Risk-Based Design of Safety Measures To Prevent and Mitigate Dust Explosion Hazards
Bibliographic record
Abstract
Dust explosion is one of the main threats to equipment safety and human health in industries. Complex factors leading to accidents, serious consequences, and relevant safety measures are the main interests of governmental agencies, researchers, and industrial companies. However, a generic risk analysis model for dust explosions is absent. The bow-tie model can be used to investigate the relationships among basic causes, safety barriers, and possible consequences of an accident scenario. In this paper, a framework is established for quantitative risk assessment of dust explosions based on bow-tie analysis via review and analysis of previous major dust explosions. A large inventory of relevant safety measures is presented, and the implementation and efficacy of such safety measures to reduce the risk of dust explosions is thoroughly discussed. Finally, the methodology is applied to a case study. The results show that the generic bow-tie developed in this study can be tailored to a wide variety of dust explosion accident scenarios with minimal manipulation; also, implementation of relevant safety measures can significantly reduce the risk of dust explosions.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".