Accepted Manuscript Experimental and modeling studies of Portuguese peach stone gasification on an autothermal bubbling fluidized bed pilot plant
Eliseu Monteiro, Tamer M. Ismail, Ana Ramos, M. Abd El-Salam, Paulo Brito, Abel Rouboa PII:
S0360-5442(17)31805-4
DOI:
10.1016/j.energy.2017.10.100
Reference:
EGY 11744
To appear in:
Energy
Received Date:
06 March 2017
Revised Date:
14 September 2017
Accepted Date:
22 October 2017
Please cite this article as: Eliseu Monteiro, Tamer M. Ismail, Ana Ramos, M. Abd El-Salam, Paulo Brito, Abel Rouboa, Experimental and modeling studies of Portuguese peach stone gasification on an autothermal bubbling fluidized bed pilot plant, Energy (2017), doi: 10.1016/j.energy.2017.10.100
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ACCEPTED MANUSCRIPT Highlights Experimental evaluation of Portuguese peach stone gasification was conducted. A homemade comprehensive CFD model for biomass gasification is proposed. The model is capable of predicting the syngas composition under different conditions. The effect of moisture, ER and SBR on peach stone gasification are studied.
ACCEPTED MANUSCRIPT Experimental and modeling studies of Portuguese peach stone gasification on an autothermal bubbling fluidized bed pilot plant
1 2 3 4 5 6 7 8 9 10 11 12 13
Abstract
14
Among the renewable energies available, biomass constitutes an auspicious option, due
15
to its environmental-friendly character allied to its significant energy supply. As a path
16
to maximize biomass energy efficiency, gasification has been reported as an adequate
17
technology. Numerical models that can predict and optimize the experimental
18
conditions as well as the equipment design for biomass gasification are imperative,
19
towards a cost-saving and sustainable performance. This work shows the experimental
20
and numerical results of thermal gasification of Portuguese peach stone. Assays were
21
performed using a thermal gasification pilot plant with a bubbling fluidized bed at
22
temperatures ranging from 750β¦ C to 850β¦ C with mass flow rates of 30 kg/h to 60 kg/h.
23
A homemade comprehensive two-dimensional CFD model is proposed to optimize the
24
operating conditions of the biomass gasification process. The numerical model results
25
were compared with experimental data and good agreement was found. A parametric
26
study was performed in order to understand the influence of moisture content, steam to
27
biomass ratio and equivalence ratio in the composition of the producer gas. The results
28
of the study showed a negative impact of moisture and equivalence ratio over
29
conversion efficiency and producer gas quality, and a positive impact for steam to
30
biomass ratio which promotes higher calorific values and overall efficiency for the
31
process.
Eliseu Monteiro1,2*, Tamer M. Ismail3, Ana Ramos1, M. Abd El-Salam4, Paulo Brito2, Abel Rouboa1,5 1CIENER-INEGI,
2
Faculty of Engineering of the University of Porto, Portugal C3i β Interdisciplinary Center for Research and Innovation, Polytechnic Institute of Portalegre, Portugal 3Mechanical Engineering Department, Suez Canal University, Ismailia, Egypt 4Department of Basic Science, Cairo University, Giza, Egypt 5MEAM Department, University of Pennsylvania, PA 19020, Philadelphia, USA
1
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Keywords: Experimental biomass gasification; gasification modeling; gasification simulation
3 4
Nomenclature
5
Ac
pre-exponential factor for char burning rate
kg m-2 s-1 Pa-1K-1
6
As
particle surface area
m2
7
Av
pre-exponential factor for devolatilization rate
s-1
8
C
gas species concentration
mol/m3
9
πΆπ·
drag coefficient
10
Cmix
mixing rate constant
11
Cp
specific heat capacity
J/kg K
12
Cw,g
moisture concentration in the gas phase
kg/mΒ³
13
Cw,s
moisture concentration at the solid phase
kg/mΒ³
14
D
mass diffusion coefficient
mΒ²/s
15
dp
particle diameter
m
16
E
activation energy
J/mol
17
Eb
blackbody emission of the gas
W
18
e
coefficient of restitution for particle collisions
19
go
radial distribution function
20
Hevp
evaporation heat of the solid material
J/kg
21
hf
enthalpy of formation
J/kg
22
hrs
radiation heat transfer coefficient
W m-2 K-1
23
hrv
effective radiation heat transfer coefficient of the voids
W m-2 K-1
24
hs
convective mass transfer coefficient between solid and gas
m/s
2
ACCEPTED MANUSCRIPT 1
hs'
convective heat transfer coefficient between solid and gas
W m-2 K-1
2
I
radiative intensity
W
3
K
turbulent kinetic energy
mΒ²s-2
4
kd
diffusion rate
kg/mΒ² s Pa
5
kf
thermal conductivity of the fluid
W/m K
6
ks
thermal conductivity of the solid
W/m K
7
keff
effective thermal conductivity
W/m K
8
keff,0
thermal conductivity for no fluid flow
W/m K
9
ls
equivalent thickness of a solid layer
m
10
M
molecular weight
kg/mol
11
PO2
partial pressure of oxygen
Pa
12
Qcr
heat absorbed by the solid
W
13
qr
radiative flux density
W
14
R
universal gas constant
J/mol K
15
Revp
moisture evaporation rate
kg/s
16
Rc
char consumption rate
kg/s
17
Re
Reynolds number
18
Rv
volatiles evolution rate
19
Sc
Schmidt number
20
Sh
Sherwood number
21
πΞ¦
Source term
22
T
temperature
K
23
t
time
s
kg/s
3
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Yv
mass fraction of volatile matter
2
U
instantaneous velocity
m/s
3
V
volume of particle
mΒ³
4
Greek Letters
5
ο‘
absorption coefficient
6
Ξ²
gas-solid interphase drag coefficient
7
Ο
void fraction in bed
8
πΎπ
dissipation of fluctuating energy
9
ΞΞ
diffusion energy
10
ο
dynamic viscosity
kg/m s
11
π
dissipation rate of turbulent kinetic energy
m-2s-3
12
Ο΅
emissivity
13
Οp
scattering coefficient
14
Ξ΄
Stephan-Boltzmann constant
W/mΒ² K4
15
ο²
density
kg/mΒ³
16
ππ
thermal dispersion coefficient
17
ππ
energy exchange between the gas and solid
18
ππππ₯ effective dispersion coefficient
19
Ξ¦
dependent variable
20
Ξs
particle phase pseudo-temperature
21
ΞΆ
random number that obeys the Gauss distribution
22
Οs
stress tensor
W/m3
mΒ²/sΒ²
Pa 4
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Subscripts
2
b
bulk
3
c
char burnout
4
eff
Effective
5
f
Fluid
6
g
Gas
7
p
Particle
8
s
Solid
9
sb
solid bulk
10
sg
solid to gas
11
T
Total
12
v
volatile matter
13 14
1. Introduction
15
Biofuels are known to soften the fading of non-renewable fuels like coal, constituting a
16
greener alternative for energy production and replacing the use of fossil fuels that,
17
besides being finite, are also more pollutant [1,2]. Among various renewable sources,
18
biomass is a broadly produced material showing enhanced conversion potential by a
19
handful of established processes [3-5]. Gasification is one of the most valuable
20
techniques as it produces a versatile synthetic gas (syngas) capable of furnishing
21
worldwide desirable assets such as heat, power and chemicals [3,6,7].
