Kinetic modeling of polyethylene pyrolysis under microwave irradiation toward predictive product distribution
Bibliographic record
Abstract
Microwave-assisted pyrolysis offers an approach for the chemical recycling of polymeric waste, contributing to sustainable energy solutions and environmental mitigation. The efficient tracking of pyrolytic gas for further upgrading necessitates a comprehensive understanding of the dominant reaction mechanisms and identifying optimal operating conditions. This study investigates the thermal pyrolysis of low-density polyethylene (LDPE) under microwave irradiation, focusing on the effects of temperature on product distribution while developing a non-isothermal kinetic model for hydrocarbon cracking. The potential decomposition mechanism was explored through ab initio molecular dynamics (AIMD) simulations, conducted over a 4–6 ps time frame at each temperature in the range of 800–4500 K. These simulations revealed fundamental product formation trends as a function of temperature, which were further validated against experimentally obtained primary gas evolution profiles. Pyrolysis experiments were conducted at microwave power levels ranging from 400 to 800 W, categorizing the resulting products into hydrogen and hydrocarbons of varying molecular weights. Model validation against experimental data confirmed a dominant second-order reaction mechanism driven by rapid microwave heating, with Sestak-Berggren Pearson's linear correlation and associated parameter values of n at 1.7 and m at 0.02. Kinetic analysis through the iso-conversional Friedman method revealed an activation energy range of 101 ± 4 kJ·mol −1 , exhibiting slight variation with conversion. A simplified reaction network was developed to predict product evolution, incorporating sinusoidal temperature fluctuations induced by microwave irradiation. The proposed kinetic model advances the design of microwave-assisted pyrolysis processes, offering potential for integration with catalytic stages and further upgrading to enhance process efficiency and product selectivity. • Non-isothermal kinetic model for LDPE microwave pyrolysis validated experimentally • AIMD simulations revealed temperature-dependent decomposition and product pathways. • Dominant second-order mechanism identified with reaction parameters n = 1.7, m = 0.02 • Activation energy of 101 ± 4 kJ·mol −1 determined by iso-conversional Friedman method • The simplified reaction network predicts product evolution under microwave fluctuations.
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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) | 0.000 | 0.001 |
| 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 | 0.001 | 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".