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Record W4232314073 · doi:10.1002/9783527699483.ch13

Operating Challenges

2020· other· en· W4232314073 on OpenAlexaff
Poupak Mehrani, Andrew Sowinski

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsAgglomerateFaraday cupParticle (ecology)ElectrostaticsEconomies of agglomerationTriboelectric effectMaterials scienceDistributorFluidized bedPolyethyleneAttritionParticle sizeNanotechnologyMechanicsComposite materialChemical engineeringChemistryWaste managementMechanical engineeringIonEngineeringPhysics

Abstract

fetched live from OpenAlex

Major nuisances can arise from electrostatic charge generation in gas–solid fluidized beds, including particle agglomeration causing deviation from the desired particle properties, adhesion of particles to the column wall and other surfaces necessitating regular shutdowns for clean-up, and electrostatic discharges endangering operators and equipment. Two methods are used to quantify the electrostatic charge on solids: employing a Faraday cage and employing an electrostatic probe. Electrostatics causes the formation of particle agglomerates in polyethylene reactors, altering the bed particle size distribution, in turn causing blockage of the distributor plate and product discharge pipe, and causing process shutdowns. Particle attrition occurs when particles break into smaller particles. A simple attrition index is used to characterize the particles. As with attrition, wear tends to be especially detrimental where local gas and entrained particle velocities are high.

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)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.118
Threshold uncertainty score0.395

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.004
Open science0.0030.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.1180.074

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 head0.195
Teacher spread0.179 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2020
Admission routes1
Has abstractyes

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Same topicGranular flow and fluidized bedsFrench-language works237,207