Model‐free control of a seeded batch crystallizer
Bibliographic record
Abstract
Abstract As the use of a batch crystallization process in several industrial applications is extensive, finding an effective control strategy is important to improve the product quality, which is typically characterized by a unimodal and narrow crystal size distribution (CSD) with a large mean crystal size. To achieve this requirement, an accurate mathematical model is needed to predict the process comportment and to design an efficient and robust controller. However, due to the highly nonlinear comportment, the difficulties of characterizing several phenomenological effects, the kinetic parameters, uncertainty, and the unknown disturbances, the used model may not describe the real process behaviour, resulting in a poor control strategy. In this work, a model‐free control and its corresponding intelligent PI (iPI), the recently introduced approach, has been proposed to ensure that the desired unimodal CSD with a desired mean size could be reached facing these problems with an easy control structure, choosing the seeded batch crystallizer of adipic acid as a case study. The proposed iPI is compared with the classic PI controller. The simulation results demonstrate the effectiveness and disturbance rejection capability of the iPI controller against the classic PI.
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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".