5.5 Conclusions 105
0 20 40 60 80 100 120
400 450 500 550 600 650 700 750 800
Reactor axis (mm)
Temperature (K)
t = 1000 s t = 2000 s t = 3000 s Experiments
Figure 5.7: Gas temperatures in the bed at three different times.
The RPM is able to predict the experimental results, describing intra-particle gradi-ents, in a much more feasible computational time compared to the discrete particle model (DPM). The importance of intra-particle gradients in fixed-bed pyrolysis is also highlighted, showing differences in the conversion time of more than 30% when the particle size is doubled, from a particle diameter of 1.24 to 2.48 cm. It is also remarked the need of more experimental data to validate fixed-bed reactor models, including information such as wall temperatures and the composition of the released volatiles.
Chapter
Conclusions 6
In this work a multi-scale approach to model fixed-bed thermo-chemical processes of biomass was developed, with the focus on fixed-bed pyrolysis. The molecular, parti-cle and reactor levels should be considered to describe the processes with adequate accuracy.
At the molecular level primary pyrolysis of pine wood is described with a non-competitive scheme with three pseudo-components. The obtained activation energies of cellulose and lignin pseudo-componenents are, respectively, lower and much higher than the usually reported values in the literature, where there is a high scattering. The importance of conducting experiments at several heating rates and to compare the results from model-fitting methods to the ones of iso-conversional methods is shown. Primary tar can further react in secondary tar cracking reactions, but nowadays information about them is scarce. To describe smouldering of pine wood three pseudo-components are needed to describe correctly the competition between wood pyrolysis and oxidation with a higher activation energy for wood pyrolysis than for wood oxidation, behaviour known from the literature and the application of iso-conversional methods. Both reactions produce char as a product, which can then undergo char oxidation. Char produced in the TGA, in the absence of secondary tar cracking reactions, has a much faster oxidation rate than char produced in a fixed-bed, where there are secondary reactions and therefore also secondary char is produced.
At the particle level the main assumptions considered to describe pyrolysis of a single biomass particle were discussed and a particle model was presented and solved for fixed and fluidized-bed typical conditions. The significant particle gradi-ents and the importance of shrinkage was shown. To decrease the computational time to solve a particle model a novel iterative solution method was introduced and compared to commonly applied solution methods taking into account typical
6.1 Future work 107
process parameters and time step sizes of fixed-bed and fluidized-bed reactors. To develop this method an analysis of the characteristic time of each phenomenon in the biomass particle was done. In order to develop the reaction scheme to describe the secondary reactions of tar cracking fluorescence measurements in close vicinity to the surface of a pyrolysing beech wood particle were done. The results indicate that there are secondary heterogeneous cracking reactions of the primary and sec-ondary tar species occurring in spherical beech wood particles with 25 mm diameter at conversions higher than 0.6 at a heating rate of 0.3 K/s, while for pyrolysis of smaller beech wood particles of 0.5 to 1 mm size complete conversion was achieved without indications of secondary reactions.
At the reactor level the framework for a multi-scale model was introduced:
the representative particle model (RPM). A split is made in the solution of the re-actor model between an incompressible flow with time-independent density and the compressible fluid with variable density. The particle model is solved with the pre-viously developed iterative method. The RPM framework was applied to fixed-bed heating up and pyrolysis and compared to experimental results available in the litera-ture. The RPM is able to predict the experimental results, describing intra-particle gradients, in a much more feasible computational time than the discrete particle model (DPM). The importance of intra-particle gradients in fixed-bed pyrolysis is also highlighted.
6.1 Future work
Further work is needed at all levels in order to develop a deeper understanding about fixed-bed pyrolysis of biomass.
At the molecular level experiments with more biomass species should be done to determine the relation between the activation energies of pure components and biomass. The application of iso-conversional methods together with model fit-ting is suggested to obtain the correct values. Composition of the obtained primary tars and permanent gases will be also valuable. It is also interesting to determine how the pyrolysis process influences the reactivity of the produced char, consider-ing secondary reactions beconsider-ing present or not together with the influence of different heating rates, maximum pyrolysis temperatures or biomass species.
More information about the reaction scheme of the secondary tar cracking reactions is needed and also data for the kinetics of the elementary reaction steps, considering not just the homogeneous reactions but also the heterogeneous reactions
6.1 Future work 108
that occur at the particle level with the presence of char. With this kinetic infor-mation a single particle model could predict the composition of the volatiles leaving the particle. More experimental data would also be required to validate the models, providing information about the temperature evolution inside the particle, the mass loss and the released volatiles.