22
Biomass concerns all the organic material derived from plants or animals, its
23
gasification featuring environmental advantages like carbon-neutral technologies 5
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(meaning that the CO2 produced in the conversion processes is further available for a
2
new photosynthesis succession, leading to the greenhouse effect decline) as well as
3
lower gaseous pollutant emissions reducing acid rain formation [1,5,8]. Either by
4
natural processes like plant and animal living cycles or as a result of human activities,
5
biomass is available on a renewable basis, contributing to a total of 62 countries in the
6
world already producing electricity from it. In fact, by 2008 Economic Co-operation and
7
Development (OECD) reported USA as the leading producer (26% share), Germany as
8
the second (15%), Brazil and Japan sharing the third position (both with 7% production)
9
[9].
10
Amid the various technologies with potential to convert biomass, gasification features
11
the most promising technique for solid residues conversion, at the same time complying
12
with the international policies that rule noxious emissions into the environment and
13
granting high conversion efficiencies [1,2, 10]. Its main advantage relies on the severely
14
higher temperatures applied, which grant a superior-quality cleaner syngas, due to the
15
incisive removal of undesired contaminants. Indeed, the high efficiencies afforded by
16
gasification support its notoriety [11] taking into account the increasing environmental
17
restrictions imposed by governments and international agencies. Therefore, gasification
18
is seen as a prominent and viable technology providing a cleaner alternative solution for
19
waste treatment with energy recovery [12, 13]. The main advantages gasification
20
depicts over traditional combustion are especially related to the final use that syngas can
21
be given, different applications being possible. At the cost of only a fraction of the
22
stoichiometric amount of oxygen when compared to other methods, gasification enables
23
the formation of dioxins, SO2 and NOx to be controlled and reduced [14]. In a recent
24
review of biomass gasification [15] its technological developments, their dissemination
6
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and economic evaluation were discussed, highlighting all the advantages that this
2
conversion technique affords.
3
Gasification comprises four main steps, aiming to enhance heating value and energy
4
density, removing components like water, nitrogen, oxygen and others (as elemental
5
sulfur, that represent a valuable by-product for the process) [8]. The first step is biomass
6
drying/heating, which is accomplished by water vaporization at low temperatures.
7
Following, pyrolysis/devolatilization at temperatures between 150ΒΊC and 400ΒΊC
8
produces liquid and gaseous fractions along with tar. Then oxidation takes place by the
9
supply of a gasification agent that gives rise to a gaseous mixture of CO, CO2 and H2O
10
and regenerates the energy needed for other steps of the process. Lastly, this mixture is
11
reduced being char converted into CO, CH4 and H2 yielding the final product (syngas),
12
which will also contain trace amounts of some other hydrocarbons, inert gases and
13
contaminants, together with ashes and tars formed in gasification phase [3,8].
14
Gasification can be held in several different types of gasifiers [5,8,16], the choice of a
15
particular equipment being supported by a set of specific variables from feedstock
16
properties to the desired syngas features [17,18]. In the specific case of biomass
17
gasification, fluidized beds depict the best performance since this kind of equipment
18
allows a wider particle size range, which is essential for this type of residues [6, 18-21].
19
Besides, fluidized beds are advantageous for their relatively low cost, ease of
20
construction and operation, scale-up potential, as well as the high efficiencies attained
21
[16].
22
There are numerous studies on biomass gasification in fluidized beds, namely
23
resourcing to microalgae [22], forestry and agro-industrial residues [20, 23-27], sludge
24
[28, 29] and wood wastes [6, 30, 31] among others [32, 33]. Within the agro-industrial 7
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residues, various types of kernel have already motivated some research considering
2
several thermochemical conversion techniques [34-36]. Peach stones are among them
3
depicting a prominent biomass source, easily available in the mediterranean region, with
4
good energetic content already studied as an alternative to fossil fuels with encouraging
5
results [36, 37]. From the major advantages accomplished by the thermochemical
6
conversion of peach stone, the emission of less noxious flue gases, the higher
7
efficiencies and higher heating values achieved when compared to coal or other biomass
8
species are highlighted [36, 37].
9
Numerical modeling can aid at maximizing biomass gasification potential, once this
10
fuel and technique are complex inputs and several parameters can influence the final
11
outcome [23]. This way, modeling serves as a tool to deepen the knowledge of the
12
process making use of simulations that can help to predict and optimize the
13
development of the experimental runs, as well as setting the best conditions on the
14
design and scaling of the gasifier and the overall experimental performance [4, 6, 38,
15
39]. For this purpose, diverse models dealing with thermodynamic equilibrium, kinetics,
16
mass and heat transfer may be developed with the possibility of each being designed for
17
the specific goal and type of biomass in order to better adjust to the desired conditions
18
[17, 40]. Computational fluid dynamic (CFD) codes are one of the most used methods
19
as they are further committed in finding solutions of conservation of mass, momentum,
20
energy, hydrodynamics and turbulence. In the case of fluidized bed reactors, these codes
21
play an essential role as they provide qualitative and quantitative information even for
22
more complicated regions of the reactor, effectively fine-tuning the operational
23
conditions and syngas composition [8]. CFD modeling engages advanced numerical
24
methods on solid or gas phase description, as well as for the mixing of the phases,
25
applying equations and complex parameters [8, 41, 42]. After validating the model, 8
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experimental data can be predicted and working conditions can be adequately changed
2
according to those predictions, if necessary. Puig-Arnavat et al. [43] analyzed several
3
biomass gasification models, describing their advantages and limitations, special
4
attention being given to the difference between kinetic and equilibrium models. The
5
authors state that while the first are able to predict the progress and syngas composition
6
along the reactor, the later point the maximum yields of specific products within the
7
reacting system. Therefore, the last ones are less computational-demanding but with the
8
shortcoming of providing low accuracy results in some cases.
9
Gerber et al. [40] present an Eulerian multiphase 2-D model for wood gasification in a
10
fluidized bed constituted of char. Operational and modeling parameters were explored
11
permitting the comparison of two different models. This way, the authors concluded
12
that some modeling parameters highly influenced syngas composition, while the
13
operational conditions had little effect on this output but presented a strong influence in
14
the tar content. Char was suggested to act as catalyst reducing tar yields, which is a big
15
advantage for the studied system once tar production is one of the limiting factors in
16
fluidized beds. Gao et al. [44] also aimed at reducing tar contents in the gasification of
17
sawdust in order to attain a high-quality syngas. With this purpose, they studied the
18
effect of air to biomass on the final characteristics of the producer gas, through the
19
development of a detailed CFD model for a cyclone gasifier. Although the model under
20
predicted CO and CO2 concentrations, high carbon conversions as well as good
21
agreement with temperature and syngas yield profiles were attained. Janajreh and Shrah
22
[45] investigated the downdraft gasification of wood chips with CFD simulations,
23
modeling the Lagrangian particle coupled evolution at several different locations inside
24
the gasifier. Reactivity was found to be heavily influenced by biomass heating value
25
and moisture content. Heterogeneous temperature distribution along the reactor was 9
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defined as a drawback for small-scale gasifiers, also contributing to efficiency loss and
2
to the complexity of the flow inside the gasifier. An interesting input given by the
3
authors was to offset biomass particle size with specific kinetic data using the discrete
4
phase method, which proved to be a suitable assumption as numerical and experimental
5
values matched. Ku et al. [46] settled a multiscale Eulerian-Lagrangian CFD model to
6
assess biomass gasification, resourcing also to the interaction between continuous gas
7
phase and discrete particles. Operating parameters such as temperature, steam/carbon
8
molar ratio, excess air ratio, biomass type and particle size were evaluated, to
9
demonstrate the accuracy of the proposed model. The authors concluded that raising
10
temperatures and steam/carbon molar ratio enhanced syngas quality, while increasing
11
the excess air ratio promoted a drop on syngas quality. Different biomass types resulted
12
in distinct yields for some syngas components. Still, these differences were not
13
significant which enabled one type of biomass to replace the other, in this particular
14
experiment case. They also verified that, for increasing particle sizes both syngas
15
quality and carbon conversion efficiency was reduced. More recently, Monteiro et al.
