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Record W2096414945 · doi:10.1002/aic.11538

Dynamics of fines deposition in an alternating semifluidized bed

2008· article· en· W2096414945 on OpenAlexafffund
Soumaı̈ne Dehkissia, Abdelaziz Baçaoui, Ion Iliuta, Faı̈çal Larachi

Bibliographic record

VenueAIChE Journal · 2008
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFluidizationFiltration (mathematics)Fluidized bedDeposition (geology)Suspension (topology)ChemistryChromatographyMaterials scienceWaste managementGeologyEngineeringMathematicsSediment

Abstract

fetched live from OpenAlex

Abstract A semifluidized bed was proposed to allay bed plugging during the filtration of fines‐hydrocarbon suspensions. Mitigation of plugging was allowed by alternating semifluidization during capture with unconstrained fluidization for removal of deposits. Solids holdup conditions were established to prevent deposition in the fluidization section. The cyclic parameters were varied to allow efficient removal of deposits during washing. Analysis with a filtration model accounting for solids detachment in the fixed bed section revealed that the fluidization section favored growth of aggregate size entering the fixed bed. Analysis of the split ratios between fixed and fluidized bed heights revealed that the shallowest fluidization sections delayed deposition in the fixed bed. In cyclic operation, reversibility of the semifluidized bed was best recovered via gas injection during the washing stages. However, the filtration times in successive cycles shortened with increasing washing times but lengthened with increasing the washing superficial suspension velocity. © 2008 American Institute of Chemical Engineers AIChE J, 2008

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.223
Teacher spread0.211 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2008
Admission routes2
Has abstractyes

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