Process Safety Fundamentals – Making Process Safety ‘Real’ in the Field
Bibliographic record
Abstract
Process safety management aims to ensure that all physical assets are well designed, safely operated and properly maintained. Process safety management is central to achieving Shell’s Goal Zero ambition of no harm and no leaks across our operations. Shell’s approach to achieving this combines our asset integrity principles with our risk management approach, which is based on the “bow-tie” model. Continuous improvement in the management of hardware barriers and the robustness of human barriers is important to our overall risk management approach. Within the overall improvement trend, the number of technical integrity related events has significantly reduced. This suggests that operating integrity incidents make up an increasing fraction of process safety incidents, and, deeper process safety leadership and a different approach to behavioural change at the front line may be required to maintain improvement. Analysis of operational integrity events in Shell identified that a small set of human barriers contribute to half of the releases and it is likely that the potential for these occurrences could have been reduced by people adhering to known good operating practices. From this analysis, a set of “Process Safety Fundamentals” were derived. The Process Safety Fundamentals were first rolled out across our Downstream Manufacturing Business. Building on the Manufacturing experience, and further incident analysis, an updated set of ten Process Safety Fundamentals are being rolled out across our businesses. The ten Process Safety Fundamentals and the roll-out uses Hearts and Minds principles to engage the workforce and unlock process safety leadership at all levels. The Fundamentals aim to leverage the knowledge of a capable workforce, supporting them to apply known safe operating techniques.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.037 | 0.016 |
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".