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Record W2020470626 · doi:10.1021/ie050643t

Control of a Process with Recycle:  Robustness of a Recycle Compensator

2006· article· en· W2020470626 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

VenueIndustrial & Engineering Chemistry Research · 2006
Typearticle
Languageen
FieldEngineering
TopicAdvanced Control Systems Optimization
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsControl theory (sociology)Robustness (evolution)PID controllerProcess dynamicsProcess (computing)Controller (irrigation)Sensitivity (control systems)Computer scienceProcess controlWork (physics)Path (computing)Process engineeringControl engineeringControl (management)EngineeringTemperature controlChemistry

Abstract

fetched live from OpenAlex

Several studies on the dynamics of processes with recycle are reported in the literature. A complete summary of the effects of the recycle, along with a detailed analysis of the disturbances behavior when matter or energy is recycled, is presented in the first part of the paper. The control of processes with recycle is then discussed, including the benefits of adding a recycle compensator to a controller. Representing the system equations with Bode plots shows that the performance of a PI (proportional and integral) controller with a recycle compensator is usually better than the use of a PI controller alone. A systematic analysis of the sensitivity of the model demonstrates that this is true, even for important model errors in the recycle path. This work suggests that the negative result of recycle addition on the process dynamics can be overcome when the recycle effects are anticipated even though the recycle path model is not error free.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.213
Threshold uncertainty score0.846

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.265
Teacher spread0.241 · 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