Application of influence diagrams to multi‐objective allocation of firefighting resources in process plants
Bibliographic record
Abstract
Abstract Identification of firefighting strategies (i.e., which endangered units to suppress or cool first) in chemical and process plants falls under the domain of multi‐objective decision‐making (MODM), where not only the safety and integrity of the affected process plant but also the safety of on‐site and off‐site vulnerable targets matter. The importance of identifying effective firefighting strategies becomes more crucial when potential domino effects (i.e., escalation of a primary fire to secondary fires) can quickly increase the number of endangered units and targets beyond what could initially be handled by firefighters. While modelling and risk assessment of domino effects have gained increasing attention over the past decade, developing methods for risk management and firefighting of domino effects has lagged. In this regard, combining the domino effect models with MODM techniques has been proposed as a viable solution for identifying effective firefighting strategies. In the present, by developing an innovative multi‐attribute utility function (MAUF), it will be shown that influence diagrams—an extension of Bayesian networks—can be applied both for modelling domino effects and for identifying multi‐objective firefighting strategies within an integrated framework. The results of the developed method are shown to be consistent with those obtained from other MODM techniques, such as goal programming.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| 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) | 0.002 | 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".