Self‐correcting modifier‐adaptation strategy for batch‐to‐batch optimization based on batch‐wise unfolded PLS model
Bibliographic record
Abstract
The problem of optimizing a batch process under model uncertainty using a batch‐wise unfolded PLS (BW‐PLS) model‐based modifier‐adaptation (MA) strategy is described. The main idea behind the strategy is to use measurements and iteratively modify the model to compensate for the mismatch of the necessary condition of optimality (NCO) between the plant and the model‐based optimization problem. It is proven that the popular data‐driven model‐based iterative learning control (ILC) strategy is equivalent to the proposed MA strategy using only zero‐order modifier. Inspired by the effectiveness of the ILC being enhanced by rebuilding the data‐driven model, a more elaborate model updating scheme is proposed in this paper to improve the optimization performances. The heuristic rules for choosing filtering gain matrix are also presented to further accelerate the convergence rate and reduce the variation of the cost during the period of evolution. Finally, the efficacy of the proposed MA strategy is illustrated via a simulated typical batch reaction and a simulated cobalt oxalate synthesis process.
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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 | 0.001 |
| 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) | 0.001 | 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".