Chemical Engineering Journal xxx (2012) xxx–xxx
Contents lists available at SciVerse ScienceDirect
Chemical Engineering Journal
journal homepage: www.elsevier.com/locate/cej
A model of biomass char gasification describing the change in catalytic activity
of ash
Kentaro Umeki a,⇑, Antero Moilanen b, Alberto Gómez-Barea c, Jukka Konttinen d
a
Division of Energy Science, Department of Engineering Sciences and Mathematics, Luleå University of Technology, 971 87 Luleå, Sweden
VTT Technical Research Centre of Finland, P.O. Box 1000, 02044 VTT, Finland
c
Bioenergy Group, Chemical and Environmental Engineering Department, Escuela Superior de Ingenieros, University of Seville, Camino de los Descubrimientos s/n, 41092 Seville, Spain
d
Renewable Natural Resources and Chemistry of Living Environment, Department of Chemistry, University of Jyväskylä, P.O. Box 35, FI-40014 University of Jyväskylä, Finland
b
h i g h l i g h t s
" The model describes the change in catalytic activity during char gasification.
" Three parallel reactions of catalytic and non-catalytic gasification were applied.
" The model was validated by TGA measurements using nine different biomasses.
" The model satisfactorily describes the reactivity with CO2 at 1023–1123 K and 0.1–3.0 MPa.
a r t i c l e
i n f o
Article history:
Available online xxxx
Keywords:
Char gasification
Biomass
Alkali and alkaline earth metals
Catalyst
Modeling
Silicon
a b s t r a c t
A comprehensive description of catalytic effects during char gasification under various conditions relevant to biomass gasification was made. A three-parallel reaction model was proposed to describe the
dynamic change in catalytic activity of ash during gasification of biomass char particles. Three different
regimes of conversion were identified by analyzing char reactivity experiments conducted in a vertical
TGA with nine biomasses under a wide range of operating conditions (temperature: 1023–1123 K, pressure: 0.1–3.0 MPa and gasification mixtures of CO2–CO–H2O–H2): (1) catalytic char gasification with
deactivation of catalyst, (2) non-catalytic char gasification, and (3) catalytic char gasification with small
amount of stable ash, without suffering deactivation. A model including the three regimes was developed
and the measurements were used to fit the kinetic coefficients. It is shown that the model accurately predicts the reactivity of biomass char in CO2–CO mixtures during the whole range of conversion. It was
detected that char gasification maintains the catalytic activity during the entire conversion process
when: (i) biomasses having small amount of silicon was used, and (ii) steam is used as part of the gasification agent. The model is still useful as predicting tool for these two conditions but its physical significance is contestable on the light of the model developed. For the conditions where the model is valid, it is
shown that the model is a useful tool as sub-model in reactor simulations, predicting the conversion rate
of single particles fast and accurately at different stages of conversion. The aspects that need to be further
investigated for expanding the applicability of the model were identified.
Ó 2012 Elsevier B.V. All rights reserved.
1. Introduction
Char gasification with steam and/or carbon dioxide is an important step during thermochemical conversion of lignocellulosic biomass because it often represents the rate-controlling phenomenon
in the gasifier. A great effort has been made to develop kinetic
Abbreviations: AAEM, alkali and alkaline earth metals; FBG, fluidized-bed
gasifier; TGA, thermogravimetric analyzer.
⇑ Corresponding author. Tel.: +46 920 492484; fax: +46 920 491074.
E-mail address: [email protected] (K. Umeki).
models to predict the rate of conversion of char particles during
gasification, i.e. the reactivity. The gasification reactivity of a single
char particle is mainly affected by char morphology, nature and
content of ash-forming constituents, as well as diffusion of mass
to and inside the particle. When the transport effects are negligible,
for instance using fine particles, intrinsic reactivity is obtained.
There are two ways of expressing intrinsic reactivity, and the following definitions are used in this paper: conversion rate,
rcg = dXcg/dt, and instantaneous reaction rate, Rcg = 1/(1 Xcg)dXcg/
dt. The term reactivity is used in general to refer any of the two defined rates. Conversion rate is usually written as[1].
1385-8947/$ - see front matter Ó 2012 Elsevier B.V. All rights reserved.
http://dx.doi.org/10.1016/j.cej.2012.07.025
Please cite this article in press as: K. Umeki et al., A model of biomass char gasification describing the change in catalytic activity of ash, Chem. Eng. J.
(2012), http://dx.doi.org/10.1016/j.cej.2012.07.025
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K. Umeki et al. / Chemical Engineering Journal xxx (2012) xxx–xxx
Nomenclature
dev
k
m
Nexp
R
S
r
t
X
y
n
Subscripts
ccg,1
catalytic char gasification in the first regime
ccg,2
catalytic char gasification in the third regime
cg
char gasification
ctloss
deactivation of catalyst
char
char
f
final
i
initial
ncg
non-catalytic char gasification in the second regime
dev
devolatilization
t
total
vol
volatiles
deviation
rate coefficient, s1
mass, g
number of data points, –
instantaneous reaction rate, s1
squared residual error, –
conversion rate, s1
time, s
conversion, –
mass fraction of precursors in the original fuel, –
coefficients for JM and CLM2, –
r cg ¼ kðT; pi Þ f ðX cg Þ
ð1Þ
The first function, k is a kinetic coefficient accounting for the effect of temperature and the partial pressure of gas species in the
gasification reactions (CO, CO2, H2O, H2). Langmuir–Hinshelwood
expressions have been utilized in the literature [1,2]. The second
term in Eq. (1), f(Xcg), expresses the change of the conversion rate
during char gasification and it has been often interpreted in terms
of the change in reactive surface area (e.g. random pore model [3]).
