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Record W2169567140 · doi:10.1109/cca.1995.555904

Nonlinear control of a wood chip refiner

2002· article· en· W2169567140 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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Control Systems Optimization
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLaguerre polynomialsNonlinear systemChipMultivariable calculusProcess (computing)Computer scienceProcess controlPulp (tooth)Controller (irrigation)Model predictive controlNonlinear modelProcess engineeringControl theory (sociology)Control engineeringControl (management)EngineeringMathematicsArtificial intelligenceTelecommunications

Abstract

fetched live from OpenAlex

This paper presents a multivariable controller for a wood chip refiner in a thermomechanical pulping (TMP) process in pulp and paper industries. A Generalized predictive control (GPC) algorithm is developed based on a nonlinear Laguerre model. In stead of conventional input/output model, a nonlinear Laguerre model is used to represent a nonlinear process properly without the accurate knowledge of the order or time delay of the process. Simulations are used to demonstrate the benefits of applying this control algorithm to a wood chip refiner.

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: none
Teacher disagreement score0.971
Threshold uncertainty score0.264

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 head0.007
GPT teacher head0.182
Teacher spread0.175 · 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

Quick stats

Citations6
Published2002
Admission routes1
Has abstractyes

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