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Record W1581406476 · doi:10.1002/cjce.22124

Development of a modelling tool representing biomass gasification step in a dual fluidized bed

2014· article· en· W1581406476 on OpenAlexvenueno aff
Halima Noubli, Sylvie Valin, B. Spindler, Mehrdji Hémati

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2014
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsnot available
FundersAgence de l'Environnement et de la Maîtrise de l'Energie
KeywordsFreeboardWood gas generatorFluidized bedBiomass (ecology)CharBubbleEnvironmental scienceProcess engineeringNuclear engineeringWaste managementMechanicsPyrolysisCoalEngineeringGeologyPhysics

Abstract

fetched live from OpenAlex

The aim of the present study is to develop a 1D modelling tool representing biomass gasification in a dual fluidized bed. This tool should be used with the objective to control and scale‐up the process for different pilot scales and operating conditions. This implies that the modelling tool should be based on a phenomenological description but also that the calculations are not too much time‐consuming. A one‐dimensional steady state model was thus developed for the gasification reactor, combining a description of the biomass thermochemical conversion phenomena and of the hydrodynamic behaviour of the fluidized bed. The reactor is divided into two zones, namely, dense zone and freeboard zone. For the dense zone, the model assumes the existence of two phases –bubble and emulsion– with chemical reactions occurring in both phases (biomass devolatilization, char gasification, water‐gas shift). In the freeboard, a plug flow is assumed and the only reaction considered is water‐gas shift. Mass balance is globally solved across the entire gasifier. The model allows calculating axial concentrations of the gas species in the reactor as well as the global yields. The parameters of the calculations are temperature of the gasifier, biomass composition, biomass and steam feeding rates.

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 score0.078
Threshold uncertainty score0.466

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 head0.013
GPT teacher head0.184
Teacher spread0.172 · 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

Citations10
Published2014
Admission routes1
Has abstractyes

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