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Record W2153594670 · doi:10.1002/cjce.21957

Hydrodynamics of turbulent bed contactor with non‐Newtonian liquids: Net pressure drop and minimum fluidisation velocity (part 2)

2013· article· en· W2153594670 on OpenAlexafffundvenue
Hadil Abu Khalifeh, M. E. Fayed, Nirav Bhagat, Ramdhane Dhib

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPressure dropMechanicsNewtonian fluidMaterials scienceTurbulenceNon-Newtonian fluidCritical ionization velocityDrop (telecommunication)ThermodynamicsVolumetric flow ratePacked bedChromatographyChemistryPhysicsMechanical engineering

Abstract

fetched live from OpenAlex

The study investigates the hydrodynamic behaviour of non‐Newtonian liquids under different operating conditions in a turbulent bed contactor (TBC). Hollow plastic spheres were fluidised in an air stream flowing counter currently to an aqueous solution of carboxy methyl cellulose (CMC) of an apparent viscosity ranging from 5 to 25 mPa s. A set of experiments was carried out to investigate the net pressure drop and minimum fluidisation velocity of the process. The column was operated at different superficial gas velocities, liquid flow rates, CMC solution concentrations and static bed heights. Results show that the net pressure drop is independent of the superficial gas velocity, but it increases steadily with liquid flow rate and static bed height. However, it remains almost invariant with a relatively low CMC concentration (0.2–0.4 wt%), and it augments gradually as the CMC percent exceeds the threshold of 0.4 wt% which corresponds to a more viscous liquid solution. The minimum fluidisation velocity decreases with a CMC concentration lower than 0.5 wt% approximately, but increases as the CMC percent exceeds 0.5 wt%. The study presents a new insight on the behaviour of non‐Newtonian solutions in TBC columns.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.682
Threshold uncertainty score0.487

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 head0.003
GPT teacher head0.147
Teacher spread0.144 · 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 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

Citations4
Published2013
Admission routes3
Has abstractyes

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