In fact, this vision lumps all the phenomena occurring during conversion that affects the conversion rate, for instance catalytic effects. In Eq. (1) the catalytic effects in f(Xcg) are assumed to be
independent of operating conditions. Only variation of these effects
with Xcg is allowed in Eq. (1). This is a reasonable approximation for
reactor simulations where the variation of operating conditions in
a given gasifier is limited [1]. Though, in the present work we will
consider that catalytic activity of the char during gasification can
vary, so f will be assumed to vary with a more general form f(Xcg,
g(T,pi)) where g is the factor in f accounting for the change in catalytic effects with operating conditions. Although a great number
of measurements have been made suggesting the catalytic effects
during char gasification, there are only a few kinetic models dealing with catalytic effect of ash content explicitly. In the present
work the change of conversion rate due to catalytic activity of
ash is investigated, i.e. the conversion rate behavior against char
conversion at fixed temperature and pressure of gasifying agents.
Ash content, especially alkali and alkaline earth metals (AAEM)
and iron, is known to act as catalyst in the gasification reactions of
carbon [4–6]. Some studies [7,8] showed that the reactivity of char
gasification can be correlated to the molar ratio of catalytic ash, i.e.
AAEM, to carbon. Other inorganic substances such as silica, alumina and phosphates, on the other hand, lower the reactivity of
char. Silica has been observed to reduce the reactivity [9] by reacting with potassium to form silicate, blocking the catalytic effect of
potassium. Alumina has also shown the function to deactivate the
catalytic activity of potassium [10]. Potassium phosphate has been
observed inactive in the catalytic carbon gasification [11]. Dupont
et al. [12] showed that the char reactivity is a linear function of the
mass ratio of potassium to silicon. Moreover, the complicated effects of various ash components are often expressed by alkali index
[13].
The dynamic change in catalytic activity of the inorganic compounds during high temperature gasification reactions is not
well-known. Three deactivation processes of catalytic activity in
char gasification have been proposed so far. First, the vaporization
of AAEM content can reduce the catalytic ash content, i.e. reactivity
[14,15]. Steep drop of AAEM retention in char was observed at
early stage of char gasification. Second, calcium dispersion affects
the contact between the calcium-catalyst and carbon, and the reactivity may be reduced by agglomeration or sintering taking place
during the char gasification reactions [16]. Finally, reactions between catalyst species (K, Ca, Na and Mg) and other inorganic substances (Si, Al and P) lower the reactivity of char as discussed in the
previous paragraph.
Several empirical models have been proposed to express the dynamic change in catalytic activity as shown in Table 1. Some studies [8,12,17,18] introduced empirical correction functions to be
multiplied to the conventional rate formula such as that in Eq.
(1). These models have shown good predictability for the conversion rate of char gasification obtained by TGA. Other models
[10,15,19] employed parallel reaction model of non-catalytic and
catalytic gasification. The predictability of these models was confirmed up to char conversion of 80–90%, expressing well the initial
decrease of conversion rate, which is due to the vaporization of catalysts at early stage of char gasification.
The main drawback of empirical models is that they are difficult
to apply to conditions other than those at which they have been
obtained. Therefore, the char preparation and gasification experiments should be carried out at similar condition to that of the process to be applied. For instance in commercial fluidized-bed
gasification (FBG) units the conversion of mm-sized char particles
occurs at temperature in the range of 1023–1173 K. Moreover, in
FBG units the char is produced from coarse fuel particle that are
rapidly devolatilized. Then, in situ char gasification (devolatilization and subsequent gasification of the char produced, i.e. without
previous cooling) is important to be reproduced in the laboratory
because significant reduction in char reactivity after cooling the
char has been reported [20–22]. Only a few works have been conducted measuring gasification char reactivity applicable to commercial FBG [15,22].
A few models have obtained rate expressions as a function of
time. This is inconvenient when the model is applied to reactor
simulation because the time of stay of char particles varies greatly
between the particles. Rate expression as a function of conversion
is more convenient because the distribution variable is normalized
(from 0 to 1) and also because the global rate in a fuel bed or calculation cells can be often approximated by average char conversion [23], allowing great simplifications while maintaining the
accuracy.
In the present work, a theoretical model based on detailed
understanding on microscopic phenomena is developed, but still
with the aim of practical applications, i.e. in reactor simulations.
The dynamic change in catalytic activity of ash particles during
gasification of biomass char is studied by analyzing measurements
conducted with a variety of biomass chars under wide range of
operating conditions [24]. These data were obtained in a lab device
Please cite this article in press as: K. Umeki et al., A model of biomass char gasification describing the change in catalytic activity of ash, Chem. Eng. J.
(2012), http://dx.doi.org/10.1016/j.cej.2012.07.025
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K. Umeki et al. / Chemical Engineering Journal xxx (2012) xxx–xxx
Table 1
Empirical models for the dynamic change of conversion rate due to catalytic activity.
Source
Fuel
[18]
14 biomasses
[8]
Coal/activated carbon
[17]
Wooda
a
Char preparation
Exp.
Temp.
Atmosphere
Models
10 K/s, 1173 K, 1 min
TGA
1123 K
H2O 0.05 Mpa
10 K/s, 1173 K, 1 min
TGA
1123 K
CO2 0.1 MPa
20 K/min, 873 K, 2.5 h
TGA
1073 K
CO2 15 vol.%
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
r ¼ kð1 X cg Þ 1 wlnð1 X cg Þ ð1 þ ðcX cg Þp Þ
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
r ¼ kð1 X cg Þ 1 wlnð1 X cg Þ ð1 þ hp Þ
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
r ¼ kð1 X cg Þ 1 wlnð1 X cg Þ ð1 þ ðp þ 1Þðbt cg Þp Þ
[12]
21 Biomasses
<10 K/min, 723 K, 4 h
TGA
1023–1173 K
H2O 0.02–0.27 atm.
