Numerical Investigation of NH3 Doped Fuels From Biomass Gasification on Fuel-Bound NOx Formation at Gas Turbine Conditions
Bibliographic record
Abstract
Abstract As our society transitioning away from fossil fuel-based energy system to reduce carbon emissions, renewable energy sources are needed to meet the increasing energy demand in the near future. Biomass, consisting mostly of plant-based organic materials, is a promising alternative to fossil fuels for carbon-neutral energy production. Unfortunately, during the gasification process, the nitrogen content in biomass is converted to NH3, which is then oxidized in the combustion process, leading to high NOx emissions through the fuel-NO pathway. The high NOx emissions from NH3 and NH3-containing fuels have prevented their implementation at scale. Depending on the gasification process, source of biomass, and combustion technology, the NH3 and water content can vary greatly. This works focuses on blends of 2% (mol) NH3 in CH4, diluted by up to 55% (mass) water. The combustion process is modelled by freeflame connected to a plug flow reactor (PFR), achieving a total residence time of 100 ms. Major chemistry involved in the fuel NO pathway were identified from reaction pathway analysis. NOx emissions were investigated under different equivalence ratios (Φ = 0.45–2.2) and inlet pressures (1 atm, 40 atm). NO concentration increases rapidly as NH3 is added to the fuel mixture, as expected. Under fuel rich conditions, NO decreases after the initial increase, and this reduction becomes more pronounced at elevated pressure and long residence times. At the same time, unburned NH3 and other reactive N species increase as NO decreases. The optimal conditions that minimize the total NOx and NH3 emissions were found to be slightly fuel rich, high pressure, and long residence time. Under these conditions, additional pathways involving NNH and N2O promotes the conversion of NH3 to N2. Large discrepancies among different thermochemical mechanisms highlight areas that still need to be researched. Results from the current study can assist in the modelling and design of low-emissions engines fuelled by biomass and NH3, thereby contributing to a carbon-neutral economy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".