Estimating uncertainties and parameters for fundamental models used in online monitoring and control
Bibliographic record
Abstract
Abstract Many model‐based online process monitoring and control applications rely on state estimation techniques that use noisy process data to update states, thereby ensuring that imperfect model predictions are consistent with process behaviour. Techniques for tuning state estimators are reviewed, and their effectiveness and limitations are summarized in this article. A new simultaneous parameter and estimator tuning (SPET) methodology is proposed, one in which parameter estimation techniques for stochastic differential equations (SDEs) are used to simultaneously estimate measurement‐error covariances and model‐error covariances along with the model parameters. The resulting information is then used to compute state‐estimator tuning information. This study shows how SPET can be used, along with old dynamic process data, to obtain reliable tuning information. The proposed methodology is tested using a nonlinear two‐state continuous stirred‐tank reactor (CSTR) model with simulated data. Comparisons are made with a more‐conventional approach that uses WLS to estimate fixed model parameters and autocovariance least squares (ALS) to estimate extended Kalman filter (EKF) tuning factors. The main benefit of the proposed approach is that fixed model parameters and tuning factors for the EKF are estimated simultaneously, resulting in significant improvements to state estimates and online model predictions.
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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".