Optimization of key process variables for enhanced hydrogen production by Enterobacter aerogenes using statistical methods

Jo, Ji Hye; Lee, Dong Hoon; Park, Donghee; Choe, Wonyoung; Park, Jong

doi:10.1016/j.biortech.2007.04.027

Cited by 134 publications

(39 citation statements)

References 23 publications

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“…2010a). In this study, the effects of different types of food waste, temperature and incubation time on bio-hydrogen and VFA production as well as on glucose consumption, were analysed by full quadratic model as shown below (Kim et al, 2008;Jo et al, 2008)…”

Section: Assay Methodsmentioning

confidence: 99%

“…There is a poor relationship between actual and predicted value as the coefficient of determination (R 2 ) was calculated to be 0.5576, which can explain only 55.76 % variability of the response. The diversity among waste type is the main reason for such a low value and this value could be at a higher level if the same waste was used in different proportions as reported in other studies (Kim et al, 2008;Jo et al, 2008). To overcome this problem, quadratic model was again developed for each waste type and the following equations were obtained: is much higher as compared to other values; this is because of the coefficient of determination that explained 98.58 % variability.…”

Section: Effect Of Temperature and Time On Bio-hydrogen Productionmentioning

confidence: 97%

“…In order to achieve this purpose, statistical modelling is an important tool to study the impact within the experimental range and can be further used for the development of response surface plots (Jo et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

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Optimizing the impact of temperature on bio-hydrogen production from food waste and its derivatives under no pH control using statistical modelling

et al. 2015

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Abstract. The effect of temperature on bio-hydrogen production by co-digestion of sewerage sludge with food waste and its two derivatives, i.e. noodle waste and rice waste, was investigated by statistical modelling. Experimental results showed that increasing temperature from mesophilic (37 • C) to thermophilic (55 • C) was an effective mean for increasing bio-hydrogen production from food waste and noodle waste, but it caused a negative impact on bio-hydrogen production from rice waste. The maximum cumulative biohydrogen production of 650 mL was obtained from noodle waste under thermophilic temperature condition. Most of the production was observed during the first 48 h of incubation, which continued until 72 h of incubation. The decline in pH during this interval was 4.3 and 4.4 from a starting value of 7 under mesophilic and thermophilic conditions, respectively. Most of the glucose consumption was also observed during 72 h of incubation and the maximum consumption was observed during the first 24 h, which was the same duration where the maximum pH drop occurred. The maximum hydrogen yields of 82.47 mL VS −1 , 131.38 mL COD −1 , and 44.90 mL glucose −1 were obtained from thermophilic food waste, thermophilic noodle waste and mesophilic rice waste, respectively. The production of volatile fatty acids increased with an increase in time and temperature in food waste and noodle waste reactors whereas they decreased with temperature in rice waste reactors. The statistical modelling returned good results with high values of coefficient of determination (R 2 ) for each waste type and 3-D response surface plots developed by using models developed. These plots developed a better understanding regarding the impact of temperature and incubation time on bio-hydrogen production trend, glucose consumption during incubation and volatile fatty acids production.

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Section: Assay Methodsmentioning

confidence: 99%

Section: Effect Of Temperature and Time On Bio-hydrogen Productionmentioning

confidence: 97%

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Optimizing the impact of temperature on bio-hydrogen production from food waste and its derivatives under no pH control using statistical modelling

et al. 2015

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“…For the first response of glucose released, the model did not show any lack of fit and the determination coefficient (R 2 ) obtained was 0.9753 which explained 97.53% of the variability in response. The model F-value of 30.656 and p-value less than 0.0001 indicates that the model terms were significant [15]. ANOVA showed that temperature is the most effective variable of glucose released.…”

Section: X3mentioning

confidence: 96%

Optimization of Enzymatic Saccharification of Alkali Pretreated Typha angustifolia for Glucose Production

Sopajarn¹,

Sangwichien²

2015

IJCEA

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Abstract-Lignocellulose ethanol is significantly sustainable bio-fuel. It is an environmentally friendly. This work is to develop a hydrolysis process of pretreated lignocellulose to ethanol production from narrow leaved cattail as a biomass material. Response surface methodology (RSM) with a central composite design (CCD) was followed to optimize the enzymatic saccharification process in order to obtain high glucose yield. Three independent variables (cellulase, β-glucosidase and temperature) that operating condition, vary at CCD five levels (-α, -1, 0, +1, +α). The optimum values for the predicted variables for glucose and xylose released were: cellulase loading 17.5 FPU/g substrate, β-glucosidase loading 0 U/g substrate and hydrolysis temperature 55°C. Under these optimal conditions, glucose and xylose yield reached to 413.25 mg/ g substrate and 75.48 mg/g substrate, respectively. The results of a confirmation experiment under the optimum conditions agreed with model predictions.

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“…The RSM designs applied in the present investigation have been successfully applied in many recent biotechnological researches (Guo et al, 2009;Jo et al, 2008;Lotfy, 2007;Mu et al, 2009). However, to the best of our knowledge, no single report was obtained on avermectins or avermectin B1a production optimization using the RSM designs.…”

Section: Validation Of the Optimized Conditionmentioning

confidence: 99%

Medium optimization for the production of avermectin B1a by Streptomyces avermitilis 14-12A using response surface methodology

Gao

Liu

et al. 2009

Bioresource Technology

132

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Optimization of key process variables for enhanced hydrogen production by Enterobacter aerogenes using statistical methods

Cited by 134 publications

References 23 publications

Optimizing the impact of temperature on bio-hydrogen production from food waste and its derivatives under no pH control using statistical modelling

Optimizing the impact of temperature on bio-hydrogen production from food waste and its derivatives under no pH control using statistical modelling

Optimization of Enzymatic Saccharification of Alkali Pretreated Typha angustifolia for Glucose Production

Medium optimization for the production of avermectin B1a by Streptomyces avermitilis 14-12A using response surface methodology

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