% (w/v) of acetone, a small amount of ethanol (
Jingbo Zhao - One of the best experts on this subject based on the ideXlab platform.
-
high titer n butanol production by clostridium acetobutylicum jb200 in Fed Batch Fermentation with intermittent gas stripping
Biotechnology and Bioengineering, 2012Co-Authors: Jingbo Zhao, Shangtian Yang, Ching I TangAbstract:Acetone–butanol–ethanol (ABE) Fermentation with a hyper-butanol producing Clostridium acetobutylicum JB200 was studied for its potential to produce a high titer of butanol that can be readily recovered with gas stripping. In Batch Fermentation without gas stripping, a final butanol concentration of 19.1 g/L was produced from 86.4 g/L glucose consumed in 78 h, and butanol productivity and yield were 0.24 g/L h and 0.21 g/g, respectively. In contrast, when gas stripping was applied intermittently in Fed-Batch Fermentation, 172 g/L ABE (113.3 g/L butanol, 49.2 g/L acetone, 9.7 g/L ethanol) were produced from 474.9 g/L glucose in six feeding cycles over 326 h. The overall productivity and yield were 0.53 g/L h and 0.36 g/g for ABE and 0.35 g/L h and 0.24 g/g for butanol, respectively. The higher productivity was attributed to the reduced butanol concentration in the Fermentation broth by gas stripping that alleviated butanol inhibition, whereas the increased butanol yield could be attributed to the reduced acids accumulation as most acids produced in acidogenesis were reassimilated by cells for ABE production. The intermittent gas stripping produced a highly concentrated condensate containing 195.9 g/L ABE or 150.5 g/L butanol that far exceeded butanol solubility in water. After liquid–liquid demixing or phase separation, a final product containing ∼610 g/L butanol, ∼40 g/L acetone, ∼10 g/L ethanol, and no acids was obtained. Compared to conventional ABE Fermentation, the Fed-Batch Fermentation with intermittent gas stripping has the potential to reduce at least 90% of energy consumption and water usage in n-butanol production from glucose. Biotechnol. Bioeng. 2012; 109: 2746–2756. © 2012 Wiley Periodicals, Inc.
-
Fed Batch Fermentation for n butanol production from cassava bagasse hydrolysate in a fibrous bed bioreactor with continuous gas stripping
Bioresource Technology, 2012Co-Authors: Congcong Lu, Jingbo Zhao, Shangtian YangAbstract:Concentrated cassava bagasse hydrolysate (CBH) containing 584.4 g/L glucose was studied for acetone–butanol–ethanol (ABE) Fermentation with a hyper-butanol-producing Clostridium acetobutylicum strain in a fibrous bed bioreactor with gas stripping for continuous butanol recovery. With periodical nutrient supplementation, stable production of n-butanol from glucose in the CBH was maintained in the Fed-Batch Fermentation over 263 h with an average sugar consumption rate of 1.28 g/L h and butanol productivity of 0.32 ± 0.03 g/L h. A total of 108.5 g/L ABE (butanol: 76.4 g/L, acetone: 27.0 g/L, ethanol: 5.1 g/L) was produced, with an overall yield of 0.32 ± 0.03 g/g glucose for ABE and 0.23 ± 0.01 g/g glucose for butanol. The gas stripping process generated a product containing 10–16% (w/v) of butanol, ∼4% (w/v) of acetone, a small amount of ethanol (<0.8%) and almost no acids, resulting in a highly concentrated butanol solution of ∼64% (w/v) after phase separation.
