Numerical investigation of thermal runaway prevention in styrene polymerization using a PCM(R-245fa)-enhanced cooling jacket
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Bibliographic record
Abstract
Styrene polymerization is a highly exothermic free-radical process that is intrinsically prone to thermal runaway due to strong Arrhenius kinetics and autoacceleration effects. In this study, a novel passive thermal management strategy based on a phase change material (PCM)-equipped cooling jacket is numerically investigated for a tubular styrene polymerization reactor. A two-dimensional axisymmetric model of a plug-flow reactor with an annular cooling jacket containing a water–R-245fa PCM emulsion is developed and solved using COMSOL Multiphysics. The model couples laminar flow, species transport, reaction kinetics, heat transfer, and PCM phase change using an apparent heat capacity formulation. Styrene polymerization is represented by a lumped global reaction with temperature-dependent Arrhenius kinetics and a heat of polymerization of -70 kJ.mol-1. The influence of PCM volumetric concentration (5–30%) on reactor temperature control, reaction rate evolution, and monomer conversion is systematically analyzed and compared with a conventional water-cooled jacket. The results show that pure water cooling fails to prevent thermal runaway, with reactor temperatures exceeding 200 °C within minutes. In contrast, PCM-enhanced cooling significantly improves thermal stability by absorbing reaction heat through latent heat effects. At PCM loadings of 20% and higher, the reactor temperature is effectively clamped near the PCM boiling point (~85 °C), completely suppressing runaway behavior and maintaining stable, high monomer conversion. The study demonstrates that nano- and emulsion-based PCM cooling jackets can provide a robust, passive, and inherently safer alternative to conventional cooling systems for highly exothermic polymerization reactors.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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