Combustion Efficiency and Methane Emission Rate of Flares Subjected to Crosswind
Bibliographic record
Abstract
An estimated 139 billion m3 of gas are burned globally each year in flares at upstream oil and gas production sites. However, data and models to reliably predict emissions from flares are notably lacking. Especially concerning are the impacts of strong crosswinds, which can reduce the overall combustion efficiency and strip unburned methane from the flare, although the specific mechanisms behind these emissions are not well understood. Building on a methodology developed by Burt et. al, this study reports quantitative measurements of carbon conversion efficiencies and methane emission rates of 25-100 mm diameter flares subjected to turbulent crosswind in a large closed-loop wind tunnel. Experiments considered multicomponent flare gas mixtures and operating conditions representative of flares at upstream oil and gas production sites which generally operate as simple non-premixed flames without supplementary air or steam injection. Emission rates and combustion efficiencies were found to be influenced by wind speed, burner diameter, exit velocity, and particularly composition of the flared gas. Comprehensive Monte Carlo uncertainty and sensitivity analysis was used to assess the uncertainties associated with such experiments. Attempting to propose a practically implementable model capable of encapsulating known affecting factors, good correlation was observed between carbon conversion inefficiency and both burner diameter and exit velocity for a single fuel mixture over a range of wind speeds. However, in different flare gas mixtures, minor changes in the composition dramatically affected the overall efficiency and emission rates. To capture the effect of fuel chemistry in a general model, different correlating factors (such as stoichiometric molar air-fuel ratio, volumetric hating value, carbon number, etc.) were considered and introduced to the model. The final semi empirical model was shown to be capable of predicting combustion efficiency with reasonable accuracy (less than 1% absolute error for conversion inefficiency at a 95% confidence interval) over a wide range of flaring conditions.
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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 | 0.000 | 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".