31st European Biomass Conference and Exhibition, 5-8 June 2023, Bologna, Italy
PREDICTION OF TOTAL PRODUCT COMPOSITION FROM PYROLYSIS AND GASIFICATION OF
LIGNOCELLULOSIC BIOMASS: A MODEL FOR REACTOR DESIGN AND OPTIMIZATION
Richard Ochieng1, Alejandro L. Ceron2, Alar Konist2 and Shiplu Sarker1
of Civil and Manufacturing Engineering, Faculty of Engineering, Norwegian University of Science and
Technology, NTNU, Teknologivegen 22,2815 Gjøvik, Norway
2Department of Energy Technology, Tallin University of Technology, TalTech, Ehitajate tee 5,12616 Tallin, Estonia
1Department
ABSTRACT: As far as thermochemical biorefineries are concerned, the development of a gasification process model
capable of accurately predicting the total composition of non-condensable gases, char, and tar remains an unsolved
problem. This paper presents a detailed kinetic mechanism for predicting the total product yield and composition of
slow pyrolysis and steam gasification. The detailed kinetic mechanism in the study extends the previously proposed
multi-step kinetic mechanism of Faravelli and co-authors to include extractives, secondary reactions for thermal
cracking, and steam reformation of primary pyrolysis products. The model has been implemented in Aspen Plus®
and validated using experimental data obtained from the slow pyrolysis and steam gasification (Steam/Biomass ~
0.21) of pine wood in a fixed-bed batch reactor at temperatures of 750, 850, and 930 oC. A comparison of the
gasification model with experimental data revealed average absolute errors of 17.8 percent for char yield and 17.3
percent for gas composition. While detailed experimental data is necessary to verify tar formation, the model can be
useful for studying pyrolysis and gasification processes in terms of their operating parameters, especially
temperatures and solid and vapor residence times.
Keywords: Lignocellulosic biomass, pyrolysis, gasification, kinetic modeling, Aspen Plus®
1
INTRODUCTION
Despite the necessity to understand the effect of
reactor operating and design parameters on tar formation,
numerous research studies have often neglected or
ignored the effect of tar formation in gasification
modeling. Instead, researchers assume the complete
conversion of tar into light hydrocarbon components via
non-conventional
biomass
simulations
[5,11].
Furthermore, previous studies have also demonstrated
that non-conventional biomass considerations usually
lead to pyrolysis and/or gasification models with an
unacceptable degree of accuracy [12]. At the same time,
previous studies have identified kinetic modeling of
pyrolysis and gasification processes as the most reliable
and effective approach to the design and optimization of
reactor performance [5,12,13].
Although kinetic analysis has the potential to offer
valuable insight into the behavior of bio-oils and tars, the
limited availability of relevant kinetic data, specifically
for cracking tars, poses a challenge. According to some
researchers, it is possible to predict the composition of tar
components based on assumptions and lumped
representations of the kinetics [14-16]. However, despite
the model simplicity, validation of results can unrealistic
due to the use a lumped set of kinetic parameters.
Moreover, studies that combine that use kinetic models to
predict both devolatilization and gasification of
biomasses are not frequently published [17]. By using
kinetic models to evaluate both the devolatilization and
gasification steps, more realistic insight into the various
factors that influence gasifier design and optimization can
be gained [18].
In order to predict the detailed product distribution of
biomass gasification, this work extends the multi-step
kinetic mechanism of biomass devolatilization, which
was first proposed by Faravelli and co-authors, to include
the decomposition of extractives and thermal and steam
cracking reactions of primary pyrolysis products. The
kinetic model was implemented in Aspen Plus® and
validated using experimental data obtained from the slow
pyrolysis and steam gasification of pine wood in a fixedbed batch reactor at various temperatures. The results
provide valuable insights into the effect of reactor
operating conditions on product distribution.
In the context of the circular economy, biorefineries
present an attractive, and sustainable way to produce
various renewable products from biomass. In the
literature, a variety of biochemical and thermochemical
methods have been investigated for the production of
biofuels and bioproducts. In contrast to biochemical
conversion techniques that use slow-acting microbes,
thermochemical conversion technologies can quickly
convert all types of dry biomass into valuable products
[1].
