Kinetics of the pyrolysis and combustion of olive oil solid waste

Jauhiainen, Jaakko; Conesa, Juan A.; Font, R.; Martı́n-Gullón, Ignacio

doi:10.1016/j.jaap.2004.01.003

Cited by 88 publications

(59 citation statements)

References 28 publications

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“…The E0,2 values were found to be 185 -186 kJ/mol, which is safely in the range of the activation energies reported for the cellulose components of biomass materials. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] On the other hand, the E0,1 and E0,3 values of Table 3 are considerably higher than the reported activation energies for the decomposition of hemicelluloses and lignin in studies based on first order or nth order kinetics. 8 One can consider here that the activation is nearly inversely proportional to the width of the DTG curves in the first order and nth order models at linear T(t).…”

Section: Notes On the Pseudocomponents And On The Magnitudes Of The Omentioning

confidence: 57%

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Thermogravimetric Study of Biomass Pyrolysis Kinetics. A Distributed Activation Energy Model with Prediction Tests

et al. 2010

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ABSTRACT. The pyrolysis of four biomasses (corn stalk, rice husk, sorghum straw and wheat straw) was studied at different temperature -time functions in inert gas flow by thermogravimetric analysis (TGA). Linear and stepwise heating programs were employed. A distributed activation energy model (DAEM) with three pools of reactant (three pseudocomponents) was used due to the complexity of the biomass samples of agricultural origin. Compensation effects were observed between the kinetic parameters similarly to the works of other investigators. The compensation effects result in ambiguous parameter values hence they were eliminated by decreasing the number of the unknown parameters. For this purpose part of the kinetic parameters was assumed to be the same for the four biomasses. This approach also helps to express the similarities of the samples in the model. The sixteen experiments were evaluated simultaneously by the method of least squares to obtain dependable kinetic parameters. 2The resulting models described well the experimental data and were suitable for predicting experiments at higher heating rates. The checks on the prediction capabilities were considered to be an essential part of the model verification.

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Section: Notes On the Pseudocomponents And On The Magnitudes Of The Omentioning

confidence: 57%

“…[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] The biomass fuels and raw materials contain a wide variety of pyrolyzing species. Even the same chemical species may have different reactivity if their pyrolysis is influenced by other species in their vicinity.…”

Section: Introductionmentioning

confidence: 99%

Thermogravimetric Study of Biomass Pyrolysis Kinetics. A Distributed Activation Energy Model with Prediction Tests

et al. 2010

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“…From Figure 3 it is clear that the presence of oxygen accelerates the thermal decomposition of SRF, as it occurs with other materials [11][12][13][14], producing the decomposition at lower temperatures. Figure 1 represents the pyrolytic decomposition of the SRF.…”

Section: Tg Runs and Kineticsmentioning

confidence: 93%

Thermogravimetric and kinetic analysis of the decomposition of solid recovered fuel from municipal solid waste

Conesa

Rey

2015

J Therm Anal Calorim

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The thermal decomposition of a Solid Recovered Fuel (SRF) has been studied using thermogravimetry, in order to get information about the main steps in the decomposition of such material. The study comprises two different atmospheres: inert and oxidative. The kinetics of decomposition is determined at three different heating rates using the same kinetic constants and model for both atmospheres at all the heating rates simultaneously. A good correlation of the TG data is obtained using three n th order parallel reactions.

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“…The cellulosic and hemicellulosic fractions are part of the holocellulose component. This is a valid approximation because the peak in DTG combustion (basically related to holocellulose devolatilization) is a symmetric peak that presents a very small hemicellulosic shoulder [33][34][35]. Therefore, it can be seen that for sample TFW 553, the peak is smaller than the others and that in the case of sample TFW 593, there is no peak observed.…”

Section: Tg Analysismentioning

confidence: 99%

Furniture wood waste as a potential renewable energy source

Plis

Kotyczka-Morańska

Kopczyński

et al. 2016

J Therm Anal Calorim

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In this study, the combustion behavior of raw waste wood from furniture and samples torrefied at temperatures of 473, 513, 553 and 593 K was investigated. TG-DTG analysis showed that the mass loss in the first stage of the process decreased with the temperature of torrefaction, whereas the temperature in the second stage increased. The influence of torrefaction and combustion parameters on greenhouse gas emissions were investigated by the FTIR technique. The characteristic combustion parameters were also determined. The ignition temperatures for the furniture wood waste and samples torrefied at 473, 513 and 553 K from 549 to 559 K, whereas that of the sample torrefied at 593 K was significantly higher (600 K). All samples were completely burnt at 813-843 K, after 29-35 min, depending on the torrefaction temperature. Kinetic parameters are determined using a two-step firstorder reaction. The activation energy value for the first stage increased with the increasing temperature of torrefaction, from 68 to 125 kJ mol -1 , whereas the temperature in the second stage decreased from 108 to 47 kJ mol -1 . A similar correlation was observed for the pre-exponential value A. In the case of the torrefied furniture wood waste at 593 K, the combustion process runs as a single first-order reaction. The calculated data were fitted to the experimental data very accurately (R 2 [ 0.9992 and standard deviation \6.7 %), and the kinetic model was correctly founded. The linear relationship between logA and E a provides a way to predict the kinetic parameters of the combustion process. Keywords

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Kinetics of the pyrolysis and combustion of olive oil solid waste

Cited by 88 publications

References 28 publications

Thermogravimetric Study of Biomass Pyrolysis Kinetics. A Distributed Activation Energy Model with Prediction Tests

Thermogravimetric Study of Biomass Pyrolysis Kinetics. A Distributed Activation Energy Model with Prediction Tests

Thermogravimetric and kinetic analysis of the decomposition of solid recovered fuel from municipal solid waste

Furniture wood waste as a potential renewable energy source

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