TOWARDS OPTIMAL MPC PERFORMANCE: INDUSTRIAL TOOLS FOR MULTIVARIATE CONTROL MONITORING AND DIAGNOSIS
Bibliographic record
Abstract
Abstract: Univariate control performance assessment (PA) was developed in late 1980s and it has been widely applied in industry. Multivariate control performance assessment was developed in 1990s, but its application has been limited. While the algorithm of univariate PA is rather straightforward to write, the multivariate control performance assessment (MVPA) is much more difficult to program. In addition, conventional MVPA algorithms alone appear not to be sufficient or sometime even irrelevant for evaluating model predictive control (MPC) systems. With demands from industries, an industrial toolbox for MVPA including variance based performance monitoring, economic performance assessment, process modeling and other diagnosis algorithms has been developed. Synthesized application of this toolbox makes MVPA highly relevant in MPC monitoring. This paper gives an overview of the toolbox developed for industrial applications and elaborates the key algorithms including model-based algorithm for MVPA, newly developed data-driven model-free approach to MVPA, and MPC economic performance assessment. It is then pointed out there is a lack of systematic and synthetic performance diagnosis means in current literature. The difficulty of developing systematic diagnosis tools is addressed. The solution strategy using Bayesian graphic network is then proposed. The proposed performance diagnosis framework is illustrated through some illustrative graphic examples.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 0.001 |
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".