Estimation of gas composition and char
conversion in a biomass FB gasifier
Alberto Gómez Barea
agomezbarea@us.us
ETSI de Sevilla
Departamento de Ingeniería Química y Ambiental
Universidad de Sevilla
España
FINNISH-SWEDISH FLAME DAYS 2013
“Focus on Combustion and Gasification Research”
International Flame Research Foundation
Jyväskylä, Jyväskylän Paviljonki, 17.-18.4.2013
Gasification Workshop (18.4.2013 )
Contents
1. Motivation and objective
2. Background for the model development an
simplification
3. Modelling
4. Results and discussion
5. Conclusions and bibliography
AGB. Estimation of gas composition and char conversion in a biomass FBG
Motivation for a new method
• In the preliminary design of a FBG, the knowledge
of the main components of the gas produced in the
gasifier is a key factor
• Advanced models for FBG exist but require
physical and kinetic inputs difficult to estimate and
sometimes are not available to industrial
applications
• Simple and reliable semi-empirical methods to
predict gas composition and reactor performance
are not common in literature, and there is a need
for such modelling tools
AGB. Estimation of gas composition and char conversion in a biomass FBG
1. Motivation and objective
Difficulty in modelling
(complex Chemestry and transport phenomena at particle level)
DEVOLATILISACION
Oxidant
Primary light gas
R-C-OP
O2
Biomass
O2
CO2
COMBUSTION
Light gas
(secondary)
Q
Gases CO2
condensables
(Tar: CiHjOz)
CO2, H2O
CO, H2,CH4
(Gas)
H2O
Tar
Secondary
(Tar + soot)
CiHjOz (L)
Char
(carbonizado)
H2O H2 CO2
GASIFICATION
Char (S)
Char
Secondary
CO H2 CH4
AGB. Estimation of gas composition and char conversion in a biomass FBG
1. Motivation and objective
Difficulty in modelling
(complex flow pattern and transport phenomena at reactor level)
N2
H2O + CO  H2 + CO2
C
C
C
Producer GAS
CO, H2, CH4,
CO2, N2, H2O,
CxHyOz
C
N2
CO2
Freeboard
H2O
H2
CO
Char particle
Volatiles
(H2, CO, CxHy)
Boundary layer
emulsion
Bed
Char
bubble
Fuel
air
AGB. Estimation of gas composition and char conversion in a biomass FBG
1. Motivation and objective
Advanced models: concept
solid
solid
flow to flow from
gas flow to cell i-1 to cell i-1 to cell i-1
gas
feed
heat
transfer
cell i
gas
gas
exchange
gas
homogenous
reactions
cell i
emulsion phase
bubble phase
homogenous
reactions
solid
homogenous
reactions
i-1
cell + 1
gas flow to cell i+1
AGB. Estimation of gas composition and char conversion in a biomass FBG
solid
recirculation
solid
solid
flow from flow to
to cell i+1 to cell i+1
solid feed
solid dischar
Advanced models: results
measured
1.0
simulated
1.0
O2
CH4
CO
0.8
0.8
H2
H2O
0.6
Height, m
Height, m
CO2
0.4
0.2
0.0
0.0
0.6
measured
0.4
simulated
0.2
0.1
0.2
0.3
0.4
0.5
0.0
0.0
Concentration, mol/mol
AGB. Estimation of gas composition and char conversion in a biomass FBG
900
1000
1100
Temperature, K
1200
1300
Past trials for simple modelling of FBG
• Equilibrium models (EM)
• Quasi-Equilibrium models (QEM)
• Empirical models
AGB. Estimation of gas composition and char conversion in a biomass FBG
1. Motivation and objective
Air ratio
Equilibrium models (EM)
AGB. Estimation of gas composition and char conversion in a biomass FBG
1. Motivation and objective
Equilibrium models (EM)
Advantages
– simple to apply
– independent of gasifier design
– widely used
Failures
–
–
–
–
overestimates yields of H2 and CO
underestimates the yield of CO2
Prediction of gas nearly free of CH4 and tar
no char in the gas phase over 1000 K
AGB. Estimation of gas composition and char conversion in a biomass FBG
1. Motivation and objective
Quasi-Equilibrium models (QEM). Concept
AGB. Estimation of gas composition and char conversion in a biomass FBG
1. Motivation and objective
Quasi-Equilibrium models (QEM)
Advantages
– Improvement of EM
– Simple to apply
Failures
–
–
–
–
Need correlations
Dependent of gasifier design
Most cases do not predict tar and/or char
Sometimes recommendations can avoid
correlation but this make QEM non-predictive
AGB. Estimation of gas composition and char conversion in a biomass FBG
1. Motivation and objective
Objective
To develop a model (method):
– Based on QEM (simple)
– With predictive capability
– Free from ad-hoc correlations
– Able to estimate tar and char
– Based on established evidences
AGB. Estimation of gas composition and char conversion in a biomass FBG
1. Motivation and objective
2. Background for the development and
simplification
Existing evidence for corrections
• Heterogeneous or homogeneous equilibrium?
