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Process simulation of a SOFC and double bubbling fluidizedbed gasifier power plant
Andrea Di Carlo a, Enrico Bocci b,*, Vincenzo Naso c
aDepartment of Chemistry, Chemical Engineering and Materials, University of Aquila, Via Campo di Pile, 67100 L’Aquila, ItalybEnergy and Mechanical Engineering Department, Marconi University of Rome, Via Virgilio 8, 00193 Rome, ItalycMechanical and Astronautical Engineering Department, Sapienza University of Rome, Via Eudossiana, 18, 00184 Rome, Italy
a r t i c l e i n f o
Article history:
Received 16 March 2012
Received in revised form
12 August 2012
Accepted 10 September 2012
Available online 12 October 2012
Keywords:
Fluidised bed biomass steam
gasification
Solid oxide fuel cells
Process model
* Corresponding author. Tel.: þ39 3288719698E-mail addresses: [email protected], e.b
0360-3199/$ e see front matter Copyright ªhttp://dx.doi.org/10.1016/j.ijhydene.2012.09.0
a b s t r a c t
The development of reliable fuel cells power plant based on renewable fuels stands out as
one of the promising energy systems solutions for the future. Indeed fuel cells can increase
the efficiency and the cleaning of the electrical energy production from renewable fuels.
Process simulations of advanced power plants fed by low cost renewable fuels like biomass
waste are a key step to develop renewable resources based on high temperature fuel cells
applications. The aim of this work is to predict the component behaviour of a specific
power plant mainly composed of a small indirectly heated gasifier and a Solid Oxide Fuel
Cell (SOFC) and fed by chestnut coppice, waste available in great quantity in Central Italy,
as well as in several other European regions. The plant’s thermodynamic behaviour is
analysed by means of the process simulator CHEMCADª in which particular models for the
SOFC and the gasifier have been developed in FORTRAN by the authors and then interfaced
to commercial software. The results of the predictive model are presented and discussed,
showing the possibility of an extremely interesting “carbon neutral” small plant configu-
ration with high electrical and global efficiency exclusively based on the use of low cost
renewable resources.
Copyright ª 2012, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights
reserved.
1. Introduction in the low-medium range following the low energy density of
The realization of many international and national strategic
plans, based upon renewable energy sources and hydrogen/
fuel cells plans, demonstrates the increasing interest in the
promotion and implementation of methods, technologies and
processes for the development of sustainable energy systems
[1e3].
In order to exploit biomass as a major source of energy in
the power generation and transport sectors, there is a need for
high efficient and clean energy conversion devices, especially
; fax: þ39 [email protected] (E. Bo2012, Hydrogen Energy P59
this fuel [4,5].
Large installations, based on boiler coupled to steam
turbine (or Integrated Gasification Combined Cycle power
plant, IGCC), are too complex at small scale, meanwhile small
biomass gasifiers coupled to Internal Combustion Engines,
ICE, have low electrical efficiency (15e30%) and generally not
negligible emissions [6].
High temperature fuel cells represent the most promising
technologies for achieving higher conversion efficiency and
reducing emissions especially at small scale. Due to its higher
cci).ublications, LLC. Published by Elsevier Ltd. All rights reserved.
i n t e r n a t i o n a l j o u rn a l o f h y d r o g e n en e r g y 3 8 ( 2 0 1 3 ) 5 3 2e5 4 2 533
power density and operating temperature, Solid Oxide Fuel
Cells (SOFCs) are considered as the major candidate for power
generation and cogeneration units [7].
In the last years many studies have assessed operating
conditions, system performance, potentials and limits of
a variety of SOFC power plant including the hybrid Gas
Turbine SOFC plant and the integration of SOFC systems with
biomass gasification where the SOFC electrical efficiency, the
total thermal efficiency and the total electrical efficiency can,
ideally, reach the values of 42%, 38% and 62% respectively,
based on the low heating value of the biomass [8e10].
This paper deals with a specific power plant configuration,
based on a particular small indirectly heated fluidised bed
gasifier, high temperature fuel cells (SOFC) and micro Gas
Turbine (mGT). In particular the gasifier is based on UNIQUE
concept. UNIQUE consists of a compact a gasifier integrating
the fluidized bed steam gasification of biomass and the hot gas
cleaning system into one reactor vessel, by means of a bundle
of ceramic filter candles that operates at high temperature
(800e850 �C) in the gasifier freeboard. Such configuration
produce a syngas free of tar and sulphur compounds and
allows for remarkable plant simplification and reduction of
costs [11e13].
The analysis is based on process models that have been
developed by the authors in earlier works [14e16] where they
carried out process simulations of MCFC system integrated
with biomass gasification and hot gas cleaning. Black-box and
empirical models were used for the gasification process.
