Corresponding Author: Tito Sumirat
E-mail: tito.sumirat@gmail.com
INTRODUCTION
Reducing emissions to mitigate the negative impacts of
climate change has become a common goal of the international community in recent
years, with more and more countries and businesses worldwide committing to net
zero emissions, mostly by mid-century (Lukmadi & Sitabuana,
2022). The race toward carbon neutrality or climate neutrality
has coincided with worsening extreme weather events, lowering the cost of
renewable energy, and increasing awareness of climate change.
Biomass is
considered a renewable energy source that is clean and environmentally friendly
and can be a good alternative to fossil fuels (Reddy et al., 2023). Biomass with appropriate process options can be
converted into solid, liquid and gaseous fuels that can be used for energy
production. Therefore, research has worked on various techniques for increasing
biomass energy production (Vassilev et al., 2015).
Biomass is a new
and renewable energy source (EBT) with abundant potential in Indonesia.
However, its use has yet to be optimal. Biomass used as an energy source (fuel)
in Indonesia generally has low economic value or is a waste that has taken its
primary product. The biomass can come from plants, trees, grass, sweet
potatoes, agricultural waste, forest waste, faeces and livestock manure (Parinduri &
Parinduri, 2020). The potential for biomass resources in Indonesia is
estimated at 49,810 MW from plants and waste (Ardilasari, 2023). The great potential of existing biomass for energy
today is plantation waste, such as oil palm, coconut and sugarcane, as well as
forest product waste, such as sawn waste and wood production waste. (Kasmaniar et al., 2023). Food crop waste (agriculture) also has a large amount.
However, most of it has been used by the community for various purposes
(agriculture, energy, industry). Several companies export plantation waste,
such as palm kernel shells, coconut cake, and molasses from sugarcane.
Meanwhile, plants containing starch and sugar are still
used for food. Several large industries have been able to create electrical
energy from waste biomass and use it for company operations. At the same time,
many small, medium and large companies have also carried out the form of direct
energy (direct combustion). Fuel production includes vegetable oil from food
crops but has yet to be produced on a large scale. Kemiri Sunan, sampling,
Jatropha curcas, water hyacinth, and algae are planted with a high potential
for energy because they do not come into contact with food. However, they have
yet to be produced on a mass scale. Animal husbandry waste in several
cities/regencies has been used as household-scale biogas.
In contrast, municipal waste has been used as a raw
material for electricity in Jakarta and Denpasar. The availability of biomass has been concentrated
geographically, but its utilization could have been better. Existing energy
policies and regulations, such as permits and energy prices, still need to
support the optimal use of biomass as a raw material for renewable energy.
Concerning the
production process biomass gasification is one of the most promising techniques
used to convert solid fuels into useful gaseous fuels, which can be widely used
in many households and industrial applications such as power generation and
internal combustion engines. (Pah, 2022) . The most common use of biomass for energy is direct
combustion, followed by gasification, carbonization and pyrolysis (Basu, 2018).
Figure
1. Biomass Gasification
Biomass gasification is increasing significantly in
terms of industrial and market applications for several reasons (Bridgwater et al., 2022) :
1. It
is a renewable energy source.
2. It
can be used as a good alternative for electricity production or to reduce
electricity consumption during peak load times.
3. Good
sewage system.
4.
It can be used in CHP
applications.
In addition, gasification also has several advantages
compared to other techniques (e.g. pyrolysis and combustion), as follows:
1. The
gasification products can be used directly in internal combustion engines and
gas turbines, making gasification more important than other technologies.
2. Negligible
emissions compared to direct combustion.
3.
Wide range of
applications, such as heat, electricity, steam, and chemicals.
4. The
resulting gas product is of high value in terms of syngas composition.
Gasification is
a thermochemical process that converts solid fuel into gaseous fuel at
temperatures between (700-900)oC (Basu, 2018). Gasification produces carbon monoxide, hydrogen and
small amounts of methane as desired products with other unwanted gases such as
nitrogen, carbon dioxide and other hydrocarbons. In gasification, organic volatiles
do not condense rapidly, so they are usually carried away with the product gas,
forming a viscous substance called tar. Both tar and char are undesirable for
any downstream application, e.g. when using producer gas in internal combustion
engines and gas turbines (Basu, 2018). Tar separation is a major challenge for using the
resulting syngas as it will condense and cause blockages in the engine and
valves.
Biomass
refers to organic matter derived from plants or animals (Simorangkir & Syaiful, nd) that
are alive or lived in the past. Universally accepted definitions take much work
to find. However, one used by the United Nations Framework Convention on
Climate Change (UNFCCC) is relevant here:
Non-fossilized and biodegradable
organic materials derived from plants, animals and microorganisms. It should
also include products, by-products, residues and wastes from agriculture,
forestry and related industries, and the nonfossil and biodegradable organic
fractions of industrial and municipal wastes.
