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Record W2048749153 · doi:10.1002/cjce.21909

Potential for flammability of gases emitted from stored wood pellets

2013· article· en· W2048749153 on OpenAlexafffundvenue
Fahimeh Yazdanpanah, Shahab Sokhansanj, Jim Lim, Anthony Lau, Xiaotao Bi, Pak Yiu Lam, Staffan Melin

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicFire dynamics and safety research
Canadian institutionsDelta-Q Technologies (Canada)University of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPelletsFlammable liquidFlammabilityPelletFlammability limitCarbon dioxideVolume (thermodynamics)MethaneCarbon monoxideOxidizing agentWaste managementNitrogenOxygenHydrogenMaterials scienceChemistryEnvironmental chemistryComposite materialThermodynamicsOrganic chemistry

Abstract

fetched live from OpenAlex

Previous measurements have shown that freshly made wood pellets continued to emit flammable gases such as CO, H 2 and CH 4 during storage and handling. The research reported in this paper examines whether the concentration of these emitted gases and the available oxygen within enclosed wood pellet spaces can reach flammable levels. Glass jars filled to 75% volume with pellets were sealed and placed in controlled environments at 25, 40 and 60°C for a period of 9 weeks. Each batch of the stored pellet had a moisture content of 4%, 9%, 15%, 35% or 50% (wet mass basis). The concentrations of carbon monoxide (CO), carbon dioxide (CO 2 ), methane (CH 4 ), oxygen (O 2 ), nitrogen (N 2 ) and hydrogen (H 2 ) were determined using gas chromatography. The flammability of the gas mixtures in the container headspace was calculated using ISO 10156 Standard ‘Gases and gas mixtures—Determination of fire and oxidizing ability for selection of cylinder valve outlets.’ It was concluded that the composition of the gas mixture does not reach flammable concentrations under all experimental conditions.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.629
Threshold uncertainty score0.350

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.005
GPT teacher head0.179
Teacher spread0.174 · 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

Citations8
Published2013
Admission routes3
Has abstractyes

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