16
[47] assessed the production of syngas from the gasification of miscanthus within a 2-D
17
CDF model, using an Eulerian-Eulerian approach to describe mass exchange,
18
momentum and energy for solid and gaseous phases. Numerical results were compared
19
to the experimental ones achieved with a semi-industrial gasification plant, and good
20
agreement for the syngas composition was obtained. Parameters such as equivalence
21
ratio, temperature and steam to biomass ratio were evaluated, monitoring the producer
22
gas composition and yield. Raising ER promoted a decrease in syngas quality as well as
23
its heating value, although carbon conversion efficiency and tar reduction were
24
enhanced. Oppositely, temperature increase has shown to favor syngas quality, heating
10
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value and efficiency. Regarding SBR, a positive relation with H2 content and tar
2
reduction was found, while negatively affecting the heating value.
3
This work reports experimental and numerical studies on an autothermal bubbling
4
fluidized bed gasification pilot plant of Portuguese peach stone. A comprehensive
5
homemade two-dimensional unsteady state CFD model is proposed in order to comply
6
with the particular needs of the fluidized bed gasification that are not readily available
7
by the computational packages such as the volatiles treated as a non-conventional
8
material just like biomass and the flexibility to deal with different biomass types. The
9
model is used to perform a parametric study as a tool to understand the influence of
10
moisture content, steam to biomass ratio and equivalence ratio in the gasification
11
process performances.
12
2. Physical model
13
The semi-industrial biomass gasification pilot plant used main components from the left
14
to the right in the Fig.1 are:
15
β Biomass feeding system with two storage tanks in series feeding the reactor through a
16
worm screw of controllable velocity;
17
β Bubbling fluidized bed reactor whose geometry is depicted in Fig. 2. This reactor has
18
three thermocouples installed along the vertical in order to monitor the gasification
19
temperature. The gasification pilot plant works at negative pressure settled by a vacuum
20
pump;
21
β Gas cooling system composed by two co-current heat exchangers. The first heat
22
exchanger cools down the producer gas to around 300ΒΊC and preheats the air feed
11
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entering the reactor. The second heat exchanger cools down the producer gas to around
2
150ΒΊC per forced air flow;
3
β Cellulosic bag filter for the removal of ashes and unconverted carbon particles;
4
β Tube heat exchanger for the removal of liquid condensates by cooling the producer
5
gas to room temperature.
6
The main component is the bubbling fluidized bed which is constituted by a reaction
7
chamber of 0.50 m internal diameter and 4.15 m height internally coated with ceramic
8
refractory material as shown in Fig. 2. The bottom bed of the reactor has a static height
9
of 0.15 m and is composed by dolomite (calcium magnesium carbonate CaMg (CO3)2,
10
with a solid density of about 2800 kg/m3 and particle size 0.3-0.7 mm; 80 kg of
11
dolomite composed the bottom bed. Preheated atmospheric air at 350 K was used as
12
gasification agent, fed through a distributor plate. The biomass is fed at the bed surface,
13
0.50 m above the distributor plate, by means of a screw feeder.
14
The start-up of the reactor until an operating bed temperature of around 400 ΒΊC was
15
done by a propane burner and after with coal to around 800ΒΊC. After reaching a bed
16
temperature of around 800 ΒΊC, the biomass feeding was started and the coal was
17
switched off. The biomass partial combustion allows the delivery of the necessary heat
18
to achieve the desired reactor temperature and the equivalence ratio was controlled by
19
adjusting the biomass feeding rate and the atmospheric air flow rate. Then, the bubbling
20
fluidized bed gasifier was operated under autothermal and steady-state conditions.
21
The reactor was operated at atmospheric pressure and in bubbling regime according to
22
Geldart [48] classification, with superficial gas velocity of around 0.25 m/s according to
23
Ergun equation [49]. 12
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The fuel used in the gasification experiments was Portuguese peach stone. This biomass
2
was characterized in terms of properties with interest for the gasification process and
3
modeling (proximate and ultimate analysis, heating value and empirical chemical
4
formula), as shown in Table 1.
5
The operating conditions of the gasifier were characterized by the biomass feed rate, air
6
feed rate, equivalence ratio, temperature along the reactor and gas composition at the
7
exit. Table 2 shows information about the operating conditions.
8
The equivalence ratio (ER) was calculated as the ratio between the actual air added to
9
the reactor and the stoichiometric air for the biomass. The stoichiometric air was
10
estimated based upon a mass balance considering the ultimate analysis of the biomass
11
expressed in Table 2 and the amount of air needed to complete oxidation of the
12
biomass. Taking the biomass feed rate and air feed rate in account, the equivalence ratio
13
values used were between 0.29 and 0.45 (Table 2).
14
The producer gas was sampled at the exit of the condenser by means of Tedlar bags
15
every time the gasification plant has reached a stationary state in terms of temperature
16
variation in the reactor in the range of 10ΒΊC. Producer gas analysis was performed in a
17
Varian 450-GC gas chromatograph with two thermal conductivity detectors that allow
18
the detection of H2, CO, CO2, CH4, O2, N2, C2H2, C2H4 and C2H6 using helium and
19
nitrogen as carrier gases. Table 2 shows the analysis of the experimental conditions and
20
the producer gas.
21
The lower heating value (LHV) of the dry gas was estimated based on the relative
22
percentage of fuel gases components present and respective LHV at reference
23
conditions of those fuel gases components. The LHV of the dry gas was found between
13
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4.1 and 5.4 MJ/Nm3, with the higher values found for equivalence ratios between 0.29
2
and 0.36 with average bed temperatures in the range 750ΒΊC to 800ΒΊC.
3
Regarding the syngas yield, in this work it was estimated according to the relation
4
between the dry gas volumetric flow rate and the biomass (dry basis) mass flow rate.
5
The dry gas volumetric flow rate was estimated based on a mass balance of the N2 in the
6
reactor, including the N2 entering with gasification agent and in the biomass fuel, and
7
the N2 exiting in the produced gas. It was assumed that at the operating temperature, the
8
N2 in the gasification agent was not converted, and exits as N2, and as approximation,
9
that all the nitrogen in the fuel exits in the produced gas as N2. The syngas yield values
10
obtained for the gasification experiments were between 1.5 and 2.2 Nm3gas/kg biomass.
11
The cold gas efficiency was calculated as the ratio between the chemical energy in the
12
produced gas and the chemical energy in the biomass fed [8]. Values of cold gas
13
efficiency between 47.9 and 67.3% were calculated for the experimental conditions
14
used. In-depth analysis on the gasification plant can be found elsewhere [50].
15
3. Mathematical model
16
Computational fluid dynamics (CFD) is a field of knowledge and techniques used to
17
solve mathematical models of fluid flow, heat transfer and associated phenomena such
18
as chemical reactions. The advance of computer capacity allowed the CFD models to
19
have a remarkable impact on reactor design and performance optimization [51].
