Pyrolysis of polyurethane foam: optimized search for kinetic properties via simultaneous K–K method, genetic algorithm and elemental analysis

Li, Kaiyuan; Pau, Dennis; Zhang, Heping

doi:10.1002/fam.2343

Cited by 24 publications

(4 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Here we explore the particle size effects on pyrolysis of polystyrene from aspects of pyrolysis behavior, kinetics, reaction model, reconstruction, and validation. The final reaction model can provide scientific guidance to polymer pyrolysis modeling [22][23][24][25][26][27]. In this study, to explore the particle size effects on pyrolysis behavior, polystyrene particles with four different sizes, 5, 10, 15, and 50 µ m, were selected to conduct a series of TG experiments.…”

Section: Discussionmentioning

confidence: 99%

“…However, sometimes the model with the maximum coefficient may be not the real reaction model, which can be checked by model reconstruction with experimental data. So, this model reconstruction [22][23][24][25][26] should be further processed to check if the obtained model can fit experimental profile well, and which procedure is necessary, but is usually ignored in previous related literatures.…”

Section: Traditional Kinetic Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Pyrolytic Kinetics of Polystyrene Particle in Nitrogen Atmosphere: Particle Size Effects and Application of Distributed Activation Energy Method

et al. 2020

View full text Add to dashboard Cite

This work was motivated by a study of particle size effects on pyrolysis kinetics and models of polystyrene particle. Micro-size polystyrene particles with four different diameters, 5, 10, 15, and 50 µm, were selected as experimental materials. Activation energies were obtained by isoconversional methods, and pyrolysis model of each particle size and heating rate was examined through different reaction models by the Coats–Redfern method. To identify the controlling model, the Avrami–Eroféev model was identified as the controlling pyrolysis model for polystyrene pyrolysis. Accommodation function effect was employed to modify the Avrami–Eroféev model. The model was then modified to f(α) = nα0.39n − 1.15(1 − α)[−ln(1 − α)]1 − 1/n, by which the polystyrene pyrolysis with different particle sizes can be well explained. It was found that the reaction model cannot be influenced by particle geometric dimension. The reaction rate can be changed because the specific surface area will decrease with particle diameter. To separate each step reaction and identify their distributions to kinetics, distributed activation energy method was introduced to calculate the weight factor and kinetic triplets. Results showed that particle size has big impacts on both first and second step reactions. Smaller size particle can accelerate the process of pyrolysis reaction. Finally, sensitivity analysis was brought to check the sensitivity and weight of each parameter in the model.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Traditional Kinetic Methodsmentioning

confidence: 99%

Pyrolytic Kinetics of Polystyrene Particle in Nitrogen Atmosphere: Particle Size Effects and Application of Distributed Activation Energy Method

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Recent treatment technologies for PUR can be grouped into four categories: Physical (Abdel Rahman et al, 2019; Junco et al, 2018), chemical (Sheel and Pant, 2018), biological (Magnin et al, 2019) and thermal (Li et al, 2016). Physical treatment could destroy the chemical structure of PUR to some extent, resulting into a degradation of performance for the regenerated products.…”

Section: Introductionmentioning

confidence: 99%

Comparative study on the pyrolysis kinetics of polyurethane foam from waste refrigerators

Yao

et al. 2019

Waste Manag Res

View full text Add to dashboard Cite

Thermal treatment offers advantages of significant volume reduction and energy recovery for the polyurethane foam from waste refrigerators. In this work, the pyrolysis kinetics of polyurethane foam was investigated using the model-fitting, model-free and distributed activation energy model methods. The thermogravimetric analysis indicated that the polyurethane foam decomposition could be divided into three stages with temperatures of 38°C–400°C, 400°C–550°C and 550°C–1000°C. Peak temperatures for the major decomposition stage (<400°C) were determined as 324°C, 342°C and 344°C for heating rates of 5, 15 and 25 K min-1, respectively. The activation energy ( Eα) from the Friedman, Flynn–Wall–Ozawa and Tang methods increased with degree of conversion ( α) in the range of 0.05 to 0.5. The coefficients from the Flynn–Wall–Ozawa method were larger and the resulted Eα values fell into the range of 163.980–328.190 kJ mol-1 with an average of 206.099 kJ mol-1. For the Coats–Redfern method, the diffusion models offered higher coefficients, but the E values were smaller than that from the Flynn–Wall–Ozawa method. The Eα values derived from the distributed activation energy model method were determined as 163.536–334.231 kJ mol-1, with an average of 206.799 kJ mol-1. The peak of activation energy distribution curve was located at 205.929 kJ mol-1, consistent with the thermogravimetric results. The Flynn–Wall–Ozawa and distributed activation energy model methods were more reliable for describing the polyurethane foam pyrolysis process.

