Dynamic Modeling and Optimization of Batch Crystallization of Sugar Cane under Uncertainty
Why this work is in the frame
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Bibliographic record
Abstract
This work presents a study on the agitation rate effects on the average diameter (% volume D (4,3)) in the batch crystallization of sugar cane in pilot-scale process. The mathematical model presented in this work includes the population balance equation (PBE), the mass and energy balances, and the kinetics equations of nucleation and growth rate. The kinetic parameters were calculated from optimization using experimental data obtained from a pilot-scale process. An uncertainty analysis was performed and used to specify robust agitation trajectories that minimize the variations of crystal size from batch to batch. Four cases studies are presented to obtain 920, 1000, 1200, and 1300 μm of D (4,3) subject to a constraint in the formed crystal mass (FCM) of 4700 g under uncertainty in the kinetic parameters. The resulting robust agitation trajectories were implemented in the pilot-scale process. Comparisons between experimental data and the model predictions are presented.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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