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Record W4362585549 · doi:10.1002/cjce.24907

Gas holdup, bubble size distribution, and mass transfer in an airlift reactor with ceramic membrane and perforated plate distributor

2023· article· en· W4362585549 on OpenAlexvenueno aff
Yanan Zhang, Zhe Lü, Yong Qiao, Kai Guo, Wei Li, Hui Liu, Xue Li, Chunjiang Liu

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaOpen Research Fund Program of Science and Technology on Aerospace Chemical Power Laboratory
KeywordsDistributorAirliftBubbleMechanicsMass transferMaterials scienceMass transfer coefficientInletCeramic membraneCeramicAnalytical Chemistry (journal)ChemistryChromatographyThermodynamicsComposite materialMechanical engineeringBioreactorPhysics

Abstract

fetched live from OpenAlex

Abstract The airlift reactor is one of the most commonly used gas–liquid two‐phase reactors in chemical and biological processes. The objective of this study is to generate different‐sized bubbles in an internal loop airlift reactor and characterize the behaviours of the bubbly flows. The bubble size, gas holdup, liquid circulation velocity, and the volumetric mass transfer coefficient of gas–liquid two‐phase co‐current flow in an internal loop airlift reactor equipped with a ceramic membrane module (CMM) and a perforated‐plate distributor (PPD) are measured. Experimental results show that CMM can generate small bubbles with Sauter mean diameter d 32 less than 2.5 mm. As the liquid inlet velocity increases, the bubble size decreases and the gas holdup increases. In contrast, PPD can generate large bubbles with 4 mm < d 32 < 10 mm. The bubble size and liquid circulation velocity increase as the superficial gas velocity increases. Multiscale bubbles with 0.5 mm < d 32 < 10 mm can be generated by the CMM and PPD together. The volumetric mass transfer coefficient k L a of the multiscale bubbles is 0.033–0.062 s −1 , while that of small bubbles is 0.011–0.057 s −1 . Under the same flow rate of oxygen, the k L a of the multiscale bubbles increases by up to 160% in comparison to that of the small bubbles. Finally, empirical correlations for k L a are obtained.

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 score0.696
Threshold uncertainty score0.466

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 head0.005
GPT teacher head0.161
Teacher spread0.157 · 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
Published2023
Admission routes1
Has abstractyes

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