Addition of hydrogen refueling for fuel cell bus fleet to existing natural gas stations: A case study in Wuhan, China
Bibliographic record
Abstract
Hydrogen refueling is an essential infrastructure for fuel cell vehicles, and currently, it appears to be a critical service needed to initiate the highly anticipated hydrogen economy in China. A practical selecting procedure of adding hydrogen refueling service to existing natural gas (NG) stations is proposed in this study. A case study in Wuhan, China, is established to assess the feasibility and future planning. The demand for hydrogen fuel and initial supply chain of hydrogen in Wuhan are estimated based on the deployment objective of fuel cell buses. The existing NG stations are evaluated based on 300 kg/day to determine whether they meet the hydrogen safety requirement using Google map or field investigation. The safety space requirement of the hydrogen refueling area on existing NG station is determined as 25.9 × 27.1 m2. The optimal hydrogen refueling plan for fuel cell buses is calculated with multi-objective analysis in economic, environmental, and safety aspects from the view of the hydrogen refueling supply chain. It is shown that adding hydrogen refueling stations to existing NG stations is feasible in technology, economics, regulation, and operation considerations. This study provides guidelines for building the hydrogen infrastructure for fuel cell buses at their early stage of commercial operation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".