Biomass
also includes gases and liquids from decomposing non-fossilized and
biodegradable organic matter (Basu, 2013). As a sustainable and renewable energy source, biomass
is continuously formed by the interactions of CO2, air, water, soil and
sunlight with plants and animals. After the organism dies, the microorganisms
break down the biomass into its constituent parts, such as H 2 O, CO
2, and their potential energy. Carbon dioxide, released by biomass
through the action of microorganisms or combustion, was absorbed by it in the
past, so burning biomass does not add to the Earth's total CO2 stock. Therefore it is called
greenhouse gas neutral or GHG neutral.
A fluidized bed gasifier offers uniform temperature
distribution and a good mixing platform for gas and solid. That, in turn,
reduces the risk of fuel stack agglomeration (Thomson et al., 2020). Silica, sand, dolomite, and glass beads are commonly
used as bed materials for fluidized bed gasifiers. However, it was reported
that magnesite could further increase H2 production compared to sand
as a coating material (Morris et al., 2018). The operating temperature of a fluidized bed reactor
is highly dependent on the melting point of the lining material and ash. Hence,
it is limited to 923 to 1223 K (Warnecke, 2020). The pressure ranges from 0 to 70 bar (Pang, 2016). Tar formation is intermediate between the updraft
and downdraft gasifiers, with an average of 10 g/N.m3 (Basu, 2018). Due to the temperature at which these gasifiers
operate, the reaction cannot move closer to equilibrium, reducing the formation
of hydrocarbons at the gas outlet [18]. In contrast, the carbon conversion
efficiency of these gasifiers is significant and can reach 95% (Chhiti & Kemiha,
2013).
Bubbling bed gasifier is classified as the oldest
reactor in fluidized bed gasifier (Wheeldon &
Thimsen, 2013). Bedding is mostly made of inert particles such as sand or silica. Moreover,
it was observed that catalytic activity in the base material significantly
increased the tar conversion rate (Wheeldon
& Thimsen, 2013). In these reactors, the
gasification medium rises to the bed at a low gas velocity of <1 ms 1 to
perform the fluidization process and maintain the state of the gas bubble
mixture. The cross-sectional area increases when the particles reach the top of
the bed. Hence, the velocity, in turn, decreases, causing the particles to
descend the bed. Since this favours the formation of particulates, the reactor
needs bubbling bed cyclone separators at the exit of the reactor (Gray, 2017).
This research has three objectives: 1. Create
a CFD model aligned with the results of lab tests from previous studies 2.
Conduct simulation tests using several types of biomass as input. 3. Optimizing the operating parameters of various types
of biomass to produce optimum syngas.
METHODS
The research method used by the author
to answer the objectives of the research is to use literature studies and
modelling using Ansys Fluent software.
The flow chart of the study conducted by
the author is as follows:
Figure
2. Research Flow Chart
For
the modelling itself, the process from making the model to the sensitivity used
for each type of biomass is as follows:
Figure 3. The Process
From Model Creation to The Used Sensitivity.
In this study, the
authors used several types of biomass as input in the simulation. Proximate
data and ultimate analysis of these biomasses are needed as input data in Ansys
Fluent. These data can be seen in the following table:
Table 1. Data Proximate and Ultimate Analysis of each type of biomass
|
Biomass
|
Proximate analysis (fraction)
|
Ultimate analysis (fraction)
|
|
volatile
|
Fixed Carbon
|
Ash
|
C
|
H
|
O
|
N
|
S
|
|
Rice husk
|
0.09
|
0.13
|
0.58
|
0.58
|
0.04
|
0.06
|
0.33
|
0
|
|
Corncob
|
0.80
|
0.19
|
0.01
|
0.47
|
0.06
|
0.45
|
0.02
|
0.0001
|
|
Wheat Straw
