A Sensitivity Study of NOx Emission to the Change in the Input Variables of a FGR Industrial Furnace
Bibliographic record
Abstract
Preliminary study has shown that the flue gas recirculation (FGR) is one of the effective ways to reduce the Nitric Oxides (NOx) emission in industrial furnaces. The research reported in this paper concentrates mainly on the development of dynamic models suitable for on-line and real-time feedback control to reduce the NOx emission in industrial furnaces with FGR. To construct an appropriate dynamic model, the relationship between the NOx emission and the furnace input variables, such as the inlet combustion air mass flow rate, inlet combustion air temperature, and the pressure head of the FGR fan, has been investigated. A moment closure method with the assumed β probability density function (PDF) for the mixture fraction is used to model the turbulent non-premixed combustion process in the furnace. The combustion model is derived based on the assumption of instantaneous full chemical equilibrium. The discrete transfer radiation model is chosen as the radiation heat transfer model, and the weighted-sum-of-gray-gases model is used to calculate the absorption coefficient.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".