Control of Product Quality in Batch Crystallization of Pharmaceuticals and Fine Chemicals. Part 1: Design of the Crystallization Process and the Effect of Solvent
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it