Sky-Scattered Solar Radiation Based Plume Transmissivity Measurement to Quantify Soot Emissions from Flares
Bibliographic record
Abstract
For gas flares typical of the upstream energy industry and similar point sources, most current methods for characterizing soot emissions are based on plume opacity rather than a quantitative measure of mass flux. The absence of more quantitative approaches is indicative of the inherent complexity of soot and the difficulties in characterizing emissions in an unbounded plume. A new experimental approach has been developed for the investigation of soot emissions in industrial plumes. Referred to as sky-LOSA, the diagnostic permits evaluation of 2D spatially resolved monochromatic sky-light transmissivity data over the width of a plume, where sky-light intensities behind the plume are obtained via an interpolation algorithm. By using Rayleigh-Debye-Gans Fractal Aggregate theory to relate transmissivity data to soot concentrations, and with knowledge of the velocity of the plume, it is possible to quantify mass flow rates of soot in a plume. Experiments on an unconfined lab-scale soot plume were used to support a detailed uncertainty analysis under a wide range of conditions and to estimate sensitivity limits of the technique. Results suggest field measurements of soot emission from flare plumes should be possible with overall uncertainties of less than 32%. This represents a significant advancement over existing techniques based on opacity measurements.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".