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Estimation of the kinetic parameters of chlorinated polyvinyl chloride waste pyrolysis by particle swarm optimization
Author(s) -
Li Ang,
Zhang Wenlong,
Huang Biqing,
Zhou Ru,
Zhang Juan,
Ding Yanming
Publication year - 2021
Publication title -
journal of vinyl and additive technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 35
eISSN - 1548-0585
pISSN - 1083-5601
DOI - 10.1002/vnl.21841
Subject(s) - dispose pattern , pyrolysis , polyvinyl chloride , materials science , activation energy , thermogravimetric analysis , kinetic energy , chemical engineering , waste management , process engineering , municipal solid waste , composite material , chemistry , organic chemistry , engineering , physics , quantum mechanics
Chlorinated polyvinyl chloride (CPVC) is a widely‐used material in various fields with excellent properties. However, CPVC waste is one of the most intractable solids to dispose of. With the development of pyrolysis technology, some advantages have been exhibited, for example, it is flexible to convert solid waste into clean products by pyrolysis, which can be used as energy. Therefore, pyrolysis is considered as an effective method to dispose of solid waste. Especially, kinetic parameters are significant for pyrolysis, which contributes to reactor design and waste management. To better apply the kinetic parameters of CPVC to dispose of waste, thermogravimetric experiments were conducted to obtain the kinetic parameters and establish the reaction mechanism. The Tang, distributed activation energy model, and Advanced Vyazovkin methods were used to calculate the activation energy, and the reaction order was obtained by the Coats‐Redfern method. The results showed that the reaction consisted of two stages, and the average activation energy of the corresponding stage was 153.27 and 290.55 kJ/mol, respectively. However, the abovementioned parameters by traditional methods were not enough to characterize the whole pyrolysis behaviors, then the obtained kinetic parameters were further optimized and extra parameters were computed by the Particle Swarm Optimization algorithm.