Automated evaluation of safety at the conceptual stage: An index-based methodology for units and processes
Bibliographic record
Abstract
Safety analysis is traditionally performed after the detailed design is concluded, and process modifications at this stage lead to extra expenses. Implementing safety indices at the conceptual stage allows to guide engineers towards an inherently safer design. Although the progress made in the field, there are still some lacks to address to help spread the utilization of such metrics. Here we leverage the tool for stream safety we presented in an earlier work to calculate units and process indices, all ranging over a fixed scale from 0 to 10. Three different analysis stages (streams, units, process) allow for a thorough analysis, comprehensive of all parameters available at the conceptual design stage. The index is fully automated, since the code retrieves the needed information either from the process simulator or a built-in Excel database. The indices are tested on two alternatives of the same methane pyrolysis process (Configuration 1 and 2), and compared to three existing indices. The automatization of our code returns the results for each Configuration (made of 40 streams and 15 units) in less than 40 s of computation time versus multiple hours required to compute state-of-the-art indices.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".