20
In the case of single-phase reacting flows it is already possible to move directly from
21
the lab-scale to the plant-scale design by using numerical simulations. However, in the
22
case of multiphase reacting flows there are significant research issues that must be
23
resolved before they can be considered reliable for reactor design and optimization [52]. 14
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Bubbling fluidized-bed gasification as a multiphase reaction flow comprises nonlinear
2
coupling of hydrodynamics, heat and mass transfer, and chemical reactions [53]. The
3
accurate modeling of the transport of mass and heat in multiphase flows is fundamental
4
to the correct design and performance optimization of biomass gasifiers.
5
Biomass gasification involves a number of fundamental processes, which can be
6
divided into four phases: drying, pyrolysis, gasification and combustion [4, 8].
7
3.1 Drying model
8
Drying consists in the evaporation of the moisture contained in the biomass. The
9
amount of heat required is proportional to the biomass moisture content, which can vary
10
considerably. In the autothermal gasification, the heat required is obtained from the
11
other stages of the gasification process. In the drying stage the biomass does not
12
experience any kind of decomposition. However, at temperatures above 100ΒΊC, the
13
biomass moisture is removed and converted into steam. Therefore, the rate of moisture
14
release (Revp) from solid biomass is modeled as expressed in Table 3.
15
The calculation of the mass transfer rate implies the determination of the moisture
16
concentrations Cw,s, Cw,g in the solid phase and gas phase, respectively. This is done by
17
assuming the vapor as an ideal gas.
18
The moisture concentration at the surface can then be calculated from the vapor
19
pressure. Pw,s(Ts) is the vapor pressure corresponding to the equilibrium line at
20
temperature Ts. By equilibrium reasons at the interface between the gas and the liquid,
21
the temperature of the vapor is equal to the surface temperature, Ts. Likewise, the
22
moisture concentration in the gas phase Cw,g can be expressed in terms of Tg. The
15
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convective mass (hs) and heat (hβs) transfer coefficients can be calculated from the
2
Sherwood (Sh) and Nusselt (Nu) numbers.
3
3.2 Pyrolysis model
4
Pyrolysis is a thermal decomposition of the biomass taking place in the absence of
5
oxidizing agents and at moderate temperatures. From the pyrolysis process a variety of
6
chemical species are formed, which can be divided into three categories: volatiles, tars
7
and char. Volatile components are a mixture of gases mainly comprised of CO, H2,
8
CO2, H2O and light hydrocarbons. Tars are a complex mixture of condensable
9
hydrocarbons assumed to be mainly aromatic [56]. Char is a solid residue containing
10
mostly carbon but also some volatiles and ash. The gas species experience
11
homogeneous gas phase reactions forming H2, CO, CO2 and H2O, which afterwards
12
gasify and combust the char.
13
The pyrolysis phenomenon has been described by various approaches: one step global
14
models (primary tar), three parallel primary reactions and one step global model with
15
generation of secondary tar [57]. Park et al. [58] reported that accounting for the
16
secondary reactions is of minor importance. Haseli et al. [57] confirms such
17
achievement through a comparative study concluding that the three parallel reactions
18
model is sufficient to achieve a reasonable agreement between the predictions and
19
pyrolysis experiments, thus any inclusion of secondary reactions (tar cracking) slightly
20
affects the final results. Nevertheless, in this model a one step global model with
21
secondary tar generation was adopted as expressed in the Table 4 in order to increase
22
the comprehensiveness of the proposed model.
23
The reaction parameters for pyrolysis model are given in Table 5.
16
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3.3 Heterogeneous gasification reactions
4
In the pyrolysis phase, char is generated that is further converted in heterogeneous
5
gasification reactions. The gases released during pyrolysis phase provide various
6
oxidizing agents such as O2, H2O and CO2 for the heterogeneous gasification reactions.
7
Therefore, gasification can be distinguished by three possibilities with different product
8
gas compositions. These are oxygen gasification, steam gasification and carbon dioxide
9
gasification as expressed in Table 6. The Kinetic/Diffusion Surface Reaction Model was
10
implemented to include both diffusion and kinetic effects on the heterogeneous
11
reactions.
12
Where Rc is the char consumption rate, Kd is the diffusion rate and Kr is the Arrhenius
13
kinetic rate.
14
3.4 Homogeneous gas reactions
15
Homogeneous reactions are reactions that include only reactants and products in the
16
same phase. In gasification, these are reactions in the gas phase. The homogeneous
17
gasification reactions rate coefficients are based on the Arrhenius law and are given in
18
Table 7.
19
In the above equations πΆπΆπ; πΆπ»2π;πΆπΆπ» πππ πΆπ» are the gas species concentrations. The
20
actual reaction rates of volatile species are obtained as:
21
π
= πππ[π
πππ,π
πππ₯]
4
2
(1)
17
ACCEPTED MANUSCRIPT 1
The mixing rate inside the bed is understood to be proportional to the pressure drop
2
throughout the bed. According to the Ergun equation [49], the mixing rate can be
3
expressed as [65]:
4
π
πππ₯ = πΆπππ₯ππ 150
{
π·π(1 β π)2/3 ππ2π
+ 1.75
} Γ πππ{
π’π(1 β π)1/3 πππ
}
πΆππ’ππ πΆπ2 πππ’ππ , ππ
2
(2)
5
Where the mass diffusion coefficient (π·π) is obtained as follows [66]:
6
π·π = 1.5 Γ 10 β 5
7
3.5 Transport equations for gas and solid phases
8
3.5.1 Mass conservation
9
The biomass feed changes from solid phase into gas phase by reacting with the
10
oxidizing agents. Therefore, the continuity equations are expressed for both phases and
11
are given in Table 8.
12
π is the void fraction in the bed, V0 is the initial volume, π1, π2 and π3 are coefficients
13
equal to 1 or 0 according, respectively, to the appearance of moisture (Revp),
14
devolatilization (Rv), and char combustion (Rc) included in the source term ππ π.
15
3.5.2 Momentum conservation
16
The momentum equations are presented for the gas and solid phases. The model follows
17
an Eulerian-Eulerian approach, which allows a realistic description of the time
18
dependent processes in reacting fluidized beds. Compared to Euler-Lagrange, the Eulerβ
19
Euler model is computationally less demanding because it assumes particles as
20
continuum instead of tracking each individual particle and allow the calculation of
[
ππ + 273.15 298
]
1.5 1.41
(3)
π
18
ACCEPTED MANUSCRIPT 1
larger reactors [69]. The Gidaspow [70] drag model is implemented to calculate the gas-
2
solid momentum exchange coefficient.
3
The turbulence model of the gas phase is of major importance in a fluidized bed
4
gasification scenario. A standard k-Ξ΅ model is implemented as turbulence model, since it
5
is the most appropriate model when turbulence transfer between phases plays an
6
important role as is the case of fluidized beds. Closure laws are needed for the particle
7
collision pressure and the granular flow based on the kinetic theory is implemented.
8
(Table 9).
9
3.5.3 Granular temperature model
10 11
The granular temperature of the solid phase is proportional to the kinetic energy of the
12
particles translation and collision. The thermodynamic temperature is a quantum of the
13
fluctuating energy of the molecules; the granular temperature expresses the macroscopic
14
kinetic energy of the random particle motion [73]. The granular temperature model is
15
implemented in order to describe the viscous forces and the solid pressure of the particle
16
phase as expressed in Table 10.
17 18
The term ( β ππ πΌ + ππ ) :βπ’π expresses the generation of energy by the solid stress tensor.
19
3.5.4 Energy conservation
20
To describe the energy conservation the following energy conservation equations must
21
be solved (Table 11).
