Hydrogen production from meat processing and restaurant waste derived crude glycerol by anaerobic fermentation and utilization of the spent broth
Bibliographic record
Abstract
Abstract BACKGROUND Crude glycerol (CG), the major by‐product of the biodiesel production process, could be used for biohydrogen production. However, fermentative hydrogen production is limited by the cost of buffer and additional nutrients required for the process. Thus, the purpose of the present study was to determine maximum H2 production potential of CG in the absence of any additional expensive supplement. Another objective was sustainable utilization of the waste from the H2 production process. RESULTS A maximum production of 2022.5 mL H2 L−1 media was achieved by CG bioconversion (without any additional nutrient) and 10 g L−1 CG was found to be optimum. Further, the addition of spent biomass (50 mg L−1) from the process into a subsequent process was found to improve production by 32.5% with a maximum rate of 1040 mL L−1 day−1. Similarly, nearly 75% of total H2 was produced at a pH as low as 3.8, indicating high acid tolerance of the strain (Enterobacter aerogenes NRRL B407) used. CONCLUSION Meat processing and restaurant waste based CG has been characterized and evaluated for maximum H2 production potential. Utilization of spent biomass from the CG bioconversion process (as supplement) was found to improve process performance. © 2013 Society of Chemical Industry
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".