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Record W2945113057 · doi:10.1002/cjce.23502

Use of axial dispersion and plug flow models for determination of axial mixing and mass transfer coefficient in an l‐shaped pulsed packed extraction column

2019· article· en· W2945113057 on OpenAlexvenueno aff
Sajad Khooshechin, Jaber Safdari, Mohammad Ali Moosavian, Mohammad Hassan Mallah

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
Fundersnot available
KeywordsMass transferMass transfer coefficientPlug flowMixing (physics)Volumetric flow rateDispersion (optics)Materials scienceIntensity (physics)Flow (mathematics)Complete mixingPhase (matter)ChemistryThermodynamicsAnalytical Chemistry (journal)MechanicsChromatographyOpticsPhysics

Abstract

fetched live from OpenAlex

In this study, the volumetric overall mass transfer and phases axial mixing coefficients have been investigated in a pilot plant of an L‐shaped pulsed packed extraction column by using two liquid systems of toluene/acetone/water and n ‐butyl/acetone/water. The mass transfer performance has been evaluated using two methods of axial dispersion and a plug flow model. The effect of the operational variables and physical properties, including the dispersed and continuous phases flow rates, pulsation intensity, and interfacial tension, on mass transfer and phases axial mixing coefficients have been considered. It has been found that the pulsation intensity and the continuous phase flow rate seriously affect the mass transfer coefficient, however, the dispersed phase flow rate has a weaker effect. Also, the axial mixing of a phase is strongly affected by the pulsation intensity and the flow rate of the phase itself and it is not affected by the second phase flow rate. Finally, new correlations are proposed to accurately predict the mass transfer and axial mixing coefficients.

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 score0.572
Threshold uncertainty score0.349

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 head0.011
GPT teacher head0.192
Teacher spread0.181 · 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

Citations13
Published2019
Admission routes1
Has abstractyes

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