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Record W1995007375 · doi:10.1002/cjce.22153

An exploratory study of explosion potential of dust from torrefied biomass

2014· article· en· W1995007375 on OpenAlexaffvenue
Andrej Boskovic, Prabir Basu, Paul Amyotte

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2014
Typearticle
Languageen
FieldEngineering
TopicCombustion and Detonation Processes
Canadian institutionsDalhousie University
Fundersnot available
KeywordsTorrefactionBiomass (ecology)Pulp and paper industryEnvironmental scienceIgnition systemRaw materialAutoignition temperatureMaterials scienceSteam explosionExplosive materialWaste managementPyrolysisChemistryAgronomyEngineering

Abstract

fetched live from OpenAlex

Torrefaction makes biomass brittle, and as such it is likely to produce more dusts during handling and transportation. So, it could increase the risk of explosion in plant using torrefied biomass. As the torrefaction technology enters commercial market the safety aspect of torrefied wood becomes important. The present paper is one of the first examinations of the question if torrefaction makes the biomass dust more explosive. Effects of torrefaction conditions on two important parameters that define the explosivity of a dust, i.e., minimum ignition temperature (MIT) and minimum explosible concentration (MEC) were studied. Above parameters were measured for Poplar wood before and after torrefaction. Three different particle size distributions of the wood, torrefied at 200, 250 and 300 °C, were tested. Exploratory experiments reported here found negligible effect of torrefaction on measured values of;Deg;ME;Deg;C and MIT of biomass. However, torrefied particles larger than 100 micron showed higher values of MIT than that for raw biomass.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.300

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 head0.010
GPT teacher head0.190
Teacher spread0.180 · 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

Citations9
Published2014
Admission routes2
Has abstractyes

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