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Record W2112410387 · doi:10.1191/0142331203tm072oa

Design and tuning of valve position controllers with industrial applications

2003· article· en· W2112410387 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

VenueTransactions of the Institute of Measurement and Control · 2003
Typearticle
Languageen
FieldEngineering
TopicIterative Learning Control Systems
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRangingPosition (finance)Control engineeringComputer scienceRange (aeronautics)Control theory (sociology)Control (management)Set (abstract data type)EngineeringTelecommunicationsArtificial intelligence

Abstract

fetched live from OpenAlex

Mid-ranging refers to control problems where there are two manipulated inputs and only one output to control. Most mid-ranging controllers in industrial use today are based on the valve position control concept. Presently, design and tuning of valve position control schemes is largely done ad hoc. As a result, much time is spent devising control strategies that may work well in one application, but not nearly so well in another. To address this problem, we set out to devise a systematic approach to the design and tuning of valve position controllers. The new method was tested in simulation, and pilot plant and paper mill examples were used to demonstrate the performance of the method in several real-life applications. Overall, the proposed design was able to reject disturbances quicker and mid-range faster, and with less oscillation than conventional schemes. This approach may be applied to virtually any mid-ranging control application and is easily implemented on any distributed control system.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.970
Threshold uncertainty score0.297

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 head0.023
GPT teacher head0.186
Teacher spread0.163 · 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