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Record W2058995950 · doi:10.2202/1542-6580.1306

Modeling a Silicon CVD Spouted Bed Pilot Plant Reactor

2006· article· en· W2058995950 on OpenAlexafffund
Juliana Piña, Verónica Bucalá, Noemí S. Schbib, Paul E. Ege, Hugo Ignacio de Lasa

Bibliographic record

VenueInternational Journal of Chemical Reactor Engineering · 2006
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaUniversidad Nacional del SurConsejo Nacional de Investigaciones Científicas y Técnicas
KeywordsPopulation balance equationFluidized bedPopulationProcess engineeringChemical reactorParticle (ecology)Pilot plantMaterials scienceMechanicsChemical engineeringWaste managementEngineeringPhysics

Abstract

fetched live from OpenAlex

This study reports a comprehensive multiphase gas-solid dynamic mathematical model that successfully describes the batch growth of silicon particles in a CVD submerged spouted bed reactor. This multiphase reactor model takes into account the hydrodynamics and interphase mass exchange between the different fluidized bed regions (spout or grid zone, bubbles and emulsion phase) and uses applicable kinetic rate models to describe both heterogeneous and homogeneous reactions. The model also incorporates a population balance equation representing particle growth and agglomeration.The CVD submerged spouted bed reactor operation is simulated by means of a sequential modular procedure, which involves the solution of the reactor model and the population balance equation.It is shown that the proposed CVD multiphase reactor model successfully simulates experimental data obtained from batch operation in a pilot scale reactor at REC Silicon Inc. The modeling of experiments obtained for different operating conditions allows correlating the scavenging factor as a function of the silane concentration for short- and long-term operations.

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 score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
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 head0.009
GPT teacher head0.202
Teacher spread0.193 · 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

Citations24
Published2006
Admission routes2
Has abstractyes

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