Dust Explosion Quantitative Risk Management for Nontraditional Dusts
Bibliographic record
Abstract
The current paper describes an approach for dust explosion quantitative risk management of the following nontraditional particulate fuel systems: (i) nanomaterials having particles with dimensions between 1 and 100 nm, (ii) flocculent (fibrous) materials characterized by a length-to-diameter ratio rather than a particle diameter, and (iii) hybrid mixtures consisting of a combustible dust and a flammable gas (or a combustible dust wetted with a flammable solvent). Experimental results are considered as input to a quantitative risk management framework so as to provide a comprehensive procedure to analyze, assess and control the likelihood and consequences of explosions of nontraditional dusts. Using concepts drawn from previous studies, the framework consists of three main components: (i) a new combined safety management protocol, (ii) use of the CFD (computational fluid dynamics) software DESC (Dust Explosion Simulation Code) and FTA (Fault Tree Analysis) to determine explosion consequences and likelihood, respectively, and (iii) application of the hierarchy of controls (inherent, engineered and procedural safety) to achieve residual risk reduction.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".