Hydrogen production from biodiesel industry waste by using a co-culture of <i>Enterobacter aerogenes</i> and <i>Clostridium butyricum</i>
Bibliographic record
Abstract
Valorization of crude glycerol (CG), a waste of biodiesel production process, was investigated for increased hydrogen production by co-culture system using Enterobacter aerogenes NRRL B-407 and Clostridium butyricum NRRL B-41122. The ability of E. aerogenes was evaluated as a replacement of expensive reducing agent to maintain anaerobic conditions for the growth of C. butyricum. The co-culture resulted in increased hydrogen production, reaching a maximum of 19.46 ± 0.95 mmol-H2/L-medium in comparison to monoculture of E. aerogenes (15.64 ± 0.47) and C. butyricum (17.44 ± 0.38) in the presence of reducing agent. The effect of inoculum ratio was investigated and hydrogen yield was 0.95 mmol-H2/mol-glycerol at 1:11 inoculum ratio with more than 85% of substrate utilization was comparable to other mixed and co-culture studies. In addition to hydrogen, value added by-products such as 1,3-propandiol, acetic acid, butyric acid and ethanol have also been produced. Co-culture indicated the possibility of valorization of CG as a stable functional consortium with higher conversion efficiency for increased hydrogen along with high value by-product production. Additionally, the study provided the evidence of medium composition controlled metabolic pathway shift during CG bioconversion. Valorization of low cost CG with efficient co-culture of hydrogen production can aid biodiesel in-house energy source.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".