Feasibility Study of Refuelling Infrastructure for Compressed Hydrogen Gas Long-Haul Heavy-Duty Trucks in Canada
Bibliographic record
Abstract
Abstract In view of serious environmental problems occurring around the world and in particular climate change caused significantly by dangerous CO2 emissions into the biosphere in the developmental process, it has become imperative to identify alternative and cleaner sources of energy. It is now indisputable that there cannot be sustained development or meaningful growth without a commitment to preserve the environment. Compressed hydrogen is being considered as a potential fuel for heavy-duty applications because it will possibly substantially reduce toxic greenhouse gas emissions. The cost of hydrogen will be a main element in the acceptance of compressed hydrogen internal combustion vehicles in the marketplace since of its effect on the levelized cost of driving. The cost of hydrogen at the pump is determined by its production cost, which is mainly a function of the feedstock and process utilised, the distribution cost and the refuelling station cost. This paper investigates the feasibility of implementing a nationwide network of hydrogen refuelling infrastructure in order to accommodate a conversion of Canada’s long-haul, heavy-duty truck fleet from diesel fuel to hydrogen. This initiative is taken in order to reduce vehicle emissions and support Canada’s commitments to the climate plans supporting active transportation infrastructure, together with new transit infrastructure, and zero emission vehicles. Two methods, Constant Traffic and Variable Traffic, along with data about hydrogen infrastructure and vehicles, were developed to estimate fuelling requirements for Canada’s long-haul, heavy-duty truck fleet. Furthermore, a thorough economic study was conducted on various test cases to evaluate how diverse variables affects the final selling price of hydrogen. This provided insight with the understanding of what factors go into pricing hydrogen and if it can compete against diesel in the trucking market. Results revealed that the cost to purchase hydrogen is the greatest factor in the pump price of hydrogen. Due to the variability in hydrogen production, however, there is no precise cost, which makes predictions difficult. Moreover, it was found that the pump price of hydrogen is, on average, 239% more expensive than diesel fuel. Future work should concentrate on the costs and logistics of high-capacity hydrogen refuelling stations, which is required to deliver fuel to a fleet of long-haul, heavy-duty trucks. A breakdown of hydrogen production costs, with regard to the Canadian landscape and the requirements of a long-haul, heavy-duty truck fleet, may possibly give further accurate predictions of those made in this study.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".