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Record W2021836654 · doi:10.1002/cjce.5450790214

Dynamic simulation and control of a paper machine wet end

2001· article· en· W2021836654 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2001
Typearticle
Languageen
FieldEngineering
TopicMaterial Properties and Processing
Canadian institutionsDow Chemical (Canada)University of British Columbia
Fundersnot available
KeywordsThroughputConsistency (knowledge bases)Dynamic simulationComputer scienceWork (physics)Controller (irrigation)MillDynamic testingSimulationProcess engineeringEngineeringMechanical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract A good simulation model for paper machines can be used to identify deficiencies in the design, bottlenecks during operation, and regions of poor control. It also allows users to test their hypotheses and innovations without potentially causing major upsets and reducing throughput. In this work, a dynamic model of the wet end system has been developed using the IDEAS TM platform, describing the distribution of fines, fillers and fibres throughout the system. The model was then tested at steady state with mill data for the low‐ash and high‐ash production grades, and the results show that over 70% of the predicted values had only 5% deviation. The dynamic simulation was also used to show that the retention aid controller would react in the wrong direction due to changes in the wire pit consistency and the stock ratio would cause major changes in stream compositions and consistencies of the wet end.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.241
Threshold uncertainty score0.217

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.004
GPT teacher head0.172
Teacher spread0.168 · 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