Rate and Peak Concentrations of Off-Gas Emissions in Stored Wood Pellets—Sensitivities to Temperature, Relative Humidity, and Headspace Volume
Bibliographic record
Abstract
Wood pellets emit CO, CO(2), CH(4), and other volatiles during storage. Increased concentration of these gases in a sealed storage causes depletion of concentration of oxygen. The storage environment becomes toxic to those who operate in and around these storages. The objective of this study was to investigate the effects of temperature, moisture, and the relative size of storage headspace on emissions from wood pellets in an enclosed space. Twelve 10-l plastic containers were used to study the effects of headspace ratio (25, 50, and 75% of container volume) and temperatures (10-50 degrees C). Another eight containers were set in uncontrolled storage relative humidity (RH) and temperature. Concentrations of CO(2), CO, and CH(4) were measured by gas chromatography (GC). The results showed that emissions of CO(2), CO, and CH(4) from stored wood pellets are more sensitive to storage temperature than to RH and the relative volume of headspace. Higher peak emission factors are associated with higher temperatures. Increased headspace volume ratio increases peak off-gas emissions because of the availability of oxygen associated with pellet decomposition. Increased RH in the enclosed container increases the rate of off-gas emissions of CO(2), CO, and CH(4) and oxygen depletion.
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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) | 0.000 | 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".