Flare Carbon Conversion Efficiency Quantification using a Long Wave Infrared Fourier Transform Spectrometer
Bibliographic record
Abstract
In recent years, measuring emissions from flares has assumed new significance due to the high global warming potential of these species and the enormous volumes of gases flared, coupled with doubt regarding the 98% combustion efficiency (CE) estimate often used to assess the impact of flaring on climate change. Hyperspectral cameras are a particularly promising avenue since they can directly visualize the species column density contained within the entire flare plume via the absorption features of these species over the infrared spectrum. Imaging Fourier transform spectrometers (IFTS) generate a datacube of images, each of these pertain to a unique wavelength. Spectroscopic and velocity submodels are then used to obtain the species mass flow rates needed to calculate the CE. This work compares the performance of mid-wavelength infrared (MWIR) and long-wavelength infrared (LWIR) IFTSs for measuring the CE for flares at petrochemical refineries, and, for the first time, uses a LWIR IFTS to fingerprint the plume species and estimate the CE. While the MWIR camera covers prominent CO2 features, the limited dynamic range of the camera can induce lens flare artifacts originating in the combustion zone that bias quantification of the CO2 in the downstream plume. Images from the LWIR camera, in contrast, are free of these artifacts, and the dynamic range is sufficient to measure unburned fuel, key to accurate CE estimates.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".