Run‐to‐run optimization of batch processes with self‐optimizing control strategy
Bibliographic record
Abstract
Abstract This paper deals with the run‐to‐run optimization problem of batch processes in the presence of uncertainty with a tailored self‐optimizing control (SOC) strategy. Firstly, the dynamic programming problem for the batch process is transformed into a static nonlinear programming (NLP) problem using the control parameterization method. Then combinations of output measurements are selected as controlled variables (CVs), which are batch‐wise controlled to account for uncertainties. However, although existing SOC methods appear directly applicable to such a static NLP formulation, a major problem therein is that the number of control parameters is generally large to maintain a satisfactory optimizing performance, which makes them inappropriate as being manipulated variables for closed‐loop optimization. To circumvent this difficulty, it is proposed to alternatively use the so‐called latent effective manipulated variables as the control system's manipulated variables, which are linear combinations of original control parameters, however, less in number whilst implicitly dominating optimal operation in the whole uncertain space. This way, the run‐to‐run self‐optimizing control system is designed with less process‐dependent CVs and operated with minimal complexity. A simulated fed‐batch reactor is provided to illustrate the proposed methodology.
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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 | 0.001 |
| 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".