Control of Product Quality in Batch Crystallization of Pharmaceuticals and Fine Chemicals. Part 2: External Control
Bibliographic record
Abstract
We use the term “external control” to refer to two process control configurations: First, the “direct or inferential feedback control” of a given product quality index such as the crystal size distribution. Second, the “optimal control” of a process variable such as the control of the cooling policy (temperature) or the reactants addition rate to optimize an objective function defined in terms of the product quality. The intent of this two-part contribution is to discuss the various approaches used for the control of crystal quality. In Part 1, the design of the crystallization process and the selection of the process variables affecting the product quality were presented. In this part the implementation of an “external controller” will be presented. Initially, state-of-the-art instrumentation in crystallization research and technology is discussed. Mathematical models involving the population density and moments equation are presented and evaluated. Feedback control of crystal size distribution and supersaturation is presented in some detail. Polymorphic outcome of pharmaceuticals is dictated by the choice of solvent, presence of impurities, and the operating variables such as the temperature and the degree of supersaturation that are controlled by the implementation of an “external controller”. A new algorithm for the solution of the integro-hyperbolic partial differential equation representing the population balance is discussed, and its potential use in the design of real-time optimal control policies for the control of batch crystallizers is highlighted. Open-loop and real-time optimal control policies in cooling batch crystallizers are presented, and their efficacy is evaluated.
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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) | 0.000 | 0.000 |
| 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 | 0.000 | 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".