Empirical prediction on boilover onset and impact for liquid hydrocarbon fire in atmospheric storage tank
Bibliographic record
Abstract
Abstract Boilover can occur several hours after the fuel in a storage tank caught fire. The delayed occurrence is an unknown strong parameter when managing the emergency response operations. Those managing response operations must be aware of the boilover potential and take the precautions to ensure safety. Modelling the phenomenon enables predicting crucial event features and assists in highlighting safety measures, with a key focus on the ignition‐to‐boilover time interval. This study focused on the predictive empirical tool development aimed at estimating the boilover onset time and consequences. This was achieved through series of small‐scale boilover experiments, followed by validation using cases of boilover incidents. The results revealed a linear relationship between the boilover onset time and the initial depth of fuel. Consequently, an empirical correlation was derived to predict the time to boilover. The developed correlation has demonstrated its ability to offer conservative predictions while also exhibiting agreement with both the observed onset time and consequences of boilover events. The reported time to boilover for the Czechowice‐Dziedzice incident is 1050 min, while the predicted time is 1413.2 min. The model showed reasonable agreement with the Amoco Refinery incident. The predicted boilover time of 811.3 min aligns with the boilover incident, reported as 790 and 925 min, respectively. It is evident that the empirical model can predict the time to boilover to a similar order of magnitude. Certain considerations in the development of effective strategies in handling fire scenario with boilover potentials can be assessed using the predictive tool developed.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".