Optimization of a Hydrogen Alternative Aviation Fuel Supply Chain Under Demand and Emissions Constraints - A Case Study of Canada
Bibliographic record
Abstract
The potential of using hydrogen-based alternative aviation fuels, to meet national greenhouse gas (GHG) emissions reduction commitments was investigated using a mixed-integer-linear-program (MILP) optimization model that minimized the total energy consumed “energy intensity” for a hydrogen-based alternative aviation fuel supply chain. The model provided (i) optimal jet fuel blend ratios and, (ii) the number and types of hydrogen production plants that would meet forecasted demand and GHG reduction target constraints. The model was optimized for time horizons of 2035, 2050, 2065, and 2080 in Canada, assessing distribution and resilience scenarios. By 2065, two resilient hydrogen infrastructure pathways emerged: one with 556 polymer-exchange-membrane-electrolysis (PEM), 4 Nuclear-Solid Oxide Electrolysis (SOE), and 2 Steam Methane Reforming Carbon Capture and Storage (SMR-CCS) plants, and another with 24 Alkaline Electrolysis (AE), 7 Nuclear-SOE, and 6 SMR-CCS plants. The 2080 net-zero solution was not found using this model due to GHG emissions in hydrogen production.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".