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Record W2165932908 · doi:10.1002/aic.690470911

Gas‐solids flow behavior: CFB riser vs. downer

2001· article· en· W2165932908 on OpenAlexaff
H. Zhang, Wenzhen Huang, Jesse Zhu

Bibliographic record

VenueAIChE Journal · 2001
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsWestern University
Fundersnot available
KeywordsFluidized bed combustionMechanicsParticle (ecology)FluidizationFlow (mathematics)ElutriationMaterials scienceThermodynamicsFluidized bedPhysicsGeology

Abstract

fetched live from OpenAlex

Abstract Comparisons are made in a circulating fluidized‐bed riser/downer system between a 15.1 m high, 0.10 m ID riser and a 9.3 m high, 0.10 m ID downer, based on the measurements of the radial distributions of the local solids holdups and local particle velocities along the two columns. Although the core‐annulus flow structures exist in both the riser and downer, the radial flow structure in the downer differs largely from that in the riser. The radial distributions of solids holdup and particle velocity in the downer are much more uniform than those in the riser, thus ensuring the low back mixing and the narrow particle residence time distribution in the downer. The axial flow structure in the downer is also more uniform than that in the riser. Due to the high particle acceleration and the high particle velocity in the downer, the overall solids holdup is significantly lower than that in the riser. The microflow structure in the downer, characterized by the low intermittency indices, is also more uniform than that in the riser. These key properties of the downer make it a very promising candidate for industrial applications where short reaction times and high product selectivity are required.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.214
Teacher spread0.204 · 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

Citations74
Published2001
Admission routes1
Has abstractyes

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