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Record W2794915676 · doi:10.1002/cjce.23213

Theoretical description of the motion of a particle in rotary dryer

2018· article· en· W2794915676 on OpenAlexvenueno aff
Mustapha Hellou, Franck Lominé, Ilham Benhsine, Y. Roques

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsnot available
Fundersnot available
KeywordsDrumMechanicsMagnetosphere particle motionContext (archaeology)Work (physics)Particle (ecology)Function (biology)Mechanical engineeringProcess (computing)Control theory (sociology)Computer scienceSimulationMathematicsEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract This paper presents a theoretical description of a particle motion in a rotary dryer equipped with straight lifters distributed on its periphery. In this configuration, the transport of granular materials occurs with cyclic cascades that can be decomposed into three phases: lifting, discharging, and falling into the air stream. In order to describe each of these phases, we have focused on the motion of a single particle in a rotating drum. In this condition, the analytical solution of the motion equation of each phase is found. With these solutions, the position and the velocity of the particle at any time are thus expressed as a function of physical parameters of the particle, physical parameters of the drum, and physical parameters of air in the drum, called operating parameters in practice. With these equations, we are able to calculate the behaviour of the particle as a function of the operating parameters. The transposition to the industrial context is made with an application. We show how these equations particularly permit the estimation of the Mean Residence Time (MRT) of material during the drying process as a function of operating parameters. Such estimations have been validated by comparison with experimental data of MRT found in the literature. The work presented in this paper gives some useful mathematical relations for whose who are interested in drum design optimization (lifter length, drum radius, drum length, etc.). More specifically, they can be used to help with the choice of operational parameters values that would achieve a given value of MRT for particular product.

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 score0.091
Threshold uncertainty score0.172

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 head0.006
GPT teacher head0.163
Teacher spread0.157 · 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

Citations12
Published2018
Admission routes1
Has abstractyes

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