Sensitivity Analysis of a FGR Industrial Furnace for NOx Emission Using Frequency Domain Method
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 sensitivity of the NOx emission from a FGR industrial furnace to the change in three major furnace input variables—inlet combustion air mass flow rate, inlet combustion air temperature, and pressure head of the FGR fan—is investigated numerically in this study. The investigation is carried out in frequency domain by superimposing sinusoidal signals of different frequencies on to the furnace control inputs around the design operating condition, and observing the frequency responses. The results obtained in this study can be used in the design of active combustion control systems to reduce NOx emission. The numerical simulation of the turbulent non-premixed combustion process in the furnace is conducted using a moment closure method with the assumed β probability density function for the mixture fraction. 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".