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Record W2949971314

Experimental Study of Volumetric Gas-Liquid Mass Transfer Coefficient in Slurry Bubble Columns

2019· article· en· W2949971314 on OpenAlexfundno aff
Afshin Fallahi

Bibliographic record

VenuePolyPublie (École Polytechnique de Montréal) · 2019
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSlurryHumanitiesPhysicsThermodynamicsPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

Slurry bubble column reactors (SBCRs) are extensively employed in a variety of processes in which efficient contact between the gas, liquid, and solid phases are of critical importance.In this context, in the past decade, SBCRs have found numerous applications in chemical, biochemical, and petrochemical industries.Bubble column reactors offer a number of advantages: superior heat and mass transfer rates, low operating and maintenance costs, excellent mixing of solids, uniform temperature distribution, and compactness.It is well established that a comprehensive understanding of the mass transfer phenomena is essential in order to design and scale-up SBCRs for industrial purposes.The fact that the mass transfer rate is connected to a large number of factors including reactor dimensions, operating conditions, physical properties of each phase makes the problem even more complicated.Despite the fact that there are several studies on the impact of solid particles on hydrodynamics and mass transport in SBCRs, still, there is no general agreement regarding the prevailing mechanism or the magnitude of such effect.More importantly, the majority of previous studies utilized laboratory scale test rig to investigate the role of solid particles; thus, the results may not be relevant to large scale reactors mainly due to the differences in hydrodynamics.To address these research gaps, in the present study, a pilot scale slurry bubble column has been used to investigate the volumetric gas-liquid mass transfer coefficient ( ) in two-phase (airwater) and three-phase (air-water-glass beads) systems.The influence of superficial gas velocity, solid particle concentration, and solid particle size on were experimentally explored.The obtained results were justified by taking into account the hydrodynamics, various mass transfer theories applicable to SBCRs.In the last part of the study, a reliable model (i.e.correlation) was developed to estimate the mass transfer coefficient based on the operating conditions, design variables and the properties of the three phases.Regarding the effect of superficial gas velocity in the air-water system, it was observed that increasing the gas velocity (in a wide range of 0.40-21.30cm/s) leads to a higher mass transfer rate.This can be attributed to the better contact between the liquid and gas phase, and the more gas holdup.Interestingly, it was noted that regardless of the gas velocity, the variation in the mass transfer coefficient in the radial direction is not significant, which can be partly ascribed to the high vii liquid phase mixing in the column.It was found that the concentration (0, 1, 3, and 5% v/v) and size (71 and 156 m) of solid particles (i.e.glass beads) strongly affect the behavior of the system and the mass transfer rate.The effect of the solid phase on depends on the gas velocity.In low gas velocities, the presence of solid particles adversely affects the mass transfer, while at high velocities, solids particles are beneficial.Using large size glass beads (i.e.156 m) caused a noticeable improvement in the mass transfer coefficient, mainly due to turbulence increasing in the gas-liquid interface by the particles.The developed correlation for predicting the mass transfer coefficient in two and three-phase systems incorporates Schmidt, Galilei, Froude and Bond numbers, and gas and liquid density, oxygen diffusivity, and column diameter.The developed correlation could reproduce the experimental data successfully: mean absolute percentage error < 4.81% and standard deviation ~ 0.27 %.

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 categoriesMeta-epidemiology (narrow)
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 score0.530
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.006
GPT teacher head0.202
Teacher spread0.196 · 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.

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

Citations2
Published2019
Admission routes1
Has abstractyes

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