-
Fed Batch Fermentation for n butanol production from cassava bagasse hydrolysate in a fibrous bed bioreactor with continuous gas stripping
Bioresource Technology, 2012Co-Authors: Jingbo Zhao, Shangtian Yang, Dong WeiAbstract:Abstract Concentrated cassava bagasse hydrolysate (CBH) containing 584.4 g/L glucose was studied for acetone–butanol–ethanol (ABE) Fermentation with a hyper-butanol-producing Clostridium acetobutylicum strain in a fibrous bed bioreactor with gas stripping for continuous butanol recovery. With periodical nutrient supplementation, stable production of n-butanol from glucose in the CBH was maintained in the Fed-Batch Fermentation over 263 h with an average sugar consumption rate of 1.28 g/L h and butanol productivity of 0.32 ± 0.03 g/L h. A total of 108.5 g/L ABE (butanol: 76.4 g/L, acetone: 27.0 g/L, ethanol: 5.1 g/L) was produced, with an overall yield of 0.32 ± 0.03 g/g glucose for ABE and 0.23 ± 0.01 g/g glucose for butanol. The gas stripping process generated a product containing 10–16% (w/v) of butanol, ∼4% (w/v) of acetone, a small amount of ethanol (
Jinyan Zhou - One of the best experts on this subject based on the ideXlab platform.
-
optimization of bacillus subtilis cell growth effecting jiean peptide production in Fed Batch Fermentation using central composite design
Electronic Journal of Biotechnology, 2014Co-Authors: Juan Zhong, Jie Yang, Xiaoyong Zhang, Yanli Ren, Hong Tan, Jinyan ZhouAbstract:article i nfo Background: Optimization of nutrient feeding was developed to improve the growth of Bacillus subtilis in Fed Batch Fermentation to increase the production of jiean-peptide (JAA). A central composite design (CCD) was used to obtain a model describing the relationship between glucose, total nitrogen, and the maximum cell dry weight in the culture broth with Fed Batch Fermentation in a 5 L fermentor. Results: The results were analyzed using response surface methodology (RSM), and the optimized values of glucose and total nitrogen concentration were 30.70 g/L and 1.68 g/L in the culture, respectively. The highest cell dry weight was improved to 77.50 g/L in Fed Batch Fermentation, which is 280% higher than the Batch Fermentation concentration (20.37 g/L). This led to a 44% increase of JAA production in Fed Batch Fermentation as compared to the production of Batch Fermentation. Conclusion: The results of this work improve the present production of JAA and may be adopted for other objective products' production.
-
the artificial neural network approach based on uniform design to optimize the Fed Batch Fermentation condition application to the production of iturin a
Microbial Cell Factories, 2014Co-Authors: Wenjing Peng, Juan Zhong, Jie Yang, Tan Xu, Song Xiao, Jinyan ZhouAbstract:Iturin A is a potential lipopeptide antibiotic produced by Bacillus subtilis. Optimization of iturin A yield by adding various concentrations of asparagine (Asn), glutamic acid (Glu) and proline (Pro) during the Fed-Batch Fermentation process was studied using an artificial neural network-genetic algorithm (ANN-GA) and uniform design (UD). Here, ANN-GA based on the UD data was used for the first time to analyze the Fed-Batch Fermentation process. The ANN-GA and UD methodologies were compared based on their fitting ability, prediction and generalization capacity and sensitivity analysis. The ANN model based on the UD data performed well on minimal statistical designed experimental number and the optimum iturin A yield was 13364.5 ± 271.3 U/mL compared with a yield of 9929.0 ± 280.9 U/mL for the control (Batch Fermentation without adding the amino acids). The root-mean-square-error for the ANN model with the training set and test set was 4.84 and 273.58 respectively, which was more than two times better than that for the UD model (32.21 and 483.12). The correlation coefficient for the ANN model with training and test sets was 100% and 92.62%, respectively (compared with 99.86% and 78.58% for UD). The error% for ANN with the training and test sets was 0.093 and 2.19 respectively (compared with 0.26 and 4.15 for UD). The sensitivity analysis of both methods showed the comparable results. The predictive error of the optimal iturin A yield for ANN-GA and UD was 0.8% and 2.17%, respectively. The satisfactory fitting and predicting accuracy of ANN indicated that ANN worked well with the UD data. Through ANN-GA, the iturin A yield was significantly increased by 34.6%. The fitness, prediction, and generalization capacities of the ANN model were better than those of the UD model. Further, although UD could get the insight information between variables directly, ANN was also demonstrated to be efficient in the sensitivity analysis. The results of these comparisons indicated that ANN could be a better alternative way for Fermentation optimization with limited number of experiments.
