Development of risk‐based process safety indicators
Bibliographic record
Abstract
Abstract Process safety performance indicators are applied to monitor and improve the safety of process plants. One of the most important and challenging issues for process safety is the early recognition of deterioration in safety performance caused by operation, maintenance, management, organization, and safety culture factors before actual events and/or mishaps occur. Most existing safety performance indicators are “lagging” indicators meaning that they monitor events after their occurrence. This article presents a risk‐based approach to measure process safety using a set of safety performance indicators. This approach uses a risk metric as a means to classify process safety. Risk provides a common ground to integrate the two main indicator types of leading and lagging indicators. It is important to note that lagging and leading indicators have a relationship, which is often ignored. The proposed methodology is a structured approach, which builds upon UK's Health Safety Executive recommended process safety indicator development framework. At present, work efforts have been made to develop a set of indicators with a common background to measure process safety. This article demonstrates a hierarchical risk aggregation approach which is used to aggregate indictors. This work was carried out with the help of the Loss Prevention Division of Qatargas Operating Company Limited (Qatargas), a Liquefied Natural Gas (LNG) company. Finally, the applicability of the approach is demonstrated by a case study on a liquefied natural gas facility. The result of this study shows a relationship between the leading and lagging indicators which together contribute to the improvement of process safety performance. © 2009 American Institute of Chemical Engineers Process Saf Prog, 2010
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".