[15]
[10]
[19]
2 Brown coals
2 Biomasses
Brown coal
In situ rapid pyrolysis
5 K/s, 773 K, 47 s
100 K/min, 1173 K, 1 h
DFR
FBF
TGA
1173 K
1123 K
1023–1373 K
H2O 53 vol.%
Various H2O–H2–N2
H2O 1.0–4.0 MPa
r ¼ kðaðmk =mSi Þ þ bÞð1 X cg Þ2=3
r ¼ k1 expðk2 tÞ þ k3 ð1 X cg Þ
r ¼ k1 þ k2 ð1 X cg Þ
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
r ¼ k1 ð1 ðk2 tÞ2 Þ þ k3 ð1 X cg Þ 1 wlnð1 X cg Þ
TGA, thermogravimetric analyzer.
DFR, drop-tube fixed-bed reactor with forced gas flow.
FBF, fixed-bed reactor with forced gas flow.
a
Samples were acid-washed and/or impregnated with alkali metals.
2. Experimental
2.1. Samples
Nine biomass species were used in this study: barley straw (BS),
forest residue of pine (FR), kenaf (Kn), miscanthus (Mc), pine bark
(PB), pine sawdust (PS), reed canary grass (RC), wheat straw (WS),
and willow (Wl). Table 2 shows the proximate and ultimate analyses as well as ash composition of raw biomass fuels. All the biomass species contain a large amount of AAEM. Some biomasses
(Kn, PB, PS and Wl) contain negligible amount of SiO2 while SiO2
content is higher than the sum of AAEM species for other biomasses (BS, FR, Mc, RC and WS).
2.2. Experimental method
The gasification tests were carried out by measuring the weight
of the sample as a function of time in a pressurized thermobalance.
The initial sample weight of raw biomass was in the range of 50
Table 2
List of samples and their properties (barley straw (BS), forest residue of pine (FR),
kenaf (Kn), miscanthus (Mc), pine bark (PB), pine sawdust (PS), red canary grass (RC),
wheat straw (WS), and willow (Wl)).
Mc
PB
RC
PS
WS
Wl
analyses, wt.%-dry
76.1
79.3
79.4
18
19.4
17.0
5.9
1.3
3.6
BS
78.5
18.2
3.3
73.1
25.3
1.7
73.5
17.6
8.9
83.1
16.8
0.1
77.7
17.6
4.7
79.9
18.9
1.2
Ultimate analyses, wt.%-dry
C
46.2
51.3
46.6
H
5.7
5.8
5.8
N
0.6
0.4
1.0
O (diff.)
41.5
40.9
42.8
S
0.08
0.02
0.1
47.9
6.0
0.6
41.6
0.6
52.5
5.7
0.4
39.7
0.03
45.0
5.7
1.4
38.9
0.14
51.0
6.0
0.1
42.8
–
47.5
5.9
0.6
41.5
0.07
49.7
6.1
0.4
42.6
0.03
Proximate
V.M.
F.C.
Ash
Ash content, wt.%
SiO2
62.0
Al2O3
0.2
Fe2O3
0.2
CaO
4.5
MgO
2.2
K2O
19.3
Na2O
0.5
TiO2
0.02
SO3
1.4
P2O5
2.5
FR
in ash
38.5
4.7
3.7
15.4
4.0
8.3
0.4
0.5
1.6
3.2
Kn
and 60 mg with initial particle size less than 0.2 mm. It was confirmed that the measurements were conducted in the chemical regime [24]. Most of experiments were carried out at the
temperature range of 1023–1123 K, total pressure of 0.1 MPa,
and gas composition with 100 vol.% of CO2 or H2O. Additional
experiments were carried out at total pressure of 3.0 MPa and
using mixtures of CO2 and CO as gasification agent. Raw biomass
sample was fed to a reactor after the reactor was kept at the desired conditions, which ensure the rapid heating of sample and
in situ char gasification. Residual weight of sample basket was
measured at constant time interval. The thermobalance setup
and the principle of the measurement procedure are presented in
detail elsewhere [24].
2.3. Analysis method of experimental data and the definition of char
gasification
Overall fuel conversion was calculated as:
Xt ¼
mi m
mi mf
ð2Þ
Since the experiments were carried out as in situ char gasification after rapid devolatilization, it is essential to divide the mass
loss by char gasification from that due to devolatilization. Previous
experiments under chemically controlled conditions showed that
char gasification started after a short break following devolatilization [25]. It means that treating devolatilization and char gasification as parallel reactions with their own precursors makes no
mathematical difference from the real reaction scheme, consecutive reactions. The overall fuel conversion can be expressed as:
1 X t ¼ yv ol ð1 X dev Þ þ ychar ð1 X cg Þ
ð3Þ
100
Conversion [−], conversion rate [s−1 ]
reproducing as much as possible the operation conditions in practice. The rate coefficients for all biomasses and the conditions for
the applications of the proposed model are identified and discussed. Further improvements to be made to increase the understanding are proposed.
1-Xt
ychar(1−Xcg)
−1
10
6.6
1.8
1.2
30.8
6.0
13.3
1.3
0.08
5.7
2.7
42.8
0.5
0.4
7.6
4.8
25.3
0.7
0.03
2.1
5.3
1.3
5.3
0.3
40.6
4.5
7.6
0.5
0.12
2.0
4.8
89.8
1.4
1.1
3.5
1.5
3.1
0.1
0.05
1.1
4.1
8.3
2.0
1.8
41.8
11.8
12.3
0.3
0.12
1.9
5.2
59.9
0.8
0.5
7.3
1.8
16.9
0.5
0.04
1.1
2.3
0.4
0.3
0.2
30.8
5.1
26.5
0.3
0.02
3.0
11.5
rcg x 100
yvol(1−Xpy)
tcg,i
−2
10
0
20
40
60
time [s]
80
100
120
Fig. 1. The analysis procedure to obtain tcg vs. (1 Xcg) from experimental data.
Please cite this article in press as: K. Umeki et al., A model of biomass char gasification describing the change in catalytic activity of ash, Chem. Eng. J.
