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Record W2398795354 · doi:10.1002/aic.15327

Cyclic operation strategies in inclined and moving packed beds—Potential marine applications for floating systems

2016· article· en· W2398795354 on OpenAlexafffund
Amir Motamed Dashliborun, Faı̈çal Larachi, Mohsen Hamidipour

Bibliographic record

VenueAIChE Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicHeat and Mass Transfer in Porous Media
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsPacked bedContactorMechanicsElectrical capacitance tomographyCapacitancePenetration (warfare)Saturation (graph theory)SwellMaterials scienceChemistryEngineeringPhysicsChromatographyThermodynamicsPower (physics)MathematicsElectrode

Abstract

fetched live from OpenAlex

The hydrodynamics of a periodically operated packed bed subjected to column oscillations and obliquity was studied. Electrical capacitance tomography (ECT) and capacitance wire mesh sensors were employed to measure the local instantaneous liquid saturation in stationary slanted and moving packed beds, respectively. A swell simulator with six‐degree‐of‐freedom motions was applied to emulate the behavior of floating packed beds. The results revealed that column inclination from the vertical position considerably decayed liquid waves excited by different cyclic operation strategies. However, ON–OFF liquid and gas/liquid alternating cyclic operations showed an attempt to conserve wave identity in the slanted bed and to decrease phase maldistribution resulting from bed inclination. It was also found that symmetric and non‐symmetric split ratios of ON–OFF liquid cyclic mode were able to noticeably decrease fluid maldistribution in the oscillating packed bed. This study opens up possible prospects for process intensification of floating packed bed reactors and contactors. © 2016 American Institute of Chemical Engineers AIChE J , 62: 4157–4172, 2016

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 score0.747
Threshold uncertainty score0.290

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 head0.008
GPT teacher head0.229
Teacher spread0.221 · 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

Citations27
Published2016
Admission routes2
Has abstractyes

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