Modelling the impacts of fire in a typical FLNG processing facility
Bibliographic record
Abstract
In the past oil and gas industry had experienced numerous major accidents with catastrophic consequences. Among oil and gas processing technologies, floating liquefied natural gas (FLNG) is an emerging technology which has no operational experiences or lesson learnt to date. In any processing facilities, fire is considered as one of the major hazards. A risk due to fire is considered as the most critical among all other potential risk in FLNG processing facilities due to inherent flammable hazards of hydrocarbons, hydrodynamic interactions, high pressures and their synergistic effects. There is a need of an adequate fire risk assessment and consequence analysis of FLNG processing facilities. Therefore, this study proposes a novel risk-based methodology for modelling the impacts of fire event in a typical FLNG processing facility. The impacts of fire event on adjacent assets and personnel are assessed considering a credible leakage of LNG with an immediate ignition. The scenario is computationally simulated using Fire Dynamic Simulator (FDS). The results of the simulation are used for impact assessment based on predefined criteria and safety measured design is considered to mitigate or avoid the impacts. As part of the safety measured design, a generic water deluge system is installed adjacent to fire location. After the activation of the water deluge system, it is found that the impacts and corresponding risk are significantly reduced. It is evident that the proposed methodology can assess fire impact and manage the associated risks. Additionally, the methodology can be used further for assessing primary propagation of domino effects in a complex processing facility.
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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) | 0.001 | 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".