Model Predictive Monitoring for Batch Processes
Bibliographic record
Abstract
In the procedure to monitor a new batch using the method proposed by Nomikos and MacGregor [ AIChE J . 1994, 40 (8), 1361−1375], an assumption about the unknown future samples in the batch has to be taken. This work demonstrates that using the missing data option and solving the score estimation problem with an appropriate method are equivalent to the use of an accurate adaptive forecast model for the future samples over the shrinking horizon of the remainder of the batch. The dynamic properties of the principal component analysis (PCA) model are illustrated by re-expressing the projection model as a time-varying multivariate prediction model. The benefits of using the missing data estimation option are analyzed by contrasting it with other options on the basis of (i) the accuracy of the forecast done for the unknown samples, (ii) the quality of the score estimates, and (iii) the detection performance during monitoring. Because of the tremendous structural information built into these multivariate PCA models for batch processes, the missing data option is shown to yield the best performance by all measures in predicting the future unknown part of the trajectory, even from the beginning of the batch. However, for the purpose of online detection of process faults (in process monitoring), the differences among the trajectory estimation methods appear to be much less critical because the control charts used in each case are tailored to the filling-in mechanism employed. All of the approaches appear to provide powerful charting methods for monitoring the progress of batch processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.002 |
| 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".