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Record W1511789986 · doi:10.1002/cjce.22240

Residence Time Distribution of Particles in a Screw Feeder: Experimental and Modelling Study

2015· article· en· W1511789986 on OpenAlexvenueno aff
Chaofei Huo, Chuigang Fan, Ping Feng, Weigang Lin, Wenli Song

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsnot available
FundersMinistry of Science and Technology of the People's Republic of ChinaChinese Academy of Sciences
KeywordsMarkov chainMechanicsRotation (mathematics)Residence time distributionMathematicsMaterials sciencePhysicsGeometryStatisticsFlow (mathematics)

Abstract

fetched live from OpenAlex

Experiments were conducted to investigate the effects of screw speed and screw feeder inclination on the residence time distribution (RTD) of particles in a screw feeder via a pulse stimulus response technique. Two models based on Markov chains were developed to simulate particle flows within and between pitches. In upward and horizontal screw feeder inclination cases, a three‐parameter two‐dimensional Markov chain model consisting of parallel active and stagnant zones fitted well with the experimental RTD data, with correlation coefficients (R 2 ) higher than 0.98, and gave a clear physical meaning for the parameters introduced. In these cases, a high screw speed or a horizontal inclination induced a high probability of forward movement from a pitch to the next pitch ( f ) during each rotation period of the screw, and a low ratio of stagnant zone to active zone ( r ) in a pitch. The upward screw feeder inclination yielded a higher diffusion probability from stagnant zone to active zone ( d ). In the downward screw feeder inclination case, a one‐dimensional Markov chain model without a stagnant zone was in agreement with the corresponding experimental data. The analysis showed that during each rotation period of the screw, the particles in a pitch could be transferred not only to the next pitch but also to the following two pitches.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.163
Threshold uncertainty score0.254

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 head0.016
GPT teacher head0.200
Teacher spread0.183 · 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 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

Citations13
Published2015
Admission routes1
Has abstractyes

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