Statistical optimization of factors affecting biohydrogen production from xylose fermentation using inhibited mixed anaerobic cultures

Chaganti, Subba Rao; Kim, Dong-Hoon; Lalman, Jerald A.; Shewa, Wudneh Ayele

doi:10.1016/j.ijhydene.2012.05.036

Cited by 31 publications

(12 citation statements)

References 45 publications

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“…Similar patterns have been observed by Chaganti et al (2012), who reported a decrease in hydrogen yield from 1.6 to 0.3 mol -1 xylose when the pH dropped from 7.6 to 5.0. A decrease in pH has caused acidogenic processes to shift to solventogenic processes, thus producing mostly acetone, butanol, and ethanol, which inhibit hydrogen production (Khanal et al 2004).…”

Section: Semi-pilot Scale-up Of H2 Productionsupporting

confidence: 88%

“…"Biohydrogen from leaves," BioResources 12(1), 2015-2030. 2022 Chaganti et al (2012) obtained an optimal hydrogen yield of 2.4 mol/mol xylose with an inoculum concentration of 1800 VSS (mg/L) and a lower hydrogen yield (1.6 mol H2/mol xylose) with an inoculum concentration of 1000 VSS (mg/L), thus suggesting that a 44% reduction in inoculum concertation led to a 50% increase in hydrogen yield. Figure 2b illustrates that by increasing the HRT from 24 to 65 h, while maintaining the substrate concentration at 14 g/L, it was possible to enhance the hydrogen yield from 6.6 to 12.51 mL H2 g -1 FS.…”

Section: Bioresourcescommentioning

confidence: 98%

“…Higher significance is inferred with larger F-values and smaller p-values (Chaganti et al 2012). The quadratic model fit was significant given the F-value and p-value of 7.46 and 0.0074, respectively.…”

Section: Model Analysismentioning

confidence: 98%

“…The quadratic model fit was significant given the F-value and p-value of 7.46 and 0.0074, respectively. A large F-value indicates that the aggression equation can account for the regression equation (Chaganti et al 2012). The coefficient of determination (R 2 ) was 0.90, thereby inferring the model's ability to explain 90% of the variations in the data.…”

Section: Model Analysismentioning

confidence: 99%

“…Response surface methodology (RSM) uses polynomial equations to model the relationships between input variables and response outputs (Pan et al 2008). It has been reported in the optimization of various bioprocesses (Pan et al 2008;Chaganti et al 2012;Faloye et al 2013). The present study modeled and optimized the physico-chemical input parameters of the substrate concentration, inoculum concentration, and hydraulic retention time for biohydrogen production from waste sugarcane leaves.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of Operational Parameters for Biohydrogen Production from Waste Sugarcane Leaves and Semi-Pilot Scale Process Assessment

Moodley¹,

Kana²

2017

BioResources

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This study modeled and optimized the operational parameters for biohydrogen production from waste sugarcane leaves and assessed hydrogen production on a semi-pilot scale. A Box-Behnken design with input variables of substrate concentration (8 to 24 g/L), inoculum concentration (10% to 50% v/v), and hydraulic retention time (HRT, 24 to 96 h) was used. A coefficient of determination (R 2 ) of 0.90 and the predicted optimum operational set-points of 14.2 g/L substrate concentration, 32.7% inoculum concentration, and 62.8 h HRT were obtained. Experimental validation produced a biohydrogen yield of 12.8 mL H2/g fermentable sugar (FS). A semi-pilot scale process in a 13-L Infors reactor under optimized conditions gave a cumulative hydrogen volume and yield of 3740 mL and 321 mL H2 g -1 FS, respectively, with a peak hydrogen fraction of 37%. Microbial analysis from the process effluent conducted by Polymerase Chain Reaction cloning indicated the presence of hydrogen-producing bacteria belonging to Clostridium sp., Klebsiella sp., and Enterobacter sp. These findings highlight the feasibility of biohydrogen production from sugarcane waste and provide preliminary knowledge on process scale up.

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Section: Semi-pilot Scale-up Of H2 Productionsupporting

confidence: 88%

Section: Bioresourcescommentioning

confidence: 98%

Section: Model Analysismentioning

confidence: 98%

Section: Model Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Optimization of Operational Parameters for Biohydrogen Production from Waste Sugarcane Leaves and Semi-Pilot Scale Process Assessment

Moodley¹,

Kana²

2017

BioResources

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Sustainable production of biofuels using different microbial consortia: the state of the art

Medina,

Bolaños,

Magalhães

et al. 2024

Biofuels Bioprod Bioref

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Population growth has generated an enormous increase in demand for energy and chemicals to supply goods and services and to maintain quality of life. Oil, coal, and natural gas are the primary sources of the chemicals and energy that directly impact the economy. However, the reserves of these resources are continuously reducing. New alternatives have emerged, including biotechnological tools such as microbial consortia (MC) to produce biofuels, chemicals such as organic acids, and processed foods. The diversity of MC as a biotechnological tool allows it to be used in complex substrates, opening the possibility of using other solid waste to implement consolidated bioprocesses (CPs). This review presents an overview of the uses of MC and CP for producing biofuels using different feedstocks.

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Screening of Medium with Different Range of Waste Frying Oil (WFO), Sodium Nitrate (NaNO3) and Sodium Chloride (NaCl) for Biosurfactant Production by Thermophilic Anoxybacillus sp. Using Fractional Factorial Design (FFD)

et al. 2016

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Statistical optimization of factors affecting biohydrogen production from xylose fermentation using inhibited mixed anaerobic cultures

Cited by 31 publications

References 45 publications

Optimization of Operational Parameters for Biohydrogen Production from Waste Sugarcane Leaves and Semi-Pilot Scale Process Assessment

Optimization of Operational Parameters for Biohydrogen Production from Waste Sugarcane Leaves and Semi-Pilot Scale Process Assessment

Sustainable production of biofuels using different microbial consortia: the state of the art

Screening of Medium with Different Range of Waste Frying Oil (WFO), Sodium Nitrate (NaNO3) and Sodium Chloride (NaCl) for Biosurfactant Production by Thermophilic Anoxybacillus sp. Using Fractional Factorial Design (FFD)

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