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Record W4403997411 · doi:10.1080/15435075.2024.2414230

2-D modeling of torrefaction of a large biomass particle: effect of <i>L/D</i> ratio, internal convection and shrinkage

2024· article· en· W4403997411 on OpenAlexaff
Biswajit Kamila, Anup Kumar Sadhukhan, Parthapratim Gupta, Prabir Basu

Bibliographic record

VenueInternational Journal of Green Energy · 2024
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsDalhousie University
Fundersnot available
KeywordsTorrefactionShrinkageBiomass (ecology)ConvectionParticle (ecology)Internal heatingPulp and paper industryEnvironmental scienceMaterials scienceWaste managementEngineeringMechanicsPyrolysisMechanical engineeringComposite materialPhysicsAgronomyGeology

Abstract

fetched live from OpenAlex

Torrefaction of a large biomass particle was investigated using a transient 2-D model, including primary and secondary reactions kinetics and heat transfer, internal convection, and shrinkage. The model predictions matched with the experimental results within +2%. Spatial and temporal distributions of temperature, residual mass fraction, velocity, and pressure inside the particle and the effect of reactor temperature, residence time, particle size, and shrinkage on the torrefaction behaviour were investigated. Evolution of moisture, volatiles and gases due to drying and torrefaction increase the pressure inside the particle, setting up a velocity field and internal convection. Average velocity and pressure reached a peak during the initial drying period due to moisture evolution, and a subsequent second peak due to the formation of volatiles and gases by torrefaction at a reactor temperature > 493 K. Internal convection influenced torrefaction significantly but the particle shrinkage did not impact it appreciably. The degree of overshoot of the particle center temperature above the reactor temperature increased with the reactor temperature and the particle diameter. Simulations suggest that a reactor temperature of 550 K and residence of about 30 min would be suitable for torrefaction of a particle with L = D = 25.4 mm.

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 score0.142
Threshold uncertainty score0.313

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 head0.228
Teacher spread0.224 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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