Supervised sparse preserving projections model based on distributed principal component analysis for chemical process monitoring
Bibliographic record
Abstract
Abstract For the monitoring of large‐scale chemical processes, the distributed method is often used to extract local feature information and model the extracted local feature information to obtain a process monitoring model. But the distributed process monitoring model often contains more process variables, which makes the local information of the process data flooded. To make up for the insufficient extraction of local information in traditional distributed process monitoring, supervised sparse preserving projections model based on distributed principal component analysis (DPCA‐SSPP) is proposed in this paper. First, the process data are decomposed by the PCA algorithm, and the principal component space and residual space are obtained. Second, the variables of each sub‐block are selected according to the maximum correlation criterion, and the SSPP process monitoring model is established for each sub‐block. Finally, the monitoring results of each sub‐block are combined together to form a global monitoring result through the Bayesian information fusion strategy. The proposed scheme can be proved to be effective through the simulation on a nonlinear numerical example and the Tennessee Eastman benchmark (TE) process.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".