As an intermediate process in thermochemical
methods, pyrolysis stands out as the most significant step
in biomass conversion via gasification and combustion
processes. In light of this, the application of a two-stage
approach involving biomass devolatilization and
secondary cracking reactions is of primary importance
since the pre-step can significantly influence the final
product [2].
While significant progress has been made in
biorefinery research, commercialization remains a
challenge due to technological immaturity [3]. While
process engineering techniques present the necessary
tools to synthesize, optimize, and up-scale technologies
in biorefineries, more research is still needed to fully
understand and predict the complex mechanisms of
biomass conversion at the reactor level [4].
According to Mutlu and Zeng [5], the absence of
kinetic models to simulate tar formation is one of the
drawbacks limiting the complete prediction of biomass
gasification products in the reactor. Tar is considered a
very complex material, whose composition depends on
many parameters such as temperature, type of gasifying
agent, and reactor design [6,7].
As part of gasification products, tar is produced by
the decomposition, oxidation, and polymerization of
different biomass devolatilization products at high
temperatures [8, 9]. In addition to temperature, the yield
and composition of tar in gasification products are also
highly dependent on the nature of the biomass feedstock
and other reactor parameters, such as the solid and vapor
residence times of the reactor contents [10].
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31st European Biomass Conference and Exhibition, 5-8 June 2023, Bologna, Italy
2
MATERIAL AND METHODS
the unreacted solid components of biomass, i.e.,
hemicellulose, cellulose, lignin, and extractives.
While the batch-type reactor in Aspen Plus® has been
recommended for a slow pyrolysis process [25], the
continuous-type (RCSTR) has been used in this study due
to constant temperature conditions during the
experiments.
For the thermodynamic properties of conventional
components in the model, the Peng-Robinson Equation of
State with Boston-Mathias (PR-BM) modification was
used. The thermophysical properties of components
including; 3-hydroxypropyl, para-coumaryl alcohol,
sinapyl aldehyde, xylose, Lignin (Lig, Lig-C, Lig-CC,
Lig-O, Lig-OH), extractives (Tannin and triglyceride),
cellulose and activated cellulose, and hemicellulose
components were estimated using an approach proposed
by Gorensek et al. [26].
2.1 Ultimate and proximate analysis
In this study, pinewood elements and proximate
compositions were analyzed. A description of the
equipment used for elemental and proximate analysis can
be found in ref. [19, 20]. The biochemical composition
of pine wood considered in this study was obtained from
the literature.
2.2 Experimental setup
The slow pyrolysis and steam gasification
experiments were performed in a laboratory-scale fixed
bed reactor, described in more detail in ref. [19, 20].
The sweeping gas flow (argon) was maintained at 100
mL/min throughout the experiment, and a steam-tobiomass ratio of 0.21 was used to study steam
gasification at all temperatures (750, 850, and 930 oC).
To ensure reproducibility, the experiments were
performed at least three times for each temperature.
As shown in Fig. 1, an FTIR was used to determine
the solid residence time, and 8 minutes was deemed
sufficient to complete devolatilization. The vapor
residence time was estimated from the carrier gas flow
rate and the reactor volume.
3
RESULTS AND DISCUSSION
3.1 Biomass characterization
The ultimate, proximate, and biochemical
compositions of pine wood (Pinus sylvestris) used in this
study are shown in Table I. The biochemical composition
of pine wood (Pinus sylvestris) was obtained from the
literature [27]. The composition of lignin components
(Lig-C, Lig-H, and Lig-O), tannin (TANN), and
triglycerides (TGL) was estimated from the lignin and
extractive weight fraction using the approach proposed
by Faravelli et al. [28], and Debiagi et al. [22],
respectively. Using Debiagi's method to estimate the total
biochemical composition was not possible due to the
overestimation of the cellulose weight fraction. In Table
II, we include the estimated composition of lignin and
extractive components.
Figure 1: Effect of solid residence time on the total
organic carbon (TOC) of pine wood.
Table I: Ultimate and proximate analysis of pine woods.
To investigate the secondary reactions, the vapor
residence time was obtained from the sweep gas flow and
the volume of the quartz tube reactor (diameter 1 inch
and length 24 inches). As the biomass sample was placed
halfway inside the reactor tube, only the first half of the
tube was considered as the secondary reaction zone.
