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Record W43671055 · doi:10.13182/nt13-a15806

Robust Nonlinear Method for Steam Generator Level Control

2013· article· en· W43671055 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueNuclear Technology · 2013
Typearticle
Languageen
FieldEngineering
TopicAdvanced Control Systems Optimization
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaUniversity Network of Excellence in Nuclear Engineering
KeywordsBoiler (water heating)Nonlinear systemNuclear engineeringGenerator (circuit theory)Computer scienceControl theory (sociology)Control (management)Power (physics)EngineeringThermodynamicsPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

AbstractIn this paper, a new, robust control method based on a multimodel predictive control scheme is developed for steam generator level (SGL) control in nuclear power plants. For a multiramp power increase from low to full power, the proposed controller is capable of keeping the SGL within the admissible range by minimizing the level transients and improving the stability of the control loop. Simulation results and a general framework for systematically studying the SGL are presented to demonstrate the effectiveness of the proposed control method by comparing the performance of the designed controller with that of a properly tuned conventional three-element proportional-integral-derivative (PID) controller. Moreover, it has been demonstrated that the proposed controller is more robust than a conventional PID controller to steam flow disturbances caused by load variations.

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: Methods
Teacher disagreement score0.302
Threshold uncertainty score0.660

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 head0.016
GPT teacher head0.221
Teacher spread0.205 · 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