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Record W2154215232 · doi:10.1109/phycon.2003.1236844

Model predictive control of an infrared-dryer

2004· article· en· W2154215232 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 institutionsToronto Metropolitan University
Fundersnot available
KeywordsModel predictive controlSetpointControl theory (sociology)Multivariable calculusProcess (computing)Controller (irrigation)Realization (probability)Process controlComputer scienceTracking (education)Infrared heaterTemperature controlControl engineeringInfraredEngineeringControl (management)Artificial intelligenceMathematics

Abstract

fetched live from OpenAlex

For product quality purposes, the drying of some delicate materials is not easily realizable and requires a tight well-designed control scheme. This study presents control results for the drying of a thin fiber sheet moving through an electric infrared dryer. The drying process dynamics is described by a minimum realization linear model that is used to develop a model predictive controller (MPC) and test its performance. The results obtained indicate that the MPC multivariable controller is able to handle process interactions and produced good results for setpoint tracking of the humidity and temperature of the material exiting the dryer and for disturbance rejection of measured and unmeasured disturbances.

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: Methods · Consensus signal: none
Teacher disagreement score0.968
Threshold uncertainty score0.310

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 head0.005
GPT teacher head0.196
Teacher spread0.191 · 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
Published2004
Admission routes1
Has abstractyes

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