Experimental and CFD investigation of the mixing of MMA emulsion polymerization in a stirred tank reactor
Bibliographic record
Abstract
Although significant advances have been achieved in emulsion polymerization in recent decades, the effect of mixing on this type of polymerization has not been fully delineated yet. In fact, mixing plays a significant role in the performance of an emulsion polymerization reaction. For instance, in case of a very low agitation rate, larger droplets are generated and phase separation, which limits the diffusion mechanism, may occur. In contrast, vigorous agitation can result in reduced nucleation of particles. Therefore, the main objective of this study is to investigate the impact of mixing parameters (e.g. impeller speed, impeller type, impeller number, and baffles) on the monomer conversion, the polymer average molecular weight, particle size and size distributions, transition glass temperature, and number of particles. To achieve this objective, the emulsion polymerization of methyl methacrylate (MMA) was carried out in a lab-scale reactor equipped with a top-entry agitator, 4 wall baffles, a U shaped cooling coil, and a temperature controller. To analyze the reactive flow inside the polymerization reactor, a novel computational fluid dynamics (CFD) model was developed. The multiple reference frames (MRF) technique, k-ε model, and mixture model approach were employed to model the impeller rotation, turbulence, and multiphase flow, respectively. The particle number density distribution within the reactor was also estimated by means of the population balance approach, which employs a discrete method to describe the nucleation and growth of the polymer particles. The experimental data and CFD results showed that the installation of the baffles enhanced the particle size and molecular weight but reduced the conversion and particle number. The number density achieved using the Rushton impeller was higher than that for the pitched blade impeller. The results revealed that the effect of the impeller speed on the characteristics of the polymer attained using the pitched-blade turbine was more prominent than that for the Rushton turbine. It was also found that the impact of the impeller speed on the polymer characteristics was much more pronounced for the double pitched-blade turbines rather than for the double Rushton turbines.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".