At the reactor level more experimental data are needed to validate fixed-bed reactor models, including information such as wall temperatures and the composition of the released volatiles, in order to check the predictive capacity of multi-scale models which apply the knowledge obtained at the molecular and particle level.
Furthermore, the multi-scale approach should be extended to other thermo-chemical processes, such as fixed-bed drying or gasification.
Appendix
Publications A
Part of the contents of the present thesis have been already published in the following peer-reviewed papers:
1. A. Anca-Couce, N. Zobel, A. Berger, F. Behrendt (2012). "Smouldering of pine wood: Kinetics and reaction heats", Combustion and Flame, 159 (4), 1708 - 1719. doi: 10.1016/j.combustflame.2011.11.015.
2. A. Anca-Couce, N. Zobel (2012). "Numerical analysis of a biomass pyrolysis particle model: Solution method optimized for the coupling to reactor models", Fuel, 97, 80 - 88. doi: 10.1016/j.fuel.2012.02.033.
3. A. Anca-Couce, N. Zobel, H.A. Jakobsen (2012). "Multi-scale modelling of fixed-bed thermo-chemical processes of biomass with the representative parti-cle model: Application to pyrolysis", Fuel, in print. doi: 10.1016/j.fuel.2012.05.063.
4. N. Zobel, A. Anca-Couce (2012). "Slow pyrolysis of wood particles: Char-acterisation of volatiles by Laser-Induced Fluorescence", Proceedings of the Combustion Institute, in print. doi: 10.1016/j.proci.2012.06.130.
Chapter 3 of this thesis is based on publication number 1, with an extended focus on pyrolysis. Chapter 4 is mainly based on publications number 2 and 3 and Chapter 5 on publication number 4.
Nomenclature
Latin symbols
A pre-exponential factor [1/s]
A area [m2]
Bi Biot number [-]
c proportion of a component [-]
cp specific heat capacity [J/(kg K)]
D mass dispersion / diffusivity [m2/s]
dp particle diameter [m]
dpor pore diameter [m]
E activation energy [J/mol]
F friction terms in momentum equation [kg/(m2 s2)]
f1 first friction factor [kg/(m3 s)]
f2 second friction factor [kg/(m4)]
fShr shrinkage factor [-]
fShr−min minimum shrinkage factor [-]
G conductance [W/K]
H convective and viscous terms in the momentum equation [kg/(m2 s2)]
H length of the control volume [m]
H˙ advection rate [W]
ˆh specific enthalpy [J/kg]
I flourescence intensity [counts]
j mass flux density [kg/(m2 s)]
Mm molecular weight [kg/mol]
m mass [kg]
N number of experiments [-]
111
N u Nusselt number [-]
p pressure [Pa]
P r Prandtl number [-]
P y Pyrolysis number [-]
Q˙ heat transfer rate [W]
˙
q heat flux density [W/m2]
r radial coordinate of particle [m]
˙
r reaction rate per unit volume [kg/(m3 s)]
R radius of the particle [m]
R¯ universal gas constant [J/(mol K)]
Rc source term in continuity equation [kg/(m3 s)]
Re Reynolds number [J/(mol K)]
S surface area [m2]
s ration surface/volume of the particle [1/m]
T F I total fluorescence intensity [counts]
t time [s]
V Volume [m3]
v general vectorial velocity [m/s]
v particle superficial velocity [m/s]
wz reactor axial velocity [m/s]
X mol fraction [-]
Y mass fraction [-]
z axial coordinate of the reactor [m]
Greek symbols
α conversion [-]
α heat transfer coefficient [W/(m2 K)]
β mass transfer coefficient [m/s]
∆h heat of reaction [J/kg]
porosity [-]
η conversion factor [-]
κ permeability [m2]
112
Λ, λ thermal dispersion / conductivity [W/(m K)]
λ wavelength [m]
µ viscosity [kg/(m s)]
ν stoichiometric coefficient [-]
ρ density [kg/m3]
σ Stefan-Boltzmann constant [W/(m2 K4)]
ω surface emissivity [-]
ζ Stefan correction [kg/m3]
Subscripts
0 initial value
b relative to the fixed-bed c relative to char
down downstream
e relative to the energy equation ef f effective
exp experimental f final value
g pertains to gas phase
i pertains to specie with index i j pertains to reaction with index j
m maximum
p relative to the particle rad radiation
sim simulation
s pertains to solid phase up upstream
V C control volume w relative to wood
∞ at large distance from particle
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