Theplant’s thermodynamicbehaviour isanalysedbymeans
of the process simulator CHEMCADª. The plant operation is
optimized in terms of energy management, including cogene-
ration. The models for the SOFC and the gasifier have been
developed in FORTRAN by the authors and then interfaced to
thecommercial softwareCHEMCADª. Indeeddifferent typesof
models can be developed, from complex fluid dynamics
models, to simpler black-box or zero-dimensionmodels, but to
really predict thebehaviour of a complex system like a biomass
gasifier under different conditions the fluid dynamics models
are the best developedmodels up to date [17].
Finally a sensitivity study of the power produced was
carried out varying the moisture content in the biomass from
10 to 30%.
Regarding the error analysis, the mass and energy
conservation principles are inherently satisfied by the soft-
ware and the model, while the results are validated by
comparing simulated with experimental data.
2. Power plant description
The proposed power plant mainly consists of a particular
gasifier that produces a woodgas to feed an SOFC while the
high temperature residual heat is used in a Capstone C15mGT
[18], to produce further electrical power.
Fig. 1 shows the CHEMCADª plant flowsheet. The incoming
biomass is gasified only by steam in the small indirectly heated
fluidized bed gasifier (100 kWth). The steam is generated by the
residual heat of themGT exhausts. From the gasifier (Stream 1)
a woodgas is obtained at 800 �C. The char and bed material are
recirculated (Stream 2) in the burner of gasifier to produce the
process heat. The woodgas is cleaned up from tars and
particulate directly in the freeboard of the gasifier. This can be
obtained by placing a bundle of catalytic ceramic candles in the
gasifier freeboard. These candles convert tars by steam
reforming and remove particulate at a temperature as high as
the gasification temperature (800e850 �C). The cleaned wood-
gas is utilized in the SOFC module to produce the electrical
power. The anode exhausts, still rich of H2, CO and CH4, are
burned with residual char in the burner of gasifier, to produce
the necessary heat for the gasification process. The hot flue
gases from burner of gasifier at 950 �C (Stream 3) are exploited
to heat the compressed air for the turbine, to obtain further
power from the mGT.
In order to simulate the power plant SOFC and steam-
gasifier specific models have been developed and described
in the following paragraphs. The remaining components of
the plant were simulated using conventional CHEMCAD
blocks, in particular the catalytic reforming downstream
gasifier was simulated by using a Gibbs reactor, while for the
combustor of the gasifier a stoichiometric reactor was used.
3. Gasifier model
The gasifiermodel receives as input the pyrolysis products and
calculates the woodgas composition. Indeed in fluidized bed
gasifier the pyrolysis reactions can be considered as flash
pyrolysis; the time necessary to reach the final products of
pyrolysis canbeneglected. For this reason, itwasassumed that
the fuel feeding the gasifier is that obtainable frompyrolysis of
wood at the same operative conditions. The experimental
results of Jand and Foscolo [19] were used in this study.
The products of pyrolysis are composed of CO2, CO, H2O,
H2, and CH4, light and heavy hydrocarbons (tar) and char, as
showed in Table 1.
The gasification reactions considered in this study are:
C þ H2O / CO þ H2 (R1)
C þ CO2/2CO (R2)
C þ 2H2 / CH4 (R3)
CH4 þ H2O 4 CO þ 3H2 (R4)
CO þ H2O ;4 CO2 þH2 (R5)
C10H8 þ 10H2O ;4 10CO þ 14H2 (R6)
Napthalene have been used as tar representative.
Regarding the reactor fluid dynamics the Kunii and Lev-
enspiel bubbling bed model [20] has been used. An outline of
their model employed in this study is shown in Fig. 2.
Fig. 1 e Plant flowsheet.
i n t e rn a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 8 ( 2 0 1 3 ) 5 3 2e5 4 2534
In the model, the fluidized bed consists of two regions,
bubble and emulsion, interacting each other through one
interchange coefficient of gas, kbe, and several assumptions
are employed as follows. The wake and cloud region is
neglected. An intermediate particles model for Geldart B
particle (dp ¼ 0.1e1 mm) was selected for the reactor model-
ling. Moreover the following equations have been used.
In the emulsion phase, gas ascends at the minimum
fluidization velocity, umf:
umf ¼h�27:22 þ 0:0408$Ar
�0:5�27:2i$
m
dprgas
In the bubble phase, bubble gas ascends at the velocity of ub:
ub
�z� ¼ 0:71
ffiffiffiffiffiffiffiffigdb
q� �QðzÞ=A� umf
�The bubble diameter is calculated by Davidson model at
each bed height:
Table 1 e Pyrolysis (devolatilization) products.
