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Record W2092321598 · doi:10.5539/mas.v3n6p88

Mixing Process of Binary Polymer Particles in Different Type of Mixers

2009· article· en· W2092321598 on OpenAlexvenueno aff
Siti Masrinda Tasirin, S.K. Kamarudin, A. M. A. Hweage

Bibliographic record

VenueModern Applied Science · 2009
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsnot available
Fundersnot available
KeywordsMixing (physics)Fluidized bedMaterials scienceRotational speedProcess (computing)MechanicsBinary numberChromatographyThermodynamicsChemistryMechanical engineeringPhysicsComputer scienceMathematicsEngineering

Abstract

fetched live from OpenAlex

The main objective of this paper is to investigate the mixing process of free flowing polymers binary mixtures at different densities and colors in two different mixers, namely the bubbling fluidized bed and V-mixer. The mixing of solids was studied by analyzing the variation of the proportions of the marked particles with time and position in the bed or in the mixer. The variation of mixture composition based on the samples was incorporated into Lacey mixing index which describes the degree of mixing of the particles at particular time. The performance and the mixing behavior in these two mixers were also presented. Results showed that gas velocity and bed depth were important parameters influencing solids mixing in a bubbling fluidized bed. While the rotation speeds and filled up levels were proved as important parameters in influencing the solids mixing in V-mixer. From the results, complete mixings were attained at a bed depth of 17 cm and gas velocity of 1.38Umf in the fluidized bed and 40 % filled up level and 40 rpm in the V-mixer. From the energy consumption point of view, it was found that the fluidized bed mixer offers the most efficient and economical process compared to V-mixer.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.172
Threshold uncertainty score0.282

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.238
Teacher spread0.225 · 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

Citations9
Published2009
Admission routes1
Has abstractyes

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