Identifying design criteria for implementing inherent safety in chemical process industries part 1: Design reasoning
Bibliographic record
Abstract
Abstract Recurrence of similar accidents is evidenced by past accidents, which show that accidents are not decreasing globally. It is recommended to implement the concept of inherent safety design (ISD) in the chemical process industry as a component of accident prevention strategies. This study aims to identify potential indicators of ISD for each inherent safety (IS) keyword, which will be referred to later as design reasoning (DR), based on 529 selected cases that suggest design changes that enhance the safety of processes, materials, or equipment. The cases were collected from accident cases, IS handbooks, chemical engineering journals, new product brochures, and chemical engineering magazines. The cases have to demonstrate that the new design is safer than the previous design. The design changes were evaluated to extract the corrective action and determine the common strategy employed. This information was then interpreted as DR. The collected DR were subsequently categorized and measured in terms of frequency according to IS keywords. From the statistical analysis, the highest percentage of DR is improve mixing (18%) which accounted from 95 out of 529 cases. The second is loose proximity (15.5%) and the third is fewer equipment (14.9%). For the IS keyword ranking, it can be summarized that: moderation (40%) >minimization (27%) >simplification (21%) >substitution (12%). Results showed that moderation inherent safety is a popular strategy used to implement IS. The findings serve as a valuable reference for designers or engineer attempting to implement ISD in their design task.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".