State and parameter estimation in closed-loop dynamic real-time optimization — A comparative study
Bibliographic record
Abstract
Dynamic real-time optimization (DRTO) schemes have risen in popularity as plant environments have become increasingly dynamic due to globalization and deregulated energy markets. Inclusion of the impact of the plant control system on the predicted response gives rise to closed-loop DRTO (CL-DRTO). To avoid using a potentially inaccurate nominal model in CL-DRTO, this work explores incorporating plant measurements through various model updating strategies: bias update, state estimation, and combined parameter and state estimation, the latter two utilizing moving horizon estimation. The strategies are applied to two case studies, a distillation column and a continuous stirred tank reactor. Our findings suggest that the combined state and parameter estimation approach provides improvement in economic performance and fewer constraint violations when parametric uncertainty affects system dynamics nonlinearly. Conversely, the bias update strategy achieves satisfactory economic performance when the propagation of parameter uncertainty in the dynamic model is linear or mildly nonlinear. • Comparing the impact of 3 model adaptation methods on closed-loop DRTO performance. • Methods: Bias updating, state estimation, and combined state/parameter estimation. • Methods evaluated on two nonlinear systems: a CSTR and a distillation column. • Inference on model adaptation method choice based on system characteristics. • Analysis based on sensitivity of model outputs with respect to uncertain parameters.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".