Colourless distributed combustion effects on a pre‐mixed coke oven gas flame
Bibliographic record
Abstract
Abstract Fuels consisting of high hydrogen concentration can be consumed under colourless distributed combustion (CDC) to suppress flashback tendency in premixed conditions. Higher NOX challenges can also be overcome through CDC. For those purposes, coke oven gas was consumed under CDC within the scope of this study. To achieve CDC, or diluents were introduced into the oxidizer so that oxygen concentration in the oxidizer would be decreased from 21% O2 to its lean blow‐off limits. Excess air ratios were determined as λ = 1.2 and λ = 1.5 along with a thermal power of 10 kW under premixed conditions. A commercial computational fluid dynamics code was used to predict temperature distribution and , CO, and emissions. 162‐step reactions created with GRI‐Mech 3.0 chemical kinetics was integrated to the eddy dissipation concept combustion model. The temperature and profiles predicted were compared with the experimental results. Consistency was achieved at ~95% for temperature profiles, and almost 100% for profiles between the measured and the predicted ones. According to the results, it is concluded that a more homogeneous temperature distribution with the ~21% decrease in the maximum temperature was observed, and there was about 96% decrease in level. It was also demonstrated that using as a diluent is more effective in terms of temperature distribution over the combustor and reduction, while dilution with is more effective in terms about 70% in decrease on CO.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".