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Record W2770462674 · doi:10.1021/acs.iecr.7b04056

Effect of Fluidization Pressure on Electrostatic Charge Generation of Polyethylene Particles

2017· article· en· W2770462674 on OpenAlexafffund
Di Song, Poupak Mehrani

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2017
Typearticle
Languageen
FieldEngineering
TopicElectrohydrodynamics and Fluid Dynamics
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for InnovationUnivation Technologies
KeywordsFluidizationPolyethyleneCharge (physics)Materials scienceChemical engineeringChemistryChemical physicsMechanicsThermodynamicsComposite materialOrganic chemistryFluidized bedPhysics

Abstract

fetched live from OpenAlex

Electrostatic charge generation in gas–solid fluidized beds results in operational challenges in some industrial processes such as polyethylene production. Such reactors operate at elevated pressures, and thus, the aim of this work was to investigate fluidized bed electrification at pressurized conditions. The degrees of particles’ charging and wall coating in a pressurized pilot-scale gas–solid fluidized bed of polyethylene resin was studied from atmospheric pressure to 2600 kPa (abs), while the gas bubble dynamics was concurrently measured by an optical fiber probe. The average gas bubble size was found to be larger at atmospheric condition along with the frequency of large bubbles, resulting in a larger degree of particle-wall contacts. The degree of fluidized bed wall coating increased with the increase of pressure. This finding was related to the decline in the gas bubble size and the increase in frequency of small bubbles found at elevated pressures, which promoted particle–particle contacts, and thus promoted bipolar charging. Bipolar charging was detected with particles that fouled on the column wall with small particles being negatively charged.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.363
Threshold uncertainty score0.675

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
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 head0.042
GPT teacher head0.309
Teacher spread0.267 · 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 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

Citations7
Published2017
Admission routes2
Has abstractyes

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