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Record W2136889239 · doi:10.1002/cjce.21764

Study of gas–solids flow in a short CFB riser by statistical and chaotic deterministic analysis of optical fibre probe signals

2012· article· en· W2136889239 on OpenAlexvenueaboutno aff
Guilherme José de Castilho, Marco Aurélio Cremasco

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicComplex Systems and Time Series Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMechanicsCorrelation dimensionAttractorPredictabilityStandard deviationRADIUSChaoticMaterials scienceChemistryThermodynamicsPhysicsMathematicsStatisticsFractal dimensionFractal

Abstract

fetched live from OpenAlex

Abstract In this work, a short CFB riser with a height of 2.42 m and an ID of 82 mm was operated under different dilute operating conditions to study the fluid dynamics of FCC catalyst particles ( d p = 80 µm, ρ p = 902 kg/m 3 ) in air. The electrical signals from the optical fibre probe were sampled at a frequency of 1000 Hz for a period of 30 s and were obtained at different positions along the radius and height of the riser. Data were analysed using both statistical methods (time average, standard deviation and frequency distribution) and chaos methods (construction of attractors; correlation dimension, D ML ; and Kolmogorov entropy, K ML ). Some results on solids holdup deduced from the electrical signal are also presented for the developed zone of the riser. It was verified that for very dilute conditions, increasing gas velocity produces more complex and less predictable fluctuations in solids concentration, while increasing solids flux generally reduces complexity and increases predictability. However, results for the most dilute condition used shows that for the radial position where solids holdup is higher (near the wall), the increase in solids concentration does not affect the mean free path of the particles, resulting in higher values of D ML than in the dilute region (core). © 2012 Canadian Society for Chemical Engineering

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.673
Threshold uncertainty score0.402

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.018
GPT teacher head0.205
Teacher spread0.187 · 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 designObservational
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

Citations6
Published2012
Admission routes2
Has abstractyes

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