Finnish-Swedish Flame Days 2013,
Gasification Workshop, 2013-04-18, Jyväskylä
Particle scale char gasification models
for biomass fuels
Kentaro Umeki
Kentaro.umeki@ltu.se
Div. Energy Science
Dept. Engineering Sciences and Mathematics
Luleå University of Technology, Sweden
Agenda
• Background
• Particle scale biomass char gasification models
– Phenomenological model for intrinsic reaction rate
• Reaction mechanism
• Surface area development
• Catalytic activity of ash (K, Na, Ca, Mg, Fe, etc.)
– Considering intra-particle diffusion
– Considering external diffusion
• Summary and future challenges
Red letters: Focus of this presentation
(especially important for biomass fuels)
Gasphase
Solid-phase
Raw biomass
Drying
(≈100 C)
Dry biomass
Devolatilization
(200-500 C)
Char
Ash
Mass transfer
Heat transfer
Gasification
(> 700 C)
Combustion
(>500 C)
Char gasification
• Rate controlling step
– Slower than devolatilization by a few orders of magnitude
• High heating value of char
– Char yield from devolatilization: ~10% on weight basis, but
15-30% on energy basis
• Target for energy balance in allothermal gasifiers
• Endothermic reactions (with CO2/H2O)
– Convert sensible heat to useful chemical energy
Reactor scale
Microscopic scale
Particle scale
Plant scale
Partice scale (mass diffusion) models?
• Important to predict char conversion accurately
hence, important for gas composition as well
• Majority of current CFD models do NOT consider
particle-scale (diffusion) phenomena
1. Develop constitutive equations to implement?
2. Find (numerically) efficient ways for coupling particle
and reactor models?
This presentation focuses on approach 1.
Important factors for char gasification
O2
CO2
H2O
Catalytic activity of
inorganic matters
Surface area
External and
intra-particle
diffusion
• Char conversion
mini  m
X
mini  m fin
• Conversion rate
dX
r
dt
Residual mass [g]
Definitions
0,4
mini
mini - m
0,3
mini - mfin
0,2
m
0,1
mfin
0
0
500
1000
1500
Elapsed time [s]
• Apparent rate
– Conversion rate observed as a lump of
chemical reaction and mass diffusion
• Intrinsic rate
– Conversion rate of pure chemical reaction
(when diffusion is negligible)
2000
Common strategy for modelling
• Intrinsic reaction rate
𝑟𝑖𝑛𝑡 = 𝑘 𝑝, 𝑇 ∙ 𝑓 𝑋
(1) reaction conditions dependent – models based on
reaction mechanisms: (k(p,T))
(2) Char conversion dependent – models describing reactive
surface area and catalytic activity: f(X)
• Intra-particle diffusion
Effectiveness factor: 𝑟𝑎𝑝𝑝 = 𝜂 ∙ 𝑟𝑖𝑛𝑡
• External particle diffusion
Mass balance in gas film
[e.g. A. Gómez-Barea and B. Leckner,
Prog. Combust. Energy Sci. 2010]
