A network based approach to envisage potential accidents in offshore process facilities
Bibliographic record
Abstract
Envisaging potential accidents in large scale offshore process facilities such as Floating Liquefied Natural Gas (FLNG) is complex and could be best characterized through evolving scenarios. In the present work, a new methodology is developed to incorporate evolving scenarios in a single model and predicts the likelihood of accident. The methodology comprises; (a) evolving scenario identification, (b) accident consequence framework development, (c) accident scenario likelihood estimation, and (d) ranking of the scenarios. Resulting events in the present work are modeled using a Bayesian network approach, which represents accident scenarios as cause‐consequences networks. The methodology developed in this article is compared with case studies of ammonia and Liquefied Natural Gas from chemical and offshore process facility, respectively. The proposed method is able to differentiate the consequence of specific events and predict probabilities for such events along with continual updating of consequence probabilities of fire and explosion scenarios taking into account. The developed methodology can be used to envisage evolving scenarios that occur in the offshore oil and gas process industry; however, with further modification it can be applied to different sections of marine industry to predict the likelihood of such accidents. © 2016 American Institute of Chemical Engineers Process Saf Prog 36: 178–191, 2017
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".