Pseudo-source parameters for flares: Derivation, implementation, and comparison
Bibliographic record
Abstract
The United States Environmental Protection Agency (US EPA) flare pseudo-source parameters are over 30 years old and few dispersion modellers understand their basis and underlying assumptions. The calculation of plume rise from the user inputs of pseudo-stack diameter, temperature and velocity have the most influence on air dispersion model predictions of ground-level concentrations. Regulatory jurisdictions across Canada, the United States and around the world have adopted their own approach to pseudo-source parameters for flares; all relate buoyancy flux to the heat release rate, none consider momentum flux and flare tip downwash as adopted by the Alberta Energy Regulator (AER). This paper derives the plume buoyancy flux for flares burning a gas in terms of combustion variables readily known or calculated without simplifying assumptions. Dispersion model prediction sensitivity to flared gas composition, temperature and velocity, and ambient conditions are now correctly handled by the AER approach. The AER flare pseudo-source parameters are based on both the buoyancy and momentum flux, thus conserving energy and momentum. The AER approach to calculate the effective source height for flares during varying wind speeds is compared to the US EPA approach. Instead of a constant source for all meteorological conditions, multiple co-located sources with varying effective stack height and diameter are used. AERMOD is run with the no stack tip downwash option as flare stack tip downwash is accounted for in the effective stack height rather than the AERMOD model calculating the downwash incorrectly using the pseudo-source parameters. The modelling approaches are compared for an example flare. Maximum ground level predictions change, generally increasing near the source and decreasing further away, with the AER flare pseudo-source parameters. It's time to update how we model flares. Implications: What are the implications of continuing to model flare source parameters using the overly simplified US EPA approach? First, the regulators perpetuate the myths that the flare source height, temperature, diameter and velocity are constant for all wind speeds and ambient temperatures. Second, that it is acceptable to make simplifying assumptions that violate the conservation of momentum and energy principles for the sake of convenience. Finally, regulatory decisions based on simplified source modelling result in predictions that are not conservative (or realistic). The AER regulatory approach for flare source parameters overcomes all of these shortcomings. AERflare is a publicly available spreadsheet that provides the "correct" inputs to AERMOD.
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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 | 0.001 | 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".