Real-Time Optimization of Batch Processes by Tracking the Necessary Conditions of Optimality
Bibliographic record
Abstract
The use of measurements to compensate for the effect of uncertainty has recently gained attention in the context of real-time optimization of dynamic systems. The commonly used approach consists of updating a process model and performing numerical optimization using the refined model. In contrast, this paper presents a two-level approach that does not require repeating the optimization: At the upper level, the constraints that are active in the optimal solution are identified from optimization of a nominal process model; at the lower level, feedback control is used to enforce the necessary conditions of optimality, i.e., meet the identified active constraints and push selected gradients to zero. A key feature of this self-optimizing control scheme is the use of an input parametrization that is tailored to the identified active constraints. Another feature that is specific to batch processes is the possibility to meet the control objectives either online or on a run-to-run basis. The self-optimizing control approach is illustrated on a semibatch reactor example.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".