Analysis of different kinetic models in the dynamic pyrolysis of cellulose

Conesa, Juan A.; Caballero, José A.; Marcilla, A.; Font, R.

doi:10.1016/0040-6031(94)02102-t

Cited by 175 publications

(102 citation statements)

References 25 publications

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“…More complex models are also employed in the literature. Among others, the use of self-accelerating kinetics has been suggested by Conesa et al 38 and Capart et al 39 In the presence of oxygen the cellulose decomposition was also found to be a self-accelerating reaction in recent studies based on evaluation strategies similar to the present work. 40,31 The selfaccelerating reactions can typically be described by an equation of type…”

Section: Evaluation By Assuming Common Parameters If Part Of the Modsupporting

confidence: 70%

Thermal Decomposition Kinetics of Woods with an Emphasis on Torrefaction

et al. 2013

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ABSTRACT. The pyrolysis kinetics of Norwegian spruce and birch wood was studied to obtain information on the kinetics of torrefaction. Thermogravimetry (TGA) was employed with nine different heating programs, including linear, stepwise, modulated and constant reaction rate (CRR) experiments. The 18 experiments on the two feedstocks were evaluated simultaneously by the 2 method of least squares. Part of the kinetic parameters could be assumed common for both woods without a considerable worsening of the fit quality. This process results in better defined parameters and emphasizes the similarities between the woods. Three pseudocomponents were assumed. Two of them were described by distributed activation energy models (DAEM), while the decomposition of the cellulose pseudocomponent was described by a self-accelerating kinetics. In another approach all the three pseudocomponents were described by n-order reactions. Both approaches resulted in nearly the same fit quality but the physical meaning of the model based on three n-order reactions was found to be problematic. The reliability of the models was tested by checking how well the experiments with higher heating rates can be described by the kinetic parameters obtained from the evaluation of a narrower subset of 10 experiments with slower heating. A table of data was calculated that may provide guidance about the extent of devolatilization at various temperatureresidence time values during wood torrefaction.

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Section: Evaluation By Assuming Common Parameters If Part Of the Modsupporting

confidence: 70%

Thermal Decomposition Kinetics of Woods with an Emphasis on Torrefaction

et al. 2013

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“…The behavior of cellulose thermal degradation has been extensively investigated [25][26][27][28] and has been explained for many mechanisms that are not completely known because the complex nature of the reaction. In this sense some pseudo-mechanistic models have been used to explain this process.…”

Section: Resultsmentioning

confidence: 99%

“…Generally, these models are based on the fact that, when cellulose is heated in a non-reactive environment, it decomposes to various pyrolysis products. These pyrolysis products can be conveniently grouped into three classes depending on their volatility: tars (mainly anhydro-compounds), char (non-volatile residue, with high carbon content) and gases (low molecular weight products as CO, CO 2 , and also water) [25] . Cellulose thermal degradation can be described by a mechanism that involves two competitive reactions.…”

Section: Resultsmentioning

confidence: 99%

Biodegradation evaluation of bacterial cellulose, vegetable cellulose and poly (3-hydroxybutyrate) in soil

et al. 2015

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“…There are various models which are able to simulate the biomass pyrolysis mathematically. These models can be classified as following: the singlereaction and the multi-reaction models (Capart et al, 2004;Conesa et al, 1995;Conesa et al, 2001;Pysiak et al, 2004;Mysyk et al, 2005). The most accurate and well defined approach for the modelling of biomass pyrolysis is to use the distributed activation energy model (DAEM) (Burnham and Braun, 1999;Burnham et al, 1995;Galgano and Biase, 2003;Ferdous et al, 2002).…”

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confidence: 99%

Parametric Study of N^TH Order Distributed Activation Energy Model for Isothermal Pyrolysis of Forest Waste Using Gaussian Distribution

Dhaundiyal

Singh

2017

Acta Technologica Agriculturae

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This paper deals with the influence of some factors relevant to isothermal pyrolysis of residual leaves of Cedrus deodara on the asymptotic solution of the non-isothermal n th order distributed activation energy model (DAEM) using Gaussian distribution. Frequency factor, integral upper limit, the reaction order and the variance of Gaussian distribution are the parameters taken under purview of this study. In order to determine the kinetic parameters of the isothermal n th order Gaussian DAEM from thermoanalytical data of loose biomass pyrolysis, the variation of these factors has been considered. The obtained results show that the predicted results for n th order DAEM hold good at upper limit of dE, E ∞ = 39 kJ mol -1 .

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Analysis of different kinetic models in the dynamic pyrolysis of cellulose

Cited by 175 publications

References 25 publications

Thermal Decomposition Kinetics of Woods with an Emphasis on Torrefaction

Thermal Decomposition Kinetics of Woods with an Emphasis on Torrefaction

Biodegradation evaluation of bacterial cellulose, vegetable cellulose and poly (3-hydroxybutyrate) in soil

Parametric Study of N^TH Order Distributed Activation Energy Model for Isothermal Pyrolysis of Forest Waste Using Gaussian Distribution

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Analysis of different kinetic models in the dynamic pyrolysis of cellulose

Cited by 175 publications

References 25 publications

Thermal Decomposition Kinetics of Woods with an Emphasis on Torrefaction

Thermal Decomposition Kinetics of Woods with an Emphasis on Torrefaction

Biodegradation evaluation of bacterial cellulose, vegetable cellulose and poly (3-hydroxybutyrate) in soil

Parametric Study of NTH Order Distributed Activation Energy Model for Isothermal Pyrolysis of Forest Waste Using Gaussian Distribution

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Parametric Study of N^TH Order Distributed Activation Energy Model for Isothermal Pyrolysis of Forest Waste Using Gaussian Distribution