Control of Product Quality in Batch Crystallization of Pharmaceuticals and Fine Chemicals. Part 2: External Control
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
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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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it