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Record W2478243609 · doi:10.1109/iecon.1994.398125

A state model for the drying paper in the paper product industry

2002· article· en· W2478243609 on OpenAlexaff
Mohamed Berrada, S. Tarasiewicz, M.E. Elkadiri, Peter Radziszewski

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Control Systems Optimization
Canadian institutionsUniversité LavalPolytechnique MontréalUniversité du Québec en Abitibi-Témiscamingue
Fundersnot available
KeywordsSet (abstract data type)Representation (politics)State (computer science)Generator (circuit theory)Boundary (topology)Boundary value problemNonlinear systemComputer sciencePartial differential equationDifferential equationMathematicsApplied mathematicsMathematical optimizationAlgorithmMathematical analysisPower (physics)Programming languageThermodynamicsLawPhysics

Abstract

fetched live from OpenAlex

The purpose of this paper is to give a state model for drying paper. This model is based on the balance equations written for the steam, the paper, the wall of cylindrical heater, and the moisture. Thus, the balance equations gives a set of six nonlinear partial derivative equations. The form of these equations changes somewhat from one cylindrical heater to other. Its solution yields the operating parameters need to realize the desired steam temperature with a good drying of the paper. Boundary conditions are specified by a stochastic generator. Initial conditions are obtained by solving the static model. In this paper we present only the state model for drying paper and compare the obtained results with the dynamic model results. Solving the set of differential equations with respect to the boundary conditions, we obtain a standard form of the state representation which represents the dynamic version of the model used for control.>

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.022
GPT teacher head0.217
Teacher spread0.196 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2002
Admission routes1
Has abstractyes

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