Control oriented identification of batch processes using latent variable models
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
Various issues on the closed-loop identification of empirical latent variable models for model predictive control (MPC) of batch processes are investigated. The concept of identifiability is explored in the context of batch processes and desirable conditions for the identification experiments to be informative for building latent variable models are proposed. It is shown that in many situations, it is possible to identify the batch process models only from historical batches without the need for external excitation of the closed-loop system. However, adding one or two batch runs with only slight set-point trajectory changes is an efficient approach to enhance the data for the identification of the batch dynamic models. The issue of model bias in closed-loop identification using nonparametric or highly parameterized modeling approaches is also investigated and it is shown that closed loop data obtained using tightly tuned PID controllers will minimize the bias.
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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 | 0.001 |
| 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