Optimal design of T<SUP align="right">2</SUP> monitoring chart for chemical processes
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, the optimal design of multivariate statistics-based monitoring charts for large scale systems is investigated. A cost function that explicitly accounts for the different quality monitoring costs is used to determine the parameters of the Hotelling's T2 monitoring chart. The proposed approach is used to economically monitor the products in the Tennessee Eastman process (TEP). The implementation of a multivariate economical criterion in the design of the Hotelling's T2 control chart provides an improved basis for the evaluation and repairs of out of control states and results in monitoring's cost minimisation through optimal sampling schedules. Due to the nature of the used economic model, the decision of switching from traditional statistical design to economic design requires the balance between the gained cost saving and the expected statistical performance of the T2 monitoring chart. Finally, in order to evaluate the reliability of the estimated optimal parameters, a detailed sensitivity analysis is presented.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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