– In EM no solid carbon in the gas phase over 1000 K
• Steam Reforming of Methane (SRM) in equilibrium?
– Steam reforming of methane is kinetically limited
below 1300 K
– methane in the exit stream of the gasifier ~ that
formed in devolatilisation
AGB. Estimation of gas composition and char conversion in a biomass FBG
2. Evidences for correction
Water Gas Shift Reaction (WGSR) in
equilibrium?
– Equilibrium for the WGSR reached at 1273 K and
residence time about 1 s
– Between 1073 K and 1273 K the attainment of
equilibrium has to be confirmed
– This confirmation depends on the use of catalysts
and steam presence:
• Synthetic (Ni) vs. minerals (dolomite, olivine, etc)
catalyst
• Steam vs. air gasification
AGB. Estimation of gas composition and char conversion in a biomass FBG
2. Evidences for correction
WGSR : in equilibrium or kinetically limited?
Experimental verification
K cal 
pCO2 pH 2
?
K (T )  0.0265  exp 3958 / T 
pCO pH 2O
1,6
1,5
1,4
Kequ (T )
1,3
1,2
1,1
1,0
0,9
0,8
Kexp
0,7
0,6
690
710
730
750
770
AGB. Estimation of gas composition and char conversion in a biomass FBG
790
810
830
2. Evidences for correction
Conclusions from the existing
evidence for the model
– Homogeneous equilibrium is enough together with a
char conversion model (kinetically limited)
– Modified equilibrium based on WGSR and SRMR is
a convenient tool
– CH4 in the exit is nearly that formed during
devolatilization. Kinetic rates of SMR should be
included in the model when there is in-bed catalyst
– Equilibrium of WGSR is not generally attained. It is
necessary to be modeled
AGB. Estimation of gas composition and char conversion in a biomass FBG
2. Evidences for correction
3. Modelling
Aim: Overall model
Heat
O2
H 2O Oxidant
CO 2
N2
Tb
GASIFIER
Biomass
CHαOβ
H2
Light Gas
Condensable
Solid
C(s) + Ash
CO
H 2O
CO 2
CH 4
N2
C jH k O l
CH α Oβ + a O 2 + b H 2O + cCO 2 + d N 2 
x1 C(s) + ash (Solid)
+x 2 H 2 + x 3CO + x4 H 2O + x 5CO 2 + x 6CH 4 + x 7 N 2 (Gas)
x8C jH k Ol (Gas condensable )
3. Modelling
Simplifications: isolation of dominant processes
D
FUEL t=t0
t1→ t∞
t∞
t4
t3
FUEL
PARTICLE
L
t2
t1
FUEL t=t0
t2
t3
t4
FUEL t=t0
Volatile release
FUEL t=t0
AIR
t1
AIR
t1
t2
t3
AIR
t4
time
(a.1) Dadev,v<< 1
(b.1) Dadev,v>> 1
(c.1) Dadev,v~ 1
Nseg = segregation time / devolatilisation time
AGB. Estimation of gas composition and char conversion in a biomass FBG
3. Modelling
Models of simplified cases
BIOMASS
PYROLYSIS
CHAR
GASIFICATION
CHAR
GASIFICATION
OXIDATION
OF CHAR AND
VOLATILES
CHAR
COMBUSTION
FUEL
BIOMASS
PYROLYSIS
(a.2) Nseg<< 1
+
OXIDATION
OF CHAR AND
VOLATILES
+
TAR
CRACKING
+
PYROLYSIS
FUEL
FUEL
OXIDANT
TAR CRACKING
CHAR
GASIFICATION
OXIDANT
(b.2) Nseg>> 1
AGB. Estimation of gas composition and char conversion in a biomass FBG
OXIDANT
(c.2) Nseg~ 1
