Silo explosion from smoldering combustion: A case study
Bibliographic record
Abstract
Smoldering combustion in power/dust deposits constitutes a significant problem in some process industries. Numerous bulk materials produce heat due to different biological and oxidation processes. Spontaneous heating occurs if the rate of heat generation by an exothermic process is fast relative to the rate of heat loss to the surroundings. Such phenomena are particularly dangerous in bulk materials storage equipment (e.g. silos, bins, hoppers, bunkers) where larger fire and dust explosions may be ignited by smoldering material. The present case study concerns an explosion that occurred in a silo containing sawdust and wood chips. Firefighters were called to extinguish a fire in the silo. During their intervention an explosion occurred. Four firefighters were injured and one of them died some months later, as a result of the explosion; the explosion caused also significant damage to the silo and minor damage to the adjacent buildings. This paper describes the investigation into the cause of the silo explosion. The CFD code FLACS was used to evaluate the consequences associated with gas explosion of pyrolysis gases produced by smoldering combustion. Simulation results showed that the most probable scenario was the explosion of pyrolysis gases accumulated in the upper part of the silo where bag filters were present. The venting system was inefficient in mitigating the explosion, due to the corrosion of metal bolts connecting the silo walls. Another factor could have been the position of relief hatches, which were located in front of the filter elements, thus not meeting the required standards.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".