Quantifying flare combustion efficiency using an imaging Fourier transform spectrometer
Bibliographic record
Abstract
Mid-wavelength infrared (MWIR) imaging Fourier transform spectrometers (IFTSs) are a promising technology for measuring flare combustion efficiency (CE) and destruction removal efficiency (DRE). These devices generate spectrally resolved intensity images of the flare plume, which may then be used to infer column densities of relevant species along each pixel line-of-sight. In parallel, a 2D projected velocity field may be inferred from the apparent motion of flow features between successive images. Finally, the column densities and velocity field are combined to estimate the mass flow rates for the species needed to calculate the CE or DRE. Since the MWIR IFTS can measure key carbon-containing species in the flare plume, it is possible to measure CE without knowing the fuel flow rate, which is important for fenceline measurements. This work demonstrates this approach on a laboratory heated vent, and then deploys the technique on two working flares: a combustor burning natural gas at a known rate, and a steam-assisted flare at a petrochemical refinery. Analysis of the IFTS data highlights the potential of this approach, but also areas for future development to transform this approach into a reliable technique for quantifying flare emissions.Implications: Our research is motivated by the need to assess hydrocarbon emissions from flaring, which is a critical problem of global significance. For example, recent studies have shown that methane destruction efficiency of flaring from upstream oil may be significantly lower than the commonly assumed figure of 98%; work by Plant et al. , in particular, suggest that this discrepancy amounts to CO2 emissions from 2 to 8 million automobiles annually, considering the US alone. Similarly, the international energy agency (IEA) estimates a global flare efficiency of 92%, which translates in 8 million tons of CH4 emitted by flares in 2020. Highlighted by these studies and supported by the World Bank initiatives toward zero routine flaring emissions, there is an urgent need for oil and gas industry to assess their flare methane emission, and overall hydrocarbon emissions. At the very least, it is critical to identify problematic flare operating conditions and means to mitigate flare emissions. Focusing on remote quantification of plume species, the measurement technique and quantification method presented in this paper is a considerable step forward in that direction by computing combustion efficiency and key components for destruction efficiency.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".