Therefore, at a sweep gas flow rate of 100 mL/min, the
vapor residence time was estimated to be 40 seconds. The
pyrolytic gas was collected in Tedlar bags as it exited the
batch reactor. The volumetric concentration of
combustible gases H2, CO, and CH4 was measured using
gas chromatography with a thermoconductivity detector
(GC-TCD) and a Gazohrom 3101 gas analyzer. Each
experiment and/or gas analysis was repeated to ensure
reproducibility of the results.
Proximate analysis
(wt.%)
Ultimate analysis
(wt.%)
Biochemical analysis
(wt.%) [27]
Moisture
Volatile matter
Fixed carbon
Ash
C
H
N
S
O
Cellulose
Hemicellulose
Lignin
Extractives
8.5
85.2
14.5
0.3
50.1
6.6
0.19
n.d.
43.1
41.0
25.7
28.6
4.8
Table II: Lignin and extractives components (wt.%)
Lig-C
16.60
2.3 Modeling in Aspen Plus®
In this study, drying was modeled based on the
kinetics of the devolatilization of water (moisture)
loosely bound in the biomass material. A multistep
kinetic scheme originally proposed by Faravelli et al. [21]
was modified to accommodate extractives [22], and
secondary reactions for tar cracking and steam
gasification [23, 24] (see support information).
In the kinetic model, char was described as carbon and
ash. However, in this work, the composition of char after
the reaction was assumed to consist of carbon, ash, and
Lig-O
2.14
Lig- H
9.75
TANN
3.36
TGL
1.44
3.2 Slow pyrolysis
Pyrolysis and gasification have three product
distributions: char, non-condensable vapors, and liquid or
condensable vapors (tar). The study was divided into two
parts: i) a two-stage process to slow pyrolysis of wood
biomass at a low sweep gas flow rate (100 mL/min), so
secondary tar reactions could be investigated; ii) a twostage process to predict steam gasification product yields
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31st European Biomass Conference and Exhibition, 5-8 June 2023, Bologna, Italy
and composition, as shown in Fig.2. For investigating the
slow pyrolysis and thermal cracking, the steam flow rate
was zero.
Due to the fact that secondary pyrolysis becomes
more dominant at temperatures above 600 oC [29], the
model considered a primary devolatilization temperature
of 500 oC and a solid residence time of 8 minutes. The
secondary reactions have been modeled in the second
reactor at vapor residence times of 40 seconds.
ethane as intermediate products from the thermal
decomposition of the tar.
B. Char yield
As shown in Figure 4, the model's predicted char
yield closely matched the experimental data. However,
the char yield obtained from experimental data was
slightly lower than that predicted by the model. A mean
absolute error of 14.1% was obtained. The difference in
Figure 2: Process flow scheme in Aspen Plus®
char amounts could be attributed to losses during the
removal of the char sample from the reactor.
A. Gas composition
As illustrated in Fig.3, the predicted hydrogen
composition increased from 25 to 31%, while that of
carbon monoxide slightly increased from 40 to 42.5%
between 750 and 930 oC. There was a slight increase in
CO due to the thermal cracking of carboxyl and carbonyl
groups in cellulose, which decompose below 750 °C [30].
Figure 4: Comparison of the char yield (wt.%) from slow
pyrolysis model with experimental data.
C. Effect of temperature on product distribution
From the simulation model, primary pyrolysis (noncracking) yields at 500 oC yielded 12.27 wt.% gas, 48.15
wt.% tar, 12.81 wt.% water, and 26.7 wt.% char. The
primary pyrolysis liquid products consisted of aromatic
and aliphatic compounds, aldehydes, alcohols,
levoglucosan, phenols, other hydrocarbon compounds,
and water [21].
Under thermal cracking conditions (750 to 930 oC), the
char yield decreased as the gas yield increased, as shown
in Table III. However, the tar yield changed only slightly.
Perhaps, under the model conditions, the increase in gas
yield was mainly due to the heterogeneous reaction of the
pyrolytic products with char [23]. At lower temperatures
(500–700 oC), previous studies have shown that the gas
fraction increased at the expense of the liquid fraction
[10]. At higher temperatures, further investigations have
revealed that vapor residence time is the controlling
Figure 3: Comparison of the char yield (wt.%) from slow
pyrolysis model with experimental data.