Char kg/kgbio,daf 0.187
Gas kg/kgbio,daf (N m3/kg bio,daf) 0.763 (0.9)
Tar kg/kgbio,daf 0.05
Gas mole fraction
H2 0.285
H2O 0.035
CO2 0.11
CO 0.38
CH4 0.19
Tar composition
C10H8 1
dB
�z� ¼ 0:54
�QðzÞ=A� umf
�0:40:2
"zþ 4
ffiffiffiffiffiffiffiffiA
s #0:8
g NorThe volume fraction of bubble in the bed is d (while that of
emulsion is 1 � d):
d�z� ¼ QðzÞ=A� umf
uBðzÞGases ascend as plug flow in each phase and are exchanged
at the rate of gas interchange coefficient kbe:
kbe
�z� ¼ umf
4þ
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi4εmfDrub
�z�
pdBðzÞ
s
With these assumptions, the transport equations at steady
state are:
v
vz½QðzÞCtot� ¼
Xi
�Rbi þ Rs
ei þ Rgei
�v
vz
��Q�z�� umfAs
�Cbi
� ¼ kbeAsðCbi � CeiÞ 6dbdþ εmfAsd
Xj;b
nijRgbj
v
vz
��umfAs
�Cei
� ¼ kbeAsðCei � CbiÞ 6db
ð1� dÞ þAsð1� dÞ24�1� εmf
�εc
rc
PMc
Xj;s
nijRcej þ εmf
Xj;e
nijRgej
þ �1� εmf
�εolivni6R
olive6
35 _mout
char
¼ _minchar �Asð1� dÞεcrc
Xj
nijRsejεc ¼
_Vin
c
_Vin
c þ _Vin
oliv
¼ _minc
rc$
_minc
rcþ _min
oliv
roliv
!�1
where j are the reactions 1,., 6, i are the chemical species H2,
., C10H8 and nij are the stochiometric coefficients of species i
in reaction j, negative for reagents and positive for products.
Fig. 2 e Kunii and Levenspiel fluidized bed reactor model.
Table 3 e Results of simulation compared with
i n t e r n a t i o n a l j o u rn a l o f h y d r o g e n en e r g y 3 8 ( 2 0 1 3 ) 5 3 2e5 4 2 535
Finally ideal gas law was used to calculate the gas
concentration:
Ctot ¼ pRT
To complete the model, the kinetic expressions for the
reaction rates must be set. Extra and intra particle diffusion
resistances were neglected owing to the small particles
diameter. For reactions (R1) and (R2) (gasification of char with
steam and CO2) the expressions deducted by Barrio et al. [21]
and Barrio and Husted [22] have been used.
R1 ¼ kw1pH2O
1þ ðkw1=kw3ÞpH2O þ ðkw2=kw3ÞpH2
ð1=sÞ
R2 ¼ kc1pCO2
1þ ðkc1=kc3ÞpCO2þ ðkc2=kc3ÞpCO
ð1=sÞ
The values of k coefficients, shown inTable 2,were deducted
from the work of Konttinen et al. [23].
Reaction (R3) was neglected because the low operative
pressure and the slow rate of reaction. Gas-phase reactions
kinetic (R4, R5) were simulated using the expressions adopted
by Wang and Kinoshita [24]
R4 ¼ 2:79e�12;579=RT
CCOCH2O � CCO2
CH2
KWGSeq
! �mol=m3 s
�
R5 ¼ 1:2863e�36150=RT
CCH4
CH2O � C3H2CCO
KREFeq
! �mol=m3 s
�
Finally for the heterogeneous reaction (R6), the expression
proposed by Espenas and Waldheim [25], was used:
R6 ¼ roliv1:46� 1012
3600e�321;000=RTpC10H8
$p�0:44H2
$p�0:56H2O
�mol=m3 s
�
Table 2 e Arrhenius and activation energy values.
k01(s�1 bar�1)
k02(s�1 bar�1)
k03(s�1)
EA1(J/mol)
EA1(J/mol)
EA1(J/mol)
R1 6.49e7 95.3 1.64e9 204,000 54,315 243,000
R2 1.64e7 4.59e2 8.83e7 188,000 88,265 225,000
3.1. Model validation
In order to validate the model, different simulations were
carried out and compared with literature results. Gasification
temperature has been taken equal to 850 �C, the steam to
biomass ratio 0.5, the superficial velocity has been set two
times theminimum fluidization velocity, the static bed height
60 cm.
Table 3 shows the comparisonof the results obtainedby the
model with the experimental data declared by Hofbauer [26].
The results obtained by simulation are in fair agreement
with that obtained experimentally on a similar gasifier except
for the concentration of tars that results higher in the model.
The main reason is probably due to the choice of tar repre-
sentative for the simulation; naphthalene is one of the most
stable tar compounds and is difficult to decompose. Anyway,
for the scope of the work the results were considered
acceptable.
Fig. 3 shows the composition of the gas (dry basis) at
different height of the bed as results of the model. Hydrogen
fraction increases from 0.285 (fraction obtained by pyrolysis)
to 0.42 reaching a plateau. This increase is a consequence of
the char gasification reaction (R1) as also of water gas shift
reaction (R5) and steam reforming of hydrocarbon ((R4)e(R6)).
