Power-to-ammonia pathways to decarbonize the agriculture, transportation,and energy sectors
Bibliographic record
Abstract
Abstract: Ammonia is a versatile chemical that is distributed and traded widely as a commodity for the fertilizer and refrigeration industries, and it also has the potential to be a low-emission fuel, hydrogen carrier and energy storage medium. Converting clean electricity to ammonia (i.e., power-to-ammonia, or P2A) is a critical process to decarbonize the use of ammonia in agriculture, transportation, and energy sectors. To understand the viability of P2A, we conducted a techno-economic analysis to study the electrified ammonia production process. The levelized cost of ammonia was found to be highly dependent on the electrolyzer efficiency and the plant’s capacity factor. Then, we designed a P2A plant using offshore wind power from Sable Island, Nova Scotia. Even though the offshore wind farms in Atlantic Canada are expected to have high-capacity factors, we still found it necessary to connect the plant with the electrical grid to maintain high ammonia outputs. Grid-connection, however, may raise the carbon intensities of the P2A process to a higher level than the conventional fossil-fuel based ammonia production. Furthermore, we studied the potential economic benefits and risks of using excess electricity for P2A in a combined ammonia use scenario, including local fertilizer sales, export, and energy storage. Results show that with higher prices and larger export demands for low-carbon ammonia, as well as technology development, the combined use scenario will be profitable compared with selling excess electricity at low prices.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".