Within-Batch and Batch-to-Batch Inferential-Adaptive Control of Semibatch Reactors: A Partial Least Squares Approach
Bibliographic record
Abstract
An inferential control strategy that combines within-batch information from process variable trajectories and information from prior batches to control multivariate product quality properties in semibatch reactors is presented. The approach extends mid-course correction (MCC) strategies by including batch-to-batch information in the controllers and an adaptive partial least squares (PLS) approach to update the models from batch to batch. As with other MCC approaches, the scheme retains the “no-control region” concept where control is taken at various stages during the batch only if the projected error in the final quality is deemed to be statistically significant. Only data on readily available process measurements (e.g., temperatures) throughout the batch, plus a measurement on a variable related to quality (e.g., particle size) at one or more discrete times during the batch, are required to achieve very precise control of the final product quality (e.g., particle-size distribution, PSD). Latent variable models based on PLS are a key element in the approach. They are able to extract information efficiently from the large number of highly correlated measurements on the process variable trajectories and relate it to high-dimensional output measurements on product quality (e.g., PSD) by projecting this information into low-dimensional latent variable spaces. The methodology is applied to the control of PSD in emulsion polymerization. The problem of regulation about a fixed set-point PSD in the face of disturbances and the problem of achieving new set-point PSDs are both illustrated.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".