A Comparative Life Cycle Assessment on Nuclear-Based Clean Ammonia Synthesis Methods
Bibliographic record
Abstract
Abstract This paper evaluates the impacts of nuclear ammonia synthesis options on the environment through the life cycle assessment (LCA) technique. Ammonia is synthesized via the mature Haber–Bosch technique that combines hydrogen and nitrogen with 3:1 ratio to yield ammonia. For hydrogen production from water, five different hydrogen production methods are used, namely, conventional electrolysis (CE), high-temperature electrolysis (HTE), and 3-, 4-, and 5-step Cu–Cl cycles. The nitrogen required for ammonia synthesis is extracted from the air by the cryogenic air separation technique. The thermal and electrical energy need of production processes is supplied from a pressurized water reactor type nuclear power plant (NPP). The simapro software is utilized for LCA in the present study. The environmental impacts of nuclear ammonia are investigated through five impact categories, namely, abiotic depletion potential, acidification potential, global warming potential (GWP), ozone depletion potential, and human toxicity potential. According to LCA results, ammonia synthesis via HTE corresponds to the lowest environmental impact in all selected impact categories. Furthermore, the GWP for ammonia production via HTE is 0.1832 kg CO2 eq/kg ammonia, followed by CE (0.2240 kg CO2 eq/kg ammonia), 4-step Cu–Cl (0.3113 kg CO2 eq/kg ammonia), 3-step Cu–Cl (0.3323 kg CO2 eq/kg ammonia), and 5-step Cu–Cl (0.3370 kg CO2 eq/kg ammonia).
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".