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Record W2099622795 · doi:10.1002/cjce.5450850211

Hydrodynamic Modelling of Internal Loop Airlift Reactor Applying Drift‐Flux Model in Bubbly Flow Regime

2007· article· en· W2099622795 on OpenAlexvenueno aff
Ladislav Chriaštel, Y. Kawase, Hussein Znad

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2007
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
Fundersnot available
KeywordsAirliftMechanicsBubbleFlow (mathematics)Flux (metallurgy)Energy balanceTwo-phase flowCirculation (fluid dynamics)Materials scienceEnvironmental scienceThermodynamicsBioreactorPhysicsChemistry

Abstract

fetched live from OpenAlex

Abstract A new model for the liquid circulation rates in airlift reactor (ALR) is presented. The model is based on the energy balance for the flow loop (riser, turn riser‐downcomer, downcomer, and turn downcomer‐riser) coupled with a drift flux theory of two‐phase flow gas‐liquid system, considering a bubbly flow regime. The predicted values of the liquid circulation rates by the developed model are compared with experimental results performed in a 22 dm 3 internal loop airlift reactor and with the results obtained in the literatures. The proposed model predicted the experimental results very well. Slip velocity relationship based on the drift flux model was proposed; including the gas holdup, bubble size and the liquid physical properties. The predicted slip velocity was similar to that obtained from the literature. The study revealed that appropriate arrangements of internal bioreactor parts can positively influence the liquid circulation velocity at the same energy consumption. The proposed models are useful in the design; scale up and characterization of the internal loop airlift reactors, and provides a direct method of predicting hydrodynamic behaviour in gas‐liquid airlift reactors.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.266
Threshold uncertainty score0.783

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.008
GPT teacher head0.175
Teacher spread0.167 · 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

Citations9
Published2007
Admission routes1
Has abstractyes

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