Multivariable real‐time optimal control of a cooling and antisolvent semibatch crystallization process
Bibliographic record
Abstract
Abstract This article presents an experimental study of simultaneous optimization with respect to two variables (cooling rate and flow‐rate of antisolvent) in an offline and online (real‐time) manner on a semibatch crystallizer. The nucleation and growth kinetic parameters of paracetamol in an isopropanol‐water cooling, antisolvent batch crystallizer were estimated by nonlinear regression in terms of the moments of the crystal population density. Moments of crystal population were estimated from the measured chord length distribution, generated by the FBRM®, and the supersaturation was calculated from the measured concentration by attenuated total reflectance‐fourier transform infrared spectroscopy. The results of real‐time optimization showed a substantial improvement of the end of batch properties (the volume‐weighted mean size and yield). For the same objective function, the real‐time optimization method resulted in an increase in the volume‐weighted mean size by ∼100 μm and 15% of theoretical yield compared with the best result obtained in an offline optimization manner. © 2009 American Institute of Chemical Engineers AIChE J, 2009
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".