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Biomass Torrefaction in a Two-Stage Rotary Reactor: Modeling and Experimental Validation

2017· article· en· W2607364121 on OpenAlexafffund
D.A. Granados, Prabir Basu, Farid Chejne

Bibliographic record

VenueEnergy & Fuels · 2017
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaDepartamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS)
KeywordsTorrefactionCharBiomass (ecology)Raw materialMass transferHeat of combustionRotary kilnProcess engineeringResidence time (fluid dynamics)Pulp and paper industryKilnEnvironmental scienceMaterials scienceThermodynamicsChemistryPyrolysisCombustionChromatography

Abstract

fetched live from OpenAlex

A mechanistic model of torrefaction was developed for a two-stage rotary reactor, and it was verified with experimental results. Mass and energy balances for each phase are considered in the model. A kinetic model that considers the progressive decomposition of biomass into volatiles and char released simultaneously from the raw biomass was coupled to the balances. Mathematical expressions for residence time, heat transfer coefficient, and bed height inside the kiln were taken from the literature for model calculations. Release of condensable and noncondensable volatiles from biomass during the process was considered in the gas phase, while the solid phase included raw and torrefied biomass. The model can predict different output parameters of torrefaction in a rotary continuous torrefier, such as final amounts of solids, gas yields, and temperatures, for different operational conditions. Properties for torrefied solid, such as high heating value, fixed carbon, and volatile matter, can also be predicted by the model through mathematical correlations obtained in a previous experimental work. The results obtained from the model were compared to experimental data, and good agreement was found.

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 score0.008
Threshold uncertainty score0.491

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 head0.017
GPT teacher head0.256
Teacher spread0.238 · 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

Citations24
Published2017
Admission routes2
Has abstractyes

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