Bibliographic record
Abstract
Hydrogen represents an excellent energy storage option as it can act as both a short and long‐term energy store. As the UK Government is strategically moving the UK towards a low carbon economy, hydrogen can play an important role as a solution to make use of grid constrained ‘green’ energy in transport. In the transport sector, green hydrogen produced from renewable sources offers one of the best opportunities to reduce green house gas emissions and significantly reduce dependence on fossil fuels. Use of zero carbon or ‘green’ hydrogen derived from renewable sources in Fuel Cell Electric Vehicles (FCEV) is expected to lead to a 90%‐ 95% reduction in well‐to‐wheel emissions by 2020 when compared to existing internal combustion engines [1]. Described within this paper is a real-world case study that utilises grid constrained renewable energy (instead of discarding it) as a source of clean energy to produce ‘green’ hydrogen for use in a transport application. A model that simulates hydrogen demand from transport has been developed. A Simulink model of hydrogen production, storage and cascade refuelling operations has also been presented. The modelling of a real world application of hydrogen transport technology demonstrates how an electrolyser could be sized to provide the daily hydrogen fuel demand for a real-world commercial hydrogen transport application.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".