Nonlinear Process Monitoring Using Supervised Locally Linear Embedding Projection
Bibliographic record
Abstract
The chemical and mineral processing industries need a nonlinear process monitoring method to improve the stability and economy of their processes. Techniques that are currently available to these industries are often too computationally intensive for an industrial control system, or they are too complex to commission. In this paper, we propose using supervised locally linear embedding for projection (SLLEP) as a new nonlinear process monitoring technique to solve these issues. In addition, we suggest using a commonly available tool in modern industrial control systems, a model predictive control, to solve the quadratic program of SLLEP in real-time and with minimal effort to commission. As a case study, we demonstrate that process monitoring with SLLEP can detect and diagnose the early onset of a semiautogenous grinding (SAG) mill overload. A SAG mill overload is a highly nonlinear operating situation, and we show that principal component analysis, the best-in-class technique currently used by the industry for monitoring an overload, is unable to detect the early onset of an overload.
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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 | 0.001 | 0.000 |
| 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".