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K. Umeki et al. / Chemical Engineering Journal xxx (2012) xxx–xxx
By assuming that devolatilization can be represented by a single
first-order reaction the following can be written as:
By integrating Eq. (4) and after substitution of Xdev into Eq. (3),
this equation can be written as
1 X t ¼ yv ol expðkdev tÞ þ ychar ð1 X cg Þ
2nd regime
ð4Þ
ð5Þ
−1
10
1-Xcg [−]
r dev ¼ kdev ð1 X dev Þ
0
10
Fig. 1 shows the procedure to derive char conversion, Xcg, as a
function of time. First, experimental data of total fuel conversion
was plotted on a logarithmic scale against time. During the first
several data plots, log(1 Xt) decreased linearly against time,
which means that devolatilization proceeded as a first order reactions. Second, these data were used to obtain the parameters of
devolatilization term in Eq. (5). Since we can assume that Xcg remains zero during pyrolysis, Eq. (5) can be rewritten as:
−2
10
3rd regime
−3
10
(a)
−4
10
0
1000
2000
3000
Time, tcg [s]
4000
0
ð6Þ
3. Model
In this section, the model of biomass char gasification describing change in catalytic activity of ash is introduced based on the
experimental observation. Fig. 2a plots the typical profile of char
conversion obtained in the present study plotted as logarithm of
1 Xcg against time. Fig. 2b shows the same plots for the zero-order reaction, first-order reaction, and the parallel reaction of zeroand first-order reactions. The conversion profile in Fig. 2a can be
divided into three regimes. In the first regime, log(1 Xcg) decreased linearly against time. It means that char gasification in
the first region followed first-order kinetics as shown in Fig. 2b.
After the short period, ca. 150 s in Fig. 2a, char gasification was
transformed to another first-order reaction, which was slower than
the initial reaction (smaller slope in Fig. 2a). Then, char gasification
was accelerated (sharp increase in the slope shown in Fig. 2a) at
the last stage of char gasification, i.e. around 3000 s and above
90% of char conversion. As shown by comparison of the results
from Fig. 2a with Fig. 2b, the reaction in the last region seems to
follow zero-order kinetics. This visual observation is in agreement
with that commonly observed for catalytic char gasification [26].
Fig. 2c shows the plot of instantaneous reaction rate of char gasification, Rcg, against char conversion. If the reaction is first-order
with respect to char, the instantaneous reaction rate should be parallel to the horizontal axis. Here, the instantaneous reaction rate of
the second regime was parallel to the horizontal axis. Considering
the instantaneous reaction rate of the second regime is lower than
that in the first regime, the reaction in the second regime can be
thought to be non-catalytic reaction or reaction with a reduced catalytic activity where the catalytic material is stable. In fact, the
zero-order
reaction
(b)
−1
parallel
reaction
10
1-Xcg [−]
considering that the sum of initial mass fractions of volatiles and
char is one. The parameters in Eq. (6) were obtained by the least
square method. Next, mass loss profile due to char gasification in
Eq. (5), ychar(1 Xcg), was obtained. By using the obtained parameters and experimental value of overall fuel conversion, Xt, mass loss
profile by char gasification, ychar(1 Xcg), can be calculated by the
difference between the left hand side and the first term of right
hand side of Eq. (5). The result is shown as diamond symbols in
Fig. 1. Then, the initial time for char gasification (t = tcg,i) was defined as the time when the derivative of ychar(1 Xcg) showed maximum. The value of ychar(1 Xcg) at the initial time of char
gasification should be equal to (1 yvol), which determines the constraint of the least square calculation in the second step. After the
iterative calculation, elapsed time of char gasification, tcg = t tcg,i,
and char conversion, 1 Xcg = (1 Xt yvolexp(kdev t))/(1 yvol),
were obtained.
5000
10
first-order
reaction
−2
10
−3
10
Instantaneous reaction rate, Rcg x 100 [s−1 ]
1 X t ¼ 1 yv ol ð1 expðkdev tÞÞ
1st regime
0
1
0.2
0.4
0.6
0.8
Normalized time, tcg/tcg,Xcg=0.001 [-]
1
(c)
0.8
0.6
3rd regime
1st regime
0.4
0.2
0
2nd regime
0
0.2
0.4
0.6
Char conversion [−]
0.8
1
Fig. 2. (a) tcg vs. log(1 Xcg) of experiments with reed canary grass at T = 1123 K
and P CO2 ¼ 0:1 MPa; (b) tcg vs. log(1 Xcg) for zero-order, first-order, and their
parallel reactions; and (c) Rcg. vs. char conversion (Xcg).
instantaneous reaction rate of initial first-order reaction
(Xcg < 0.4) decreased at higher char conversion in agreement with
previous studies [15,19], where these observations were explained
on the basis of vaporization of catalytic ash. Then they introduced a
first-order reaction independent from char conversion assuming
that the reaction was a first-order with respect to the amount of
catalyst [15,19]. The zero-order reaction at the final stage of reaction can be explained by assuming that small amount of catalytic
ash remains in the ash and deactivation/vaporization process is
negligible.
On the basis of the visual findings discussed and the past results
from literature it seems reasonable to assume that during the second regime non-catalytic char gasification governed the overall
conversion rate because of the negligible fraction of catalytic ash.
However, catalytic gasification mechanism became dominant since
Please cite this article in press as: K. Umeki et al., A model of biomass char gasification describing the change in catalytic activity of ash, Chem. Eng. J.
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K. Umeki et al. / Chemical Engineering Journal xxx (2012) xxx–xxx
1
(a)
Conversion, Xcg [−]
0.8
0.6
Exp.
CLM1
JM
CLM2
0.4
0.2
0
0
1500
Time, tcg [s]
3000
4500
−3
7
x10
(b)
Conversion rate, rcg [s −1]
6
Exp.
CLM1
JM
CLM2
5
4
3
2
1
0
0
0.2
0.4
0.6
Conversion, Xcg [−]
0.8
1
Fig. 4. Comparison of the proposed models with experimental data. (a) Conversion
vs. time, (b) conversion rate vs. conversion. Operating conditions: red canary grass
at T = 1123 K and P CO2 ¼ 0:1 MPa.
