Black carbon emissions from turbulent buoyant non-premixed flames representative of flares in the upstream oil and gas sector
Bibliographic record
Abstract
Gas flaring, the pervasive oil and gas industry process of using turbulent buoyant non-premixed flames to destroy unwanted flammable gases, is an important global source of carbonaceous soot or black carbon (BC). However, experimental data and reliable models with which to predict these emissions over a range of flare gas compositions and operating conditions relevant to upstream oil and gas production sites (which account for 90 % of global flaring) do not exist. In the absence of alternatives, most official reporting and inventory estimates are based on single-valued emission factors that are known to be insufficient and inaccurate. This work addresses this gap through parametric experiments to measure BC emissions from vertical lab-scale flares at Reynolds and Froude number conditions directly relevant to flares at upstream oil and gas production sites, burning multicomponent C1-C7 alkane, CO 2 , and N 2 flare gas mixtures typical of flares in North Dakota, USA and Alberta, Canada. Analysis reveals that BC emission rates fall under two different regimes corresponding to the transition buoyant and transition shear regimes of turbulent buoyant non-premixed flares previously proposed by Delichatsios based on visual flame observations and distinguished by the product of Reyolds number and the square of the fire Froude number. Within the transition buoyant regime, BC emissions are simply proportional to total volumetric fuel flowrate whereas within the transition shear regime BC emission are inversely proportional to the exit strain rate. For a narrow range of methane-dominated hydrocarbon mixtures relevant to upstream oil and gas flaring, empirical models are presented for each regime that reliably predict BC emissions scaled by the mean carbon number of the fuel. Though ultimately limited to the specific conditions considered in this study, these empirical models nevertheless are a significant improvement over existing, widely used single valued emission factors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".