22
οqr represent the radiative flux, assuming the bed as a gray optically medium
23
characterized by the extinction coefficient (K) and the emissivity (Ο΅). 19
ACCEPTED MANUSCRIPT 1
3.5.6 Species conservation
2
To describe the species conservation the following species conservation equations must
3
be solved (Table 12).
4
πππ is the mass fraction of the considered chemical species; πππ is the mass fraction of
5
the particles such as moisture, volatile, fixed carbon and ash. The source term of the
6
species conservation equation for gas and solid phases is calculated individually for
7
each species and particles.
8
3.6 Numerical procedure
9
The finite volume method is used for the discretization of the geometrical domain and
10
the partial differential equation set. A staggered grid is used instead of a collocated one
11
in order to properly represent the influence of the pressure in the discretized momentum
12
equations. Therefore, a staggered grid is implemented for the velocity components. The
13
scalar variables are calculated at nodal points and the velocity components on staggered
14
grids centered on the cell faces. This also helps to avoid some convergence problems
15
and oscillations in velocity and pressure fields. The problems associated with the non-
16
linearities in the equation set and the pressure-velocity linkage is solved by adopting the
17
semi-implicit method for pressure-linked equations algorithm (SIMPLE). From the
18
discretization procedure results a set of algebraic equations that is solved using the
19
point-by-point iterative method of Gauss-Seidel in combination with the algebraic
20
multi-grid solver.
21
The accuracy and convergence of the decomposition of the differential equation set
22
depends to a large extent on whether the boundary information can be given correctly.
23
The boundary conditions of the proposed model are given in Table 13. 20
ACCEPTED MANUSCRIPT 1
The boundary conditions for mass, species, and enthalpy comprise biomass feed points,
2
gas inlet, and non-permeability of the reactor walls. The boundary conditions for the
3
momentum are the velocities of gas and particles at the boundaries. It is assumed that
4
the gasifying agent flow at uniform velocity and that only the gas phase is allowed to
5
outflow the reactor. The no-slip condition is applied to the upright gas velocity
6
component at the side walls. The horizontal components of the gas and particle
7
velocities are set to zero. The outlet pressure is set equal to the normal atmospheric
8
pressure.
9
The boundary condition is closely related to the shape of the mesh, so the shape of the
10
mesh largely determines the computational solution accuracy and convergence. A grid
11
independence test is carried out to reduce the computational time as well as to build up
12
confidence level. Three meshes for the fluidized bed gasifier were created with the
13
present code. These meshes were utilized for sensitivity studies to determine whether
14
mesh size would affect the gasification metrics. The information regarding the meshes
15
characteristics is presented in Table 14.
16
Molar fraction compositions for H2, CO, CH4 and CO2 at gasifier outlet are compared
17
for the described meshes as shown in Fig. 3.
18
The simulation results for the various meshes are found to be in an increasing agreement
19
with each other. As can be seen, the mesh with number of cells exceeding 200,000 cells
20
reveal a small variation in parameter convergence less than 1%. Consequently, the
21
medium grid system having 200,000 cells was chosen for the rest of the numerical
22
analysis under different operation condition.
21
ACCEPTED MANUSCRIPT 1
The computational effort of a numerical solution grows almost linearly with the size of
2
the problem which mainly depends on the complexity of the equation set to solve and
3
the size of the grid. The geometrical domain of the bed is divided into 200,000 small
4
cells. The time step is 10-4 s and the gasification time was resolved by 18000 time steps.
5
In such a complex model, it is sometimes difficult to define a good initial condition. For
6
this reason, the process was first simulated considering only flow and non-reacting heat
7
transfer (also known as βcold flowβ) and after reaching conversion, reactive multiphase
8
flow was added.
9
4. Results and discussion
10
4.1. Syngas composition
11
In order to validate the proposed mathematical and simulation model, the numerical
12
results were compared with the experimental data collected from the gasification pilot
13
plant already described. Figure 4 shows a comparison between the numerical results
14
predicted by the proposed model and the experimental data under the operation
15
conditions described in Table 2.
16
From the analysis of the Fig. 4, a good agreement between the experimental and
17
numerical results is found even for different operating conditions.
18
From the gasification process results the formation of three fractions: syngas, ashes (and
19
eventually char) and condensates. The syngas is the most important fraction, accounting
20
for more than 70 % and comprises light gases, notably CO, H2, CH4, CO2 and N2.
21
Figure 5 shows the mole fraction contours of the main syngas fractions.
22
ACCEPTED MANUSCRIPT 1
The O2 and CO2 contours show an opposite profile; one is maximum where the other is
2
minimal. This can be seen observing the bottom of the reactor, where the O2 quantity is
3
more pronounced due to the proximity of the air inlet, while CO2 is in minimal amounts.
4
Its quantity is raised along the reactor, due to the oxidation reactions taking place,
5
which partially burn the volatiles and producing more CO2. Logically, at the top of the
6
reactor CO2 is present in a great extension whereas O2 and CO shows their minimal
7
amounts, due to the oxidation reactions. Similar results have been reported in the
8
literature [19, 77].
9
These numerical contours also depicts analogous profiles for H2, CH4 and C2H4
10
(ethylene) although with different intensities. These components present their maximum
11
yields near the biomass inlet. Especially in the case of H2, the lower temperatures found
12
at reactor greater heights promote primary water-gas and steam-methane reforming
13
reactions enhancing its production [77, 78]. Concerning CH4 and C2H4, they tend to
14
decompose at higher temperatures which normally also means a decrease in tar content
15
[19], as we were able to find.
16
4.2. Effect of process parameters on gasification
17
4.2.1 Moisture content
18
The moisture content of biomass considerably affects gasification, because it is
19
vaporized inside the reactor, absorbing heat and reducing the temperature, while the
20
produced steam is able to react with other compounds present within the various
21
reactions during all stages of the gasification process [79]. Figure 6 shows the effect of
22
moisture content on each of the syngas components.
23
ACCEPTED MANUSCRIPT 1
As can be seen, higher moisture contents promote an accentuated decrease in the CO
2
yield, as well as a small increase for H2, more pronounced in the case of CO2 and a
3
slight decrease in the case of light hydrocarbons. This has been reported in some other
4
published works [19] and can be explained based on the fact that when more moisture is
5
present, CO levels decrease through their consumption in the water-gas shift reaction,
6
which subsequently raises CO2 and H2 contents [2]. Referring to the hydrocarbons, for
7
higher biomass moisture contents, they tend to crack incompletely due to the
8
temperature reduction [2].
9
In what regards the influence of moisture content in the efficiency of the overall process
10
of gasification, cold gas efficiency (CGE), carbon conversion efficiency (CCE) and low
11
heating value (LHV) are good parameters to take into account. Figure 7 shows these
12
trends and also depicts tar behavior for different moisture contents.
13
From Fig. 7 it can be seen that higher moisture contents on biomass lead to enhanced
14
CCE and tar yields, as well as decreased CGE and LHV. Regarding the increase of tar
15
content with moisture, this is related to the inherent drop of temperature inside the
16
reactor which difficult tar cracking before syngas is released [2]. Concerning CGE, the
17
presented behavior was similarly reported elsewhere and associated not only to the
18
difficulty of the system to reach the required temperature for gasification due to the high
19
energy demanding conditions (less energy being available to ensure the endothermic
20
reactions), but also to the dilution effect of syngas with water and CO2 [79]. The
21
progressive decrease of LHV with moisture increase is associated to the loss of energy
22
suffered by the system as a result of the reduced CO, CH4 and hydrocarbon contents
23
which are consumed in the steam reforming reactions, lowering the energy conversion
24
efficiency as reported for other feedstocks [80].
