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Estimation of Vinyl Chloride Emissions from Gasholders and Validation of In Situ Emission Reduction Methods

2004· article· en· W2011727849 on OpenAlexafffund
Julie Verville, Christophe Guy, R.F. Caron

Bibliographic record

VenueJournal of the Air & Waste Management Association · 2004
Typearticle
Languageen
FieldEngineering
TopicVehicle emissions and performance
Canadian institutionsPolytechnique MontréalSNC-Lavalin (Canada)
FundersPolytechnique Montréal
KeywordsVinyl chlorideIn situEnvironmental scienceEnvironmental chemistryReduction (mathematics)ChemistryWaste managementEnvironmental engineeringEngineeringOrganic chemistryMathematics

Abstract

fetched live from OpenAlex

Gasholders consist of a floating bell in a tank filled with water. Water provides a seal between the enclosed gas and ambient air. Gasholder emissions come from the contaminated water seal exposed to ambient air and the wet bell wall. The study objectives were to define parameters influencing gasholder emissions, determine the quantities of vinyl chloride (VC) emitted, generate a correlation equation between parameters of influence and mass emissions, and evaluate the efficiency of emission reduction methods. The research project was carried out on a laboratory-scale representation of a gasholder. The classic two-phase resistance model was used successfully to generate a correlation equation, which can be used to calculate the gasholder water seal emissions. A strictly empirical model was generated to estimate the wet wall emissions. Two in situ reduction methods were evaluated with the laboratory installations: floating objects and an oil layer. Both methods showed significant emission reductions, but the oil layer was the most effective. To reduce emissions even further, it is recommended that the water level of the gasholder be set to the lowest achievable level, that a windshield be placed around the water seal perimeter, and that hydrophobic paint be used on the bell wall.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.777
Threshold uncertainty score0.259

Codex and Gemma teacher scores by category

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 communication0.0000.000
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 head0.010
GPT teacher head0.260
Teacher spread0.250 · 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 designBench or experimental
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

Citations0
Published2004
Admission routes2
Has abstractyes

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