Evaluation of available indices for inherently safer design options
Bibliographic record
Abstract
Abstract Inherent safety is a proactive approach for loss prevention during process plant design. It has been proven that, considering the lifetime costs of a process and its operation, an inherent safety approach can lead to a cost‐optimal option. Application of inherent safety at the early stages of process design yields the best results with respect to process selection, conceptual design, and engineering design. However, in spite of being an attractive and cost‐effective approach to loss prevention, it is not widely used. Reasons have been suggested for this lack of widespread use, but the lack of systematic tools to apply inherent safety principles is perhaps the most important one. A detailed study was conducted to analyze the performance of available hazard indices with reference to various inherent safety principles (guidewords). The performance of four main indices (Dow, Mond, Inherent Safety, and Safety Weighted Hazard [SweHI] indices) was studied for five inherent safety guidewords. None of the indexing procedures can capture all of the inherent safety guidewords, although the SWeHI and Dow Index were found to be robust on many accounts. It is recommended that a new specific index be developed for inherently safer design evaluation. The SWeHI and Dow indexing procedures may be a good basis on which to build.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".