Integration of Scheduling and Control for Plants Controlled by Distributed MPC Systems
Bibliographic record
Abstract
Increasingly dynamic market conditions have fueled efforts to integrate scheduling and control in recent years. While a large number of formulations have been proposed for different control systems, the integration of scheduling and control for plants controlled by distributed MPC systems has received scant attention. In this article, we present a framework for the integration of scheduling and control for plants controlled by distributed MPC systems. We also propose strategies to approximately model the distributed MPC action within the integrated problem formulation, reducing its complexity and computation time. The integrated problem is solved at the dynamic real-time optimization (DRTO) level, in a moving horizon fashion, to compute set-points assigned to the lower-level distributed MPC subsystems, whose formulation is kept intact. Case study results suggest that these strategies can significantly reduce the solution time of the closed-loop integrated scheduling and control problem without severely impacting plant performance. We demonstrate that the proposed integrated scheduling and control formulation can successfully coordinate the MPC subsystems to meet market demands of distinct product grades, even in the presence of plant–model mismatch.
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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 | 0.002 |
| 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".