Robust optimization of cascaded <scp>MSMPR</scp> crystallization unit using unsupervised machine learning
Bibliographic record
Abstract
Abstract The use of mixed suspension mixed product removal (MSMPR) system in the pharmaceutical industry to produce active pharmaceutical ingredients is well known. In industrial settings, the MSMPR system is subject to lot of process uncertainty which, if ignored, might result in poor product quality. In this work, the process uncertainty involved in MSMPR is targeted during the process optimization stage to find robust optimal operating conditions. The temperature and the residence time inside each cascaded MSMPR unit, altogether six, are considered as uncertain parameters. A sampled set of uncertain data points for such six different uncertain parameters are clustered using a novel support vector clustering (SVC) based algorithm. The uniqueness of this algorithm lies in its ability to fine‐tune the hyper‐parameters of SVC while intelligently clustering the uncertain data points into optimal number of clusters. Such identified clusters are helpful to generate more samples from the intended regions rather than generating them randomly to avoid proposing conservative solutions. Both best‐case and worst‐case scenarios for robust oOptimization (RO) are considered with , and samples. As the model has to be evaluated for a large number of samples and the MSMPR models are time‐consuming to evaluate, a surrogate model of the MSMPR process is developed to perform optimization under uncertainty. Performance metrics are used to quantitatively establish the superiority of the SVC based RO over the box‐sampling based RO.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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".