NOx Emission of Ammonia Combustion: Analyzing the Impact of Mixing With a Novel Monte Carlo Model
Bibliographic record
Abstract
Abstract Ammonia is attracting a high level of interest as a green fuel produced by regenerative H2 and N2 using the Haber Bosch process. The transport of ammonia from areas featuring good regenerative sources to areas where power will be produced using gas turbines is straightforward. However, burning ammonia in conventional gas turbine combustors optimized to burn natural gas will generate NOx emissions of several 1000 ppm. Staged combustion systems have been proposed to keep NOx emissions below 200 ppm enabling further NOx emission reduction via conventional exhaust catalysts to acceptable levels. In a staged combustor the ammonia is burnt under fuel rich conditions at high temperatures towards water, hydrogen and nitrogen. The final dilution with excess air to meet the target turbine inlet temperature is provided in the secondary zone. Important factors influencing NOx generation are the fuel/air mixing in the primary zone of a staged combustor and dilution with secondary air in the second stage of the combustor. To investigate the mixing impact, a Monte Carlo method has been added to a reacting flow network model. This paper outlines the Monte Carlo technique and demonstrates the impact of mixing on NOx emissions for a staged combustion system burning ammonia and blends of ammonia with hydrogen or natural gas. Considering the mixing effects in the primary and secondary stage of the combustor an increase of NOx emissions by 100% has been shown. To obtain the low NOx emission minimum, the primary zone needs to be operated at equivalence ratios near to 1.2 over the whole gas turbine operation range. This requires additional measures on air management like combustor air bypass.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".