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Record W4392758766 · doi:10.5194/egusphere-egu24-14031

Evaluation of a UAV-Based Methodology for Measuring Flare Combustion Efficiency

2024· preprint· en· W4392758766 on OpenAlexaff
Simon A. Festa-Bianchet, Milad Mohammadi, A. E. Tanner, Greg Kopp, Matthew R. Johnson

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnergy
TopicOil, Gas, and Environmental Issues
Canadian institutionsWestern UniversityCarleton University
Fundersnot available
KeywordsFlareCombustionComputer scienceEnvironmental scienceAerospace engineeringAutomotive engineeringAeronauticsEngineeringChemistry

Abstract

fetched live from OpenAlex

This work will present a quantitative evaluation of the potential for measuring gas flare combustion efficiency using an aspirating sensor platform mounted under an uncrewed aerial vehicle (UAV).  The UAV sensor package contains lightweight commercial gas analyzers capable of precise measurements of atmospheric methane (CH4), ethane (C2H6), carbon dioxide (CO2), and carbon monoxide (CO).  By sampling the flare’s plume of combustion products with the help of a UAV, flare efficiency measurements can be safely and remotely completed without affecting the flare’s operation.  The relative mole fraction of the measured major carbon containing species can be used to close a carbon mass balance, which permits calculation of a local carbon conversion efficiency of the flare.  However, because the composition of the flare plume can be inhomogeneous as well as turbulent, it is not straightforward to determine whether the measured incomplete combustion products are representative of total inefficiencies.  Further uncertainty arises if the flared gas contains additional hydrocarbon species (e.g., C3+ hydrocarbons) that may not be directly measurable by the UAV platform.  To address these challenges, controlled experiments were completed on large scale (100-mm diameter) flares burning within Western University’s Boundary Layer Wind Tunnel.  With the wind tunnel running in an open circuit configuration, the UAV/sensor package was suspended within the wind tunnel test section downstream of the flare where it measured combustion efficiency while being moved in and out of the combustion plume.  Results were compared with known combustion efficiencies for identical operating conditions obtained following the established method of Burtt et al. (J. Energy Inst. 2022).  Further, combustion efficiency measurements from operating flares will be made using the developed sensor to validate the proposed measurement approach.  Ultimately, this tool could close a known gap in our ability to quantify carbon conversion efficiency and methane slip from flares under field conditions as required under emerging measurement, reporting, and verification (MRV) programs.  

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.352
Threshold uncertainty score0.856

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.277
GPT teacher head0.377
Teacher spread0.099 · 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 teacher head, 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

Citations1
Published2024
Admission routes1
Has abstractyes

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