Limitations of model-fitting methods for kinetic analysis: Polystyrene thermal degradation

Jiménez, Pilar; Pérez‐Maqueda, Luis A.; Criado, J. M.

doi:10.1016/j.resconrec.2013.02.014

Cited by 45 publications

(15 citation statements)

References 22 publications

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“…Recently, different authors (Sánchez-Jiménez et al, 2013;Snegirev et al, 2012) have reported a wide variety of kinetic data for PS thermal degradation. The values of the kinetic parameters determined by thermogravimetric techniques are of the same order of those reported by other authors for PS pyrolysis.…”

Section: Kinetic Study In Thermobalancementioning

confidence: 99%

“…The values of the kinetic parameters determined by thermogravimetric techniques are of the same order of those reported by other authors for PS pyrolysis. The differences are related to the different experimental conditions, PS compositions and, especially, the analysis methodology (Sánchez-Jiménez et al, 2013). Encinar and González (2008) used different heating rates (5-25°C min À1 ) and obtained values for the activation energy and frequency factor in the 168-286 kJ mol À1 and 4.24 Á 10 13 À4.31 Á 10 19 s À1 ranges, respectively, for a first order reaction.…”

Section: Kinetic Study In Thermobalancementioning

confidence: 99%

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Styrene recovery from polystyrene by flash pyrolysis in a conical spouted bed reactor

et al. 2015

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Section: Kinetic Study In Thermobalancementioning

confidence: 99%

Section: Kinetic Study In Thermobalancementioning

confidence: 99%

Styrene recovery from polystyrene by flash pyrolysis in a conical spouted bed reactor

et al. 2015

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“…There are two kinds of kinetic methods used for the analysis of biomass pyrolysis kinetics: model-fitting methods and isoconversional methods [26][27][28]. According to Sánchez-Jiménez et al [29], the use of model-fitting methods may reach analogous conclusions: almost any conversion function can satisfactorily fit experimental data at the cost of estimating drastically different kinetic parameter values. The uncertainty in estimating kinetic parameters caused by the use of model-fitting methods can be avoided in the use of isoconversional methods.…”

Section: Introductionmentioning

confidence: 99%

Processing thermogravimetric analysis data for isoconversional kinetic analysis of lignocellulosic biomass pyrolysis: Case study of corn stalk

Cai

Dong

et al. 2018

Renewable and Sustainable Energy Reviews

308

139

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Modelling of lignocellulosic biomass pyrolysis processes can be used to determine their key operating and design parameters. This requires significant amount of information about pyrolysis kinetic parameters, in particular the activation energy. Thermogravimetric analysis (TGA) is the most commonly used tool to obtain experimental kinetic data, and isoconversional kinetic analysis is the most effective way for processing TGA data to calculate effective activation energies for lignocellulosic biomass pyrolysis. This paper reviews the overall procedure of processing TGA data for isoconversional kinetic analysis of lignocellulosic biomass pyrolysis by using the Friedman isoconversional method. This includes the removal of "error" data points and dehydration stage from original TGA data, transformation of TGA data to conversion data, differentiation of conversion data and smoothing of derivative conversion data, Copyright: Elsevier 2017, Licensed under CC BY NC ND 2 interpolation of conversion and derivative conversion data, isoconversional calculations, and reconstruction of kinetic process. The detailed isoconversional kinetic analysis of TGA data obtained from the pyrolysis of corn stalk at five heating rates were presented. The results have shown that the effective activation energies of corn stalk pyrolysis vary from 148 to 473 kJ mol-1 when the conversion ranges from 0.05 to 0.85.

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“…It was previously shown from a theoretical analysis [62] that only the f(α) function that really describes the kinetic model obeyed by the reaction fulfil Eqn. (2), contrarily to what occurs in rising temperature experiments [82][83][84][85][86][87][88][89][90][91][92][93]. In this latter case, a number of different kinetic models can fit simultaneously a given α-T plots, making impossible to discriminate the kinetic model from a single experiment recorded under a linear heating program.…”

Section: Discrimination Of the Kinetic Modelmentioning

confidence: 99%

Applications of sample-controlled thermal analysis (SCTA) to kinetic analysis and synthesis of materials

Pérez‐Maqueda

Criado

Jiménez

et al. 2014

J Therm Anal Calorim

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The advantages of the Sample Controlled Thermal Analysis (SCTA) for both the kinetic analysis of solid state reactions and the synthesis of materials are reviewed. This method implies an intelligent control of the temperature by the solid state reaction under study in such a way that the reaction rate as a function of the time fits a profile previously defined by the user. It has been shown that SCTA has important advantages for discriminating the kinetic model of solid state reactions as compared with conventional rising temperature methods. Moreover, the advantages of SCTA methods for synthesizing materials with controlled texture and structure are analyzed.

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Limitations of model-fitting methods for kinetic analysis: Polystyrene thermal degradation

Cited by 45 publications

References 22 publications

Styrene recovery from polystyrene by flash pyrolysis in a conical spouted bed reactor

Styrene recovery from polystyrene by flash pyrolysis in a conical spouted bed reactor

Processing thermogravimetric analysis data for isoconversional kinetic analysis of lignocellulosic biomass pyrolysis: Case study of corn stalk

Applications of sample-controlled thermal analysis (SCTA) to kinetic analysis and synthesis of materials

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