Fig. 3. Suggested mechanisms describing the dynamic change in catalytic activity
during char gasification.
the fraction of catalyst in char that hardly deactivate/vaporize increased due to the carbon consumption. A proposed physical interpretation of the dynamic change in catalytic activity during char
gasification is summarized in Fig. 3. As shown in Fig. 2b, transition
from the second to third regimes can be expressed by applying parallel reaction mechanism of zero- and first-order reactions.
On the basis of the above discussion a three-parallel reaction
model is assumed to describe the overall conversion rate of biomass char gasification.
r cg ¼ r ccg;1 þ r ncg þ rccg;2
ð7Þ
rccg,1 corresponds to the part of conversion rate in first regime,
where catalytic char gasification with the deactivation of catalyst
occurs (CCG1). rccg,1 is independent from char conversion because
its dynamic change is governed by the loss of catalytic activity.
The loss of catalytic activity has been expressed by exponential
function of time in previous studies [15] as shown in Eq. (8). We call
this model Catalyst Loss Model 1 (CLM1) in this paper.
r ccg;1ðCLM1Þ ¼ kccg;1 expðkctloss tcg Þ
ð8Þ
The drawback of CLM1 is, as discussed above, that it is less suitable for the application to reactor simulation because the expression includes the residence time of char particle explicitly.
Therefore, model accounting catalytic effects during gasification
as a function of conversion is preferred. Johnson model [27] (JM:
Eq. (9)) fulfills this conditions, so that it is considered in this work.
Table 3
Comparison of the three models developed for catalytic char gasification with
deactivation (regime 1 in Fig. 2): CLM1 (Eq. (8)), JM (Eq. (9)) and CLM2 (Eq. (10)).
kccg,1
kctloss, n
kncg
kccg,2
Nexp
Deviation (%)
CLM1
JM
CLM2
5.27 103
1.44 102
4.86 104
3.18 105
545
0.50
6.01 103
15.3
4.36 104
3.82 105
545
0.28
5.65 103
17.0
4.39 104
3.78 105
545
0.28
rccg;1ðJMÞ ¼ kccg;1 ð1 X cg Þ2=3 expðnX 2cg Þ
ð9Þ
2/3
Here, the term, (1 Xcg) , expresses the change of surface area
for char gasification, which can be introduced according to the
shrinking core model without the formation of ash layer. The exponential term represents the relative reactivity of the surface area,
which is, in our case, deactivation of catalyst. Although the deactivation rate of catalyst is independent of char conversion, it can be
expressed by applying a correlation factor, n. However, the reactivity of catalytic char gasification is known to be independent of the
char conversion [15,26], but of the reactive surface area of catalyst
[16,28]. Therefore, we modified JM to exclude the surface area
term, and examined its predictability as shown in Eq. (10)
(CLM2: catalyst loss model 2).
rccg;1ðCLM2Þ ¼ kccg;1 expðnX 2cg Þ
ð10Þ
The ability of the three models presented for the regime 1 (catalytic char gasification with deactivation) will be assessed in the
results in order to select the most appropriate one.
Please cite this article in press as: K. Umeki et al., A model of biomass char gasification describing the change in catalytic activity of ash, Chem. Eng. J.
(2012), http://dx.doi.org/10.1016/j.cej.2012.07.025
6
K. Umeki et al. / Chemical Engineering Journal xxx (2012) xxx–xxx
1.2
Conversion rate, rcg/rcg,0 [-]
(a)
1123 K, 0.1 MPa H2O
1123 K, 3.0 MPa H2O
0.8
0.6
0.4
0.2
0
1.2
Conversion rate, rcg/rcg,0 [-]
(b)
1023 K, 0.1 MPa CO2
1123 K, 0.1 MPa CO2
1123 K, 3.0 MPa CO2
1123 K, 2.7 MPa CO2+0.3 MPa CO
1
(c)
(d)
1023 K, 0.1 MPa CO2
1123 K, 0.1 MPa CO2
1123 K, 3.0 MPa CO2
1123 K, 2.7 MPa CO2+0.3 MPa CO
1
1123 K, 0.1 MPa H2O
1123 K, 3.0 MPa H2O
1123 K, 2.7 MPa H2O+0.3 MPa H2
0.8
0.6
0.4
0.2
0
0
0.2
0.4
0.6
Conversion, Xcg [−]
10
0.8
0.2
0.4
0.6
Conversion, Xcg [−]
0.8
1
Fig. 5. Xcg vs. rcg/rcg,i by the experiments under various reaction conditions. (a) Barley straw under CO2 atmosphere, (b) barley straw under H2O atmosphere, (c) miscanthus
under CO2 atmosphere, and (d) miscanthus under H2O atmosphere.
Table 4
Rate coefficients for various reaction conditions according the three-parallel model given by Eqs. (6), (9), (10), (11).
Temp.
Pressure
Atmosphere
kccg,1
n
kncg
kccg,2
Dev. (%)
Barley straw
1023 K
1123 K
0.1 MPa
0.1 MPa
3.0 MPa
CO2
CO2
CO2
CO2:CO = 9:1
5.07 104
6.16 103
3.99 103
8.36 104
37.9
48.3
124.0
30.4
2.15 104
3.27 103
5.33 103
2.27 103
2.75 106
1.27 105
4.21 104
1.37 104
0.75
0.40
1.36
0.20
Miscanthus
1023 K
1123 K
0.1 MPa
0.1 MPa
3.0 MPa
CO2
CO2
CO2
CO2:CO = 9:1
3.64 103
2.69 103
3.44 103
5.70 103
344.9
120.5
276.3
110.2
4.29 104
2.68 103
3.28 103
1.76 103
1.05 105
8.62 105
9.96 104
2.08 104
1.72
1.11
2.06
1.03
X
2
The second term in Eq. (7) represents the part of conversion rate
by non-catalytic gasification. As observed from Fig. 2a and c, conversion rate corresponding to the second regime was expressed
by the first-order with respect to char conversion.
