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Record W4389206774 · doi:10.22215/etd/2023-15841

Development of a Tunable Diode Laser Absorption Spectroscopy System for Quantification of Transient Emissions from Liquid Hydrocarbon Storage Tanks

2023· dissertation· en· W4389206774 on OpenAlexafffund
Fraser Keegan Kirby

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldChemistry
TopicSpectroscopy and Laser Applications
Canadian institutionsCarleton University
FundersGovernment of Canada
KeywordsTunable diode laser absorption spectroscopyAbsorption (acoustics)Storage tankSpectroscopyMaterials scienceEnvironmental scienceAbsorption spectroscopyLaserTunable laserFluorescence spectroscopyOptoelectronicsOpticsAnalytical Chemistry (journal)Waste managementChemistryWavelengthEnvironmental chemistryFluorescenceEngineering

Abstract

fetched live from OpenAlex

Fixed-roof atmospheric storage tanks are used throughout the oil and gas sector to store liquid hydrocarbons and are a key source of volatile organic compound (VOC) emissions.Despite their prevalence, vented emissions from these tanks are rarely monitored and remain poorly understood.This thesis presents a non-intrusive, inline optical measurement system to quantify oxygen fraction (which can be used to infer VOC fraction) in the vent line of an uncontrolled tank.The sensor utilizes tunable diode laser absorption spectroscopy with wavelength modulation to target near infrared absorption bands of diatomic oxygen ingested into the tanks during diurnal tank breathing processes and is compact, robust, and compliant with CSA zoning standards.Lab characterizations of two variants of the system are presented.Over the full-scale (0 -20.95% O2) measurement range, the optical variant had a long-term precision of < 0.72% absolute (at 95% confidence) while the detector variant improved this to < 0.31% absolute.

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)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.019
GPT teacher head0.283
Teacher spread0.264 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations0
Published2023
Admission routes2
Has abstractyes

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