AN INDUSTRIAL REHEATING FURNACE WITH FLUE GAS RECIRCULATION MODELED BY LINEAR TRANSFER FUNCTIONS
Bibliographic record
Abstract
To design an effective active combustion control system, it is important to have detailed knowledge about the dynamic relationships of the furnace from its inputs to its outputs. A novel approach has been proposed to construct a set of dynamic models in the form of transfer functions for an industrial furnace with flue gas recirculation (FGR) based on the computational fluid dynamics (CFD) simulation results. The inputs to the furnace are the flow rate of the combustion air, the temperature of the combustion air, and the pressure head of the FGR fan. The outputs are nitric oxide (NO) and oxygen (O2) concentrations. Therefore, the furnace has been represented by a three-input and two-output system. The model construction relies on the CFD simulations of the combustion process and NO formation in the furnace as well as frequency domain system identification techniques. Low-amplitude sinusoidal signals of different frequencies have been administered at the furnace inputs around a designed furnace operating condition. The dynamic relationships among the furnace inputs and outputs at these frequencies are first established in terms of frequency responses. These frequency responses are further processed by a least-squares-based system identification technique to convert them to a set of parametric models. These models are validated against the results obtained from the CFD simulations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".