scholarly journals Kinetic compensation effect in logistic distributed activation energy model for lignocellulosic biomass pyrolysis

2018 ◽  
Vol 265 ◽  
pp. 139-145 ◽  
Author(s):  
Di Xu ◽  
Meiyun Chai ◽  
Zhujun Dong ◽  
Md. Maksudur Rahman ◽  
Xi Yu ◽  
...  
Keyword(s):  
Activation Energy ◽  
Lignocellulosic Biomass ◽  
Compensation Effect ◽  
Energy Model ◽  
Biomass Pyrolysis ◽  
Distributed Activation Energy Model ◽  
Kinetic Compensation Effect

Kinetic analysis of biomass pyrolysis using a double distributed activation energy model

2015 ◽  
Vol 121 (3) ◽  
pp. 1403-1410 ◽  
Author(s):  
Benedetta de Caprariis ◽  
Maria Laura Santarelli ◽  
Marco Scarsella ◽  
Carlos Herce ◽  
Nicola Verdone ◽  
...  
Keyword(s):  
Activation Energy ◽  
Kinetic Analysis ◽  
Energy Model ◽  
Biomass Pyrolysis ◽  
Distributed Activation Energy Model

Analysis of coals and biomass pyrolysis using the distributed activation energy model

2009 ◽  
Vol 100 (2) ◽  
pp. 948-952 ◽  
Author(s):  
Zhengqi Li ◽  
Chunlong Liu ◽  
Zhichao Chen ◽  
Juan Qian ◽  
Wei Zhao ◽  
...  
Keyword(s):  
Activation Energy ◽  
Energy Model ◽  
Biomass Pyrolysis ◽  
Distributed Activation Energy Model

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.

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