Hydrogen pathways for green fertilizer production: A comparative techno-economic study of electrolysis and plasmalysis
Bibliographic record
Abstract
Decarbonizing ammonia production is critical to meeting global climate targets in agriculture. This study evaluates two hydrogen pathways, plasmalysis and electrolysis, at Ontario's Courtright Complex using detailed techno-economic modeling. The natural gas–based plasma system achieves the lowest hydrogen cost ($1.35/kg) but incurs high annual fuel expenses ($297.7 M/y) and shows strong sensitivity to natural gas prices. Electrolysis, powered by 110 MW PV, 1700 MW wind, 60 MW biomass, 95 MWh battery storage, and a 2.0 GW electrolyzer, produces hydrogen at $2.07/kg with lower fuel costs ($29.7 M/y) and significant grid interaction (2.67 TWh/y imports and 1.89 TWh/y exports), enhancing operational flexibility. Over a 15-year horizon, both pathways deliver substantial CO 2 reductions (plasmalysis: 27,000 kt; electrolysis: 26,045 kt). Extending plant lifetimes from 10 to 30 y reduces the levelized cost of hydrogen from $2.25 to $1.91/kg in the plasmalysis case and from $1.52 to $1.18/kg in the electrolysis case, while increasing overall net present cost. Although electrolysis requires higher capital investment ($5.53 B compared with $1.79 B), it demonstrates resilience to fuel price volatility and provides additional grid revenue. In contrast, plasmalysis offers near-term cost advantages but remains dependent on fossil gas, underscoring its role as a transitional rather than fully green option for ammonia decarbonization. • Plasmalysis hits $1.35/kg; electrolysis reaches $2.07/kg with grid support. • Electrolysis achieves 74 % renewables with higher capital requirements. • Hybrid system balances PV, wind, biomass, storage, and grid exchange. • Lifetime extension cuts LCOH by 15 % but raises resource use and NPC. • Both pathways cut CO 2 ; plasmalysis avoids slightly more emissions.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".