S¼
r ncg ¼ kncg ð1 X cg Þ
4.1. Comparison of models for the catalytic gasification with
deactivation
ð11Þ
The third term in Eq. (7), i.e. conversion rate corresponding to
the third regime, represents catalytic char gasification by stable
catalyst. It was expressed by zero-order reaction.
r ccg;2 ¼ kccg;2
ð12Þ
Substitution of Eqs. (8)–(12) in Eq. (7) yields the overall conversion rate of char gasification. There are four unknown rate coefficients in the proposed model. These rate coefficients were
calculated by minimizing the error between the experimental
and calculated values of char conversion. Analytical solution for
the integration of Eq. (7) was found only when CLM1 is assumed,
so numerical integration is applied when using JM and CLM2. Minimization of the sum of squared residual error between the experimental data and calculated value was applied.
X cg;exp X cg;cal
ð13Þ
4. Results and discussion
The ability of the three models developed to fit the measurements in the regime 1 (catalytic char gasification with deactivation, i.e. CLM1, JM and CLM2) was assessed. The aim was to
select the most suitable model for the application in reactor
simulation.
Fig. 4 compares the results from these three models with the
experimental data at T = 1123 K with 100 vol.% of CO2 under atmospheric pressure. Experimental data shown in Fig. 4 was obtained
with reed canary grass as fuel. All three models fitted sufficiently
well with experimental profile of char conversion against time. Table 3 shows the rate coefficients and deviation, dev = (S/Nexp)1/2, for
three models. All the models showed very small deviation although
JM and CLM2 showed slightly better fitting. Fig. 4b shows char conversion vs. conversion rate curve with three suggested models and
Please cite this article in press as: K. Umeki et al., A model of biomass char gasification describing the change in catalytic activity of ash, Chem. Eng. J.
(2012), http://dx.doi.org/10.1016/j.cej.2012.07.025
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K. Umeki et al. / Chemical Engineering Journal xxx (2012) xxx–xxx
Conversion rate, rcg x100 [s -1]
1
Conversion rate, rcg x100 [s -1]
Forest residue (FR)
Miscanthus (Mc)
Pine bark (PB)
Kenaf (Kn)
0.8
0.6
0.4
0.2
0
1
Pine sawdust (PS)
0.8
0.6
0.4
0.2
0
1
Conversion rate, rcg x100 [s -1]
Barley Straw (BS)
Reed canary grass (RCG)
Wheat straw (WS)
Willow (Wl)
0.8
0.6
0.4
0.2
0
0
0.2
0.4
0.6
Conversion, Xcg [−]
0.8
10
0.2
0.4
0.6
Conversion, Xcg [−]
0.8
1 0
0.2
0.4
0.6
Conversion, Xcg [−]
0.8
1
Fig. 6. Char conversion vs. conversion rate curves with experiments and the proposed model for various biomass species. Operating conditions: T = 1123 K and
P CO2 ¼ 0:1 MPa.
Table 5
Rate coefficients for various biomasses according the three-parallel model given by Eqs. (6), (9), (10), (11) (T = 1123 K, CO2 0.1 MPa).
Barley straw
Forest residue
Kenaf
Miscanthus
Pine bark
Reed canary grass
Pine sawdust
Wheat straw
Willow
kccg,1
n
kncg
kccg,2
Dev. (%)
6.16 103
4.58 103
1.21 102
2.69 103
2.13 103
5.65 103
9.84 103
3.36 103
3.22 103
48.3
14.8
2.97
120.5
16.6
17.0
1.70
9.52
38.3
3.27 103
2.19 103
2.07 102
2.68 103
5.46 104
4.39 104
1.13 102
3.00 103
3.44 103
1.27 105
3.39 104
8.18 104
8.62 105
4.33 104
3.78 105
1.89 103
1.59 105
1.07 103
0.40
0.45
0.45
1.11
0.62
0.28
0.26
0.29
1.19
experiments with the rate coefficients that gave the best fitting for
time vs. char conversion curves. Here, the difference is more significant: conversion rate prediction by JM and CLM2 agreed with the
experimental data for the entire range of char conversion while
CLM1 showed considerable error for char conversion below 0.4,
i.e. during the first catalytic char gasification regime.
Besides the good fitting, an important aspect is the computational cost. During the estimation of rate coefficients, computa-
tional times to calculate a certain number of numerical
integration were measured. The exact number is not shown here
because it varies with the environment of computation and other
factors. However, the computational time of CLM2 was 2–5 times
lower than that of JM. The three models were compared for all
the reaction conditions discussed in the following sections, and
the same tendency with the example (Fig. 4 and Table 3) was observed. Therefore, CLM2 was identified as the most suitable model
Please cite this article in press as: K. Umeki et al., A model of biomass char gasification describing the change in catalytic activity of ash, Chem. Eng. J.
(2012), http://dx.doi.org/10.1016/j.cej.2012.07.025
8
K. Umeki et al. / Chemical Engineering Journal xxx (2012) xxx–xxx
for the application in reactor simulation. In the following sections
we shall call ‘‘suggested/proposed model’’ to mean the three-parallel reaction model (Eq. (7)) with rccg,1 given by CLM2 in Eq. (10) the
other two regime rates in Eq. (7) are represented by Eq. (11) (rncg)
and Eq. (12) ((rccg,2)).
4.2. Model validity under various reaction conditions
Fig. 5 shows the char conversion vs. normalized conversion rate
(rcg/rcg,i) curves obtained by the experiments under various reaction conditions. Two biomass species, barley straw and miscanthus, were selected for the analysis of Fig. 5. At all reaction
conditions under CO2 and CO2/CO atmosphere, typical rcg/rcg,i
curves like those presented in Fig. 4b were observed. On the other
hand, rcg/rcg,i curve under H2O and H2O/H2 atmosphere showed no
sign of initial conversion rate drop, showing instead, rather similar
shape of the random pore model. The proposed model seems to be,
therefore, applicable only under CO2 and CO2/CO atmosphere under examined conditions. There is no clear evidence for the difference of rcg/rcg,i curve under H2O based atmosphere and CO2 based
atmosphere. Though, it is known that the reaction atmosphere affects the release of Na and K from fuel particles [29]. Then it can be
argued that chemical reactions occurring in the ash were decisively
influenced by the atmosphere, resulting in different deactivation
mechanism of catalyst. Apparently, the deactivation of catalytic
activity was not significant under steam based atmosphere in the
present study. Further study on AAEM behavior under various
atmospheres is necessary to understand the different shape of conversion rate curves under different atmosphere.
