Computationally efficient quantification of unknown fugitive emissions sources
Bibliographic record
Abstract
Fugitive emissions or unintentional losses of gas (e.g. leaks) are a significant source of greenhouse gases within the oil and gas sector. Previous work has demonstrated the potential of a scalar transport adjoint method for using sparse sensor data to locate and quantify multiple simultaneous unknown fugitive emission sources within a bluff-body dominated facility environment. This paper builds directly on that work and demonstrates the significant computational time reductions that can be achieved by modifying this approach to use a database of pre-computed retro-tracers (PRT). The computational cost, as well as estimated source emission rates and locations, were compared for both an open field release and multiple releases in a bluff-body dominated domain when using the PRT method versus the concurrent gas transport computations from previous work. For the open-field release, given the same wind input there were no significant differences in results of the two approaches. For the bluff-body dominated multiple source case (a domain representative of an actual gas plant), using simplified wind fields for the PRT database generation allowed major sources to be successfully located. The emission rates were computed within −75% to −32% of their actual value. When the wind direction coverage was increased to 110° from ∼60°, the emission rate computations improved to within approximately −30% to −25%. The total computational cost for both methods was of a similar order of magnitude when including the initial database generation for the PRT method, but non-reusable computational time was reduced by a factor of 200–600 times making the PRT method feasible on a standard desktop computer once the database is generated. This is a noteworthy achievement as it raises the possibility of continuous or near-continuous characterization of unknown fugitive emissions sources within a complex facility which could allow sources to be identified as they arise.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".