Microbial shifts and VFA production in the optimization of anaerobic digestion by thermal hydrolysis coupled with vacuum fermentation
Bibliographic record
Abstract
• Vacuum fermentation showed a potential for VFA recovery from sludge. • Thermal hydrolysis improved the vacuum fermentation for VFA recovery. • Fermentate from the vacuum fermentation used as a carbon source for denitrification. • Vacuum application shifted the microbial community enriching the VFAs producers. This study investigated a novel thermal-hydrolysis combined with a vacuum fermentation system for high-grade volatile fatty acids (VFA) recovery, and the corresponding changes in the microbial community. Four systems with and without hydrothermal pre-treatment (HTP) and vacuum were mobilized; results revealed that integration of HTP with vacuum has the highest potential in terms of VFA recovery, sludge disintegration, and solid reduction. HTP and vacuum fermentation systems were associated with the highest COD solubilization (45 %), and VFA yield (0.32 g COD/g VSS added). Vacuum fermenters with and without pre-treatment have the highest specific denitrification rates of 7.6 and 7.2 mg NO 3 -N/g VSS.h, respectively, compared to all other samples and control (acetate). Changes brought about by vacuum fermentation included a shift in the microbial community toward enriching fermenters, mainly Caprothermobacteria and Thermotagea, responsible for VFA production .
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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".