Phenomenological models for intrinsic reaction rate
– Reaction mechanism
– Surface area development
– Catalytic activity of ash (K, Na, Ca, Mg, Fe, etc.)
For more detail: K. Umeki, A. Moilanen, A. Gómez-Barea, J. Konttinen, A model
of biomass char gasification describing the change in catalytic activity,
Chemical Engineering Journal 207-208 (2012) 616-624.
Single-step reaction model
• C+γ·O22(1-γ)·CO+(2γ-1)·CO2
• C+H2OCO+H2
• C+CO22CO
𝑛
𝑘 𝑝, 𝑇 = 𝐴𝑃𝑔𝑎
𝑒𝑥𝑝 −𝐸/𝑅𝑇
Arrhenius plots from the reference will be shown.here.
[C. Di Blasi, Prog. Energ. Comb. Sci. 35 (2009) 121-140]
Detailed reaction model
C+γO2 2(1-γ) CO+ (2γ-1) CO2
2C+O2↔2C(O)
C(O)  CO
2C(O)  CO2 + C
C+CO2↔C(O) + CO
O2, H2O,
CO2
H2, CO,
CO2
O
C
C+H2OCO+H2
C
H
C
C
C
Char (carbon)
C+H2O↔C(O) + H2O
C(O)  CO
Examples (Langmuir-Hinshelwood model)
𝑘1𝑓 𝑘2 𝑃𝑂2
C+H2↔C(H)2
𝑘 𝑝, 𝑇 =
𝑘1𝑓 𝑃𝑂2 + 𝑘2
C+1/2H2↔C(H)
C+CO22CO
C+CO2↔C(O) + CO
C(O)  CO
𝑘 𝑝, 𝑇 =
𝑘 𝑝, 𝑇 =
𝑘1𝑓 𝑃𝐻2𝑂
1 + 𝑘1𝑓 /𝑘3 𝑃𝐻2𝑂 + 𝑘3𝑓 /𝑘3𝑏 𝑃𝐻2
𝑘1𝑓 𝑃𝐶𝑂2
1 + 𝑘1𝑏 /𝑘2 𝑃𝐶𝑂 + 𝑘1𝑓 /𝑘2 𝑃𝐶𝑂2
Phenomenological models for surface area
Models
Overall reaction
Shrinking core
[Yagi 1953]
Random pore
[Bhatia 1980]
Overlapped grain
[Adschiri 1987]
Percolation
[Reyes and Jensen 1986]
Reaction
Throughout particle
Particle surface
Pore surface
Overlapped grains’
surface
Each carbon site in
lattice
Function f(X) for different models
Models
f(X) with f(X=0)=1
Overall reaction
(1  X )
Shrinking core
(1  X ) 2 3
Random pore
(1  X ) 1   ln(1  X )
Overlapped grain [1  (1   0 1 ) X ]{1  (ln  0 ) 1 ln[1  (1   0 1 ) X ]}2 / 3
Percolation
1
(1  X )[1  (1   0 ) 0 X ]
Ψ= 4πLo(1-εo)/So
εo: Initial porosity
Lo: Total length of pores per unit volume
So: Pore surface area per unit volume
Models
1. Overall reaction
1.
2.
2. Shrinking core
3. Random pore
3.
4. Overlapped grain
5. Percolation
4.
5.
Catalytic activity of inorganic content
• K, Ca, Na, Mg and Fe known as ”catalyst”
• Si, P and Al known to deactivate catalytic activity
• Difficult to observe actual catalytic activity due to its
own transformation (vaporization, reaction,
sintering)
• ”In-situ” experiments are necessary for the
observation of catalytic activity
Previous models describing catalytic activity
Ref.
Model
Zhang et al. Fuel
(2008)
rint  k (1  X ) 1   ln(1  X )  1  cX 
Zhant et al. Fuel
(2010)
rint  k (1  X ) 1   ln(1  X )  1   p
Struis et al. Chem Eng
Sci (2002)
rint  k (1  X ) 1   ln(1  X )  1   p  1bt 
Dupont et al. Bioresour
Technol (2011)
rint  k amk mSi   b 1  X 
Kitsuka et al. Energy
Fuel (2007)
rint  k1 exp k 2  t   k 2 (1  X )
Kajita et al. Energy
Fuel (2010)
rint  k1  k 2 (1  X )
Löwenthal, PhD thesis
(1993)
rint  k1 1  k 2  t   k3 (1  X ) 1   ln(1  X )



