Prevention and mitigation of dust and hybrid mixture explosions
Bibliographic record
Abstract
Abstract The results presented in this article focus on the importance of the prevention and mitigation of dust and hybrid mixture explosions. The main objective is to demonstrate the use of the inherent safety principle of moderation to achieve a significant reduction of the risk of explosions. Experiments and a companion modeling study were conducted with a test matrix composed of various size fractions of polyethylene powder together with concentrations of hydrocarbon gas (ethylene, hexane, and propane). The results quantitatively show the increased hazard posed by fine particle sizes of dust and the addition of flammable gases. There are clear implications for industry in terms of moderating the risk of an explosion. Gas concentrations used in this work were all less than the lower flammability limit (LFL) of the particular chemical species and the ratio of gas concentration to LFL concentration was at least 75%. The enhancement of mixture reactivity brought about by a flammable gas admixture could therefore be correlated with the burning velocity of the gas. This article describes how to predict K St for hybrid mixtures and includes the concept of using propane as a surrogate for hexane. Additionally, it shows that the avoidance of both fine dust sizes and hybrid mixtures is a beneficial approach in the process industries to reduce the risk arising from the hazards posed by combustible dusts and their mixtures with flammable gases. © 2009 American Institute of Chemical Engineers Process Saf Prog, 2010
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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 | 0.001 |
| 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".