A holistic approach for assessing occupational health risk due to fugitive emissions in petrochemical processes: Leak hazard index ( <scp>LHI</scp> )
Bibliographic record
Abstract
Abstract Fugitive emissions (FE) are unintentional and undesirable leaks of hazardous gases that come from petrochemical piping components such as valves, flanges, and pumps. Fugitive emissions represent a serious threat to the health of petrochemical workers. Based on the source, pathway, receptor (SPR) model, the occupational health (OH) risk due to fugitive emissions is a combination of the health hazards that originate from the source (i.e., process materials, conditions, and design), the leak hazard that represents the pathway, and the exposure hazard that takes place at the receptor. These three hazards bear a resemblance to the severity, probability of leakage, and probability of exposure, respectively. The severity was covered in a previous article related to this study. This paper concentrates on the probability of leakage. The exposure will be covered in a subsequent work to be published later. The OH risk is usually evaluated based on FE amount estimations made based on emission factors developed for different process piping components. This type of evaluation, however, does not consider maintenance that is put in place to control leaks from process piping components. This paper attempts to address and reduce this gap through the development of an index‐based method named the leak hazard index (LHI). The LHI is meant to determine the probability of leakages, taking into consideration the effectiveness of maintenance programs that are regularly executed to reduce or prevent leaks from process piping components. The demonstration of the LHI reveals a reliable and realistic evaluation of the probability of leakage.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".