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Record W1976274307 · doi:10.1080/00986440903574891

A COMPARISON OF PARTICLE WEAR IN PNEUMATIC TRANSPORT

2010· article· en· W1976274307 on OpenAlexaff
Luis A. Borzone

Bibliographic record

VenueChemical Engineering Communications · 2010
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsBreakageParticle (ecology)Materials scienceSodium nitrateFlow (mathematics)Phase (matter)Pressure dropBrittlenessPotassium nitrateMechanicsParticle sizeComposite materialChemistryPotassiumChemical engineeringMetallurgyEngineeringGeology

Abstract

fetched live from OpenAlex

Flow regimes and particle degradation in pneumatic transport depend on the operating conditions, granular material properties, and dynamic behavior of the system in a very complex way. In this work, an experimental study was carried out using a blow tank system, which was operated under several phase regimes with the help of a secondary air line. Flow maps were constructed to predict horizontal flow patterns under conditions that ranged from plug to dilute phase flow, using two test materials: sodium and potassium nitrate prills. They have identical particle size distributions, but very different strength properties, allowing a comparative study of the particle degradation (breakage and dusting) of soft and hard materials in a wide range of flow conditions, identified as plug-, slug-, dune-, and dilute-phase regimes. The results demonstrated clear advantages in dust reduction for dense-phase conveying compared to dilute phase; however, particle breakage rates were similar in both types of systems. Dusting and breakage were the dominant wear processes for the softer material (sodium nitrate), while breakage, to a lesser extent, prevailed for the case of the brittle material (potassium nitrate).

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.017
GPT teacher head0.261
Teacher spread0.245 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2010
Admission routes1
Has abstractyes

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