24
ACCEPTED MANUSCRIPT 1
4.2.2. Steam-to-biomass ratio
2
The steam-to-biomass ratio (SBR) is defined as the relation between the steam flow rate
3
and the biomass flow rate fed into the gasifier and is a key process parameter involved
4
in steam gasification. Figure 8 details the syngas composition for some SBR values.
5
The abrupt decrease in CO content, associated with the increase of H2 and CO2 for
6
higher SBR is explained by the raise of partial pressure inside the reactor, promoted by
7
the steam injection, as stated by other researchers [26, 30, 81]. The steam injection
8
favors the water gas, water gas shift and steam reforming reactions which, besides the
9
described effects also endorse hydrocarbon breakdown as reported by some authors [30,
10
81].
11
As with the moisture content, SBR influence on CGE, CCE, LHV and tar yield was also
12
investigated and is shown in Fig. 9.
13
SBR increase leads to higher CGE, CCE and LHV accompanied by lower tar yields.
14
This is due to the steam reforming reactions that convert H2O available from the
15
gasifying agent into H2, which is consistent with Fig. 8 and with reports from other
16
authors [82,83]. In what regards tar contents in the produced syngas, they decrease for
17
higher SBR once their decomposition is favored improving syngas quality as proved by
18
high CGE, CCE and LHV.
19
3.2.3 Equivalence ratio
20
Equivalence ratio (ER) is the ratio of the actual air to fuel ratio to the stoichiometric air
21
to fuel ratio, and this measure is very important as it permits to distinguish between
22
gasification and combustion. Gasification presents ER values below 1, the optimum
25
ACCEPTED MANUSCRIPT 1
range for biomass lying between 0.2 and 0.4, depending on other operation conditions
2
[3, 6]. Figure 10 shows syngas composition for growing ER values.
3 4
From Fig. 10 it can be seen growing contents of H2 and CO until a peak is reached.
5
Afterwards, they start to decrease returning to a value slightly below the initial one.
6
These two components are negatively affected by the oxidation reactions occurring in a
7
progressively more oxygenated atmosphere [84]. The increase in the ER means that
8
more air is added to the gasifier favoring the oxidation reactions. An ER value lower
9
than 0.2 results in various problems, including incomplete gasification, excessive char
10
formation, and a low heating value of the syngas. An ER higher than 0.4 results in
11
excessive formation of combustion products, such as CO2 and H2O, at the expense of H2
12
and CO [8]. For these reasons an optimal ER value should appear in the ER window of
13
0.2-0.4 for biomass gasification. This situation is observed in the Fig. 10, where the H2
14
and CO contents are increasing up to a certain ER and then decrease assuming an
15
elbow-shape function.
16
Regarding CO2, an opposite behavior was registered, with initial values decreasing
17
nearly until the same peak region, from where they start to raise reaching similar
18
contents of the initial ones. This can be explained based on the thermal cracking of the
19
volatiles, once in the presence of more oxygen, more CO2 will be produced at the
20
expense of CO [84].These trends were also found by other researchers for different
21
feedstocks and with distinct peak values [85]. Regarding the hydrocarbons, their
22
contents shows lower decreases within the interval of ER values under study, due to the
23
oxidation reactions favored with the presence of more oxygen and, in some extent, to
24
the steam reforming reactions [84].
26
ACCEPTED MANUSCRIPT 1
The influence of ER on tar content, lower heating value, cold gas efficiency and carbon
2
conversion efficiency was investigated in the referred interval and is shown in Fig. 11 at
3
constant temperature.
4
As can be seen, ER increase means a general trend of improvement of CGE and CCE
5
whereas LHV and tar yield diminish. This is caused by the extra air introduced in the
6
gasifier. This effect is more visible in the case of LHV, which seems to start increasing
7
but, at an ER peak value it begins to drop due to the diluting effect of N2 and also to the
8
progressive reduction of H2 and CO contents, together with the CO2 augment, as seen in
9
Fig.10. As there are so many variables to balance, the maximum LHV can only be
10
attained through a compromise range for ER, forming an asymmetric curve as depicted
11
in Fig. 10. Cold-gas efficiency (CGE) is the energy input over the potential energy
12
output based on the LHV of both the solid fuel and the product gas, strongly depending
13
on the LHV [8]. Therefore, CGE also presents an elbow-shape function. These
14
tendencies in the calorific value parameters are in agreement with other reported works
15
[85].
16
The increase in the ER favors the oxidation reactions that being exothermic promote the
17
increase of the reactor temperature. The increase of the temperature favors the steam
18
reforming reactions, which in turns promote the carbon conversion efficiency as shown
19
in Fig. 11. This also leads to the increase of gas yield, which improves the tar
20
decomposition due to the reforming and cracking reactions [86], which is also seen in
21
Fig. 11.
22
5. Conclusions
27
ACCEPTED MANUSCRIPT 1
In the experimental work, peach stones were used in the autothermal pilot scale
2
bubbling fluidized-bed gasifier as a feedstock. The result showed the final gas
3
compositions about 51.1 - 53.5% of N2, 13.1-16.7% of CO2, 12.4 -18.1% of CO, 7.7-
4
11.5% of H2, 2.8-4.2% of CH4, and 0.3-1.1% of C2H4 for equivalence ratios in the range
5
0.29-0.45 and gasification temperature from 750 ΒΊC to 850ΒΊC.
6
The LHV of the dry gas was found between 4.1 and 5.4 MJ/Nm3, with the higher values
7
found for equivalence ratios between 0.29 and 0.36 with average bed temperatures in
8
the range 750ΒΊC to 800ΒΊC. The specific dry gas production was between 1.5 and 2.2
9
Nm3/kg biomass db and the cold gas efficiency between 47.9 and 67.3%.
10
A comprehensive homemade two-dimensional unsteady state CFD model was proposed
11
to investigate the effects of moisture content, steam to biomass ratio and equivalence
12
ratio in the process performances such as (1) producer gas composition, (2) producer
13
gas LHV, (3) carbon conversion efficiency, (4) cold gas efficiency, and (5) tar yields.
14
Obtained results show good agreement between experimental and numerical runs,
15
which suggests that the homemade proposed model is capable of predicting the
16
producer gas composition as well as the trends for the main conditions governing the
17
gasification process for the selected feedstock.
18
By parametric study, the five process performances were evaluated for the pilot
19
gasification plant with respect to ER, SBR and biomass moisture. The results of the
20
study showed a negative impact of moisture and equivalence ratio over conversion
21
efficiency and producer gas quality, and a positive impact for steam to biomass ratio
22
which promotes higher calorific values and overall efficiency for the process.
23
Acknowledgments
28
ACCEPTED MANUSCRIPT 1
Ana Ramos thanks the Portuguese Foundation for Science and Technology for her PhD
2
grant [SFRH/BD/110787/2015].
3
4
5
6
7
8
9
10
11
12 13
14
15
16
17
18 29
ACCEPTED MANUSCRIPT 1
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21
catalytic steam co-gasification process. Energy 2014; 75: 40-4.
39
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2
Portuguese municipal solid wastes. Int J Hydrogen Energy 2016; 41: 10619-30.
3
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downdraft biomass gasifier. Biomass Bioenergy 2002; 23(4): 283-9.
5
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6
review. Appl Energ 2013; 111: 129-41.