|
0.71
|
0.20
|
0.09
|
0.43
|
0.05
|
0.39
|
0.12
|
0.0011
|
|
Sugarcane Bagasse
|
0.74
|
0.15
|
0.11
|
0.45
|
0.05
|
0.40
|
0.10
|
0.0001
|
|
Cotton Dregs
|
0.67
|
0.15
|
0.18
|
0.40
|
0.05
|
0.36
|
0.19
|
0
|
The chemical reaction equation
used as a basis in Ansys Fluent is as follows (Murugan & Sekhar, 2017) :
Table 2. The reaction
equations used in the simulation model
|
Reactions
|
Pre-exponential factor (sec-1)
|
The activation energy
(J mol-1)
|
References
|
|
H20(1)
- H20(v)
|
5.3 x 1010
|
88 x 103
|
Franciso et al. (2008)
|
|
C+O2
- CO2
|
93.5 x103
|
82.8 x 103
|
Fletcher et al. (2000)
|
|
2CO
+ O2 - 2CO2
|
10 x 1017
|
166.28 x 103
|
Franciso et al. (2008)
|
|
CH
4 + 1.502 - CO + 2H2 0
|
92 x 105
|
80.23 x 103
|
Franciso et al. (2008)
|
|
2H2
+ O2 - 2H20
|
10 x 1011
|
42 x 103
|
Avdhesh (2008)
|
|
CH20
- CO + H2
|
14 x 107
|
179.50 x 10ł
|
Fletcher et al. (2000)
|
|
C
+ CO2 - 2CO
|
34 x 106
|
179.50 x 103
|
Fletcher et al. (2000)
|
|
C
+ 2H2 - CH4
|
4.189 x 10-3
|
19.21 x 103
|
Babu and Pratik (2006)
|
|
CH4
+ H20 - CO + 3H2
|
16.50 X 1010
|
33.90 x 107
|
Luc et al. (2008)
|
|
CO
+ H20 - CO2 + H2
|
2.824 X 10-2
|
32,840 X 103
|
Ningbo and Aimin (2008)
|
RESULTS AND DISCUSSION
Design
of 3D reactor gasifier P2 and mesh quality check
The 3D design of the reactor is made based on the
initial design draft of the P2 mobile gasifier using Space claim. After the
reactor design, meshing was carried out on the model by checking the mesh
quality using orthogonal quality with an average of 0.76.
Figure
4. Schematic of the reactor included in the model and the mesh used
Figure
5. Mesh quality diagram
Making
the Ansys fluent model
After meshing and checking the mesh quality,
modelling in Ansys Fluent is continued by importing the mesh into the Ansys
Fluent application, then inputting initial data in the form of biomass
composition, elements involved, reactions that occur, boundary conditions, and
settings other. A summary of the processes that occur in Ansys Fluent can be seen in the following table:
Table 3.
Summary of processes and models used in Ansys Fluent
|
Process
|
Sub-models
|
Type
|
|
Model
|
turbulence
|
k-epsilon 2
equations
|
|
Radiation
|
Discrete
Ordinates
|
|
Non-premixed
Combustion
|
|
Solution
Method
|
Pressure-velocity
coupling
|
coupled
|
|
Momentum
and Energy
|
2nd order
upwind discretization scheme
|
|
Pressure
discretization scheme
|
PRESTO
|
|
Residual
levels
|
10e-3 for all variables, for energy and radiation, 10e-6
|
The next process is carried out after all the input data is entered to do
the initial run to see the convergence of our created process. This is done by simulating with a high number of
iterations. The author uses the initial number of iterations at 2000, then
optimizes so that the number of iterations becomes 1000 iterations. Then
initialization is carried out before the next simulation process is carried
out. As input data, the feed rate and gas flow rate are used as follows:
Table 4. Feed
rate and gas flow rate for each ER
|
Biomass
|
Air
inlet ea (ER=0.15)
|
Air
inlet ea (ER=0.27)
|
Air
inlet ea (ER=0.4)
|
Air
inlet ea (ER=0.6)
|
|
kg/hour
|
kg/s
|
kg/s
|
kg/s
|
kg/s
|
kg/s
|
|
5
|
0.001
|
0.0003
|
0.0005
|
0.0008
|
0.0012
|
|
12
|
0.003
|
0.0007
|
0.0013
|
0.0019
|
0.0028
|
|
15
|
0.004
|
0.0009
|
0.0016
|
0.0023
|
0.0035
|
|
20
|
0.006
|
0.0012
|
0.0021
|
0.0031
|
0.0047
|
Data
matching
Data matching is
carried out on the results of previous research based on the following table:
Table 5.
Experimental results from previous research
|
ER
|
H2
|
CO
|
CH4
|
LHV
|
|
Experiment
|
|
0.18
|
1.43%
|
3.73%
|
0.96%
|
0.97
|
|
0.23
|
1.66%
|
4.64%
|
0.94%
|
1.1
|
|
0.27
|
3.34%
|
5.25%
|
0.87%
|
1.34
|
|
0.31
|
2.75%
|
5.24%
|
0.44%
|
1.11
|
After matching the
data to ER 0.27 (the ER that produces the maximum syngas), the following
results are obtained:
Table 6.
