Experiment design, identification and control in large-scale chemical processes
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.
Bibliographic record
Abstract
Experiment design for parameter identification, state and parameter estimation, and model reduction have been studied extensively in the literature. However, most of the methods proposed in the literature are not suitable for, or have not been tested for, large scale and complex systems. In this contribution, we investigate modifications to methods developed for the design of optimal experiments and system identification in order to make them suitable for application to large scale systems. The optimal experiment design and system identification are demonstrated on two different examples. Parameter clustering and principal component analysis are used with D optimal design of experiments for a catalytic kinetic system, the preferential oxidation of carbon monoxide on platinum catalyst. A reparameterization of autoregressive integrated moving average models are used for identification and control of a multiscale stochastic thin film growth process.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it