Clean Energy Powered Fertilizer Production System Integrated with Commercial Greenhouse
Bibliographic record
Abstract
This thesis investigates the integration of clean energy technologies, including wind power, green ammonia and urea synthesis, and Small Modular Reactors (SMRs) with commercial greenhouse operations. Through detailed techno-economic assessments with environmental benefits, the study evaluates the feasibility of decarbonizing both energy supply and fertilizer production in greenhouse agriculture. The thesis is structured into three complementary phases. In the first phase, an integrated wind-powered greenhouse system coupled with on-site green ammonia production is assessed. While the Levelized Cost of Ammonia (LCOA) ranges from USD 530 to 1,600 per metric ton depending on electricity prices, the system also offers significant emissions reductions, including a potential annual reduction of 3,134 metric tons of CO2 when surplus hydrogen is used in hydrogen-fired Combined Heat & Power (CHP) engines. The second phase extends the analysis to green urea production, achieving a competitive Internal Rate of Return (IRR) of 23.3% and a payback period (PBT) of 4.2 years under favorable market conditions while enabling the opportunity of near-zero or even carbon-negative greenhouse operations. The final phase explores SMR-powered systems that eliminate reliance on both the electrical grid and natural gas. Although the Levelized Cost of Urea (LCOU) in this configuration is higher at USD 1,394 per metric ton, the system offers long-term sustainability and zero direct emissions, supporting decentralized, year-round agricultural production. Overall, this thesis demonstrates that while economic challenges remain, integrating clean energy systems into greenhouse and fertilizer production presents a promising pathway toward carbon-neutral agriculture. The findings offer strategic insights for policymakers, researchers, and growers, highlighting how renewable energy and nuclear technologies can work in synergy to create resilient, low-emission agricultural systems in regions like Southwestern Ontario.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".