Identifying design criteria for implementing inherent safety in chemical process industries part 2: Design mechanism
Bibliographic record
Abstract
Abstract Inherent safety concepts are common knowledge today, but accidents with similar characteristics do recur. Research on accident causes found that design errors, especially equipment failure, contribute the highest percentage of accidents. The development of inherent safety tools has been increasing at a positive pace, but only a small number of these tools are applicable to equipment design. Ample amounts of inherent safety tools are only suitable to be used at the early design stage. This amplifies the fact that inherent safety tools are still conceptual (e.g., change process routes, change safer materials). This is in contrast to the circumstances, where tools are expected to reinforce the inherent safety of equipment. The objective of this research is to identify design mechanism that can help trigger design thinking for implementation of inherent safety in the chemical process industry. To identify such a design mechanism, 526 cases were collected, and knowledge of the mechanism was extracted from design changes and presented in this research paper. The mechanisms were classified according to equipment and inherent safety keywords. The significant design mechanisms from the overall summation were listed (turbulence, change heat transfer fluid, continuous removal, large surface area, corrosion resistance, seamless joint, thin film, dividing wall, on demand, and double wall).
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 | 0.020 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".