Preventive control of odor emissions through manipulation of operational parameters during the active phase of composting
Bibliographic record
Abstract
Better understanding of the effects of key operational parameters or environmental factors on odor emission is of critical importance for minimizing the generation of composting odors. A series of laboratory experiments was conducted to examine the effects of various operating conditions on odor emissions. The results revealed that airflow rates that were too high or too low could result in higher total odor emissions. An optimal flowrate for odor control would be approximately 0.6 L/min.kg dry matter with intermittent aeration and a duty cycle of 33%. Temperature setpoint at 60 degrees C appeared to be a turning point for odor emission. Below this point, odor emissions increased with increasing temperature setpoint; conversely, odor emissions decreased with increasing temperature setpoint above this point. With regard to the composting material properties, odor emissions were greatly affected by the initial moisture content of feedstock. Both peak odor concentration and emission rate generally increased with higher initial moisture content. Odor emission was significant only at moisture levels higher than 65%. An initial moisture level below 45% is not recommended due to concern with the resulting lower degree of biodegradation. Biodegradable volatile solids content (BVS) of feedstock had pronounced effect on odor emissions. Peak odor concentration and emission rate increased dramatically as BVS increased from 45% to 65%, thus, total odor emission increased exponentially with BVS.
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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) | 0.000 | 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) | 0.000 | 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".