Extension of QRA Event Trees for Risk Assessment of R290 Refrigerant Applications
Bibliographic record
Abstract
After the 1987 Montreal Protocol banned chlorofluorocarbons (CFCs) for their ozone-depleting effects, hydrochlorofluorocarbons (HFCs) became the preferred refrigerants. Although HFCs do not damage the ozone layer, they contribute significantly to global warming. HFCs possess, indeed, a global warming potential (GWP) hundreds of times higher than CO2. Therefore, the 2016 Kigali Amendment introduced an international plan to phase out high-GWP HFCs. The most suitable alternatives for residential refrigerants are hydrocarbons (HCs) such as R600 (isobutane) and R290 (propane), which exhibit a negligible GWP but are characterised by high flammability. While R600 has been safely used in refrigerators due to its low charge requirements, safety concerns about using R290 in air conditioning have been raised. These concerns have led to international standards capping the permissible R290 charge to amounts that made it unfeasible for widespread HVAC applications. Still, IEC 60335-2-40:2022 increased the maximum allowable charge in 2022 to remove a barrier to heating electrification and energy transition, effectively. Risk mitigation measures have focused on R290 leak control in enclosed spaces. According to current research and regulations, the leakage of R290 is considered the primary source of fire hazard for these applications. However, despite empirical, experimental, and numerical evidence, less attention has been given to the behaviour of air conditioning and refrigeration units in independent fires. As a result, quantitative risk assessment (QRA) methods have been mainly applied with leakage as a starting event. Conversely, this study introduces new event trees for QRA, considering an independent fire as a starting event. Preliminary probability assessments are also provided to compare the likelihood of fire consequences. The findings stress the importance of considering fire reactions of HC equipment together with leakage risks.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".