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Record W2463606834 · doi:10.1021/ie001149m

Liquid Vaporization in a Fluidized Bed

2001· article· en· W2463606834 on OpenAlexaff
Karine Leclère, Cédric Briens, Thierry Gauthier, Jérôme Bayle, M.A. Bergougnou, Pierre Guigon

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2001
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsWestern University
Fundersnot available
KeywordsAgglomerateVaporizationFluidized bedEconomies of agglomerationEvaporationMaterials scienceMechanicsChemical engineeringChemistryComposite materialThermodynamicsOrganic chemistry

Abstract

fetched live from OpenAlex

Feedstock injection in the riser is a critical step of the fluid catalytic cracking process. However, although a few models have been developed, no experimental study of the vaporization of droplets in a hot fluidized bed has been published. Experimental studies were thus conducted. Agglomeration of wet particles greatly increases the evaporation time. The first step was thus the study of the different agglomeration processes which may occur when liquid droplets are injected into a hot fluidized bed. There are two kinds of agglomeration. Discrete, round agglomerates are formed when droplets are larger than a critical value, which depends on the bed temperature. Large, flat agglomerates are formed when the amount of liquid injected per unit area is larger than a critical value, which is a linear function of the bed temperature. This implies that the height of the bed remains the same when the bed temperature varies. Experiments were then conducted under agglomerate-free conditions in the case of nonpenetrating droplets. In this limiting case, droplets remain within a few millimeters of the bed surface and contact only a restricted number of particles. An estimation of the vaporization time was obtained by deconvolution of the acquisition signal.

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 categoriesMeta-epidemiology (narrow)
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 score0.033
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.065
GPT teacher head0.295
Teacher spread0.229 · 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.

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

Citations12
Published2001
Admission routes1
Has abstractyes

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