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Record W1967750991 · doi:10.1002/aic.11500

Electric conductance method to study the contact of injected liquid with fluidized particles

2008· article· en· W1967750991 on OpenAlexafffund
Federica Portoghese, Peter K. House, Franco Berruti, Cédric Briens, Kazimierz Adamiak, Edward Chan

Bibliographic record

VenueAIChE Journal · 2008
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsSyncrude (Canada)Western University
FundersSyncrude
KeywordsFluidized bedNozzleOperabilityConductanceElectrical resistance and conductanceChemistryMaterials scienceMechanicsNuclear engineeringChromatographyMechanical engineeringComposite materialEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract A sensitive and reliable experimental technique was developed to assess the performance of gas‐atomization nozzles injecting liquid into a gas‐solid fluidized bed. Gas atomizers are used to inject liquid feeds into industrial fluid cokers, fluid catalytic crackers, and gas‐phase polymerization reactors. In the fluid coking process, for example, both liquid yields and reactor operability are affected by how well the injected liquid contacts the fluidized particles. The new experimental method assesses the quality of the liquid‐solid contact from the electric conductance of the bed solids wetted with the injected liquid. The validity of the method was corroborated by direct comparison with the results provided by alternative experimental techniques, as well as by numerical modeling of the evolution of the bed conductance for various types of liquid distribution within the bed solids. The electric conductance technique can help optimize liquid injection into fluidized beds. © 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 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)0.001
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 score0.021
Threshold uncertainty score0.375

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.021
GPT teacher head0.253
Teacher spread0.232 · 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

Citations22
Published2008
Admission routes2
Has abstractyes

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