Optimization of Dry Anaerobic Digestion of Food Waste in Leachate Bed Reactors
Bibliographic record
Abstract
This study investigates the optimization and performance of a single-stage leachate bed reactor (LBR) system for the dry anaerobic digestion (AD) of food waste (FW). Three different parameters were assessed in the LBR run at a reaction time of 10 days: the inoculum-to-substrate ratio (ISR), leachate recirculation rate, and type of inoculum. For ISR optimization, four different ISRs were investigated ranging between 10 and 60%. Results indicated that a higher ISR of 60% with an acclimated inoculum led to a 3.35-fold increase in cumulative methane yield compared to an ISR of 10%, while volatile solids (VS) reduction with an ISR of 10% was better than that with an ISR of 60%. Furthermore, increasing leachate recirculation rates improved methane yield, with a notable 78% increase observed when the recirculation rate was elevated from 0.3 to 7.5 L/h. These results demonstrate high methane production of 349 mLCH 4 /gVS reduced within a short digestion time of 10 days.
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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 | 0.000 |
| 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".