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Record W4293825700 · doi:10.1002/cjce.24609

Bubbles in a sand fluidized bed unit for the gasification of coffee waste biomass. A probabilistic based fluid‐dynamic description

2022· article· en· W4293825700 on OpenAlexafffundvenue
Nicolas Torres Brauer, Marcos G. Navarro Salazar, Hugo de Lasa

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaUniversidad Autónoma de ZacatecasUniversidad de Costa Rica
KeywordsFluidized bedBubbleProbabilistic logicBiomass (ecology)Range (aeronautics)MechanicsPopulationEnvironmental scienceParticle (ecology)Petroleum engineeringMaterials scienceWaste managementPulp and paper industryMathematicsComposite materialPhysicsGeologyEngineeringStatistics

Abstract

fetched live from OpenAlex

Abstract The present study shows the applicability of the chemical reactor engineering centre (CREC) Optiprobes engineered with a graded refractive index (GRIN) lens and fibreoptics to establish both the bubble rising velocity (BRV) and the bubble axial chord (BAC) of bubbles in a 240–955 μm sand fluidized bed, filled with different types and loadings of biomasses. It is confirmed via the application of the CREC Optiprobes that the formed bubbles display both BRV and BAC normal probabilistic distributions, leading to characteristic BRV–BAC bands of bubble behaviour, with this being true for an ample range of superficial gas velocities (0.188–0.282 m/s) and broza biomass loadings (0–30 vol.%). It is also proven that the observed BRV and BAC distributions in biomass‐loaded sand fluidized beds are of the quasi‐normal distribution type, as attested by the Shapiro–Wilk test (S–W test). Furthermore, a probabilistic prediction model (PPM) proposed in a previous contribution can be used effectively to predict, in all cases, that a close to 80% of the total bubble population falls within a proposed probabilistic band of bubble behaviour.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.520
Threshold uncertainty score0.391

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.013
GPT teacher head0.185
Teacher spread0.172 · 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

Citations2
Published2022
Admission routes3
Has abstractyes

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