Adaptive Predictive Control Algorithm for Batch Processes: Application to a Rotational Molding Process
Bibliographic record
Abstract
The present manuscript addresses the problem of handling process nonlinearity in batch process operations and control via a re-identification-based subspace identification approach deployed within a model predictive control (MPC) framework. In contrast to existing re-identification algorithms for continuous and batch processes, where all of the recent and past experimental data is chosen to re-identify the model, the proposed approach is designed to use the most appropriate subset of the data. In particular, the data for re-identification is determined by first determining the equivalent of a “locator” index from the training data set, and only using the portion of training batches from the locator to batch termination. The idea is to try and build the model using data pertaining to the state-space region that the system is presently passing through. The proposed approach is implemented on a rotational molding lab-scale setup coordinated via an existing model monitoring technique deployed within the MPC, which detects the model mismatch and triggers the re-identification algorithm. Validation data sets are first used to demonstrate the improved model resulting from the proposed re-identification approach, followed by experimental results demonstrating improved closed-loop performance.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".