A full‐condition monitoring method for nonstationary dynamic chemical processes with cointegration and slow feature analysis
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Bibliographic record
Abstract
Chemical processes are in general subject to time variant conditions because of load changes, product grade transitions, or other causes, resulting in typical nonstationary dynamic characteristic. It is of a considerable challenge for process monitoring to consider all possible operation conditions simultaneously including multifarious steady states and dynamic switchings. In this work, a novel full‐condition monitoring strategy is proposed based on both cointegration analysis (CA) and slow feature analysis (SFA) with the following considerations: (1) Despite that the operation conditions may vary over time, they may follow certain equilibrium relations that extend beyond the current time, and (2) there may exist certain dynamic relations that stay invariant under normal process operation despite process may operate at different operating conditions. To monitor both equilibrium and dynamic relations, in the proposed method, nonstationary variables are separated from stationary variables first. Then by CA and SFA, the long‐term equilibrium relation is distinguished from the specific relation held by the current conditions from both static and dynamic aspects. Various monitoring statistics are designed with clear physical interpretation. It can distinguish between the changes of operation conditions and real faults by checking deviations from equilibrium relation and deviations from the specific relation. Case study on a chemical industrial scale multiphase flow experimental rig shows the validity of the proposed full‐condition monitoring method. © 2017 American Institute of Chemical Engineers AIChE J , 64: 1662–1681, 2018
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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 | 0.000 | 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 it