Methane Emissions Reduction in Liquefied Natural Gas Off-Loading Process in Refueling Stations
Bibliographic record
Abstract
Abstract Recent research into methane emissions from the liquefied natural gas (LNG) supply chain has revealed uncertainty in the overall greenhouse gas emissions reduction associated with the use of LNG in heavy-duty vehicles. Methane is the main component of natural gas and a potent greenhouse gas. This study investigates the impact of five methods used to offload LNG from a tanker truck to an LNG refueling station and estimate the amount of fugitive methane emissions. The LNG offloading process time, and the final pressures of the tanker truck and refueling station are considered to evaluate the performance of the LNG offloading methods. The modeling results show that the LNG transfer by using a pressure buildup unit has a limited operating range and can increase methane emissions by 10.4% of LNG offloaded from the tanker truck. The results indicate that the LNG transfer by using a pump and an auxiliary pressure buildup unit without vapor return provides the shortest fuel offloading time with the lowest risk of venting methane to the atmosphere.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".