7
8
9
10
11
12
13
14
15
16
17
18
40
ACCEPTED MANUSCRIPT 1
2
(f) (d) (a)
(e)
(c)
(g)
(b) (k)
(j)
(i)
(h)
3 4 5 6
Figure 1 - Schematic of the biomass gasification pilot plant. (a) Feed system; (b) Bubbling fluidized bed gasifier; (c) Heat exchangers; (d) Bag filter; (e) Condenser; (f) Flare; (g) Condensate storage tank; (h) Vacuum pump; (i) Air compressor; (j) Air fan; (k) Air fan.
7
41
ACCEPTED MANUSCRIPT
1
1.0 m
2 0.2 m
Refractory
3
Reactor
4
Syngas
Steel wall
5
0.5 m
6 4.15 m
7 8 9 10 11
13
Biomass
0.2 m
12
14 15 1 2
Air flow
Figure 2- Bubbling fluidized bed reactor scheme
3
4
42
ACCEPTED MANUSCRIPT 22
Coarse
20
Medium
Fine
18
Molar Fraction (%)
16 14 12 10 8 6 4 2 0
1 2
H2
CO
CH4
CO2
Figure 3 - Grid independency study for the described meshes
3
4
5
6
7
8
9
10
43
ACCEPTED MANUSCRIPT
Syngas fraction (% vol. dry basis)
60 50
Experimental work
40
Theoretical Model
a)
30 20 10 0 H2
1
CO
CH4
CO2
N2
CO2
N2
CO2
N2
Syngas fraction (% vol. dry basis)
60 50
Experimental work
40
Theoretical Model
b)
30 20 10 0 H2
2 3 4
Syngas fraction (% vol. dry basis)
60 50 40
CO
Experimental work
CH4
c)
Theoretical Model
30 20 10 0
5 6 7 8
H2
CO
CH4
Figure 4 - Experimental and predicted syngas compositions of peach stone from table 2 a) run# 1 b) run# 4 and c) run# 7 44
ACCEPTED MANUSCRIPT
1 2 3
4 5 6 7 8
O2
CO
CH4
CO2
C2H4
H2
Tar
Figure 5 - Numerical contours for syngas composition produced within the fluidized bed gasifier for peach stone under operating conditions of run# 5
9
10
11
12
13
45
ACCEPTED MANUSCRIPT
25
Syngas composition (%)
CH4
C2H4
C2H2
20
15
10
5
0 5
1 2 3 4
10
15
20 25 Moisture content (%)
30
35
40
Figure 6 - Effect of moisture content on the produced syngas for peach stone under the operating conditions of run# 5
5
6
7
8
9
10
11
12
13
14 46
7
Efficiency ( %)
100
LHV (MJ/Nm3), Tar yield (g/Nm3)
ACCEPTED MANUSCRIPT
6
80
5
60
4 3
40
2 20
CGE CCE Moisture content (%)
1
LHV
Tar
Moisture content (%) 0
0 0
1 2 3
10
20
30
40
Figure 7 - Effect of moisture content on CGE, CCE, LHV and tar yield for peach stone under the operating conditions of run# 5
4
5
6
7
8
9
10
11
12
13
47
ACCEPTED MANUSCRIPT 1
25 H2
CO
CO2
Syngas composition (%)
20
15
10
5
0 0
1
2 Steam biomass ratio
3
4
2 3
Figure 8 - Effect of steam biomass ratio on the produced syngas for peach stone under the operating
4
conditions of run# 5
5
6
7
8
9
10
11
12
48
Efficiency ( %)
100
LHV (MJ/Nm3), Tar yield (g/Nm3)
ACCEPTED MANUSCRIPT
7 6
80
5 60
4 3
40
2 20
Steam biomass ratio
1
CGE LHV
Steam biomass ratio 0
CCE Tar
0 0
1
2
3
4
1 2 3
Figure 9 - Effect of steam biomass ratio on CGE, CCE, LHV and tar yield for peach stone under the operating conditions of run# 5
4
5
6
7
8
9
10
11
12
13 49
ACCEPTED MANUSCRIPT 1
2
Syngas composition (%)
25
C2H2 CO2
H2 CH4
CO C2H4
20
15
10
5
0 0.15
0.2
0.25
ER
0.3
0.35
0.4
3 4 5
Figure 10- Effect of equivalence ratio on the produced syngas for peach stone under the operating conditions of temperature 800Β°C, admission biomass 45 kg/h and moisture content 10%
6
7
8
9
10
11
12
13 50
ACCEPTED MANUSCRIPT 1
Efficiency ( %)
100
LHV (MJ/Nm3), Tar yield (g/Nm3)
2
6 5
80
4 60 3 40 2 20
1
ER CGE
0
CCE
ER
0 0.15
0.2
0.25
LHV
0.3
Tar
0.35
0.4
3 4 5
Figure 11 - Effect of equivalence ratio on CGE, CCE, LHV and tar yield for peach stone under the operating conditions of temperature 800Β°C, admission biomass 45 kg/h and moisture content 10%
6
7
8
9
10
11
12
13 51
ACCEPTED MANUSCRIPT 1 2
Table 1 - Proximate and ultimate analysis of peach stone Biomass proprieties
Peach stone
Proximate analysis (wt % wet basis) Volatile Fixed carbon Ash Moisture
63.0 29.0 1.0 7.0
Ultimate analysis (wt % dry and ash free basis) N C H O
4.9 41.0 5.7 48.4
S
0.0
Density (kg/m3)
480
Higher heating value (MJ/kg)
18.8
Empirical formula
CH1.668O0.885N0.102
3 4
5
6
7
8
9
10
11
12
13
52
ACCEPTED MANUSCRIPT 1 2
Table 2. Experimental operating conditions and producer gas analysis for peach stone Experimental conditions
Peach Stone Run 1 Run 2 Run 3 Run 4 Run 5 Run 6 Run 7 Run 8 Run 9
Temperature of gasification (ΒΊC) Biomass feed rate (kg/h) Air feed rate (Nm3/h)
750 30 45
750 45 55
750 60 87
800 30 49
800 45 57
800 60 91
850 30 57
850 45 60
850 60 93
Equivalence ratio
0.36
0.29
0.35
0.39
0.3
0.36
0.45
0.32
0.37
H2 CO CH4 CO2
8.3 18.2 3.7 13.1
8.5 15.7 4.1 15.1
10.3 12.4 3.2 16.7
8.2 16.5 3.6 14.7
7.4 14.9 4.2 15.2
11.5 14 2.8 15.9
8.1 14.1 2.9 16
7.7 12.9 3.7 16.5
11 13.7 3.1 16.0
N2 O2 C2H2
51.4 3.9 0.2
51.1 4.1 0.1
52.9 3.9 0.1
52 3.8 0.1
52.2 4.8 0.1
51.7 3.6 0.1
53.2 3.1 0.1
53.5 4.6 0.2
51.9 4.1 0.1
C2H4
1.1
1.1
0.5
1.0
1.1
0.4
0.3
0.9
0.3
C2H6
0.1
0.2
0.0
0.1
0.1
0.0
0.0
0.0
0.0
5.4
5.2
4.2
5.0
5.0
4.3
4.1
4.4
4.3
1.8
1.5
1.7
2.0
1.5
1.9
2.2
1.6
1.9
67.4
54.2
49.5
67.2
52.3
54.5
60.0
47.9
54.8
Producer gas fraction (%vol. db)
Gas LHV (MJ/Nm3) Gas Yield
3
(Nm3/kg
biomass)
Cold gas efficiency (%)
4
5
6
7
8
9
10
11
12 53
ACCEPTED MANUSCRIPT 1
Table 3 - Drying model
Ref.
