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Record W2080671797 · doi:10.1002/cjce.5450850105

Measuring Flow Velocity and Uniformity in a Model Batch Digester Using Electrical Resistance Tomography

2007· article· en· W2080671797 on OpenAlexaffvenue
Q. F. Lee, Chad P. J. Bennington

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2007
Typearticle
Languageen
FieldEngineering
TopicElectrical and Bioimpedance Tomography
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsReynolds numberFlow (mathematics)Materials scienceElectrical resistance and conductanceMechanicsFlow velocityTomographyFlow resistancePoint (geometry)Environmental scienceComposite materialMathematicsPhysicsGeometryTurbulenceOptics

Abstract

fetched live from OpenAlex

Abstract The literature shows that the extent of delignification in batch digesters varies as a function of chip location in the vessel. This non‐uniformity may be exacerbated by a number of factors but is commonly attributed to poor and/or non‐uniform liquor flow through the reactor (which causes poor chemical and heat distribution to the chip mass during the cook). Electrical resistance tomography (ERT) was used to evaluate the uniformity of liquor flow in a laboratory model digester under scaled industrial conditions (a 1:15 geometrically scaled vessel, a vessel to particle diameter ratio of 93:1 to minimize wall effects, and close approximation of liquor superficial velocity and pore Reynolds number). Local interstitial flow velocities were measured using pixel‐pixel cross correlation techniques. It was possible to create uniform zones in the digester, but a stagnation point was observed in the centre of the vessel at the screen level. This point coincides with the location of highest kappa numbers (lowest degree of cooking) reported in industrial tests.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.198
Threshold uncertainty score0.547

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.012
GPT teacher head0.179
Teacher spread0.167 · 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 designSimulation or modeling
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

Citations13
Published2007
Admission routes2
Has abstractyes

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