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Record W2000163563 · doi:10.5539/mas.v4n8p75

Prediction of Riser Gas Holdup in Three- Phase External Loop Air Lift Fluidized Bed Reactor

2010· article· en· W2000163563 on OpenAlexvenueno aff
Kannan Kandasamy, Akilamudhan Palaniappan

Bibliographic record

VenueModern Applied Science · 2010
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
Fundersnot available
KeywordsDraft tubeAirliftMechanicsMaterials scienceFluidized bedNewtonian fluidNon-Newtonian fluidFluidizationFlow (mathematics)ThermodynamicsChemistryPhysicsBioreactor

Abstract

fetched live from OpenAlex

Hydrodynamics is an important issue for design and development of three phase external-loop airlift fluidized bed. This paper deals on the experimental investigations on the effect of superficial gas and liquid velocities and properties of solids on the riser gas holdup of a three phase external-loop airlift fluidized bed reactor and it was characterized using Newtonian and non Newtonian systems. Water, 65% and 85% of glycerol and n-butanol were used as Newtonian liquids and different concentration of Carboxyl Methyl Cellulose (CMC) i.e., 0.2%, 0.5% and 1% were used as non Newtonian liquids. Spherical glass beads, bearl saddles and rasching rings of different sizes were used as solid phase. The phase flow rates and properties of solid particle had significant effect on the hydrodynamic characteristics of the external-loop airlift fluidized bed reactor such as riser gas holdup. Unified correlations have been developed to estimate the riser gas holdup as a function of superficial phase velocities, properties of solid particle and physical properties of both Newtonian and Non-Newtonian liquid systems. The predicting ability of the correlations were tested with the experimental data and found to be good fit with an absolute average relative error (AARD) of ± 9.7 % for riser gas holdup.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.746
Threshold uncertainty score0.527

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 head0.012
GPT teacher head0.221
Teacher spread0.208 · 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 designBench or experimental
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

Citations3
Published2010
Admission routes1
Has abstractyes

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