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Kinetic Model of Steam Gasification of Biomass in a Dual Fluidized Bed Reactor: Comparison with Pilot-Plant Experimental Results

2017· article· en· W2756874373 on OpenAlexafffund
Bijan Hejazi, John R. Grace, Xiaotao Bi, Andrés Mahecha‐Botero

Bibliographic record

VenueEnergy & Fuels · 2017
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCarbon Management CanadaBioFuelNet Canada
KeywordsWood gas generatorFluidized bedtar (computing)PyrolysisBiomass (ecology)Waste managementCrackingNuclear engineeringEnvironmental scienceFluidized bed combustionProcess engineeringMaterials scienceChemical engineeringChemistryCoalEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

By incorporating reaction kinetics and reactor hydrodynamics, a steady-state two-phase one-dimensional reactor model for biomass steam gasification in the bubbling fluidized bed gasifier of a dual fluidized bed reactor is developed. The generic two-step kinetic model adopted for biomass pyrolysis allows for prediction of tar generation and cracking, as well as predicting pyrolysis product yield and composition based on CHO elemental balances. This model is capable of predicting species concentrations, solids holdup, temperature, pressure, and superficial gas velocity profiles along the gasifier. By performing mass and energy balances over the two interconnected fluidized beds, key operating parameters such as solids circulation rate and additional fuel required for stable operation of the process are approximated. This predictive reactor model, which will provide a useful tool for designing, evaluating, and improving a dual fluidized bed gasifier, is compared with experimental data from the pilot dual fluidized bed gasification unit at the University of British Columbia.

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)0.001
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 score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.029
GPT teacher head0.243
Teacher spread0.214 · 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

Citations27
Published2017
Admission routes2
Has abstractyes

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