Comparison of experimental results with simulations
|
Case
|
Results
|
|
H2 (%)
|
CO (%)
|
CH4 (%)
|
|
Experiment
|
3.34
|
5.25
|
0.87
|
|
Simulation
|
4.20
|
5.78
|
0.85
|
Figure
6. Rice husk temperature profile, ER 0.27, feed rate 12 kg/hour
Figure 7. Profile H 2, CO, CO 2 and
CH 4 of rice husk, ER 0.27, feed rate 12 kg/hour
Sensitivity
Sensitivity to several types of biomass was
carried out by changing the proximate and ultimate analysis according to 849able 3: Data Proximate and
Ultimate Analysis of each type of biomass. From the simulation results for
several types of biomass, the following results are obtained:
Table 7. Simulation results of several types of
biomass
|
Biomass
|
Result (ER
= 0.27, Feed rate = 12 kg/hour)
|
|
H2 (%)
|
CO(%)
|
CH4 (%)
|
|
Rice Husk (Experiment)
|
3.34
|
5.25
|
0.87
|
|
Rice Husk
(Simulation)
|
4.20
|
5.78
|
0.85
|
|
Corncob
|
10.18
|
6.26
|
6.43
|
|
Wheat Straw
|
9.63
|
6.31
|
5.87
|
|
Sugarcane
Bagasse
|
9.49
|
6.38
|
5.66
|
|
Cotton
Dregs
|
9.41
|
6.39
|
5.55
|
The table shows that for conditions
of equivalent ratio and the same biomass flow rate, corn cobs produce the most
hydrogen, followed by wheat straw, bagasse and cotton waste. Carbon monoxide is
produced by cotton dregs the most, followed by bagasse, wheat straw and
corncobs. Methane is produced mostly by corn cobs, wheat straw, bagasse and
cotton waste.
After that, sensitivity to ER was carried out for
each type of biomass to see its effect on the resulting syngas output, and the
results are as follows:
Table
8. Sensitivity to Equivalent Ratio
|
Biomass
|
Syngas
Produced (H2+CO+CH4) in an hour, kg
|
|
Feed rate
12 kg/hour
|
|
ER 0.15
|
ER 0.27
|
ER 0.40
|
ER 0.60
|
|
Rice Husk
|
0.62
|
1.30
|
1.15
|
0.76
|
|
Corncob
|
1.21
|
2.74
|
2.41
|
1.91
|
|
Wheat Straw
|
1.12
|
2.62
|
2.33
|
1.77
|
|
Sugarcane
Bagasse
|
1.16
|
2.58
|
2.28
|
1.83
|
|
Cotton
Dregs
|
1.14
|
2.56
|
2.24
|
1.76
|
The table above
shows that maximum syngas is produced at ER conditions of 0.27 with a biomass
flow rate of 12 kg/hour for each biomass. Sensitivity to changes in the biomass
flow rate was also carried out to see the effect of the biomass flow rate on
the syngas produced with the following results:
Table 9. Sensitivity to biomass flow rate from
Equivalent ratio 0.27
|
Biomass
|
Syngas
Produced (H2+CO+CH4) in an hour, kg
|
|
ER 0.27
|
|
Feedrate 5
kg/hour
|
Feedrate 12
kg/hour
|
Feedrate 15
kg/hour
|
Feedrate 20
kg/hour
|
|
Rice Husk
|
1.20
|
1.30
|
1.34
|
0.97
|
|
Corncob
|
1.94
|
2.74
|
3.04
|
2.98
|
|
Wheat Straw
|
1.94
|
2.62
|
2.77
|
2.70
|
|
Sugarcane
Bagasse
|
2.07
|
2.58
|
2.83
|
2.72
|
|
Cotton
Dregs
|
1.93
|
2.56
|
2.75
|
2.67
|
Then for each biomass, a sensitivity to the biomass
flow rate for ER 0.15, 0.27, 0.4, and 0.6 is carried out so that a feed rate vs
ER surface chart can be made for each biomass, as follows:
Table 10. Sensitivity to Feed rate and ER for Rice Husk
|
Rice Husk
|
Syngas
Produced (H2+CO+CH4) in an hour, kg
|
|
ER 0.15
|
ER 0.27
|
ER 0.40
|
ER 0.60
|
|
Feedrate 5
kg/hour
|
0.56
|
1.20
|
0.99
|
0.83
|
|
Feedrate 12
kg/hour
|
0.62
|
1.30
|
1.15
|
0.76
|
|
Feedrate 15
kg/hour
|
0.61
|
1.34
|
0.96
|
0.58
|
|
Feedrate 20
kg/hour
|
0.60
|
0.97
|
0.70
|
0.35
|
