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Record W2591057134 · doi:10.1002/cjce.22817

Prediction of spout diameter in gas‐solid spouted beds using factorial design of experiments approach with the aid of advanced optical fibre probe

2017· article· en· W2591057134 on OpenAlexvenueno aff
Muthanna H. Al‐Dahhan, Shreekanta Aradhya, Haidar Taofeeq

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsnot available
FundersU.S. Department of Energy
KeywordsInletFactorial experimentParticle sizeMaterials scienceFactorialFractional factorial designRegression analysisLinear regressionWork (physics)MechanicsMathematicsStatisticsChemistryEngineeringMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

ABSTRACT In this study, the effects of five operating and design variables (solid density, static bed height, particle diameter, superficial gas velocity, and inlet diameter) on the average spout diameter of a 0.152 m inside diameter gas‐solid spouted bed have been assessed experimentally using advanced gas‐solid optical fibre probe technique. Statistical analysis of the experimental data including the factorial design of the experiments using MINITAB17 statistical software has been performed to determine the extent of the effects of these variables on the spout diameter as a case study. It was found that all five operating and design variables, except the solid density together with the 2‐ways interactions between particles size and inlet diameter, in addition to gas velocity and inlet diameter, have a significant effect on the average spout diameter. Regression analysis was performed to correlate these variables with the average spout diameter for the gas‐solid spouted beds. The obtained preliminary regression correlation was able to closely predict the average spout diameter of this work with the mean relative deviation value of 0.7 %.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.395

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 head0.024
GPT teacher head0.210
Teacher spread0.186 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

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