The model and experimental results obtained in this study
were in close agreement with other studies in the
literature [31]. The mean absolute error between the
model and experimental results was 21.53%. The model
predicted a slightly higher H2 composition (26.5 %) than
the experiment (21 %) at 750 oC. The model prediction
for CO concentration was about 14% lower than that
observed in the experiment. The CO composition
predicted by the model is within the range of that
reported by Tao et al.[31]. However, the model estimated
a low methane concentration at all temperatures when
compared to the experimental data in this study and that
of Tao et al. [31]. It is possible that the low methane yield
in the model is caused by the inclusion of ethylene and
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31st European Biomass Conference and Exhibition, 5-8 June 2023, Bologna, Italy
factor during thermal cracking. A detailed analysis of the
effect of vapor residence time was discussed in the
subsequent subsection.
thermal cracking is not a major contributor to char yield
in the process.
Furthermore, the simulation results revealed that within 1
second, the gas yield increased by over 112 percent at the
expense of tar thermal cracking, as shown in Figures 5
and 6.
Table III: Effect of temperature on the gas, tar, and char
yield from the model.
Weight percent
750 oC
850 oC
930 oC
Gas
57.7
64.1
65.5
Tar
5.7
5.8
5.8
Char
22.3
15.7
14.2
Water
14.2
14.3
14.4
The total product composition after thermal cracking
is shown in Table IV.
Figure 5: Effect of vapor residence time on the gas yield
Table IV: Product composition of slow pyrolysis.
The tar decomposition reactions became even faster
as the temperature increased from 750 to 930 oC. These
findings indicated that the thermal cracking of unstable
hydrocarbons in tar occurs rapidly at higher temperatures.
The char yield increased by less than 1.0 % within 1
second of the vapor residence time, as shown in Figure 7.
mg/g (biomass)
750 oC
850 oC
930 oC
H2
14.5
17.69
18.77
CO
305.9
353.7
367.66
CO2
140.7
136.0
134.28
CH4
21.4
29.50
30.18
C2H4
88.6
94.71
94.67
Methanol
34.2
34.49
34.57
Formaldehyde
0.011
0.003
0.002
Acetaldehyde
12.19
12.41
12.50
5-Hydroxymethylfurfural
0.011
0.004
0.002
Benzene
1.41
2.14
2.22
Toluene
0.000
0.000
0.000
Naphthalene
6.16
9.36
9.74
Cyclopentadiene
0.001
0.011
0.048
H2O
142.45
143.47
143.84
Char
223.30
157.30
142.40
Figure 6: Effect of vapor residence time on the tar
cracking.
Although not experimentally validated, the model
predicted the composition of heavy hydrocarbons such as
benzene, toluene, levoglucosan,
1,3-cyclopentadiene, furan, and phenols in tar produced
from biomass pyrolysis [10, 32].
D. Effect of vapor residence time
To investigate the effect of vapor residence times on
gas, tar, and char yields. As the temperature increased
from 750 to 930 oC, the gas yield increased at the
expense of tar thermal cracking (Figure 5). However, the
char yield did not show any significant decrease in the
temperature range of 750 to 930 oC. Even though soot
formation occurred during tar thermal cracking, the
increase did not have a significant impact on the overall
yield of char in the process. This observation also agrees
with previous literature studies [10]. The results suggest
that the thermal cracking of tar occurs at a higher
temperature than that of char. This indicates that tar
Figure 7: Effect of vapor residence time on the char
yield
The slight increase in char yield due to secondary
reactions has also been reported in previous studies [10].
The findings from this study illustrate that at higher
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31st European Biomass Conference and Exhibition, 5-8 June 2023, Bologna, Italy
temperatures, very low vapor residence times can be
adequate to crack the thermally unstable tar components.
For instance, Tao et al. [31] considered vapor residence
times as low as 9 seconds for tar cracking at 700 oC.
3.3 Steam gasification
A. Gas composition
Figure 8 shows a comparison of the experimental and
model compositions of hydrogen, carbon monoxide, and
methane from steam gasification. At all temperatures
with a steam-to-biomass ratio of 0.21, the results indicate
that the model composition closely matches the
experimental values. A mean absolute error of 17.4%
results from the model's prediction. This suggests that the
model can be used to predict the gas composition of
steam gasification with a reasonable degree of accuracy.
Figure 9: Comparison of the char yield (wt.%) from
gasification model with experimental data.