Figure 8. Surface chart of rice husks and corn cobs
Table
11. Sensitivity to Feed Rate and ER for Corn Cobs
|
Corncob
|
Syngas
Produced (H2+CO+CH4) in an hour, kg
|
|
ER 0.15
|
ER 0.27
|
ER 0.40
|
ER 0.60
|
|
Feedrate 5
kg/hour
|
0.89
|
1.94
|
1.81
|
1.57
|
|
Feedrate 12
kg/hour
|
1.21
|
2.74
|
2.41
|
1.91
|
|
Feedrate 15
kg/hour
|
1.26
|
3.04
|
2.48
|
1.91
|
|
Feedrate 20
kg/hour
|
1.23
|
2.98
|
2.33
|
1.93
|
Table
12. Sensitivity to Feed rate and ER for Wheat Straw
|
Wheat Straw
|
Syngas
Produced (H2+CO+CH4) in an hour, kg
|
|
ER 0.15
|
ER 0.27
|
ER 0.40
|
ER 0.60
|
|
Feedrate 5
kg/hour
|
0.84
|
1.94
|
1.67
|
1.52
|
|
Feedrate 12
kg/hour
|
1.12
|
2.62
|
2.33
|
1.77
|
|
Feedrate 15
kg/hour
|
1.20
|
2.77
|
2.33
|
1.88
|
|
Feedrate 20
kg/hour
|
1.27
|
2.70
|
2.33
|
1.73
|
Figure 9. Surface chart of wheat straw and bagasse
Table
13. Sensitivity to Feed Rate and ER for Sugarcane Bagasse
|
Sugarcane Bagasse
|
Syngas
Produced (H2+CO+CH4) in an hour, kg
|
|
ER 0.15
|
ER 0.27
|
ER 0.40
|
ER 0.60
|
|
Feedrate 5
kg/hour
|
0.88
|
2.07
|
1.75
|
1.53
|
|
Feedrate 12
kg/hour
|
1.16
|
2.58
|
2.28
|
1.83
|
|
Feedrate 15
kg/hour
|
1.18
|
2.83
|
2.33
|
1.85
|
|
Feedrate 20
kg/hour
|
1.20
|
2.72
|
2.30
|
1.68
|
Table
14. Sensitivity to Feed rate and ER for Cotton Dregs
|
Cotton
Dregs
|
Syngas
Produced (H2+CO+CH4) in an hour, kg
|
|
ER 0.15
|
ER 0.27
|
ER 0.40
|
ER 0.60
|
|
Feedrate 5
kg/hour
|
0.86
|
1.93
|
1.69
|
1.47
|
|
Feedrate 12
kg/hour
|
1.14
|
2.56
|
2.24
|
1.76
|
|
Feedrate 15
kg/hour
|
1.17
|
2.75
|
2.22
|
1.79
|
|
Feedrate 20
kg/hour
|
1.24
|
2.67
|
2.13
|
1.74
|
Figure 10. Surface chart of cotton dregs
In addition to sensitivity to ER and Feed Rate, it can also be
seen the correlation between the oxygen content of each biomass and the syngas
produced as follows:
Table 15. Correlation of oxygen content to the syngas produced
|
Biomass
|
Oxygen
Content (mass fraction)
|
Syngas
produced (kg/hour)
|
|
Rice Husk
|
0.06
|
1.30
|
|
Corncob
|
0.45
|
2.74
|
|
Wheat Straw
|
0.39
|
2.62
|
|
Sugarcane
Bagasse
|
0.40
|
2.58
|
|
Cotton
Dregs
|
0.36
|
2.56
|
Figure 11. Graph of Oxygen Content vs Syngas produced
The graph shows that the more oxygen content in the
biomass, the greater the total amount of syngas produced. This graph can be
used for a P2 gasifier with an ER of 0.27 and a feed rate of 12 kg/hour. So
when there are other types of biomass whose ultimate analysis is known, the
syngas that will be produced can be predicted using this graph.
CONCLUSION
Biomass is a new and
renewable energy source (EBT) with abundant potential in Indonesia. However,
its use has yet to be optimal. Biomass gasification is one of the most
promising techniques used to convert solid fuels into useful gaseous fuels,
which can be widely used in many households and industrial applications such as
power generation and internal combustion engines.
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(Calatropis gigantea) pada Briket Tongkol Jagung dengan Penambahan Plastik Low
Density Polyethylene. Politeknik Negeri Jember.
Basu, P. (2013). Chapter 3 - Biomass Characteristics
(P. B. T.-B. G. Basu Pyrolysis and
Torrefaction (Second Edition) (ed.); pp. 47–86). Academic Press.
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