Nitric oxide emissions and temperature evolution during the char grate‐fired process
Bibliographic record
Abstract
Understanding the char grate‐fired process is key to developing a low‐nitric oxide (NO) technology for industrial boilers. In this work, char combustion and NO emissions during a grate‐fired process were studied in a small‐scale one‐dimensional fixed‐bed system by adjusting the char/oxygen (O2) ratio. Evolution of the surface temperature of the char bed was measured using an infrared temperature measurement system. As the char/O2 ratio increased, a reaction layering of the char bed occurred. The char bed can be divided into oxygen‐absent and ‐present parts in time, and into reduction and oxidation layers in space. This kind of division was determined by the complete oxidation layer that could deplete all O2. The reduction layer could reduce NO emissions well. With the increase of the char/O2 ratio, the char mass proportion of the oxygen‐absent part increased, while that of the oxygen‐present decreased; and the NO emissions and conversion rate of char nitrogen decreased. When combustion began, char started to burn and released a large amount of heat, and the surface temperatures of both the oxidation and reduction layers increased, with a larger rise of the former of about 260 °C. As the reaction proceeded, the surface temperature of the oxidation layer gradually decreased, while that of the reduction layer increased until the char bed was burnt through.
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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) | 0.000 | 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".