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Record W1996560139 · doi:10.1021/ie300843z

Pilot-Scale Examination of Mixing Liquid into Pulp Fiber Suspensions in the Presence of an In-Line Mechanical Mixer

2012· article· en· W1996560139 on OpenAlexaff
Wisarn Yenjaichon, John R. Grace, C. Jim Lim, Chad P. J. Bennington

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2012
Typearticle
Languageen
FieldEngineering
TopicElectrical and Bioimpedance Tomography
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsImpellerTurbulenceMixing (physics)MechanicsMaterials scienceRotational speedChemistryMechanical engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

The quality of liquid mixing into the main stream for an in-line mechanical mixer was investigated for water and pulp suspensions over a range of mass concentrations (0–3.0%), main-stream velocities (0.5–3.0 m/s), jet velocities (3.8–12.6 m/s), and rotational speeds (0–800 rpm) based on electrical resistance tomography and a modified mixing index, derived from the coefficient of variation of conductivity values. The mixing quality was worse when the jet penetrated to the far wall of the pipe for all fiber mass concentrations investigated, whereas this only applied at higher mass concentrations without the impeller. For water flow, the residence time had a significant effect on mixing at higher impeller speeds. With the impeller present, the mixing quality in pulp suspensions improved substantially and was similar to that for water when the flow approached the turbulent regime, with a considerably lower main-stream velocity required for mixing compared to a tee mixer alone. At higher mass concentrations, the energy supplied was insufficient to provide the same level of turbulence as that in water, even at the highest main-stream velocity and impeller speed examined. Improved mixing with increasing impeller speed primarily occurred in the high-shear zone around the impeller, with turbulence decaying rapidly downstream, likely aided by reflocculation.

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)0.002
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 score0.009
Threshold uncertainty score0.485

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.072
GPT teacher head0.314
Teacher spread0.242 · 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

Citations6
Published2012
Admission routes1
Has abstractyes

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