State and Parameter Estimation in Dynamic Real-Time Optimization with Embedded MPC
Bibliographic record
Abstract
The goal of dynamic real-time optimization (DRTO) applications is to compute an optimal operational trajectory for a plant by generating set-points for the lower-level control algorithm to track. This approach can be further improved by directly incorporating the control algorithm (such as Model Predictive Control, MPC) into a closed-loop DRTO (CL-DRTO). By doing so, CL-DRTO can predict both the plant and controller responses to set-point adjustments, enhancing the performance of the entire system. However, CL-DRTO schemes require a mechanism to utilize plant measurements to adapt the model to the current plant conditions. Otherwise, the decisions will be based on a nominal model and are likely to be suboptimal. This study proposes a plant feedback scheme using an extended Kalman filter within a CL-DRTO framework that embeds an MPC model. In this novel model adaptation approach in the context of CL-DRTO, not only the states and parameters of the plant model are updated but also the embedded linear MPC model, which is adapted via an output disturbance scheme. Moreover, by adding input constraints to the CL-DRTO problem, this formulation allows a simplified representation of the MPC solution at the CL-DRTO level without directly accounting for input constraints at the MPC level, which reduces computation time. The efficacy of the proposed CL-DRTO approach is demonstrated through application to a multi-input multi-output CSTR where a critical parameter is not measurable.
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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 | 0.001 | 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".