-
the artificial neural network approach based on uniform design to optimize the Fed Batch Fermentation condition application to the production of iturin a
Microbial Cell Factories, 2014Co-Authors: Wenjing Peng, Juan Zhong, Jie Yang, Song Xiao, Jinyan Zhou, Yanli Ren, Hong TanAbstract:Background: Iturin A is a potential lipopeptide antibiotic produced by Bacillus subtilis. Optimization of iturin A yield by adding various concentrations of asparagine (Asn), glutamic acid (Glu) and proline (Pro) during the Fed-Batch Fermentation process was studied using an artificial neural network-genetic algorithm (ANN-GA) and uniform design (UD). Here, ANN-GA based on the UD data was used for the first time to analyze the Fed-Batch Fermentation process. The ANN-GA and UD methodologies were compared based on their fitting ability, prediction and generalization capacity and sensitivity analysis. Results: The ANN model based on the UD data performed well on minimal statistical designed experimental number and the optimum iturin A yield was 13364.5 ± 271.3 U/mL compared with a yield of 9929.0 ± 280.9 U/mL for the control (Batch Fermentation without adding the amino acids). The root-mean-square-error for the ANN model with the training set and test set was 4.84 and 273.58 respectively, which was more than two times better than that for the UD model (32.21 and 483.12). The correlation coefficient for the ANN model with training and test sets was 100% and 92.62%, respectively (compared with 99.86% and 78.58% for UD). The error% for ANN with the training and test sets was 0.093 and 2.19 respectively (compared with 0.26 and 4.15 for UD). The sensitivity analysis of both methods showed the comparable results. The predictive error of the optimal iturin A yield for ANN-GA and UD was 0.8% and 2.17%, respectively. Conclusions: The satisfactory fitting and predicting accuracy of ANN indicated that ANN worked well with the UD data. Through ANN-GA, the iturin A yield was significantly increased by 34.6%. The fitness, prediction, and generalization capacities of the ANN model were better than those of the UD model. Further, although UD could get the insight information between variables directly, ANN was also demonstrated to be efficient in the sensitivity analysis. The results of these comparisons indicated that ANN could be a better alternative way for Fermentation optimization with limited number of experiments.
Fuli Wang - One of the best experts on this subject based on the ideXlab platform.
-
modeling and parameter updating for nosiheptide Fed Batch Fermentation process
Industrial & Engineering Chemistry Research, 2016Co-Authors: Dapeng Niu, Long Zhang, Fuli WangAbstract:Nosiheptide is a sulfur-containing peptide antibiotic obtained through Fermentation. It can be used as feed additives because of its relative safety and good effect. However, nosiheptide Fermentation does not have a high yield. Keeping the Fermentation environment or operating conditions optimum through optimization is an effective way to improve nosiheptide’s yield, while accurate and reliable process models are the basis to achieve process optimization. Based on the reaction mechanism of the nosiheptide Fed-Batch Fermentation process, we establish its mechanism models. Fermentation processes have slow time-varying characteristics and the conditions usually change due to disturbances during the production process, so the accuracy of established models tends to decline. Thus, models do not match the actual process gradually, leading to model mismatch, which have bad effects on the optimization and control of the process. Therefore, it is necessary to update the process models in time. In this paper, we up...
-
optimization of nosiheptide Fed Batch Fermentation process based on hybrid model
Industrial & Engineering Chemistry Research, 2013Co-Authors: Dapeng Niu, Mingxing Jia, Fuli WangAbstract:Nosiheptide, a sulfur-containing peptide antibiotic obtained through Fermentation, is a perfect feed additive, but its yield in industry is not high. Process optimization is a good way to increase nosiheptide yield, maintaining the optimum operating conditions of the Fermentation process, while optimization of the process requires a sufficiently accurate and robust process model. In this paper, the mechanism model for nosiheptide Fed-Batch Fermentation is first established. Then, in order to improve performance of the mechanism model, a hybrid model is built using least-squares support vector machines to compensate the errors between the mechanism model and the process. The hybrid model not only overcomes pure black-box model’s shortcoming that it often has poor generalization ability but improves the mechanism model’s accuracy. A yield optimization model of nosiheptide Fed-Batch Fermentation process is then established based on the hybrid model. An improved particle swarm optimization algorithm is used t...