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Record W2135723487 · doi:10.1139/l2012-051

Analyzing volatile organic siloxanes in landfill biogas

2012· article· en· W2135723487 on OpenAlexafffundvenue
Chris Clark, Richard G. Zytner, Edward A. McBean

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2012
Typearticle
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsUniversity of GuelphPublic Works and Government Services Canada
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiogasSiloxaneWaste managementGas chromatographyFlame ionization detectorEnvironmental scienceSorptionDesorptionCombustionMaterials scienceChemistryOrganic chemistryChromatographyComposite materialEngineeringAdsorption

Abstract

fetched live from OpenAlex

Many municipalities are turning to landfill biogas to generate electricity. A major challenge is the presence of volatile cyclic organic siloxanes in the biogas stream, which upon combustion form silica deposits, decreasing the lifetime and warranty of reciprocating engines and turbines. With literature reporting that the analysis of biogas for siloxanes is complex, an investigation was completed to identify best practices for measuring siloxane concentrations in the biogas. The result is an easy-to-use collection and analytical technique that used commercially available XAD-2 resin ORBO ® tubes to capture siloxanes through sorption. Following desorption, analysis was done on a gas chromatograph equipped with a flame ionization detector. Using the developed technique at the Region of Waterloo landfill, the sampled biogas showed the presence of D4-siloxane at an average concentration of 37.6 ± 1.7 mg/m 3 , while the D5-siloxane concentration was 21.9 ± 1.2 mg/m 3 . These measured concentrations were higher than those reported by an accredited lab. Investigation suggests that the use of Tedlar ® bags by the outside lab to collect the biogas sample affected the reliability and variability of the latter approach.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.377
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.008
GPT teacher head0.192
Teacher spread0.183 · 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 designObservational
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

Citations11
Published2012
Admission routes3
Has abstractyes

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