Model‐free control of a seeded batch crystallizer
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
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.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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