Kinetics Estimation and Single and Multi-Objective Optimization of a Seeded, Anti-Solvent, Isothermal Batch Crystallizer
Bibliographic record
Abstract
The nucleation and growth kinetic parameters of paracetamol in an isopropanol−water antisolvent batch crystallizer were estimated by nonlinear regression in terms of the moments of the crystal population density. The moments were calculated using the measured chord length distribution (CLD) generated by the FBRM. The measured supersaturation by ATR-FTIR spectroscopy was also used to calculate the nucleation and growth rates using power law correlations. Using the estimated kinetic parameters, the crystallization model based on the population and mass balance, was validated using the open-loop experimental particle size distribution and supersaturation results. Subsequently, the solution to the optimal antisolvent flow-rate profiles was obtained by applying nonlinear constrained single- and multiobjective optimization on the validated model. These profiles were implemented on the crystallizer and crystal-size distributions were compared with the open-loop experiments. The bimodality in the particle size distribution (PSD), which was present in the open-loop experiments, was either minimized or completely eliminated with the optimal profile policies. The results of the multiobjective optimization showed an improvement of 27.5 μm and 3% in the volume weighted mean size and yield, respectively, in comparison to the best results obtained from the open-loop experiments.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".