3. Modelling
Simplified model of a FB gasifier (Nseg>>1)
Initial conditions
for Char
Reduction Zone
(CRZ)
FUEL
PARTICLE
Flamingpyrolysis zone
(FPZ)
FUEL
CHAR
GASIFICATION
(CRZ)
C(s)+R(g) P(g)
RFPZ=(CO2+H2O)FPZ
OXIDATION
OF CHAR AND
VOLATILES
TAR CONVERSION
(OZ)
FUEL
DEVOLATILISATION
(FDZ)
FUEL
FLUIDISATION
AGENT
(a)
FLUIDISATION
AGENT
(b)
3. Modelling
Model concept adopted
Discharged ashes
Produced gas
OVERALL MASS AND HEAT BALANCE,
PSEUDO-EQUILIBRIUM IN THE GAS PHASE
xCH4 ,gp
WGSR
FACTOR
 f WGSR 
xchar ,gp
xtar ,gp
METHANE
CONVERSION
CHAR
GASIFICATION
TAR
CONVERSION
( X CH 4 )
( X char )
( X tar )
xCH 4 ,d
xchar ,d
xtar ,d
DEVOLATILIZATION
GASIFICATION AGENT
FUEL
3. Modelling
Steps in modelling
1.
Estimation yields of light gases, char and tar from FPZ (Ideal:
experiments in lab FB)
2.
Estimation tar, methane and char conversion in CRZ by
application kinetic models
3.
QE model:
- Unconverted CH4, tar and char are removed from this
analysis formulation of C-H2-O2-N2 mass balances
- Mass balances with corrected C-H-O inputs
- two equilibrium (or approach to equilibrium) relationships
(WGSR and SRMR)
4.
Restoration of unconverted CH4, tar and char
Application of heat balance over the corrected exit streams
AGB. Estimation of gas composition and char conversion in a biomass FBG
3. Modelling
Model of the flaming pyrolysis zone (FPZ)
•
Char, methane, and tar yield as a function
of temperature (other)
•
Light gases from mass balances with extraassumptions to close the system:
– H2 and CO consume completely the
oxygen, and CH4 and tar are not
converted in the OZ
– Char conversion in the oxidation zone
(OZ) is neglected
AGB. Estimation of gas composition and char conversion in a biomass FBG
3. Modelling
Char conversion model
GAS
PRODUCED
FLY ASH
(ENTRAINED SOLIDS)
x3 , xch,3 , wc,ch,b
ADDITIVE
FUEL
Control
volume
VOLATILES
xadd
wc ,ch ,b
xch ,d
mch ,b
wc ,ch ,d
mT ,b
T

u0
p , p
 H 2O CO2
CHAR
BOTTOM ASH
GASIFICATION
(DISCHARGED SOLIDS)
AGENT
(a)
SOLID
x2 , xch,2 , wc,ch,b
GAS
3. Modelling
Tar and CH4 conversion sub-model
Da i
CSTR: X i 
1  Da i
i=CH 4 , Tar
ki
Da i 
 ki  CRZ Damköhler number
(uFPZ / L f )
•
Difficulties in the adequate selection of tar and
methane model:
–
Tar: mainly depends on the biomass nature, operating
conditions (T, t), presence of catalyst
–
Methane: The use of catalyst and the presence of steam
AGB. Estimation of gas composition and char conversion in a biomass FBG
3. Modelling
4. Results and discussion
The impact of the char conversion model
-∙- Xch calculated with complete model
Gas lower heating value (LHV), MJ/Nm3
1100
1050
Temperature (T), ºC
1000
950
Xch=0.25
0.50
900
850
0.75
800
1.00
750
700
650
600
0.2
0.22
0.24
0.26
ooo
0.28
0.3
Xch calculated with simplified model
8
7.5
7
6.5
6
5.5
1.00
0.75
0.50
