scholarly journals Coal Pyrolysis Distribution

2021 ◽  
Author(s):  
◽  
Sione Paea
Keyword(s):  
Activation Energy ◽  
Frequency Factor ◽  
Activation Energies ◽  
Energy Model ◽  
Smoothing Function ◽  
Coal Pyrolysis ◽  
Distributed Activation Energy Model ◽  
Constant Frequency ◽  
Underlying Distribution ◽  
First Order

<p>Coal pyrolysis is a complex process involving a large number of chemical reactions. The most accurate and up to date approach to modeling coal pyrolysis is to adopt the Distributed Activation Energy Model (DAEM) in which the reactions are assumed to consist of a set of irreversible first-order reactions that have different activation energies and a constant frequency factor. The differences in the activation energies have usually been represented by a Gaussian distribution. This thesis firstly compares the Simple First Order Reaction Model (SFOR) with the Distributed Activation Energy Model (DAEM), to explore why the DAEM may be a more appropriate approach to modeling coal pyrolysis. The second part of the thesis uses the inverse problem approach together with the smoothing function (iterative method) to provide an improved estimate of the underlying distribution in the wide distribution case of the DAEM. The present method significantly minimizes the error due to differencing and smoothes the chopped off parts on the underlying distribution curve.</p>

scholarly journals Coal Pyrolysis Distribution

2021 ◽  
Author(s):  
◽  
Sione Paea
Keyword(s):  
Activation Energy ◽  
Frequency Factor ◽  
Activation Energies ◽  
Energy Model ◽  
Smoothing Function ◽  
Coal Pyrolysis ◽  
Distributed Activation Energy Model ◽  
Constant Frequency ◽  
Underlying Distribution ◽  
First Order

<p>Coal pyrolysis is a complex process involving a large number of chemical reactions. The most accurate and up to date approach to modeling coal pyrolysis is to adopt the Distributed Activation Energy Model (DAEM) in which the reactions are assumed to consist of a set of irreversible first-order reactions that have different activation energies and a constant frequency factor. The differences in the activation energies have usually been represented by a Gaussian distribution. This thesis firstly compares the Simple First Order Reaction Model (SFOR) with the Distributed Activation Energy Model (DAEM), to explore why the DAEM may be a more appropriate approach to modeling coal pyrolysis. The second part of the thesis uses the inverse problem approach together with the smoothing function (iterative method) to provide an improved estimate of the underlying distribution in the wide distribution case of the DAEM. The present method significantly minimizes the error due to differencing and smoothes the chopped off parts on the underlying distribution curve.</p>

scholarly journals Distributed Activation Energy Modelling Using a Parabolic Heating Profile

2021 ◽  
Vol 24 (1) ◽  
pp. 27-34
Author(s):  
Alok Dhaundiyal ◽  
Suraj B. Singh
Keyword(s):  
Activation Energy ◽  
Frequency Factor ◽  
Energy Model ◽  
Distributed Activation Energy Model ◽  
Linear Temperature ◽  
First Order ◽  
Forest Waste ◽  
Energy Modelling ◽  
Pyrolysis Reactor ◽  
Heating Profile

AbstractThis work investigates the thermal decomposition of forest waste for a non-linear temperature distribution inside the pyrolysis reactor. Quantitative analysis of the distributed activation energy model is explained graphically. It has been assumed that thermal profile varies according to the general parabolic equation with the initial condition (0, T0). The approximated solution of the non-analytical integral is determined by the Laplace integral method. The integral limit for the distributed activation energy model (DAEM) is found to vary from 211 to 810 kJ·mol−1; whereas the frequency factor (the first-order reactions) for the corresponding range of the activation energy lies in the domain of 400–2000 min−1. The acceleration in the char formation has been found for the reactions other than that of the first order.

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

2017 ◽  
Vol 20 (1) ◽  
pp. 23-28 ◽  
Author(s):  
Alok Dhaundiyal ◽  
Suraj B. Singh
Keyword(s):  
Activation Energy ◽  
Gaussian Distribution ◽  
Frequency Factor ◽  
Energy Model ◽  
Distributed Activation Energy Model ◽  
Cedrus Deodara ◽  
Upper Limit ◽  
Forest Waste ◽  
Thermoanalytical Data ◽  
Distribution Frequency

Abstract 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 nth 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 nth 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 nth order DAEM hold good at upper limit of dE, E∞ = 39 kJ mol-1.

scholarly journals Thermogravimetric and Kinetic Analysis of Melon (Citrullus colocynthis L.) Seed Husk Using the Distributed Activation Energy Model

2015 ◽  
Vol 15 (1) ◽  
pp. 77-89 ◽  
Author(s):  
Bemgba Bevan Nyakuma
Keyword(s):  
Activation Energy ◽  
Thermal Decomposition ◽  
Kinetic Analysis ◽  
Biomass Conversion ◽  
Clean Energy ◽  
Frequency Factor ◽  
Energy Model ◽  
Distributed Activation Energy Model ◽  
Solid Biofuel ◽  
Seed Husk

