Switching from wet to dry anaerobic digestion of food waste with different dilution times under no mechanical mixing condition
Bibliographic record
Abstract
Previous research on anaerobic digestion of food waste has primarily focused on either wet or dry anaerobic digestion (AD), typically accompanied by continuous mechanical mixing. However, the necessary dilution rates and the extent of mixing required have yet to be addressed. In this study, we investigated switching from wet to dry AD of food waste without mechanical mixing, employing different dilution rates. Lab-scale anaerobic reactors were operated with dilution rates of 10, 5, and 2 times during Phases I (0–56 days), II (57–121 days), and III (122–209 days), respectively. The methane production rates were not significantly different (p > 0.05) across the dilution rates decreased from 10 to 2 times. Remarkably, the methane production in the anaerobic reactors exhibited fluctuations due to variations in feeding, with the methane production rate ranging from 2.0 to 2.7 g CH4-COD/(L d), without mechanical mixing, as the solids content transitioned from wet to near-dry digestion conditions (15 %, food waste). The distribution of sludge volatile solids concentrations remained uniform in the reactor, even at high solids concentrations of up to 15 %. A dynamic microbial community response to changes in dilution rates, with a shift from aceticlastic to hydrogenotrophic methanogenesis pathways. Syntrophic acetate oxidization bacteria (the genus Syner-01 (4.2–8.9 %) and f_Synergistaceae (3.6–4.2 %)) were highly enriched as switching from wet AD to dry AD. The study's findings provide crucial operational insights for anaerobic food waste treatment, potentially resulting in decreased water usage and operational costs, particularly in scenarios with low dilution rates and without mechanical mixing.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".