Risk Assessment and Safety Evaluation Using Probabilistic Fault Tree Analysis
Bibliographic record
Abstract
Risk assessment is an essential prelude to the development of accident prevention strategies in any chemical or petrochemical industry. Many techniques and methodologies such as HAZOP, failure mode effect analysis, fault tree analysis, preliminary hazard analysis, quantitative risk assessment and probabilistic safety analysis are available to conduct qualitative, quantitative, and probabilistic risk assessment. However, these methodologies are limited by: extensive data requirements, the length of study, results are not directly interpretable for decision making, simulation is often difficult, and they are applicable only at the operation or late design stage. Khan et al. (2001a) recently proposed a detailed methodology for risk assessment and safety evaluation. This methodology is simple, yet it is effective in safety and design-related decision making, and it has been applied successfully to many case studies. It is named SCAP, where S stands for safety, C and A stand for credible accident respectively, and P stands for probabilistic fault tree analysis. This paper recapitulates the SCAP methodology and demonstrates its application to a petrochemical plant.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".