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Record W2330933580 · doi:10.2202/1934-2659.1545

Fast Pyrolysis of Biomass in Bubbling Fluidized Bed: A Model Study

2011· article· en· W2330933580 on OpenAlexaff
Priyanka Kaushal, Seyed Amin Mirhidi, Jalal Abedi

Bibliographic record

VenueChemical Product and Process Modeling · 2011
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPyrolysisBiomass (ecology)Fluidized bedYield (engineering)Mass transferNitrogenMixing (physics)Chemical engineeringKineticsProcess engineeringMaterials scienceWaste managementEnvironmental sciencePulp and paper industryChemistryOrganic chemistryChromatographyEngineeringEcology

Abstract

fetched live from OpenAlex

This study proposes a model for the fast pyrolysis of biomass. A reaction scheme of a set of three parallel reactions followed by a set of two parallel reactions has been used to describe the primary and secondary reactions of biomass pyrolysis in a stationary bed reactor. A simple first-order kinetic approach has been applied to predict the product yields. The bed hydrodynamics, the mass transfer between phases and the reaction kinetics have been mathematically formulated. The effects of the operating parameters on the biomass pyrolysis product yield were simulated; the results show that the reaction temperature and nitrogen flow rate plays an important role in the yield of bio-oil. Good agreement between the predicted and measured results was obtained.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.234
Teacher spread0.203 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2011
Admission routes1
Has abstractyes

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