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Record W2046297013 · doi:10.1002/cjce.21726

Statistical analysis of effects of experimental variables on mass transfer coefficient in a novel hybrid bubble column using Box–Behnken design

2012· article· en· W2046297013 on OpenAlexvenueaboutno aff
S. Dhanasekaran, T. Karunanithi

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2012
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
Fundersnot available
KeywordsBox–Behnken designMass transferMass transfer coefficientReciprocating motionBubbleCoefficient of determinationMaterials scienceMechanicsChromatographyResponse surface methodologyMathematicsChemistryThermodynamicsStatisticsPhysics

Abstract

fetched live from OpenAlex

Abstract Statistical analysis of effects of experimental variables on volumetric mass transfer coefficient in a novel hybrid rotating and reciprocating perforated plate bubble column is studied. The novel bubble column is designed indigenously using bevel gear arrangement. Agitation level, superficial gas velocity, superficial liquid velocity, perforation diameter and plate spacing are the experimental variables. Air–sodium sulphite solution system is used in this investigation. The Box–Behnken design in response surface methodology is employed for statistical analysis. The relationship between experimental variables and the desired response of volumetric mass transfer coefficient is established for this novel hybrid column. The linear, quadratic and interactive effects of experimental variables are found to be significant on the desired response of volumetric mass transfer coefficient. Results show that the data adequately fit into the second‐order polynomial model. An F ‐test and P ‐value show the significance of parameters on volumetric mass transfer coefficient. © 2012 Canadian Society for Chemical Engineering

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.355
Threshold uncertainty score0.497

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 head0.010
GPT teacher head0.196
Teacher spread0.187 · 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

Citations2
Published2012
Admission routes2
Has abstractyes

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