Numerical investigation of thermal runaway prevention in styrene polymerization using a PCM(R-245fa)-enhanced cooling jacket
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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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".