Thermo‐environomic evaluation of the ammonia production
Bibliographic record
Abstract
Within the global challenge of sustainable energy supply and greenhouse gas emissions mitigation, carbon capture and storage and the deployment of renewable resources are considered as promising solutions. In this study the production of ammonia mainly used in the fertilizer industry and that is responsible for around 2–3 % of the world greenhouse gas emissions is analyzed. Considering natural gas and biomass as a resource and the option of CO2 capture and storage, different process configurations are systematically compared with regard to energy, economic and environmental considerations. A consistent thermo‐environonomic optimization approach combining flowsheeting, process integration techniques, economic performance evaluation, life cycle assessment and multi‐objective optimization is applied for the conceptual process design and competitiveness evaluation. It is highlighted that the quality of the process integration is a key factor for improving the performance by valorizing the heat excess through electricity cogeneration. Including CO2 mitigation in the ammonia production allows to reduce the emissions but leads to a slight efficiency decrease due to the energy consumption for the CO2 compression. For the natural gas fed process yielding an energy efficiency around 65 %, the overall life cycle emissions can be reduced to 0.79 kgCO2/kgNH3 with CO2 capture compared to 1.6 kgCO2/kgNH3 without capture. Considering the biogenic nature of the carbon in the biomass, the emissions drop to −1.79 kgCO2/kgNH3 for the biomass process having an energy efficiency of 50 %. The economic competitiveness highly depends on the resource price and the introduction of a carbon tax. This study reveals the potential of the decarbonization of the fertilizer industry.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".