Optimization of firefighting strategies in process plants with emphasis on domino effects and safe evacuation
Bibliographic record
Abstract
Abstract Effective firefighting and evacuation are integral parts of emergency response plans in process plants, which play a key role in protecting human lives and assets in the event of major fires. Given sufficient firefighting resources, firefighters would suppress all the burning vessels and cool off all the exposed vessels in order to contain the fire and prevent a fire‐induced domino effect. However, when the number of critical units—whether on fire or exposed to fire—exceeds the firefighting resources, firefighters should decide how to optimally allocate the resources so as to best satisfy the safety goals. To facilitate such decisions, the present work aims to develop a methodology for effective firefighting under insufficient resources. The methodology seeks out two safety goals via optimal firefighting strategies: (1) providing for the safety of evacuees, and (2) reducing the risk of domino effects. Although both safety goals are attempted to be satisfied at the same time, a higher priority is assigned to the first goal as long as the evacuation is underway. When the evacuation is complete, all the resources are focused on the second goal. The study shows that a multi‐objective optimization approach to identifying firefighting plans outdoes single‐objective optimization approaches in that several safety goals could be met at once. Although only two safety goals are considered in the present study, the methodology is flexible enough to accommodate several goals such as safety of offsite people and assets.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".