The shape of rcg/rcg,i vs. Xcg curve, i.e. f(Xcg) in Eq. (1), varied with
composition of the gasification mixture (CO2/CO). In general, having the same f(Xcg) for different reaction condition is necessary to
apply conventional char gasification models such as random pore
model. On the other hand, the proposed model does not require
to have the same f(Xcg) for different reaction conditions. Since four
rate coefficients represent the different processes, each coefficient
has different temperature and pressure dependence, meaning that
it is acceptable for the model to have different f(Xcg) at different
reaction conditions. In Fig. 5a and c, the increase of temperature resulted in relatively slower deactivation of catalyst than char conversion process, meaning that the deactivation of catalyst is less
sensitive to the reaction temperature than to catalytic effect itself.
Table 4 shows the rate coefficients and the deviation under CO2
and CO2/CO atmosphere. The proposed model showed good fitting
ability for all the reaction conditions.
4.3. Model validity for various biomasses
Fig. 6 shows the char conversion vs. conversion rate curves with
experiments and the proposed model for various biomass species.
Table 5 shows the rate coefficient and the deviation for these data.
Experimental conditions of these figures were at the reaction temperature of 1123 K, with 100 vol.% of CO2, and under atmospheric
pressure.
The proposed model showed good fitting ability to nine biomass
species examined. However, kenaf and pine sawdust did not show
the typical conversion rate curve. In fact, the parameters with best
fit for these two biomass species included negative values, which
physically mean the char conversion decreased with reaction. A
common physical property of these two biomass species is that
they contain less silicon in ash-forming elements than other biomass (see Table 2). Other two biomasses with less silicon contents,
pine bark and willow, showed typical shape of conversion rate, but
they showed slightly higher deviation than other biomasses. It is
concluded that silicon might have played a significant role on catalyst deactivation process in the present study by forming silicates
[9]. To generalize the conclusion it is recommended to validate
with larger number of biomass species.
5. Conclusions
This work was aimed to identify a comprehensive description of
catalytic effects during char gasification under various conditions
relevant for biomass gasifiers. Based on measurements conducted
in a thermobalance, a reaction model of char gasification was proposed to describe the dynamic change in catalytic activity, comprising three regimes of reaction (1) catalytic char gasification
with the deactivation of catalyst, (2) non-catalytic char gasification, and (3) catalytic char gasification with small amount of stable
catalyst, i.e. without deactivation. Three functions describing the
deactivation of catalyst were compared, and the model with the
best fitting ability requiring the less computational time was selected. The ability to describe the conversion rate obtained by
experiments under various conditions (temperature: 1023–
1123 K, pressure: 0.1–3.0 MPa and gasification mixtures of CO2–
CO–H2O–H2) was examined.
The proposed model described the conversion rate of char gasification well with most biomass samples examined. However, biomass samples with small amount of silicon in their ash-forming
elements maintained its catalytic activity for the entire gasification
process. The model was still able to describe the conversion rate for
these biomass species when the catalytic activity was maintained,
but the best fit parameters gave negative rate coefficient for the
first regime. For high silicon content biomass species the model
was shown to work for gasification using mixtures of CO2 and CO
for the ranges of temperature of 1023–1123 K and pressure of
0.1–3.0 MPa. Char gasification under steam atmosphere was observed to maintain its catalytic activity (like biomass with low silicon content) so the model seems to be not applicable for steam
gasification for the operating conditions investigated.
To further strengthen the application of the model, some issues
to be further investigated were identified (i) the AAEM transformation behavior during char gasification; (ii) the reactions affecting
the catalytic activity of the char during steam to be able to apply
the model (or a modified model) under steam atmosphere; (iii)
the dependency of rate coefficients on the reaction conditions in
order to make the model more generally applicable.
Acknowledgments
The authors acknowledge the financial support of Nordic Energy Research Top-level Research Initiative for supporting this
work through Nordsyngas project. The support of the project
GASIFREAC, financed by the Academy of Finland, is also gratefully
acknowledged. KU thanks Bio4Energy, a strategic research environment appointed by the Swedish government, for supporting
this work.
References
[1] A. Gómez-Barea, B. Leckner, Modeling of biomass gasification in fluidized bed,
Prog. Energy Combust. Sci. 36 (2010) 444–509.
[2] N.M. Laurendeau, Heterogeneous kinetics of coal char gasification and
combustion, Prog. Energy Combust. Sci. 4 (1978) 221–270.
[3] S.K. Bhatia, D.D. Perlmutter, A random pore model for fluid–solid reactions:
Part 1. Isothermal, kinetic control, AIChE J. 26 (1980) 379–386.
[4] T. Suzuki, H. Ohme, Y. Watanabe, Alkali metal catalyzed CO2 gasification of
carbon, Energy Fuels 6 (1992) 343–351.
[5] Y. Huang, X. Yin, C. Wu, C. Wang, J. Xie, Z. Zhou, L. Ma, H. Li, Effects of metal
catalysts on CO2 gasification reactivity of biomass char, Biotechnol. Adv. 27
(2009) 568–572.
[6] K. Mitsuoka, S. Hayashi, H. Amano, K. Kayahara, E. Sasaoka, Md. A. Uddin,
Gasification of woody biomass char with CO2: the catalytic effects of K and Ca
species on char gasification reactivity, Fuel Process. Technol. 92 (2011) 26–31.
Please cite this article in press as: K. Umeki et al., A model of biomass char gasification describing the change in catalytic activity of ash, Chem. Eng. J.