CO reaches a negative peak and then it slightly increases
again. This increase is probably due to the slower reforming
reactions ((R4)e(R6)) that continuously produce CO and H2 but
require much more residence time (and thus bed height) to
reach the equilibrium conditions, if compared to thewater gas
shift reaction. The reduction of steam and the not favored
equilibrium conditions at 800 �C for the water gas shift reac-
tion are also further reasons (Fig. 4).
Finally the cold gas efficiency of the gasifier was calculated
as:
hcold ¼ LHVH2_mH2
þ LHVCO _mCO þ LHVCH4_mCH4
LHVBIO _mBIO
where LHV is the low heating value (kJ/N m3 or kJ/kg) of each
compoundwhile _m is its flowrate (Nm3/s or kg/s). The cold gas
efficiency of the gasifier is around 90%. It is worth stressing
here that this result does not consider the required additional
fuel that must be burned in the combustor of the gasifier (see
Fig. 1); combustion of the residual char from gasification is not
sufficient to supply the required heat for the gasification
process and additional fuel is necessary, in this way the cold
gas efficiency would be lower. This problem is solved burning
the residual fuel outgoing the anode of the SOFC. The
experimental data.
Results Simulation Experimental
H2 42% 35e45%
CH4 13% 8e12%
CO 22% 20e30%
CO2 21% 15e25%
Dry gas/kg biomass daf 1.22 1e1.5
Tar 12 g/N m3 1.5e4.5 g/N m3
Fig. 3 e Composition of the produced gas (dry basis) during
gasification vs. the bed height.
i n t e rn a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 8 ( 2 0 1 3 ) 5 3 2e5 4 2536
necessary amount of residual fuel affects the fuel utilization
in the SOFC and thus also the efficiency of the entire system: if
the residual fuel from the SOFC is not enough for the gasifi-
cation process, the fuel utilization must be reduced,
increasing the fuel for the combustor but decreasing the SOFC
(and thus system) efficiency.
4. SOFC model
In this paragraph the model developed for a SOFC is illus-
trated. The fuel cell model is taken one-dimensional on the
horizontal cell layer, while temperature variations along the
vertical coordinate are neglected. The model geometry was
divided in three distinct zones: a planar solid zone (S)
comprehensive of the two electrodes, the bipolar plate and the
electrolytic matrix invested by the two counter-flow gaseous
stream (Anodic A and Cathodic C).
The model is based on the following hypotheses:
1. steady state conditions;
2. adiabatic conditions;
3. no radiation heat exchanges between solid components
and gas streams;
4. continuous description of the gas flow (distributed into
a number of discrete channels) in terms of a specific rate of
reactants per unit length of the fuel cell side;
5. fully developed velocity and temperature profiles in the gas
streams;
6. plug-flow balance equations for the gas streams where gas
species diffusion on gas phase is neglected;
7. the rate of the electrochemical reaction has been calculated
on the basis of Faraday’s law;
8. water gas shift reaction was considered at equilibrium;
9. reaction rate for steam reforming of CH4 was calculated
using the expressions proposed by Achenbach and Rien-
sche [27]:
_sref ¼ 4274pCH4exp
��82; 000RT
� �mol=m2 s
�
4.1. Electrochemical model
The difference between the thermodynamic potentials of the
electrode reactions determines the reversible cell voltage or
open-circuit potential, This open-circuit potential is a local
quantity, as it depends on the gas composition and tempera-
ture at the electrodes, and can be determined by the Nernst
equationwritten for theelectrochemical oxidationofhydrogen
E ¼ E0 � RTs
neFln
Yi
pnii
!
where E is the maximum theoretical potential that can be
achieved by a fuel cell. As current is drawn from a fuel cell, the
cell voltage falls due to internal resistances (Ohmic) and
overpotential losses. Electrode overpotential losses are asso-
ciated with the electrochemical reactions taking place at the
electrode/electrolyte interfaces and can be divided into
concentration and activation overpotentials.
Due to these internal losses the real voltage V obtainable
from a fuel cell can be calculated as:
V ¼ E0 ��RUJþ hconc;a þ hconc;c þ hact;a þ hact;c
�
4.2. Activation polarization
The activation polarization is the result of the kinetics
involved with the electrochemical reactions. Each reaction
has a certain activation energy barrier that must be overcome
in order to proceed and this barrier leads to the polarization.
This energy barrier is called the activation energy and results
in activation or charge-transfer polarization, which is due to
the transfer of charges between the electronic and the ionic
conductors. The activation polarization may be regarded as
the extra potential necessary to overcome the energy barrier
of the rate-determining step of the reaction to a value such
that the electrode reaction proceeds at a desired rate. Acti-
vation polarization is normally expressed by the Butlere
Volmer equation [28]:
J ¼ J0
exp
�bneFhact
RT
�� exp
� ð1� bÞneFhact
RT
��
where b is the transfer coefficient and J0 the exchange current
density. The transfer coefficient is usually 0.5 for the fuel cell
applications. The exchange current density is the forward and
reverse electrode reaction rate at the equilibriumpotential; the
higher the exchange current density and the electrochemical
reaction rate, the better fuel cell performance can be expected.
