Comparison of six implicit real-time optimization schemes
Bibliographic record
Abstract
ABSTRACT. Real-time optimization (RTO) is a class of methods that use measurements to reject the effect of uncertainty on optimal performance. This article compares six implicit RTO schemes, that is, schemes that implement optimality not through numerical optimization but rather via the control of appropriate variables. For unconstrained processes, the ideal controlled variable is the cost gradient. It is shown that, because of their structural differences, model-free and model-based techniques exhibit different features in terms of required excitation, convergence, scalability with the number of inputs and rejection of uncertainty. This comparison is illustrated through a simulated CSTR. RÉSUMÉ. L’optimsation en temps réel (RTO) est une classe de méthodes où les mesures sont util-isées pour rejeter l’effet de l’incertitude. Cet article compare six techniques de RTO implicites qui optimisent un procédé en contrôlant certaines variables. En l’absence de contraintes, la grandeur commandée idéale est le gradient de la fonction coût. A cause de leurs différences structurelles, les méthodes sans modèle et les méthodes basées sur le modèle se comportent différemment en termes de besoin d’excitation, de temps de convergence, de capacité de mise à l’échelle et d’aptitude à rejeter l’effet d’incertitudes. Cette comparaison est illustrée en simu-lation au moyen d’un réacteur chimique à marche continue.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".