Techno-economic and environmental analysis of Cu–Cl cycle for hydrogen production in hybrid refuelling stations: A case study of Highway 401, Canada
Bibliographic record
Abstract
The significant environmental impact of carbon dioxide emissions in the energy sector, especially from transportation, calls for exploring alternative energy carriers to reduce these emissions. Promoting hydrogen as an eco-friendly energy vector can significantly lower greenhouse gas emissions from heavy-duty vehicles. Assessing hydrogen production infrastructure is therefore critical. This research evaluates a network of pilot refuelling stations for heavy-duty applications along Highway 401, a key trade route in Canada. A feasibility analysis combines thermodynamic assessment and cost estimation of the 4-step Cu–Cl cycle, addressing a key gap in its techno-economic study. The integrated energy system proposed utilizes solar panels, the grid, wind turbines, and industrial waste heat to produce hydrogen via the Cu–Cl cycle. The findings reveal the substantial impact of location on the optimal solution configuration, emphasizing the importance of considering local weather data. Employing an optimization algorithm, the best hybrid energy system is composed of 1891 kW PV arrays, 3822 kW WTs, 13.22 kgH2/hour, a 220 kg H2 tank, and 1064 kW CNV with a renewable fraction of 98% in Windsor, Ontario. The levelized cost of hydrogen for Windsor, Toronto, Kingston, and Cornwall are $2.78/kgH2, $2.79/kgH2, $3.25/kgH2, and $3.22/kgH2, respectively. Additionally, a comprehensive sensitivity analysis explores the effects of energy market fluctuations, fleet size variations, and greenhouse gas emissions on key economic and environmental parameters. The levelized cost of hydrogen decreases significantly from $12.48 for a 10-truck fleet size to $1.56 for a 100-truck fleet size. This research contributes to fostering synergies between the transportation sector and electricity networks, advancing the transition towards a net-zero energy future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".