Hydrogen supply via power‐to‐gas application in the renewable fuels regulations of petroleum fuels
Bibliographic record
Abstract
Abstract Power‐to‐gas (PtG) is an evolving energy storage technique that can transfer surplus and intermittent renewable generated power into a marketable hydrogen, among other ancillary services for the electrical grid. This study provides a comparative assessment of blending 10 % corn‐ethanol and using an electrolytic hydrogen supply via PtG on the well to wheel of gasoline fuel, based on Ontario's energy system. The analysis is performed using the GREET® model to investigate the energy and emissions results of the subject comparison. Consequently, PtG renewable hydrogen, when used for gasoline production, is found to decrease 4.6 % of the natural gas consumption of the gasoline cycle and, therefore, increase the renewable content of gasoline. Furthermore, the deployment of electrolytic hydrogen at the refinery results in minimizing gasoline carbon intensity by 0.15 kg CO2e per 100 km (0.5 g CO2e per MJ) of the fuel. When associated with the annual gasoline sales in Ontario, the use of electrolytic hydrogen can offer a reduction of 0.26 MT of greenhouse gas emissions yearly. Moreover, the hydrogen supply from the PtG method used for gasoline production may contribute to lowering VOCs, NOx, PM10, and PM2.5 criteria air pollutants from the gasoline cycle, which cannot be achieved with blending corn‐based ethanol. Therefore, the results of this paper support the inclusion of the PtG concept in renewable fuels regulations for petroleum fuels.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".