Real time, non-intrusive measurement of particle emissivity and gas temperature in coal-fired power plants
Bibliographic record
Abstract
We present a novel, remote technique for measuring in situ and in real time (every 2 s) the spectral emissivity of particles at λ = 3.95 µm, in optically thick combustion environments. The novelty lies in the use of spectral information in the mid-IR (the blackbody emission profile of the 4.3 µm CO2 band and the gray emission profile of particles between 3.8 and 4.1 µm) to determine the physical and brightness temperatures of the gas–particle medium, from which particle emissivity can be calculated. The retrieved particle emissivity at 3.95 µm is a reasonable average of total particle emissivity between 1 and 15 µm. Thus, CFD researchers who work with radiation sub-models may use this technique to obtain in situ emissivities at different locations, with a portable, rugged and inexpensive device. A small prototype was built with off-the-shelf components: standard light collection optics, a grating spectrometer and a linear-array pyroelectric detector. The particle emissivity is calculated from the asymptotic solution of the radiative transfer equation for optically thick media with isotropic scatterers. Results from a proof-of-concept test at a full-scale, coal-fired boiler 10 m above the top row of burners showed an average particle emissivity of 0.41 and an average gas temperature of 1533 K. Intrinsic and prototype error as well as the impact of temperature gradients in the line of sight of the instrument are discussed.
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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 | 0.001 | 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".