Robust economic model predictive control: disturbance rejection, robustness and periodic operation in chemical reactors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study investigates the properties of a robust economic model predictive control (REMPC) algorithm with respect to rejection of disturbances in initial conditions and non-stationary disturbances, robust stabilization and closed-loop performance in the presence of model parameters’ errors, and the enforcement of optimal economic periodic operations. A key characteristic of the algorithm is that it enforces robustness to model errors without requiring terminal conditions (unless periodic operation is desired) and instead a set-point trajectory is calculated at each time interval. Robust stability and convergence to the calculated set-point trajectory are enforced online by a set of constraints. Three case studies are used to illustrate the closed-loop performance of the REMPC algorithm for reactor operation under different conditions. The algorithm is shown to preserve stability in the presence of model parameters’ mismatch. In the face of disturbances, the algorithm leads to higher profits when a variable set-point is used compared to a fixed one. Furthermore, the periodic operation obtained through the application of the robust algorithm confers an improved average cost compared to steady-state operation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it