A new look at optimal control of a 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 The dynamic optimization of batch crystallizers has been widely studied. A simplified method of optimization inspired from nonlinear model predictive control with a receding horizon is introduced and tested on many different objective functions with various constraints. The proposed optimization method with a receding horizon gives excellent results with no noticeable difference from those obtained by rigorous dynamic optimization and is well adapted to online dynamic optimization. Two different crystallizer models are compared. It is shown that the dynamic optimization problem is constituted of several subproblems related to the constraints on the crystallizer temperature, on the concentration compared to the metastable concentration or on the final moments. Finally, the authors propose simple online control algorithms that result in quasi‐optimal temperature profiles provided that the type and sequence of arcs have been previously determined. This method is well adapted to industrial situations. © 2008 American Institute of Chemical Engineers AIChE J, 2008
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 | 0.000 | 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