Biohydrogen production by co-digesting whey and glycerin in an AnSBBR: Performance optimization, metabolic pathway kinetic modeling and phylogenetic characterization
Bibliographic record
Abstract
The co-digestion of cheese whey with glycerin for biohydrogen production, in an anaerobic sequencing batch biofilm reactor (AnSBBR) with recirculation of the liquid phase (at 30 °C with 3.5 L of working volume and treating 1.5 L per cycle), was assessed. The feeding time (1.5 h) was equal to half of the cycle length (3 h). Two variables were studied: the ratio between whey and glycerin (100:0; 75:25; 50:50; 25:75; 0:100% on a COD basis) and the organic loading rate (from 10.1 to 23.9 kgCOD m−3 d−1) by altering the influent concentration (from 3 to 7 kgCOD m−3). The highest hydrogen productivity (129.0 molH2 m−3 d−1) and yield (5.4 molH2 kgCOD−1) with complete inhibition of methanogenisis were achieved when the reactor was fed with 75% cheese whey and 25% glycerin with an applied volumetric organic load of 23.9 kgCOD m−3 d−1; an increase in productivity of almost 145% and 27% when compared to the anaerobic digestion of cheese whey and glycerin separately, respectively, showing the significant benefit of glycerin addition for anaerobic whey digestion, most likely due to its buffering capacity. A kinetic metabolic model was efficiently fitted to the process to help understand the metabolic routes. Cloning analyses targeting 16S rRNA genes indicated the dominant microorganisms were phylogenetic affiliated to Ethanoligenens and Megasphaera genera.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".