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A molecular-level kinetic model for the primary and secondary reactions of polypropylene pyrolysis.

Authors :
Fu, Zhe
Sun, Qidian
Hua, Fang
Yang, Shiqi
Ji, Ye
Cheng, Yi
Source :
Journal of Analytical & Applied Pyrolysis. Oct2023, Vol. 175, pN.PAG-N.PAG. 1p.
Publication Year :
2023

Abstract

The environmental pollution caused by waste plastics and their potential of resource recovery are receiving increasing attention. In this study, a molecular level kinetic model was established to simulate the polypropylene pyrolysis based on the SOL method, Poisson distribution model and genetic algorithm, which was simplified and effective without the introduction of extensive chemical databases and stiff differential equations. The automatic generation of complex reaction network was based on the representation of species and reactions at molecular level and the design of 14 reaction rules consistent with the mechanism. The model prediction achieved good agreement with experimental data affected by the primary and secondary reactions, verifying the generalizability of the models. The model prediction according to the kinetic parameters provided guidance for the pyrolysis experiments. In general, the molecular-level model behaves exquisite in the evaluation, validation, and prediction, and can gain an insight into the pyrolysis of polymers. • A molecular level kinetic model with automatic reaction network generation is established for polypropylene pyrolysis. • The automatic network generation is based on the Poisson distribution model, the SOL method and the design of reaction rules. • This model achieves excellent agreement with experimental data and can predict pyrolysis performance in more details. • The important role of temperature on the pyrolysis and the competition of two scissions are quantified by this model. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01652370
Volume :
175
Database :
Academic Search Index
Journal :
Journal of Analytical & Applied Pyrolysis
Publication Type :
Academic Journal
Accession number :
173341862
Full Text :
https://doi.org/10.1016/j.jaap.2023.106182