Control of Particle Size Distributions in Emulsion Semibatch Polymerization Using Mid-Course Correction Policies
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Bibliographic record
Abstract
The manufacture of emulsion polymers with broad and bimodal particle size distribution (PSD) through in situ particle nucleation in semibatch reactors is difficult because of the sensitivity of particle nucleation phenomena to variations in reactor conditions, impurities, and surfactant and initiator properties. In this paper, we present several control strategies based on the use of readily available online and offline measurements to predict the final PSD and, if necessary, to compute mid-course corrections. Partial least squares (PLS) models are used to extract the necessary information from different sets of measurements, to predict the final PSD, and to define a control region, outside of which mid-course control actions are deemed necessary. Using a simulated styrene emulsion polymerization process as an example, these control strategies are shown to be highly effective and very practical for industrial implementation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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