Oxygen-enriched combustion studies with the low NOx CGRI burner
Bibliographic record
Abstract
This paper described a study involving an oxygen-enriched-natural gas combustion system with a modified low nitrogen oxide (NOx) Canadian Gas Research Institute (CGRI) burner. The authors examined the effects of oxygen enrichment at various stack oxygen levels and a single furnace operating temperature, on NOx and carbon dioxide (CO2) emissions, fuel efficiency and furnace temperature distribution. In addition, a study of the combined effects of oxygen enrichment and air infiltration was conducted. A single sidewall mounted burner was used in the pilot scale furnace at the Centre for Advanced Gas Combustion Technology (CAGCT). There was a linear decrease of the firing rate needed to maintain the furnace temperature at 1100 Celsius with increasing oxygen enrichment. A reduction in the 40 to 45 per cent range was required in the firing rate to maintain constant furnace temperature at full oxygen enrichment. Changes in oxygen enrichment (up to 60 per cent) had a minimal effect on NOx emissions, which remained relatively constant. Higher oxygen enrichment decreased NOx emissions. Increasing stack oxygen concentration at all oxygen enrichment levels resulted in increased NOx emissions. NOx emissions similar to those observed with no air infiltration but with similar stack oxygen concentrations resulted from air infiltration. The standard deviation of the temperature distribution with no oxygen enrichment fell in the 19 to 27 Celsius range, while the range for 90 per cent oxygen enrichment was 31 to 34 Celsius. 8 refs., 2 tabs., 9 figs.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".