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 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 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 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 Distributed activation energy modelling for pyrolysis of forest waste using Gaussian distribution

2016 ◽  
Vol 70 (2) ◽  
pp. 64-70 ◽  
Author(s):  
Alok Dhaundiyal ◽  
Suraj B. Singh
Keyword(s):  
Activation Energy ◽  
Thermogravimetric Analysis ◽  
Asymptotic Solution ◽  
Kinetic Parameters ◽  
Gaussian Distribution ◽  
Frequency Factor ◽  
Upper Limit ◽  
Forest Waste ◽  
Energy Modelling ◽  
Distribution Energy

Abstract The paper focuses on the influences of some factors significant to pyrolysis of forestry biomass on the asymptotic solution of the non-isothermal nth-order distribution energy model (DAEM) using Gaussian distribution. Investigated parameters are the integral upper limit, the frequency factor, and the heating rate parameters of the Gaussian distribution. The influence of these factors has been used for evaluating the kinetic parameters of the non-isothermal nth-order Gaussian distribution from thermogravimetric analysis of forest waste.

scholarly journals Asymptotic Approximations to the Non-Isothermal Distributed Activation Energy Model for Bio-Mass Pyrolysis

2017 ◽  
Vol 11 (4) ◽  
pp. 293-301 ◽  
Author(s):  
Alok Dhaundiyal ◽  
Suraj B. Singh
Keyword(s):  
Activation Energy ◽  
Kinetic Parameters ◽  
Gamma Distribution ◽  
Numerical Solutions ◽  
Frequency Factor ◽  
Energy Model ◽  
Erlang Distribution ◽  
Biomass Pyrolysis ◽  
Distributed Activation Energy Model ◽  
Thermoanalytical Data

AbstractThis paper describes the influence of some parameters significant to biomass pyrolysis on the numerical solutions of the non-isothermal nth order distributed activation energy model (DAEM) using the Gamma distribution and discusses the special case for the positive integer value of the scale parameter (λ), i.e. the Erlang distribution. Investigated parameters are the integral upper limit, the frequency factor, the heating rate, the reaction order, and the shape and rate parameters of the Gamma distribution. Influence of these parameters has been considered for the determination of the kinetic parameters of the non-isothermal nth order Gamma distribution from the experimentally derived thermoanalytical data of biomass pyrolysis. Mathematically, the effect of parameters on numerical solution is also used for predicting the behaviour of the unpyrolysized fraction of biomass with respect to temperature. Analysis of the mathematical model is based upon asymptotic expansions, which leads to the systematic methods for efficient way to determine the accurate approximations. The proposed method, therefore, provides a rapid and highly effective way for estimating the kinetic parameters and the distribution of activation energies.

scholarly journals Kinetic Parameters for the Thermal Decomposition of Forest Waste Using Distributed Activation Energy Model (DAEM)

2017 ◽  
Vol 19 (1) ◽  
pp. 15-32 ◽  
Author(s):  
Alok Dhaundiyal ◽  
Pramod Tewari
Keyword(s):  
Experimental Data ◽  
Activation Energy ◽  
Thermal Decomposition ◽  
Asymptotic Expansion ◽  
Nitrogen Atmosphere ◽  
Energy Model ◽  
Pine Needle ◽  
Distributed Activation Energy Model ◽  
Exponential Factor ◽  
Forest Waste

Abstract The purview of paper pivoted around the pyrolysis decomposition of forest waste (pine needle litter) by thermogravimetric analysis (TGA). Experiments were carried out in the presence of Nitrogen atmosphere. The experimental data was compared with those obtained by numerical solution of distributed activation energy model (DAEM). Asymptotic expansion is adopted to evaluate the pre-exponential factor, mean activation energy and variance. The correction factor Bi has been invoked to describe accurately the differential thermogravitmeric curves of thermal decomposition of pine needles.

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 Asymptotic Approximations to the Isothermal Pyrolysis of Deodara Leaves using Gamma Distribution

2017 ◽  
Vol 22 (3) ◽  
pp. 263 ◽  
Author(s):  
Alok Dhaundiyal ◽  
Suraj Singh
Keyword(s):  
Activation Energy ◽  
Mathematical Model ◽  
Gamma Distribution ◽  
Numerical Solutions ◽  
Reaction Order ◽  
Frequency Factor ◽  
Asymptotic Approximations ◽  
Distributed Activation Energy Model ◽  
Upper Limit ◽  
The Mathematical Model

The main aim of th is paper pivote d ar ound th e influence of some parameters relev ant to biomass pyrolys is on the numerical solutions of the nth order distributed activation energy model (DAEM) using the Gamma distribution. The upper limit of ‘dE’ integral, frequency factor, reaction order, and the shape and rate parameters of the Gamma distribution are investigated. Analys is of the mathematical model is done with the help of asymptotic expansion.

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>

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