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doi:10.1016/j.enconman.2006.08.018    
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Copyright © 2006 Elsevier Ltd All rights reserved.

Most sensitive parameters in pyrolysis of shrinking biomass particle

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A.S. Chaurasiaa, Corresponding Author Contact Information, E-mail The Corresponding Author, E-mail The Corresponding Author and B.D. Kulkarnib

aChemical Engineering Department, MAEER’s, MIT’s, Maharashtra Academy of Engineering (MAE), Pune 412 105, Maharashtra, India

bNational Chemical Laboratory (NCL), Dr. Homi Bhabha Road, Pune 411 008, Maharashtra, India


Received 9 February 2006; 
accepted 27 August 2006. 
Available online 10 October 2006.

Abstract

In the present study, the impact of shrinking and non-shrinking biomass particles on pyrolysis is studied employing a kinetic model coupled with a heat transfer model using a practically significant kinetic scheme consisting of physically measurable parameters. The numerical model is used to predict the effects of the most important physical and thermal properties (thermal conductivity, heat transfer coefficient, emissivity, reactor temperature and heat of reaction number) considering cylindrical geometry. A finite difference pure implicit scheme utilizing the tri-diagonal matrix algorithm (TDMA) is employed for solving the heat transfer model equation. The Runge–Kutta 4th order method is used for the chemical kinetics model equations. The computer simulations are performed for wide ranges of particle size and temperatures. The results obtained are in excellent agreement with many experimental studies, much better than the agreement with earlier models reported in the literature. The most dominant design variable is reactor temperature and exothermic reaction. The applications of these findings are important and useful for optimum design of biomass gasifiers and pyrolysis reactors.

Keywords: Modelling; Pyrolysis; Shrinkage; Dominant; Biomass

Nomenclature

A1, A2, A3
frequency factor, s−1
b
geometry factor (slab = 1, cylinder = 2, sphere = 3)
B
virgin biomass
G1
(gases and volatiles)1
C1
(char)1
G2
(gases and volatiles)2
C2
(char)2
CB
concentration of B, kg m−3
CG1
concentration of G1, kg m−3
CC1
concentration of C1, kg m−3
CG2
concentration of G2, kg m−3
CC2
concentration of C2, kg −3
Cp
specific heat capacity, J kg−1 K−1
d
pore diameter, m
D1, D2
constants defined by expressions of k1 and k2, respectively, K
E3
activation energy defined by expression of k3, J mol−1
h
convective heat transfer coefficient, W m−2 K−1
H
modified Biot number
k
thermal conductivity, W m−1 K−1
k1, k2, k3
rate constants, s−1
l
axial length of cylinder, m
L1, L2
constants defined by expressions of k1 and k2, respectively, K2
M
mass, kg
n1, n2, n3
orders of reactions
N
total number of equations used in simulation of model
Q
defined by Eq. (35), m3 kg−1
r
radial distance, m
R
radius for cylinder and sphere; half thickness for slab, m
Rc
universal gas constant, J mol−1
t
time, s
T
temperature, K
V
total particle volume, sum of volume occupied by pores and by solid phase, m3
Vg
volume occupied by pores, i.e. by gases and volatiles, m3
VS
solid phase (wood and char) volume, m3
VS0
initial effective solid volume, m3
x
dimensionless radial distance
X
conversion of biomass

Greek letters

ΔH
heat of reaction, J kg−1
Δτ
axial grid length
Δx
radial grid distance
ρ
density, ρ0 at initial condition, kg m−3
α
thermal diffusivity, m2 s−1
τ
dimensionless time
θ
normalized temperature
ε
emissivity coefficient
ε
void fraction of particle as defined by Eq. (26), ε0″ at initial condition
σ
Stefan Boltzmann constant, W m−2 K−4
η
reaction progress variable
α
shrinkage factor (defined by Eq. (19))
β
shrinkage factor (defined by Eq. (20))
γ
shrinkage factor (defined by Eq. (22))

Dimensionless numbers

Bi
Biot number
Pr
Prandtl number
Q
Heat of reaction number
Re
Reynolds number

Subscripts

g
gas
0
initial
f
final
B
wood
C
char
eff
effective
m
mean

Article Outline

Nomenclature
1. Introduction
2. Background
3. Description of mathematical model
4. Numerical solution and simulation
5. Results and discussion
5.1. Models’ validation and comparison
5.2. Simulation results
5.2.1. Effect of heat of reaction number (Q″)
5.2.1.1. Exothermic reactions
5.2.1.2. Endothermic reactions
5.2.2. Effect of thermal conductivity of biomass (k)
5.2.3. Effect of convective heat transfer coefficient (h)
5.2.4. Effect of emissivity (ε)
5.2.5. Effect of temperature (Tf)
5.3. Sensitivity analysis
6. Conclusions
References



























Corresponding Author Contact InformationCorresponding author. Present address: Imperial College London CPSE, Department of Chemical Engineering, South Kensington Campus, London SW7 2AZ, UK. Tel.: +44 20 7594 6624; fax: +91 20 27185759.

 
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