If b is set to 0.5 the equation can be rewritten as follows:
J ¼ 2J0sinh
�neFhact
2RT
�
And thus
hact ¼2RTneF
sinh�1
�J2J0
�
In order to evaluate hact for the two electrodes J0 must be
defined. The following form was adopted:
J0;electrode ¼ RTneF
kelectrodeexp
��Eelectrode
RT
�
i n t e r n a t i o n a l j o u rn a l o f h y d r o g e n en e r g y 3 8 ( 2 0 1 3 ) 5 3 2e5 4 2 537
Data used by Aguiar et al. [29] were adopted for k and E
whose values are reported in Table 4.
4.3. Concentration polarization
The rate of mass transport to the reaction sites in a porous
electrode of an SOFC can be described by the diffusion of gases
in the pores. Concentration polarization results from restric-
tions to the transport of the fuel gases to the reaction sites.
This usually occurs at high currents because the forming of
product water blocks the reaction sites. This polarization is
also affected by the physical restriction of the transfer of
a large atom, oxygen, to the reaction sites on the cathode side
of the fuel cell. Diffusion through the porous material is
typically described by either ordinary or Knudsen diffusion
and has been found to play an important role in catalytic
reaction. Ordinary diffusion occurs when the pore diameter of
the material is large in comparison to the mean free path of
the gas molecules. Molecular transport through pores which
are small in comparison to the mean free path of the gas
is regarded as a Knudsen type of diffusion. According to
hConc;c ¼ �RT4F
ln
"�pc=dO2
�� �pc=dO2
�� pfO2
�exp
�ðRT=4FÞ�dO2lc=Dcathode;effpc
�J�
pfO2
#
Knudsen diffusion, molecules collide more frequently with
the pore walls than with other molecules. Upon collision, the
atoms are instantly adsorbed on to the surface and are then
desorbed in a diffusive manner. As a result of frequent colli-
sionswith thewall of the pore, the transport of themolecule is
prevented. The Knudsen diffusion coefficient can be predicted
using kinetic theory by relating the diameter of the pore and
the mean free path of the gas.
Molecular diffusion Dm( g) is calculated using the Wilke
equation [30]:
DA ¼ 1PAsB
yB
DAB
The binary diffusion coefficient Dmn( g) has been calculated
using the ChapmaneEnskog equation:
DAB ¼ 0:0018583
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiT3
�1MA
þ 1MB
�s1
ps2ABUAB
where the expressions to calculate the required parameters
can be found in Bird [31]. The effective diffusion coefficient in
the particle (DA,eff) is calculated by the following equation:
DA;eff ¼ DA0ε
s
Table 4 e Activation overpotential data.
kcathode 2.35e11 U�1 m�2 Ecathode 137 kJ mol�1
kanode 6.54e11 U�1 m�2 Eanode 140 kJ mol�1
DA0 was calculated using Bosanquet equation as reported in
Hayes [32]
DA;eff ¼ 11DA
þ 1DkA
DA was calculated with Wilke equation while DkA is the
Knudsen diffusion, calculated as:
DkA ¼ 23rav
ffiffiffiffiffiffiffiffiffiffiffi8RTpMA
s
To calculate hconc, a relation between the partial pressures
of H2, H2O, and O2 at the three-phase boundaries and the
current density is necessary. Different porous-media gas-
phase transport models have been developed to predict
concentration overpotentials [33e36]. In this work the model
developed by Chan et al. [33] was adopted.
hConc;a ¼ �RT2F
ln
" ð1� RT=2FÞ la=Danode;effpf
H2
�J
ð1þ RT=2FÞ la=Danode;effpf
H2O
�J
#
where pfH2; pf
H2O;pf
O2are the partial pressure of the chemical
species involved in the reaction in the flow phase, thus at the
boundary of the electrodes (anode, cathode) and la and lc the
thickness of anode and cathode respectively.
Danode,eff and Dcathode,eff are computed as
Danode;eff ¼ pfH2O
pa
!DH2 ;eff þ
pfH2
pa
!DH2O;eff
Dcathode;eff ¼ DO2 ;eff
Finally dO2is calculated as:
dO2¼ DkO2 ;eff
DkO2 ;eff þ DO2N2 ;eff
4.4. Ohmic polarization
Ohmic losses occur because of resistance to the flow of ions in
the electrolyte and resistance to flow of electrons through the
electrode materials. The dominant ohmic losses, through the
electrolyte, can be reduced by decreasing the distance of
electrode separation and enhancing the ionic conductivity of
the electrolyte. The ionic flow in the electrolyte obeys Ohm’s
law, thus the ohmic losses can be expressed by the equation:
hU ¼ RUJ
with
RU ¼X
i¼a;e;c
lisi
Table 5 e Main parameters adopted for the validation ofthe SOFC model.
