Risk-based approach for safe terminal operation and route planning of on-road hydrogen distribution network
Bibliographic record
Abstract
Hydrogen is a critical energy carrier in the transition to sustainable energy, but its properties such as high diffusivity, wide flammability range, and low ignition energy, present unique safety challenges during transportation. This study aims to improve on-road hydrogen transport safety by developing a dynamic, traffic-dependent risk assessment framework for both Compressed Gaseous Hydrogen (CGH₂) tube trailers and Liquid Hydrogen (LH₂). A key advancement in this study is the use of dynamic occupancy data, capturing variations in traffic density throughout the day, instead of relying on average traffic density to estimate ignition source distribution. Additionally, a qualitative Hazard and Operability (HAZOP) study was conducted for a potential central distribution terminal in Fort Saskatchewan, Alberta, Canada, to systematically identify process hazards during the loading of hydrogen on-road carriers. Results reveal that the ignition probability for minor CGH2 leaks significantly increases with road occupancy, rising from 0.003 at 0.1 % to 0.149 at 5 %, emphasizing the importance of scheduling transport during off-peak hours Vapor Cloud Explosions (VCE) from LH2 extend up to 257 m, compared to 122.42 m for CGH₂, underscoring the need for stricter land-use planning in densely populated areas. The analysis suggests prioritizing lower-traffic rural routes, which exhibit lower release frequencies (e.g., 1.80E-05 per year), over high-traffic urban routes with higher release frequencies (e.g., 6.47E-05 per year).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".