Effect of temperature and reaction atmosphere on nitric oxide emission during a char grate‐fired process in local flue gas recirculation
Bibliographic record
Abstract
Abstract Local flue gas recirculation (LFGR) is an effective technology for reducing nitrogen oxide (NOx) emissions from coal‐fired industrial boilers. The temperature and reaction atmosphere changes when flue gas is recycled, thereby affecting both the grate‐fired process and NOx emission. In this paper, the boundary of LFGR was simulated by changing the experimental parameters. On a small‐scale one‐dimensional fixed‐bed system, the effects of temperature, O2 flux, and CO, CO2, and recycled NO concentrations on oxidation‐reduction layering and char nitrogen conversion during the char grate‐fired process were studied. The effect of temperature and recycled flue gas components on nitric oxide (NO) emissions during the char grate‐fired process was then analyzed based on the mass proportion of oxygen‐absent and oxygen‐present parts. The results show that, with the introduction of recycled flue gas, increasing the temperature will also increase the reduction layer mass and proportion, and, subsequently, inhibit NO emissions; increasing the O2 flux will reduce the reduction layer proportion and subsequently promote NO emissions; increasing the CO and CO2 concentrations will reduce the NO emitted from the oxidation layer, yet has limited effects on the entire char bed; and recycled NO will significantly reduce the NO emissions. The effect of LFGR‐induced changes in temperature and reaction atmosphere on NO emissions can be ascribed to the negative effect of the increase in O2 flux and the positive effect of the increase in temperature and CO, CO2, and recycled NO concentrations.
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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 | 0.000 | 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".