Control of Product Quality in Batch Crystallization of Pharmaceuticals and Fine Chemicals. Part 1: Design of the Crystallization Process and the Effect of Solvent
Bibliographic record
Abstract
The product quality in a crystallization process refers to the crystal size distribution (CSD), crystal morphology, polymorphic outcome and the degree of crystallinity, and purity. In addition, the product yield is also important. Properties such as the filterability and solid bulk density are directly related to the CSD. To obtain the desired product quality, attention should be paid to the various operating conditions such as the local and average levels of supersaturation, the type of the solvent, the operating temperature and pressure, the type and concentration of impurities and tailor-made additives, degree of mixedness, geometry and the mode of operation of the crystallizer, and seeding and feeding policies. In addition to these variables, the implementation of external control either in the form of a feedback controller or an optimal control policy can further improve the product quality. In Part 1 of the present communication, an attempt is made to present a systematic approach to investigate the effect of various operating conditions, i.e., the design of the crystallization processes, on the product quality. In particular the effect of the solvent in terms of the solubility and its ability to participate in forming hydrogen bonds with the solute molecules will be studied. The effect of mixing, the seeding policy, and the design of the feed system on the product quality will also be discussed. Experimental results are presented to demonstrate the effect of the operating conditions in improving the filterability and solid bulk density of ranitidine hydrochloride and another pharmaceutical compound. In Part 2, the effect of the “external control” on the product quality will be discussed.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".