Control of Supersaturation in a Semibatch Antisolvent Crystallization Process Using a Fuzzy Logic Controller
Bibliographic record
Abstract
A fuzzy logic control approach is developed for the control of a seeded semibatch crystallizer. Crystallization of paracetamol (PA) in 2-propanol−water mixtures was used as the model system. The concentration and cord length counts were measured using an in situ attenuated total reflection Fourier transform infrared probe and an in situ focused beam reflectance measurement probe, respectively. Three open-loop feeding policies, concave (CCFP, similar to natural cooling), linear (LFP), and convex (CVFP, near controlled cooling), were employed to investigate the process dynamic behavior to construct the fuzzy controller structure for the control of supersaturation within a predefined zone close to the solubility curve. It is found that the fuzzy controller can ensure tracking of the concentration within the zone, leading to substantial improvement of the end product size distribution compared to the open-loop results. Selecting the initial PA concentration above the upper limit of the concentration results in longer process times. The open-loop results show that the feeding policy (addition of water) does not prevent nucleation and agglomeration; however, both phenomena can be minimized by the LFP.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".