Effects of Headspace and Oxygen Level on Off-gas Emissions from Wood Pellets in Storage
Bibliographic record
Abstract
Few papers have been published in the open literature on the emissions from biomass fuels, including wood pellets, during the storage and transportation and their potential health impacts. The purpose of this study is to provide data on the concentrations, emission factors, and emission rate factors of CO(2), CO, and CH(4) from wood pellets stored with different headspace to container volume ratios with different initial oxygen levels, in order to develop methods to reduce the toxic off-gas emissions and accumulation in storage spaces. Metal containers (45 l, 305 mm diameter by 610 mm long) were used to study the effect of headspace and oxygen levels on the off-gas emissions from wood pellets. Concentrations of CO(2), CO, and CH(4) in the headspace were measured using a gas chromatograph as a function of storage time. The results showed that the ratio of the headspace ratios and initial oxygen levels in the storage space significantly affected the off-gas emissions from wood pellets stored in a sealed container. Higher peak emission factors and higher emission rates are associated with higher headspace ratios. Lower emissions of CO(2) and CO were generated at room temperature under lower oxygen levels, whereas CH(4) emission is insensitive to the oxygen level. Replacing oxygen with inert gases in the storage space is thus a potentially effective method to reduce the biomass degradation and toxic off-gas emissions. The proper ventilation of the storage space can also be used to maintain a high oxygen level and low concentrations of toxic off-gassing compounds in the storage space, which is especially useful during the loading and unloading operations to control the hazards associated with the storage and transportation of wood pellets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".