show abstract

“…A alumina é preferível frente aos compostos contendo halogênio e fósforo que são extremamente tóxicos e prejudiciais ao meio ambiente. [16][17][18][19] O estudo cinético sobre a decomposição térmica das ERPU, sem e com adição de carga inorgânica, será realizado no presente trabalho com o principal objetivo de identificar a contribuição de modelos cinéticos na descrição dos dados experimentais de decomposição térmica e, consequentemente, identificar os mecanismos de decomposição das espumas, com e sem a presença de carga. O estudo sobre o mecanismo, nas duas situações, fornece informações valiosas sobre as propriedades fundamentais do material, como por exemplo suas propriedades mecânicas.…”

Section: Introductionunclassified

Estudo Cinético De Decomposição Térmica De Espumas Rígidas De Poliuretano Por Rede Neural Artificial

et al. 2017

View full text Add to dashboard Cite

publicado na web em 16/10/2017 KINETIC AND THERMAL DECOMPOSITION STUDIES OF RIGID POLYURETHANE FOAMS MODELED BY ARTIFICIAL NEURAL NETWORKS. Kinetic models of solid thermal decomposition are traditionally used for individual fit of isothermal experimental data. However, this methodology presents unacceptable errors in some regions of the data. To solve this problem, a neural network was adopted in this work. The implemented algorithm uses the rate constants as predetermined weights between the input and intermediate layer and kinetic models as activation functions of neurons in the hidden layer. The contribution of each model in the overall fit of experimental data is calculated as the weights between the intermediate and output layer. In this way, the phenomenon is better described as a sum of kinetic processes. Two rigid polyurethane foam samples: loaded with Al 2 O 3 and no inorganic filler were used in this work. The R3 and D2 models described the thermal decomposition kinetic process for all temperatures for both foams with smaller residual error. However, the network, combining the kinetic models, presented residual errors on average 10 2 times lower compared to these individual models. The determined activation energy is 12.44 kJ mol -1 higher for the loaded foam. This result corroborates the use of this material as flame retardant, even with the presence of a small amount of charge in its structure.Keywords: thermal decomposition; rigid polyurethane foams; polyurethane; artificial multilayer neural network. INTRODUÇÃOEstudos cinéticos de decomposição térmica no estado sólido são de grande interesse científico e industrial.1 Três metodologias relevantes no tratamento teórico de cinética de decomposição em sólidos são: (i) descrição do processo por modelos cinéticos, 2 (ii) associação e correção dos modelos cinéticos por rede neural artificial 3,4 e (iii) modelo isoconversional. 5A metodologia de ajuste por modelos cinéticos considera que as reações de decomposição térmica de sólidos ocorrem na interface do produto-reagente. Este processo cinético, com base na formação e crescimento de núcleos, pode ser estudado por análise de dados de decomposição térmica nos quais a redução da massa é medida num intervalo de tempo à temperatura constante. Os núcleos de reação são preferencialmente formados em imperfeições da estrutura e os modelos cinéticos são usados para explicar as isotermas experimentais, em que a fração de decomposição é medida ao longo do tempo. Um determinado modelo é escolhido devido a sua melhor correlação para ajustar os dados experimentais. No entanto, em muitas situações, o erro residual do modelo não é aceitável na descrição do processo total, embora, para regiões específicas de decomposição, o modelo possa ser apropriado, sendo apenas uma primeira aproximação. Os modelos podem ser representados por uma equação geral do tipo: 2 (1) em que α representa a fração de decomposição, t o tempo, k a constante de velocidade. Os parâmetros de ajuste p e q definem o modelo físico.Em alguns trabalhos já ...

show abstract

Pyrolysis of polyurethane foam: optimized search for kinetic properties via simultaneous K–K method, genetic algorithm and elemental analysis

Cited by 24 publications

References 26 publications

Pyrolytic Kinetics of Polystyrene Particle in Nitrogen Atmosphere: Particle Size Effects and Application of Distributed Activation Energy Method

Pyrolytic Kinetics of Polystyrene Particle in Nitrogen Atmosphere: Particle Size Effects and Application of Distributed Activation Energy Method

Comparative study on the pyrolysis kinetics of polyurethane foam from waste refrigerators

Estudo Cinético De Decomposição Térmica De Espumas Rígidas De Poliuretano Por Rede Neural Artificial

Contact Info

Product

Resources

About