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Record W2153736494 · doi:10.1002/apj.5500100103

Time Optimal Control of a Binary Distillation Column

2002· article· en· W2153736494 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

VenueDevelopments in Chemical Engineering and Mineral Processing · 2002
Typearticle
Languageen
FieldEngineering
TopicAdvanced Control Systems Optimization
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsOptimal controlBang–bang controlColumn (typography)Fractionating columnBinary numberControl (management)Dynamic programmingControl theory (sociology)DistillationComputer scienceBatch distillationMathematical optimizationMathematicsChemistryFractional distillationChromatographyArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Time optimal control problems involving high dimensional systems are generally very difficult to solve. Instead of using suboptimal control based on reduced system, iterative dynamic programming is used directly to establish the time optimal control policy of a binary distillation column described by 11 ordinary differential equations. The resulting optimal control policy that is bang‐bang in nature was obtained very rapidly. The minimum time of 2236 seconds obtained here is substantially better than 4120 seconds obtained by a suboptimal control policy. Simulations show that the procedure may be used for on‐line optimal control of the distillation column, since the optimal control policy can be established very fast on a personal computer.

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: Empirical
Teacher disagreement score0.262
Threshold uncertainty score0.607

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 head0.178
Teacher spread0.174 · 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