Key factors for biohydrogen production by dark fermentation
Bibliographic record
Abstract
Abstract Among biological ways of hydrogen production, the dark fermentation process gives important prospects. One of the key factors of bioreactor operation is the biogas extraction. Thus, this work gives a parametric optimization of the bioreactor as a function of the sweep gas nature and flow rate. The bacterial inoculum was activated sludge of wastewater treatment plant, which was thermally treated to inhibit the activity of hydrogenotrophic and non‐hydrogen producing micro‐organisms. The fermentation was performed in a semi‐batch bioreactor with a model substrate. Biogas was analyzed online by GC‐TCD and metabolites (volatile fatty acids and alcohols) were analyzed by GC‐FID. Kinetics parameters of hydrogen production were obtained by modelling. Increase the sweep gas (N2) flow rate in the bioreactor results in an increase of hydrogen yield and cumulative production of 35 %. In the purpose of recycling the CO2 produced by fermentation, the addition of CO2 to N2 as sweep gas gives a yield improvement from 2.10 molH2/molhexose with N2 alone, to 2.37 molH2/molhexose with a mixture of CO2:N2 (75:25). In order to reduce the amount of suspended matter in initial biomass, filtration tests show an equivalent hydrogen yield but with a higher lag time than with unfiltered biomass (30 h towards 4.4 h). The presence of hydrogen producing bacteria mainly stick with the solid part of sludge was highlighted.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".