Optimal Control of Batch MMA Polymerization with Specified Time, Monomer Conversion, and Average Polymer Molecular Weights
Bibliographic record
Abstract
Abstract Summary: In this paper, the optimal control policies are determined for the free‐radical polymerization of methyl methacrylate (MMA) in a non‐isothermal batch reactor. The temperature of the fluid inside reactor‐jacket is used as the control function to realize four different optimal control objectives. Each objective is formulated to optimize a given variable simultaneously specifying another. The first two objectives target the maximization of monomer conversion in a specified operation time, and the minimization of operation time for a specified, final monomer conversion. The last two objectives target the maximization of monomer conversion for specified, final number‐ and weight‐average polymer molecular weights. The realization of these objectives is expected to be very useful for the batch production of polymers. To meet the specification of an optimization variable other than time, the differential model of batch process is expressed and utilized in the range of specified variable. Equations are provided for Jacobian evaluations to help in the accurate solution of process model. A genetic algorithms‐based optimal control method is applied to realize the four optimal control objectives. The results of this application show considerable improvements in the performance of batch MMA polymerization. magnified image
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".