Abstract This study seeks to characterize the thermochemical fuel properties of melon seed husk (MSH) as a potential biomass feedstock for clean energy and power generation. It examined the ultimate analysis, proximate analysis, FTIR spectroscopy and thermal decomposition of MSH. Thermogravimetric (TG) analysis was examined at 5, 10, 20 °C/min from 30-800 °C under nitrogen atmosphere. Subsequently, the Distributed Activation Energy Model (DAEM) was applied to determine the activation energy, E, and frequency factor, A. The results revealed that thermal decomposition of MSH occurs in three (3) stages; drying (30-150 °C), devolatization (150-400 °C) and char degradation (400-800 °C). Kinetic analysis revealed that the E values fluctuated from 145.44-300 kJ/mol (Average E = 193 kJ/mol) while A ranged from 2.64 × 1010 to 9.18 × 1020 min-1 (Average E = 9.18 × 1019 min-1) highlighting the complexity of MSH pyrolysis. The fuel characterization and kinetics of MSH showed it is an environmentally friendly solid biofuel for future thermal biomass conversion.

scholarly journals Analysis of the non Isothermal Distributed Activation Energy Model for Biomass Pyrolysis by Fuzzy Gaussian Distribution

2016 ◽  
Vol 35 (330) ◽  
pp. 32-41 ◽  
Author(s):  
Alok Dhaundiyal ◽  
Suraj B. Singh
Keyword(s):  
Activation Energy ◽  
Gaussian Distribution ◽  
Fuzzy Numbers ◽  
Analytical Data ◽  
Frequency Factor ◽  
Energy Model ◽  
Membership Functions ◽  
Biomass Pyrolysis ◽  
Distributed Activation Energy Model ◽  
Upper Limit

Abstract The main aim of this paper is to fuzzify the kinetic parameters, which have crisp nature, in order to obtain the realistic and accurate results. In the present study, the variance, upper limit of ‘dE’ and the frequency factor are assumed to be fuzzy numbers. The Gaussian distribution is considered as the distribution function, f (E), of Distributed Activation Energy Model (DAEM). The membership and the non-membership functions are evaluated by the trapezoidal fuzzy number. Thermo-analytical data has been found experimentally with the help of TGA/DTG analysis. The approximated solution of DAEM is obtained with the help of asymptotic expansion.

scholarly journals Approximations to the Non-Isothermal Distributed Activation Energy Model for Biomass Pyrolysis Using the Rayleigh Distribution

2017 ◽  
Vol 20 (3) ◽  
pp. 78-84 ◽  
Author(s):  
Alok Dhaundiyal ◽  
Suraj B. Singh
Keyword(s):  
Activation Energy ◽  
Numerical Solutions ◽  
Frequency Factor ◽  
Rayleigh Distribution ◽  
Energy Model ◽  
Biomass Pyrolysis ◽  
Distributed Activation Energy Model ◽  
Scale Parameters ◽  
Thermoanalytical Data

AbstractThis paper deals with the influence of some parameters relevant to biomass pyrolysis on the numerical solutions of the nonisothermalnthorder distributed activation energy model using the Rayleigh distribution. Investigated parameters are the integral upper limit, the frequency factor, the heating rate, the reaction order and the scale parameters of the Rayleigh distribution. The influence of these parameters has been considered for the determination of the kinetic parameters of the non-isothermalnthorder Rayleigh distribution from the experimentally derived thermoanalytical data of biomass pyrolysis.

scholarly journals Pyrolytic and Kinetic Analysis of Two Coastal Plant Species:Artemisia annuaandChenopodium glaucum

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Lili Li ◽  
Xiaoning Wang ◽  
Jinsheng Sun ◽  
Yichen Zhang ◽  
Song Qin
Keyword(s):  
Activation Energy ◽  
Kinetic Analysis ◽  
Plant Species ◽  
Energy Production ◽  
Artemisia Annua ◽  
Frequency Factor ◽  
Distributed Activation Energy Model ◽  
First Order ◽  
Coastal Plant ◽  
Simplified Mathematical Model

The large amount of coastal plant species available makes them ideal candidates for energy production. In this study, thermogravimetric analysis was used to evaluate the fuel properties of two coastal plant species, and the distributed activation energy model (DAEM) was employed in kinetic analysis. The major mass loss due to devolatilization started at 154 and 162°C at the heating rate of 10°C min−1forArtemisia annuaandChenopodium glaucum, respectively. The results showed that the average activation energies ofArtemisia annuaandChenopodium glaucumwere 169.69 and 170.48 kJ mol−1, respectively. Furthermore, the activation energy changed while the conversion rate increased, and the frequency factork0decreased greatly while the activation energy decreased. The results also indicated that the devolatilization of the two coastal plant species underwent a set of first-order reactions and could be expressed by the DAEM. Additionally, a simplified mathematical model was proposed to facilitate the prediction of devolatilization curves.

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