p


p
2/3

2


Isothermal TGA
Schematic diagram of isothermal TGA will be shown.here.
Mimic reaction conditions of commercial
gasifiers
• Rapid heating rate and in-situ
gasification (no cooling and reheating)
• Reaction conditions of FBG and cyclone
gasifiers (973-1123 K; CO2-CO-H2O-H2;
0.1-3.0 MPa)
[A. Moilanen, Doctoral thesis, VTT publication 607, 2006]
Conversion rate analysis by integral method
If conversion rate is first-order: 𝑑𝑋/𝑑𝑡 = 𝑘 1 − 𝑋
By integration:1 − 𝑋 = 𝑒𝑥𝑝(−𝑘𝑡)
ln(1-X) should be linear function of t
1st/2nd regimes: linear functions
Which is first-order reaction
with respect to char conversion?
Three regimes were observed.
Typical experimental data
Instantaneous reaction rate
2nd regime is first-order reaction!
If 𝑑𝑋/𝑑𝑡 = 𝑘 1 − 𝑋
1
𝑑𝑋

∙ = 𝑐𝑜𝑛𝑠𝑡.
1−𝑋
Typical experimental data
Initial stage of char gasification: K, Na evaporation
1st regime… Loss of catalytic activity?
𝑑𝑡
Observed reaction mechanism
1. Catalytic char gasification w/ fast catalyst loss
2. ”Non-catalytic” char gasification
3. Catalytic char gasification w/o catalyst loss
Three-parallel reactions model
𝑑𝑋
𝑟=
𝑑𝑡
= 𝑟𝑐𝑔,1 + 𝑟𝑛𝑐𝑔 + 𝑟𝑐𝑔,2
1st regime: Catalytic gasification with deactivation
𝑟𝑐𝑔,1 = 𝑘𝑐𝑔,1 𝑒𝑥𝑝 −𝜉𝑋 2
2nd regime: Non-catalytic gasification
𝑟𝑛𝑐𝑔 = 𝑘𝑛𝑐𝑔 1 − 𝑋
3rd regime: Catalytic gasification without deactivation
𝑟𝑐𝑔,2 = 𝑘𝑐𝑔,2
Effect of biomass species
Biomasses with low Si in ash
Poor predictability.
Negative rate coefficient
Governing deactivation mechanism
Reaction with Si?
Biomasses with high Si in ash
Good model predictability!!
Effect of reaction atmosphere
H2O-H2 system
Poor predictability.
CO2-CO system
Good model predictability!!
Catalytic activity maintained under H2O-H2 system?
Intra-particle mass diffusion
K. Umeki, S. Roh, T. Min, T. Namioka, K. Yoshikawa, A simple expression for the
apparent reaction rate of large wood char gasification with steam,
Bioresource Technology 101 (2010), 4187-4192
Models describing pore diffusional effect
◎: very good
Pseudo- Effective
○: good
activation
factor
×: not so good
energy
Simplified
numerical
simulation
Direct
numerical
simulation
Effect of
initial
diameter
×
○
◎
◎
Diameter
change
×
×
○
○
Comp. time
◎
◎
○
×
General
versatility
×
○
○
◎
Calc. method
Arrhenius
plot
Theoritical
value
Modify!
(Effective factor) = rapp/rint
Quantum
method
Numerical
solution of
PDEs
Macro-TG experiments
Precision scale
Gas
cylinder
MFC
Pump
Temp.: 1123, 1173, 1223 K
Steam: 0.02, 0.04, 0.06 Mpa
Gas velocity: 5 L/min
Syringe pump
(water)
Thermocouple
Effect of particle diameter
(1173 K, PH2O =0.04 MPa)
Simplified reaction model
X1 < X < X2
0 < X < X1
X
X2 < X <1
X
1
X1
r
r
r
Change of local char conversion and particle size
0 < X < X1 – Uniform reaction
X1 < X < X2 – Shrinking reaction
X2 < X < 1 – Uniform reaction
r
Modification of effective factor
Modified expression
Original
Thiele module
Thiele module
V

S
n 1
n  1  c k cg C AS
2
DeA
Effective factor
1
1
1
 
 
  tanh(3 ) 3 
Effect of particle size change
Effective factor
[Bischoff 1965]
η= rapp/rint
Effect of local intrinsic rate
Predictability with theoretical parameters
(1173 K, H2O: 0.04 MPa)
Experimental data: solid lines
Calculated data: dashed lines
External mass diffusion
Overall conversion rate with external diffusion
C+a Agases (CO, H2 and CO2)
(Amount of agent consumed in particle) =
(Amount of agent diffusing to char surface)
S p kC  As  S p D  Ab   As 
Mass loss rate of carbon from stoichiometry
dmc
dm A
a
dt
dt
M k
 a C c D S p  Ab
M A kc   D

dmc
M
 a C S p ke  Ab
dt
MA
Effective rate constant:
k c D
ke 
kc   D
 D  Sh
DAB
dp

 As 
D
 Ab
kC   D
Summary
• Intrinsic rate can be usually expressed as k(p,T)f(X),
but parallel reaction model may be appropriate for
describing catalytic activity
• Effect of intra-particle diffusion can be included by
applying effectiveness factor
• Effect of external diffusion can be included by
effective rate constant
Future works
• More understanding of ash transformation and
modification of models of catalytic activity
• Consideration of fragmentation in particle scale
models
• Generalization of models to various shape
Acknowledgment
• Co-workers
– Catalyst model: Dr. A. Moilanen (VTT); Prof. A. Gómez-Barea (U.
Sevilla); Prof. J. Konttinen (U. Jyväskylä)
– Intra-particle diffusion model: Dr. S. Roh and Dr. M. Taijin (Korean
Inst. Machinery and Materials); Prof. T. Namioka and Prof. K.
Yoshikawa (Tokyo Inst. Tech.)
• Finance
– Catalyst model: Bio4Energy, Nordic Energy Top-Level Initiative
– Pore diffusion model: Japan Society for the promotion of Science
and Korea Science and Engineering Foundation