Cell length, L 0.4 m
Cell width, W 0.1 m
Anode thickness 500 mm
Cathode thickness 50 mm
Electrolyte thickness 20 mm
Fuel feed Completely reformed CH4 with S/C ¼ 2
Fig. 4 e Cell potential depending on current density, H2 flux
360 ml/min, air flux 2 l/min, 50 cm2 active area [37].
i n t e rn a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 8 ( 2 0 1 3 ) 5 3 2e5 4 2538
where li is thickness of anode, cathode and electrolyte while si
is the anode, cathode and electrolyte electronic conductivity.
4.5. Transport equations
4.5.1. Massespecies balanceAnode:
d�ugCi
�dx
¼Xj
vij _sj1
wa
where Ci is the molar concentration of species i, ug is the gas
velocity, wa is the height of the fuel channel and j are the
reactions that occur in the anode:
1. _sel ¼ J=neF for electrochemical reaction.
2. _sWGS WGS reaction was considered at equilibrium.
3. _sref ¼ 4274pCH4expð�82; 000=ðRTÞÞ ðmol=m2 sÞ [27].
Cathode:
d�ugCi
�dx
¼ _sel1wc
4.5.2. Energy balanceAnode:
ugd�cpCiTa
�dx
¼ haðTs � TaÞ 1wa
Cathode:
ugd�cpCiTc
�dx
¼ hcðTs � TcÞ 1wc
Solid layer:
hcðTs � TcÞ þ haðTs � TcÞ ¼ l$s
�v2Ts
vx2
�þ Qreac
where Qreac ¼Pj
_sjDHj � VJ.where cp is the specific heat of the
species i, T is the temperature of anode gas (a), cathode gas (c),
solid zone (s), h is the convection coefficient between gas and
solid, l is thermal conductivity of the solid, s is the thickness
of the solid, Qreac is the heat of reactions, and VJ is the electric
power produced per unit area.
The proposed equations system was solved using a finite
difference method with relaxation writing a sub-routine in
Fortran 90.
4.6. Model validation
The results obtained by Autissier et al. [37] and those of Aguiar
et al. [29] were used as reference for the validation of the SOFC
model. In the work of Autissier et al. [37] a button cell anode
supported SOFC were tested to validate a CFD model. The
button cell used had an active area of 1 cm2. Cells are anode
supported, 200 mm thick with a 6e8 mm thick electrolyte layer.
The results available in [37] were obtained with a flow of
360 ml/min of pure Hydrogen at the anode and a flow of air at
the cathode of 2 l/min. The temperature of the cell was set to
750 �C (Fig. 4).
Even if the general trend of the curves is similar, the
experimental open-circuit voltage is still lower than the
calculated one of about�10MV. Similar results were observed
by [37] comparing the results of their model with the same
experimental data. Autissier et al. justified this difference
by omitting to calculate back-diffusion through outlets in the
model, so modifying the partial pressure near the outlet and
thus the OCV. This diffusion could lead to the observed losses.
Another difference between the results obtained by this work
and the experimental data of [37] is the different slope of the
two curves. The main reason of this difference can be attrib-
uted at the different polarization curves adopted in this work
compared with [37]. A much more accurate calibration of the
model with experimental data would be required to reduce
this small error but this is beyond the presented work’s scope.
Fuel composition plays a significant role in fuel cell
performance and further validation of the model is required
when the gas has no negligible concentration of CO, CO2 and
CH4. In order to verify the model with a syngas containing not
negligible concentration of CO in the gas the results of Aguiar
et al. [29] were also used as reference. In the work of Aguiar
et al. a numerical model for anode supported planar SOFC
were developed and tested with a syngas obtained by steam
reforming of CH4. The same conditions adopted by [29] were
utilized in order to compare the results of Aguiar and those of
this work. Table 5 shows the most important values adopted.
Fig. 5 shows the power density obtained from the model
compared with those from Aguiar et al. [29] at different
operative temperatures and current densities. The results
Fig. 5 e Cell power density depending on current density,
results of the model compared with that obtained by
Aguiar et al. [29] using syngas from steam reforming of
CH4.
i n t e r n a t i o n a l j o u rn a l o f h y d r o g e n en e r g y 3 8 ( 2 0 1 3 ) 5 3 2e5 4 2 539
obtained by the model are in agreement with that available in
literature, small differences can be detected only at high
current densities. As expected, the performance of a cell is
significantly hindered when the operating temperature is
decreased: the maximum power density reduces from
840 W m�2 at 800 �C, to 303 W m�2 at 700 �C.As the fuel flows through a SOFC fuel channel, hydrogen
and carbon monoxide are consumed by the electrochemical
reactions and by the WGS reaction and the fuel stream
becomes diluted, being less rich close to the exit of the
channel. Therefore, in this region, concentration over-
potentials become more significant and result in the decrease
of SOFC output.