Xch=0.25
5
0.2
Equivalence ratio (ER)
(a)
AGB. Estimation of gas composition and char conversion in a biomass FBG
0.22
0.24
0.26
0.28
0.3
Equivalence ratio (ER)
(b)
4. Results and discussion
The effect of air ratio (ER)
0.25
Molar fraction in wet gas (%v/v)
Char conversion (Xch), kg/kg
1
0.95
0.9
0.85
0.8
0.75
0.7
CO
0.2
0.15
CO2
0.1
0.22
0.24
0.26
0.28
0.3
H2O
0.05
CH4
0.65
0.6
0.2
H2
0
0.2
0.22
0.24
0.26
0.28
Equivalence ratio, ER
Equivalence ratio (ER)
(a)
(b)
AGB. Estimation of gas composition and char conversion in a biomass FBG
0.3
4. Results and discussion
The effect of Throughput
0.906
1
Char conversion (Xchar ), kg/kg
Char conversion (Xch), kg/kg
ER=0.30
0.98
0.96
0.94
0.92
ER=0.25
0.9
0.904
0.902
0.9
0.898
0.896
0.894
0.892
ER=0.25
0.89
0.888
0.88
300
400
500
600
700
800
900
0.886
1000
400
Throughput (Th), kg/(m 2h)
AGB. Estimation of gas composition and char conversion in a biomass FBG
600
800
1000
1200
1400
Throughput (Th), kg/(m 2h)
4. Results and discussion
The effect of solids removal rate, 1/τ2
1/τ2=kg/h mecahnically removed/ kg bed
1
Char residence time ( ), h
3
Char conversion (Xch), kg/kg
ER=0.30
0.8
0.25
0.6
0.4
0.2
0.2
0
0
2.5
2
ER=0.20
1.5
0.25
1
0.5
0.30
0.5
1/2, h-1
1
1.5
0
0
AGB. Estimation of gas composition and char conversion in a biomass FBG
0.5
1/2, h-1
1
1.5
4. Results and discussion
The effect of solids removal rate, 1/τ2
1/τ2=kg/h mecahnically removed/ kg bed
Molar fraction in wet gas (%v/v)
0.25
0.2
CO
0.15
0.1
H2
CO2
H2O
0.05
CH4
0
0
0.5
-1
1/2, h
AGB. Estimation of gas composition and char conversion in a biomass FBG
1
1.5
4. Results and discussion
Summary and conclusions
1. A model based on QEA with predictive capability and easy to
apply has been developed
2. Used as tool for design and optimisation: improves
significantly equilibrium predictions
3. Valid for preliminary design
4. Details of the model efforts:
•
Char conversion need to be modelled taking into account entrainment
and kinetics
•
Yields of char, methane and tar during devolatilisation depends on the
fuel and shoulkd be determined in the lab
WGS, tar and CH4 reforming need to be modelled with proper kinetics
Kinetics of char need to be determined in the lab
•
•
AGB. Estimation of gas composition and char conversion in a biomass FBG
Bibliography
Details of the model developed and
comparison with measurements, in:
• Gómez-Barea, A., Leckner, B. Estimation of gas composition
and char conversion in a fluidized bed biomass gasifier. Fuel
2013, 107, 419–431
Extensive description of the process ocurring
in a FBG and the way to model them, in:
• Gómez-Barea A.. Leckner B. Modeling of biomass gasification in
fluidized bed. Progr. En. Comb. Sc. 2010, 36 (4), pp. 444-509
AGB. Estimation of gas composition and char conversion in a biomass FBG