Effectiveness of purging on preventing gas emission buildup in wood pellet storage
Bibliographic record
Abstract
Storage of wood pellets has resulted in deadly accidents in connection with off‐gassing and self‐heating. A forced ventilation system should be in place to sweep the off‐gases and control the thermal conditions. In this study, multiple purging tests were conducted in a pilot scale silo to evaluate the effectiveness of a purging system and quantify the time and volume of the gas needed to sweep the off‐gases. To identify the degree of mixing, residence time distribution of the tracer gas was also studied experimentally. Large deviations from plug flow suggested strong gas mixing for all superficial velocities. As the velocity increased, the system dispersion number became smaller, which indicated less degree of mixing with increased volume of the purging gas. One‐dimensional modelling and numerical simulation of the off‐gas concentration profile gave the best agreement with the measured gas concentration at the bottom and middle of the silo.
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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 | 0.001 | 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".