Fig. 6 illustrates the predicted cell voltage as a function of
current density for a fully reformed fuel mixture with 10%,
75%, and 90% fuel utilisation. For low fuel utilization (10%)
concentration overpotentials are not significant. As seen in
Fig. 6, for this case, the voltage versus current density curve
does not present a concave curvature for high current densi-
ties. However, for 75% and 90% fuel utilisation, the concavity
Fig. 6 e Cell voltage as a function of current density at
800 �C for a fully reformed fuel mixture with 10%, 75%, and
90% fuel utilisation.
becomes visible and voltage and power density drop more
rapidly to zero, due to concentration overpotentials in the
anode. It is clear from the Figure that the curve loses its
linearity around 2.25 A/cm2 for fuel utilization equal to 75%
and 2 A/cm2 for fuel utilization equal to 90%.
Finally the influence of gas composition on the cell voltage
was verified. Three different simulations were carried out, the
first with pure H2 humidified with 10% of steam, the second
with a syngas obtained by steam reforming of CH4 with SC ¼ 2
(H2 ¼ 64%, H2O ¼ 16%, CO2 ¼ 5%, CO ¼ 14% CH4 ¼ 1% vol/vol),
the third with a reformed woodgas that can be obtained by
biomass gasification (see Table 5). Total amount of H2 þ CO
was maintained constant (per each current density) for the
three cases. Fuel utilization was maintained constant at 70%
(Fig. 7).
As expected the decrease of H2 fraction and the increase of
steam and other diluents (like CO2) decrease dramatically the
output voltage at each current density. Concentration resis-
tances effect due to diffusion is evident for the woodgas: the
typical concavity become visible at 1.5 A/cm2, while it is
negligible for the pure H2 case. As expected the use of pure
hydrogen would improve the efficiency of the cell but the
necessary devices to purify hydrogen from other contaminant
would reduce the entire system efficiency. Therefore the use
of woodgas could be anyway a reasonable choice.
5. Catalytic filter candles
Ceramic filters are mainly used for removal of dust from hot
gases produced by industrial processes such as combustion
and gasification. Recently, advanced particle filtration
systems using ceramic candle filters for cleaning hot gases
have been developed by Pall Schumacher GmbH [38]. The
candle filters are hollow cylinders (closed on one end) typi-
cally with a diameter of about 6 cm and a length of 1e1.5 m;
the outer surface is made of a thin, micro porous layer that
forms the actual filtering surface. The dusty feed gas flows
outside such cylinders and percolates through their porous
structure driven by a differential pressure: fine particles
accumulate on the filtering surface building up a cake, while
the clean gas is collected in the hollow space. A filter vessel
Fig. 7 e Cell voltage vs current density for different gas
composition.
Table 7 e Comparison of the results obtained by themodel with those of [42,43].
Model Rapagna et al [42,43]
Gas yield (N m3/kg daf) 1.53 1.49e2.09
Composition mole frac (dry) mole frac (dry)
H2 0.53 0.50e0.56
CO2 0.18 0.19e0.23
CO 0.22 0.19e0.22
CH4 0.07 0.02e0.05
Tar (g/N m3) 0.0 0.15e0.91
i n t e rn a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 8 ( 2 0 1 3 ) 5 3 2e5 4 2540
for a commercial power plant requires a large number
(several hundreds) of candle filters arranged in several clus-
ters. Such clusters are periodically cleaned by an instanta-
neous reverse-gas flow back-pulse procedure to remove the
dust cake that builds up on the filtering surface. In addition,
a high catalytic capacity can be achieved by simply filling the
free hollow cylindrical volume of the filter element with
catalyst particles of a high active surface area. Several tar
reforming catalysts with different NiO loadings and different
catalyst support materials have been tested with synthetic
gases, resulting in complete conversion of naphthalene at
800 �C [39].
The catalytic filter candles of UNIQUE system were simu-
lated using two blocks of the CHEMCADª library:
1. a separator that removes all the fines particles;
2. a Gibbs reactor downstream the baghouse filter.
The experimental tests described in the work of Di Carlo
and Foscolo [40] have shown that the cake on the candle
surface increases continuously its thickness without particle
shedding and no fine particles could be detected upstream the
filter. Consequently it was assumed in the model that all
particles reaching the filter candles are “trapped” in the cake
and removed from the gas stream. Thanks to the back-pulse
cleaning system the pressure drops due to the cake forma-
tion are of the order of 50 mBar and thus they were neglected
in the simulation.
Experimental works developed by Rapagna et al. [41e43]
have shown that tars can be almost completely reformed
thanks to the catalytic filter candles, also steam reforming of
methane can reach equilibrium. For this reason a Gibbs
reactor was considered a good choice to simulate the catalytic
reaction occurring in the candles.
6. Results of the entire system simulation
Finally a sensitivity analysis of the power produced by the
power plant has been carried out, by varying the cathode gas
inlet temperature (and thus also the operative temperature of
the fuel cell) and varying the moisture content of the feeding
biomass. The composition of gas at anode inlet (and thus at
the catalytic filter candles outlet) is reported in Table 6.
Table 6e Composition of gas at anode inlet (catalytic filtercandle outlet).
