Bio-hydrogen production from thin stillage using conventional and acclimatized anaerobic digester sludge

Nasr, Noha; Elbeshbishy, Elsayed; Hafez, Hisham; Nakhla, George; Naggar, M. Hesham El

doi:10.1016/j.ijhydene.2011.07.032

Cited by 47 publications

(27 citation statements)

References 34 publications

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“…r e f e r e n c e s 5 e Experimental and predicted hydrogen production profile using data from [24,26,28]. Fig.…”

Section: Resultsmentioning

confidence: 99%

“…The ANN model was then used to estimate the temporal hydrogen evolution for three new data sets adopted from Chen et al [24], Nasr et al [26], and Wang and Wan [28] that were not used in the training process. Chen et al [24] investigated biohydrogen production from sucrose in batch studies using ADS at 36 C and an initial pH of 5.5.…”

Section: Hydrogen Production Prediction Using Annmentioning

confidence: 99%

“…Chen et al [24] investigated biohydrogen production from sucrose in batch studies using ADS at 36 C and an initial pH of 5.5. Nasr et al [26] investigated biohydrogen production from thin stillage as the substrate using ADS as the seed microflora at 37 C and an initial pH of 5.5. Wang and Wan [28] investigated biohydrogen production from glucose in batch studies at 35 C using preheated ADS at an initial pH of 7.…”

Section: Hydrogen Production Prediction Using Annmentioning

confidence: 99%

“…4 shows the correlation between the predicted and experimental data points from the aforementioned sets of data, where a correlation coefficient of 0.965 and an MAE of 11.2 mL were obtained. Average percentage error (APE), defined as the summation of the absolute difference between the experimental and predicted values divided by the experimental values, averaged over the number of data points were 1.4%, 9.6%, and 8.5% for the data sets adopted from Nasr et al [26], Chen et al [24], and Wang and Wan [28], respectively. Fig.…”

Section: Hydrogen Production Prediction Using Annmentioning

confidence: 99%

“…5 shows the experimental and predicted hydrogen production profile using the three sets of data. Although Nasr et al [26] used thin stillage from a bioethanol plant as a substrate as opposed to glucose or sucrose that were mostly used in establishing the model, the model was able to predict the hydrogen production profile accurately. The reason is that thin stillage is composed predominantly of carbohydrates.…”

Section: Hydrogen Production Prediction Using Annmentioning

confidence: 99%

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Application of artificial neural networks for modeling of biohydrogen production

Nasr

Hafez

Naggar

et al. 2013

International Journal of Hydrogen Energy

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“…r e f e r e n c e s 5 e Experimental and predicted hydrogen production profile using data from [24,26,28]. Fig.…”

Section: Resultsmentioning

confidence: 99%

Section: Hydrogen Production Prediction Using Annmentioning

confidence: 99%

Section: Hydrogen Production Prediction Using Annmentioning

confidence: 99%

Section: Hydrogen Production Prediction Using Annmentioning

confidence: 99%

Section: Hydrogen Production Prediction Using Annmentioning

confidence: 99%

See 3 more Smart Citations

Application of artificial neural networks for modeling of biohydrogen production

Nasr

Hafez

Naggar

et al. 2013

International Journal of Hydrogen Energy

Self Cite

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Optimization of hydrogen production from organic fraction of municipal solid waste (OFMSW) dry anaerobic digestion with analysis of microbial community

Elsamadony

Tawfik

Danial

et al. 2015

Int. J. Energy Res.

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Summary Effect of mixed culture bacteria (MCB) addition, initial substrate concentration to inoculum (So/Xo), carbon to nitrogen (C/N) ratio on the hydrogen production (HP) from organic fraction of municipal solid waste (OFMSW) via dry anaerobic digestion was investigated. The results showed that supplementation of OFMSW with MCB substantially improved hydrogen yield (HY) and HP. HYs were 306.2 ± 33.0, 149.8 ± 16.6, and 155.3 ± 22.9 mlH2/gCODremoved for OFMSW supplemented with MCB, OFMSW, and MCB, respectively. HP at So/Xo ratio of 9.2 gCOD/gVSS was 3.1 times higher than those obtained at So/Xo ratio of 1.7 gCOD/gVSS. The maximum HY of 338.4 ± 18.9 mlH2/gCODremoved was achieved at So/Xo ratio of 9.2 gCOD/gVSS. The HP and HY were substantially increased from 169.7 ± 17.1 to 524.2 ± 34.4 ml and from 163.1 ± 23.6 to 338.4 ± 18.9 mlH2/gCODremoved with increasing the C/N from 16 to 25, respectively. However, HP and HY decreased at C/N ratio exceeding 26.6. The modified Gompertz equation model was highly fitted to the experimental data with correlation coefficient (R2 > 0.984). The 16S rRNA sequences showed the dominance of Pseudomonas fulva with similarity of 99%. Copyright © 2015 John Wiley & Sons, Ltd.

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Continuous two-staged co-digestion process for biohydrogen production from agro-industrial wastes

Gómez-Romero

Gonzalez-Garcia

Chairez

et al. 2015

Int. J. Energy Res.

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SUMMARYMexico produces large amounts of organic residues. Twenty milliard tons of fruit and vegetable wastes (FVW) are produced yearly. On the other hand, the cheese processing industry produces crude cheese whey (CCW) with an annual production of 1 million metric tons. These types of residues are well characterized, and both constitute a potential feedstock for biohydrogen production, individually or as a mixture. Recently, we demonstrated the feasibility of utilizing CCW and FVW in a co-digestion process to produce biohydrogen. The possibility to design an effective process in a two stage configuration was evaluated based in our previous studies of the by-products and description of microbial ecology evolution of the co-digestion process. An initial assimilation of easily degradable carbohydrates and protein allowed cell growth and acetate and lactate as main byproducts. The co-digestion and two-stage processes increased several times the biohydrogen yield compared with those obtained from single substrate and in a co-digestion single stage process. Higher biohydrogen productivity was a result of reaction stability in comparison to single-substrate digestion and one-stage processes.The effect of hydraulic retention time (HRT) and organic loading rate (OLR) on the volumetric hydrogen production rate (VHPR) and yield (Y H2 ) was evaluated in a two-staged co-digestion process of FVW and CCW. The system consisted of hydrolytic and hydrogenic bioreactors operated in batch mode and continuous regime (89 days), respectively. Eight conditions of HRTs and respective OLRs were evaluated. As HRT decreased, the VHPR increased proportionally, reaching the highest average values around 7 L L À1 d À1 at an HRT between 15 and 17.5 h. The highest Y H2, (813.3 mL H 2 g COD À1 ) was determined at 17.5 h (OLR, 80.02 g COD L À1 d À1 ). Based on the distribution of the final products, the processes occur sequentially and reach a steady state set by the OLR. Biohydrogen production is a result of a syntrophic microbial activity. In general, the two stages of the co-digestion process provided higher stability, reliability, and efficiency.

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Bio-hydrogen production from thin stillage using conventional and acclimatized anaerobic digester sludge

Cited by 47 publications

References 34 publications

Application of artificial neural networks for modeling of biohydrogen production

Application of artificial neural networks for modeling of biohydrogen production

Optimization of hydrogen production from organic fraction of municipal solid waste (OFMSW) dry anaerobic digestion with analysis of microbial community

Continuous two-staged co-digestion process for biohydrogen production from agro-industrial wastes

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