Integrated operation optimization strategy for batch process based on process transfer model under disturbance
Bibliographic record
Abstract
Abstract An integrated operation optimization strategy based on the process transfer model (PTM) is proposed in this work, which combines the batch‐to‐batch optimization method and the within‐batch optimization method. The data‐driven tool with the joint‐Y partial least squares (JYPLS) model is utilized to transfer rich information from a similar old process to the new process to assist the establishment of the model of the new process. However, differences invariably exist between similar batch processes, which can bring about a fateful necessary condition of optimality (NCO) mismatch. Although the traditional batch‐to‐batch optimization method can overcome the problem of plant‐model mismatch between batches, it is helpless to deal with the problem of mismatch and disturbance during a single batch operation. For the sake of settlement of problems, the within‐batch optimization method is introduced. The main advantages of the integrated operation optimization strategy are (i) the plant‐model mismatch and disturbances within the batch or during batches can be solved, (ii) the suboptimal results of batch‐to‐batch optimization can be further compensated, and (iii) the optimization performance is better by discretizing the control profile into several intervals. Taking the cobalt oxalate synthesis process as a simulation study, the superior performance of the proposed strategy is illustrated.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".