Modeling and Optimal Control of Solution Mediated Polymorphic Transformation of <scp>l</scp>-Glutamic Acid
Bibliographic record
Abstract
The solution-mediated polymorphic transformation (SMPT) of l -glutamic acid is modeled using the method of moments (MoM) with the addition of a dissolution term to account for the transformation of the metastable to the stable polymorph. The numerical solution methodology involves the kinetics of nucleation, growth, and dissolution for the polymorphic system. The effects of the cooling profile, initial solute concentration, and seeding conditions on the product quality were investigated. In supersaturated solutions with respect to both polymorphs, the natural cooling yielded the highest mass of the metastable form, while the nonlinear cooling resulted in the highest mass of the stable form (13.41 g/kg of solvent). The ratio of the stable to metastable form masses was higher with the higher cooling rate parameters. In solutions supersaturated with respect to the stable form and undersaturated relative to the metastable form, the dissolution of the metastable form favored the production of the stable form. The number-weight average size of the stable particles was 148.5 μm with the nonlinear cooling policy which was 51% and 134% more than those corresponding to the linear and natural cooling policies. Finally, nonlinear programming (NLP) was used in a dynamic mode to investigate the optimal control of the process with different objective functions. It was shown that the optimal control policy had a favorable effect on the yield of the stable or metastable form as well as the particle sizes at the end of the batch. The optimal control using an objective function to maximize the mass of the metastable form at the end of the batch resulted in 7.8 g of crystals/kg of solvent for metastable form which was 33% and 381% higher than the natural and linear cooling policies. For an objective function to maximize the mass of the stable form, the optimal cooling policy increased the mass of the stable form by 3.2% compared to the nonlinear cooling policy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".