A <scp>HAZOP</scp> of dust explosion testing and explosibility modelling using artificial neural networks
Bibliographic record
Abstract
Abstract This work presents artificial neural network (ANN) models to determine explosion severity parameters (e.g., maximum explosion pressure and maximum rate of pressure rise) of given dust samples. ANN‐based models for explosibility parameters are presented for carbon black, zinc, urea, and oat grain flour dust samples based on data generated in a 20‐L explosion chamber. The optimal hyper‐parameters of the models have been explored using the Broyden–Fletcher–Goldfarb–Shanno, stochastic gradient descent, and Adam solvers. A hazard and operability study has also been conducted for each of the following to diagnose issues at different stages in developing the ANN‐based dust explosibility models: dust testing, model selection, parameter learning, and evaluation. Using different guidewords, the deviation of numerous factors from their design intent, causes, consequences, and specific safeguards have been provided for enabling optimal performance. This can be helpful in understanding, evaluating, and analyzing dust explosions for safer operation of industrial activities handling combustible dusts.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".