C. Effect of temperature on product distribution
Table V shows the simulation results of the variation
in gas, tar, char, and water yield with temperature. The
gas yield increased from 76.4 wt% at 750 oC to 88.8 wt%
at 930 oC.
Table V: Effect of temperature on the gas, tar, and char
yield from the gasification model.
Weight percent
oC
Figure 8: Comparison of gas compositions from
gasification model with experimental data.
Gas
Generally, hydrogen composition increased while
carbon monoxide composition decreased with
temperature. These trends could be attributed to the steam
reforming and water gas shift reactions that subsequently
occur to increase and reduce the concentration of H2 and
CO, respectively [33]. As a result of methane reforming
reactions, both experimental and simulation models
predict only traces of methane gas. Similar observations
have been reported in the literature [34].
Char
Tar
Water
750
76.4
850 oC
82.8
930 oC
88.8
4.79
4.8
4.8
11.3
7.8
3.54
7.5
4.54
2.7
Table VI: Product composition of the gasification
mg/g (biomass)
H2
B. Char yield
As shown in Figure 9, the model predicted lower char
yields when compared to the experimental results. The
mean absolute error for the prediction was 17.1%.
However, for both the experiment and simulation, the
char yield significantly decreased between 750 and 930
oC. For instance, experimental findings indicate a
decrease in the char yield from 13.2 wt% at 750 oC to 5
wt% at 930 oC. The decrease in char yield can be
attributed to the heterogeneous and endothermic reaction
of steam and carbon, which is favored by increasing
temperatures [23]. The prediction of low char yield in the
model can be attributed to the assumption that char is
made up of only carbon, which makes its reaction with
steam easier. Despite that, the model predicted the small
amount of soot that remains during the steam reforming
of biochar [34].
CO
CO2
CH4
C2H4
Methanol
Formaldehyde
Acetaldehyde
5-Hydroxymethylfurfural
Benzene
Toluene
Naphthalene
Cyclopentadiene
H2O
Char
750 oC
36.24
850 oC
42.700
930 oC
47.94
471.35
485.303
513.40
184.67
220.304
241.29
2.42
2.490
2.40
67.71
75.189
79.76
28.83
29.066
29.14
0.144
0.047
0.022
10.28
10.464
10.53
0.152
0.050
0.024
0.365
0.579
0.820
0.000
0.000
0.000
1.60
2.533
3.59
0.000
0.003
0.018
75.10
45.414
27.86
113.0
78.0
35.40
Like pyrolysis, the increase in gas yield coincided
with the decrease in char yield, signifying that the
increase in gas yield could be attributed to the
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31st European Biomass Conference and Exhibition, 5-8 June 2023, Bologna, Italy
heterogeneous reaction of char and steam. There was no
noticeable change in the tar yield over the temperature
range, signifying that the gas yield was mainly obtained
from char-steam reactions at higher temperatures and
vapor residence time [32]. As shown in Table VI, the
gasification products consisted mainly of aromatic
compounds such as benzene, toluene, naphthalene, and
cyclopentadienyl, which mainly exist as secondary
compounds [[32]. The presence of aromatic compounds
indicates that steam has only limited influence on the
conversion of aromatics, even at high temperatures [34].
4
[5]
[6]
[7]
CONCLUSIONS
[8]
In this work, a rigorous pyrolysis and gasification
model capable of predicting the thermal and steam
cracking of pyrolytic products was proposed. The model
was validated using experimental data from a laboratoryscale fixed-bed reactor. The proposed model can be
applied to study the influence of reactor parameters such
as temperature, pressure, residence times, and steam flow
rate.
Unlike the other models described in the literature,
the key novelty in the proposed model was the ability to
predict the effect of solid and vapor residence time on
pyrolysis and gasification. At an overall error of 15.1 %,
the proposed model can be useful in the design and
optimization of thermochemical biorefineries, especially
when the prediction of tar and pollutant composition such
as benzene is required. Despite the advantages, more
detailed experimental data that includes analysis of tar
components is needed to further improve and optimize
the kinetic data used in the model.
[9]
[10]
[11]
[12]
5
ACKNOWLEDGEMENTS
The authors would like to acknowledge the Nordic
Energy Agency for providing financial support via the
BIOELEC project, grant no. 120006.
[13]
6. SUPPORT INFORMATION
[14]
Support information will be made available on
request.
[15]
7
[1]
1]
[2]
[3]
[4]
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