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Record W2948173255 · doi:10.1016/j.aeaoa.2019.100035

Computationally efficient quantification of unknown fugitive emissions sources

2019· article· en· W2948173255 on OpenAlexafffund
Carol A. Brereton, Lucy J. Campbell, Matthew R. Johnson

Bibliographic record

VenueAtmospheric Environment X · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsCarleton University
FundersNatural Resources CanadaNatural Sciences and Engineering Research Council of CanadaPetroleum Technology Alliance Canada
KeywordsFugitive emissionsBluffComputationGreenhouse gasEnvironmental scienceScalar (mathematics)Work (physics)Computational fluid dynamicsWind directionComputer scienceMeteorologyWind speedMechanicsEngineeringAlgorithmMathematicsGeologyGeometryPhysicsMechanical engineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.005
GPT teacher head0.192
Teacher spread0.187 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2019
Admission routes2
Has abstractyes

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