Solid state anaerobic digestion of mixed organic waste: the synergistic effect of food waste addition on the destruction of paper and cardboard
Bibliographic record
Abstract
ABSTRACT Full-scale anaerobic digestion processes for organic solid waste are common in Europe, but generally unaffordable in Canada and the United States because of inadequate regulations to restrict cheaper forms of disposal, particularly landfill. We investigated the viability of solid-state anaerobic digestion (SS-AD) as an alternative that reduces the costs of waste pretreatment and subsequent wastewater treatment. A laboratory SS-AD digester, comprising six 10L leach beds and an upflow anaerobic sludge blanket reactor treating the leachate, was operated continuously for 88 weeks, with a mass balance of 101±2%. The feed was a mixture of cardboard, boxboard, newsprint, and fine paper, and varying amounts of food waste (from 0% to 29% on a COD basis). No process upset or instability was observed. The addition of food waste showed a synergistic effect, raising CH 4 production from the fibre mixture from 52.7 L.kg -1 COD fibre added to 152 L.kg -1 COD fibre added , an increase of 190%. Substrate COD destruction efficiency reached 65% and a methane yield of 225 L.kg -1 COD added was achieved at 29% food waste on a COD basis, and a solids retention time of 42 days. This performance was similar to that of a completely stirred tank reactor digesting similar wastes, but with much lower energy input. Abstract Figure
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".