(2012), http://dx.doi.org/10.1016/j.cej.2012.07.025
K. Umeki et al. / Chemical Engineering Journal xxx (2012) xxx–xxx
[7] K. Matsumoto, K. Takeno, T. Ichinose, T. Ogi, M. Nakanishi, Gasification reaction
kinetics on biomass char obtained as a by-product of gasification in an
entrained-flow gasifier with steam and oxygen at 900–1000 °C, Fuel 88 (2009)
519–527.
[8] Y. Zhang, S. Hara, S. Kajitani, M. Ashizawa, Modeling of catalytic gasification
kinetics of coal char and carbon, Fuel 89 (2010) 152–157.
[9] M.P. Kannan, G.N. Richards, Gasification of biomass chars in carbon dioxide:
dependence of gasification rate on the indigenous metal content, Fuel 69
(1990) 747–753.
[10] M. Kajita, T. Kimura, K. Norinaga, C.-Z. Li, J.-i. Hayashi, Catalytic and
noncatalytic mechanisms in steam gasification of char from the pyrolysis of
biomass, Energy Fuels 24 (2010) 108–116.
[11] D.W. Mckee, S. Chatterji, Gasification of graphite in carbon dioxide and water
vapor—the catalytic effects of alkali metal salts, Carbon 20 (1982) 59–66.
[12] C. Dupont, T. Nocquet, J.A. Da Costa Jr., C. Verne-Tournon, Kinetic modeling of
steam gasification of various woody biomass chars: influence of inorganic
elements, Bioresour. Technol. 102 (2011) 9743–9748.
[13] B.B. Hattingh, R.C. Everson, H.W.J.P. Neomagus, J.R. Bunt, Assessing the
catalytic effect of coal ash constituents on the CO2 gasification rate of high
ash, South African coal, Fuel Process. Technol. 92 (2011) 2048–2054.
[14] B. Bayarsaikhan, J. Hayashi, T. Shimada, C. Sathe, C. Li, A. Tsutsumi, et al.,
Kinetics of steam gasification of nascent char from rapid pyrolysis of a
Victorian brown coal, Fuel 84 (2005) 1612–1621.
[15] T. Kitsuka, B. Bayarsaikhan, N. Sonoyama, S. Hosokai, C.-Z. Li, K. Norinaga, J.-i.
Hayashi, Behavior of inherent metallic species as a crucial factor for kinetics of
steam gasification of char from coal pyrolysis, Energy Fuel 21 (2007) 387–394.
[16] D. Cazorla-Amorós, A. Linares-Solano, C. Salinas-Martinez de Lecea, H.
Yamashita, T. Kyotani, A. Tomita, M. Nomura, XAFS and thermogravimetry
study of the sintering of calcium supported on carbon, Energy Fuels 7 (1993)
139–145.
[17] R.P.W.J. Struis, C. von Scala, S. Stucki, R. Prins, Gasification reactivity of
charcoal with CO2. Part I: Conversion and structural phenomena, Chem. Eng.
Sci. 57 (2002) 3581–3592.
9
[18] Y. Zhang, M. Ashizawa, S. Kajitani, K. Miura, Proposal of a semi-empirical
kinetic model to reconcile with gasification reactivity profiles of biomass
chars, Fuel 87 (2008) 475–481.
[19] G. Löwenthal, Makrokinetisches Modell zurBeschreibung von Gas-FeststoffReaktionenangewendet auf die Wasserdampfvergasung grosser Kohlekörner
(Macro Kinetic Model for the Description of Gas–Solid Reactions Applied on
Steam Gasification of Larger Coal Particles). PhD thesis, Universität-GHS-Essen,
1993, p. 155 (in German).
[20] K. Miura, K. Hashimoto, P.L. Silveston, Factors affecting the reactivity of coal
chars during gasification, and indices representing reactivity, Fuel 68 (1989)
1461–1475.
[21] H. Liu, H. Zhu, M. Kaneko, S. Kato, T. Kojima, High-temperature gasification
reactivity with steam of coal chars derived under various pyrolysis conditions
in a fluidized bed, Energy Fuels 24 (2010) 68–75.
[22] S. Nilsson, A. Gómez-Barea, D.F. Cano, Gasification reactivity of char from dried
sewage sludge in a fluidized bed, Fuel 92 (2012) 346–353.
[23] A. Gómez-Barea, B. Leckner, D. Santana, P. Ollero, Gas–solid conversion in
fluidised bed reactors, Chem. Eng. J. 141 (2008) 151–168.
[24] A. Moilanen, Thermogravimetric Characterisations of Biomass and Waste for
Gasification Processes, VTT, Espoo, 2006. <http://www.vtt.fi/inf/pdf/
publications/2006/P607.pdf>.
[25] K. Umeki, T. Namioka, K. Yoshikawa, The effect of steam on pyrolysis and char
reactions behavior during rice straw gasification, Fuel Process. Technol. 94
(2012) 53–60.
[26] K. Hashimoto, K. Miura, J.-J. Xu, A. Watanabe, H. Masukami, Relation between
the gasification rate of carbons supporting alkali metal salts and the amount of
oxygen trapped by the metal, Fuel 65 (1986) 489–494.
[27] J.L. Johnson, Kinetics of Coal Gasification, Willey, New York, 1979.
[28] A.A. Lizzio, L.R. Radovic, Transient kinetic study of catalytic char gasification in
carbon dioxide, Ind. Eng. Chem. Res. 30 (1991) 1735–1744.
[29] J.-Y. Lin, Y. Liu, Y. Qiao, Release rules of alkali and alkaline earth metals during
coal gasification in CO2 and O2/CO2, Adv. Mater. Res. 396–398 (2012) 1206–
1209.
Please cite this article in press as: K. Umeki et al., A model of biomass char gasification describing the change in catalytic activity of ash, Chem. Eng. J.
(2012), http://dx.doi.org/10.1016/j.cej.2012.07.025