Development of a layers of protection incorporated inherent safety index for chemical processes
Bibliographic record
Abstract
Abstract To date, numerous inherent safety indices, tailored to different process design stages and hazards, have been proposed to assess the inherent safety of chemical processes. However, traditional inherent safety indices mainly focus on identifying and evaluating process hazards, with limited consideration of risk mitigation. This work develops an enhanced inherent safety index (EISI), which integrates risk reduction strategies (RRSs) within layers of protection (LOP) into the various hazard sub‐indices of the pioneering inherent safety index (ISI), thereby enabling a more comprehensive inherent safety assessment. According to the LOP framework, RRSs are identified for each sub‐index, with different scores assigned to reflect the relative effectiveness of each measure. A designed formula is used to calculate the value of EISI. The proposed index was applied to the acetic acid process as a case study. The results indicate that the safety level of the acetic acid process significantly improves when appropriate RRSs are incorporated. Based on a comparison of three distinct risk mitigation schemes, the EISI has been demonstrated to be practical for assessing the safety performance of chemical processes with control measures of varying priorities. The EISI provides stakeholders with a list of RRSs to address various process hazards during the design stage. It expands conventional inherent safety indices by incorporating risk mitigation elements, thereby facilitating industries in developing a comprehensive safer chemical process.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".