T (�C) 620
p (bar) 1
Total flow (kg/h) 28.5
Composition mole frac (wet)
H2 0.44
H2O 0.17
CO2 0.15
CO 0.18
CH4 0.06
In order to partially validate the results obtained Table 7
shows the composition obtained during different tests
carried out by Rapagna et al. [42,43] compared with the dry
composition obtained by the model.
Most of the results are in line with that obtained experi-
mentally. As expected the model predicted the complete
removal of tar, while the experiments showed a small residual
concentration. This result is due to the inaccuracy of the Gibbs
reactor (equilibrium reactor), anyway for the purpose of this
work the model results were considered acceptable.
Inorder tomakemorerealisticsimulations thedimensionof
the cell utilized by themodel were taken from [44,45] (Table 8).
The current density was extrapolated from the data
available in [44,45]. A value of 0.4 A/cm2 was chosen for the
preliminary dimensioning of the system. A sensitivity study
would be probably necessary to optimize the value of the
current density but it is beyond the scope of this work.
Number of cells was chosen in order to have fuel utilization
around 0.75 with the current density chosen, flowrate and
composition of Table 6. Oxidant flowrate ratio was set equal to
10 in agreement with data also available in [44]. Once the
number of cells was set, current density was varied around
0.4 A/cm2 to obtain different fuel utilization (and thus
different amount of residual fuel from anode) as required by
the gasification process.
A sensitivity study of the power produced was carried out
varying the moisture content in the biomass from 10% to 30%.
The steam to carbon ratio wasmaintained constant and equal
to 0.5. This means that when the moisture is increased the
necessary external steam amount was reduced. Figs. 8 and 9
show the results obtained by the simulations.
One can observe that the electrical efficiency decreases
when the moisture content increases (from 48% to 34%). The
main reason is due to the greater amount of heat that must be
supplied from the burner to the gasifier. Indeed, in order to
vaporize the moisture of the biomass, a greater amount of
Table 8 e Dimensions of the cell used in the simulations[44,45].
Cell active area 200 cm2
Single cell power 60 W
Anode thickness 240 mm
Cathode thickness 40 mm
Electrolyte thickness 8 mm
Fig. 8 e SOFC power vs cathode inlet temperature and
moisture content.Fig. 10 e Temperature distribution inside the cell for
different cathode inlet temperatures moisture 10%.
i n t e r n a t i o n a l j o u rn a l o f h y d r o g e n en e r g y 3 8 ( 2 0 1 3 ) 5 3 2e5 4 2 541
woodgas must be circulated to the burner, reducing the
amount exploitable in the SOFC for electrical power produc-
tion (reduction of fuel utilization). The opposite is for the
cogeneration efficiency, because lower amount of heat is
required from the exhausts gases to generate steam.
Simulations showed that the best case occurs with
a temperature of the cathode gas of 800 �C and moisture of
10%, in this case the fuel utilization could be set equal to 0.79
and the electrical efficiency of the entire system is 48%. In the
worst case (temperature of the cathode gas of 650 �C and
moisture 30%) fuel utilization must be reduced to 0.73. In this
case the electrical efficiency of the entire system is 34%.
Fig. 10 shows the temperature distribution in the cell for
different anode and cathode inlet temperatures, for the case
of 10% of moisture.
As depicted the maximum temperature difference are
limited and lower than 100 �C. The worst case is that obtained
at 650 �C where the maximum temperature difference was
93 �C while the best was for the case at 800 �C with
a maximum difference of 74 �C. The main reason of these
results is explained by the higher electrical efficiency that
increases with the increase of operative temperature:
a smaller fraction of the incoming energy is dissipated in heat.
Fig. 9 e Power plant electrical efficiency (SOFC D mGT) vs
cathode inlet temperature and moisture content.
7. Conclusion
The paper deals with a new small cogeneration system con-
sisting of a fluidised bed gasifier, coupled to a SOFC and
a mGT. A detailed model for the gasifier and the SOFC has
been discussed and validated. The simulation showed higher
stack power production (29e42 kWe) and higher electrical
efficiency (34e48%). Thus the proposed coupling of a recircu-
lated fluidized bed gasifier and a SOFC/mGT system presents
conversion efficiency higher than those reached by the stan-
dard biomass power plants even at bigger size. Considering
the realistic model (e.g. overestimation of tar formation) and
the low performance of the biomass as fuel, the performance
obtained look fine even if are below the performance obtained
in bigger or traditional fossil fuel (natural gas) power plants.
Another related important innovation is the feeding of the
mGT via the high temperature flue combustor gases that
allowed a better mGT operation producing other electrical
power. As the most of the heat is recovered from exhausts at
quite high temperature (400 �C), it could be also used in
Organic Rankine Cycle to further increase the electrical power
produced and thus improving electrical efficiency of the plant.
Moreover, the very low environmental impacts make this
solution particularly suitable for distributed energy produc-
tion also in place with high environmental constraints.
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