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Record W1995321964 · doi:10.1002/ceat.200900075

Particle Shape, Density, and Size Effects on the Distribution of Phase Holdups in an LSCFB Riser

2009· article· en· W1995321964 on OpenAlexafffund
Shaikh Abdur Razzak, Jesse Zhu, Shahzad Barghi

Bibliographic record

VenueChemical Engineering & Technology · 2009
Typearticle
Languageen
FieldEngineering
TopicMineral Processing and Grinding
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials sciencePhase (matter)Fluidized bed combustionDistributorMechanicsThree-phaseParticle (ecology)Particle sizeComposite materialFluidizationFluidized bedChemistryGeologyThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Abstract Electrical resistance tomography (ERT) as a non‐invasive technique based on conductivity measurement of the continuous phase was employed for the study of phase holdup in a liquid‐solid circulating fluidized bed (LSCFB). Local solid holdup was also measured by an optical fibre probe and pressure transducers to compare and verify the results obtained by ERT. Good agreement was observed among the three methods. Tap water was used as the continuous and conductive phase and glass beads (spherical shape) and lava rocks (irregular shape) of two different sizes were used as the solid and non‐conductive phase. Radial non‐uniformities of solid holdups were observed for all four types of particles under different superficial liquid velocities in different axial locations. The solid holdup was higher in regions close to the wall and low in the central region. Non‐uniformity in the phase holdup decreased with increasing liquid velocity. The axial flow profile was found uniform along axial locations of the riser except at the lower location closer to the distributor zone.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0010.000

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 head0.006
GPT teacher head0.216
Teacher spread0.210 · 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 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

Citations14
Published2009
Admission routes2
Has abstractyes

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