Bibliographic record
Abstract
Abstract The role of inherently safer design (ISD) in process safety assurance has changed significantly over the past 40 years. When first introduced by Professor Trevor Kletz following the 1974 Flixborough explosion, the ISD concept challenged the manner in which process risk was addressed in the chemical industry. The prevailing view of adding on safety devices and implementing procedures aimed at controlling hazards was now complemented by a way of thinking that sought to remove or reduce hazards at their source. The past 20 years have seen ISD mature into an established risk reduction strategy that is widely known in principle and increasingly adopted in practice. The current paper reviews the authors' collaborative research efforts aimed at integrating ISD into various process safety systems, activities, and applications. The primary inherent safety principles (minimization, substitution, moderation, and simplification) are explained with example‐based guidance provided for their use. ISD features and performance indices are examined throughout the early design and operational stages of a typical process life cycle. Preventing and mitigating undesirable occurrences such as domino effects and dust explosions are shown to be feasible by adopting an inherent safety approach. The importance of reviewing ISD case studies developed from incident investigations is also discussed. Finally, we present our personal opinions on the current status of inherently safer design and future possibilities for its continued growth.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".