π
ππ£π = π΄π βπ (πΆπ€,π β πΆπ€,π) ππ < 100β ππ = 100β
(βπ , (ππ β ππ ) + ππΏ(πβ4 β π4π )) πΆπ€,π =
[54] [54]
ππ€,π (ππ )
[54]
π
π
( π (π) = 10
πππ
π
ππ£π = π» ππ£π πππ = π΄π
0.622 +
7.5π +π 238
)
[55]
π€
[55]
1 3
πβ = 2 + 1.1ππ π
π0.6 1 3
ππ’ = 2 + 1.1ππ π
π
[55] 0.6
2
3
4
5
6
7
8
9
10
11 54
ACCEPTED MANUSCRIPT 1
2 3
Table 4 - Chemical reactions [59] πΆπ₯π»π¦ππ§βπβππ + π£ππππ‘πππ πππ ππ (πΆπ2 + π»2π + πΆπ»4 + π»2 + πΆπ) + πππππππ¦ π‘ππ πππππππ¦ π‘ππβπ£ππππ‘ππππ + π ππππππππ¦ π‘ππ Volatile matter in solid rate (kg/s): πππ£ πΈπ£ π
π£ =β ππ π = ππ πππ£π΄π£ππ₯π β ππ‘ π
π
( )
π΄π£ = 3.63 Γ 104 s β 1 ,
πΈπ£ π
= 9340 K
4 5 6 7 8 9 10 11 12 13 14 15 16 17
55
ACCEPTED MANUSCRIPT 1 2 3 4 5
Table 5- Reaction parameters for pyrolysis Reaction
π΄ (π β 1)
πΈ (ππ½πππ β 1)
Heat of reaction (MJ/kg)
Ref.
π1
1.44 Γ 104
88.6
-0.42
[60]
π2
4.13 Γ 106
112.7
0.42
[60]
7.38 Γ 105
106.5
0.42
[60]
π4
4.28 Γ 106
107.5
0.04
[61]
π5
1 Γ 105
107.5
0.04
[61]
ππππππ π βπππ ππππππ π βπ‘ππ π3
ππππππ π βπβππ π‘ππβπππ π‘ππβπβππ 6
7
8
9
10
11
12
13
14
56
ACCEPTED MANUSCRIPT 1
2
Table 6 - Heterogeneous gasification reactions Heterogeneous reactions [62]:
Kinetic/Diffusion Surface Reaction Model [63]: ππ
πΆ + 0.5π2βπΆπ
πΆ + πΆπ2β2πΆπ
π
π =
2
1 1 + πΎπ πΎπ
ππ + ππ 5.06 Γ 10 β 7 πΎπ = Γ ππ 2
(
C + H2OβCO + H2
[
πΎπ = 3.0 Γ ππ ππ₯π
3
4
5
6
7
8
9
10
11
12 57
)
]
10300 π
ππ
0.75
ACCEPTED MANUSCRIPT 1
2
3
Table 7 - Gasification homogeneous reactions Homogeneous reactions [62]:
Reaction rate [64]:
1 π»2 + π2βπ»2π 2
π
π» = 5.159 Γ 1015ππ₯π
1 πΆπ + π2βπΆπ2 2
π
πΆπ = 1.0 Γ 1015ππ₯π
πΆπ»4 + 2π2βπΆπ2 + 2π»2π
(
2
(
)πΆ
0.5 πΆππΆ π 2
(
)π
π
πΆπ» = 3.552 Γ 1014ππ₯π
(
π
π€π = 2.78 ππ₯π
4
5
6
7
8
9
10
11
12
58
β 1.5 1.5 π πΆπ2πΆπ»2
β 16000 ππ
4
πΆπ + π»2πβπΆπ2 + π»2
)π
β 3430 ππ
β 15700 ππ
)( πΆ
β 12600 π
ππ
β1 π πΆπ2πΆπΆπ»4 πΆπΆπ2πΆπ»2
πΆππΆπ»2π β 0.0265ππ₯π(65800/π
ππ)
)
ACCEPTED MANUSCRIPT 1
2
Table 8 - Mass conservation Gas phase: β(πππ) βπ‘
+ β(ππππ’π) = ππ π
Solid phase: β((1 β π)ππ ) βπ‘
+ β((1 β π)ππ π’π ) =β ππ π
Void fraction [67]: π=πβ
πβ π
Biomass volume shrinkage [68]:
πβ π
β β π
πππ¦) β π2(π
π£β β π
π£) β π3(π
πβ β π
π) = 1 β π1(π
πππ¦
Source term:
ππ π = π
ππ£π + π
π£ + π
π 3
4
5
6
7
8
9
10
59
ACCEPTED MANUSCRIPT Table 9 - Momentum conservation Gas Phase: β(Οπππ’π) + β(ππππ’ππ’π) =β Οβπg + ππππ β Ξ²(π’π β π’π ) + βπππ βπ‘ Gasβsolid drag coefficient, ο’ οο΅οΉοοΊ π½=
{
150
(1 β π)2ππ πππ2
+ 1.75
ππ(1 β π)|π’π β π’π | ππ
ππ(1 β π)|π’π β π’π | 3 π β 2.65 π , 4πΆπ· ππ
, π β€ 0.8
π > 0.8
where : πΆπ· =
{
and:
24 π
ππ
(1 + 015π
π0.687 π ),
π
ππ β€ 1000 0.44, π
ππ > 1000
πππ|π’π β π’π |ππ
π
ππ =
ππ
Gas phase stress tensor [71]: 2 ππ = ππ[βπ’π + βπ’ππ] β ππ(βπ’π) 3 where: π2
ππ = ππ + ππ‘ ; ππ‘ = πππΆπ π ; πΆπ = 0.09. The governing transport equations for k and ο₯ [72]: β βπ‘
(
ππ‘
ππ‘
)
)
(ππππ) + β(ππππ’ππ) = + β πππβπ + ππΊπ β ππππ
(
β βπ‘
(ππππ) + β(ππππ’ππ) + β πππβπ + π(πΆπ1πΊπ β πΆπ2πππ)
Gk represents the generation of turbulence kinetic energy due to the mean velocity gradients [72]: 2 πΊπ = ππ‘βπ’π.[βπ’π + βπ’ππ] β 3βπ’π(ππ‘βπ’π + πππ) and: πΆπ1= 1.44 ; πΆπ2 = 1.92, the turbulent Prandtl numbers for π and Ξ΅ are ππ = 1 and ππ = 1.3 [71]. Solid phase: β((1 β Ο)ππ π’π ) + β((1 β π)ππ π’π π’π ) = βπ‘ =β (1 β π)βππ + (1 β π)ππ π β π½(π’π β π’π ) + β(1 β π)ππ Stress tensor of the solid phase: 2 ππ = ππ β 3ππ βπ’π + ππ (βπ’π + π’ππ )
(
)
Where [70]: 4 ππ = 3(1 β π)ππ ππππ; 4
ππ = 5(1 β π)ππ ππππ(1 + π)
Ξπ Ο
10ππ ππ πΞπ
[
4
+ 96(1 + π)ππ 1 + 5ππ(1 β π)(1 + π) π
Solid pressure [72]: ππ = (1 β π)ππ Ξπ + 2(1 + π)(1 β π)2ππππ Ξπ Where [70]:
[
3
ππ = 5 1 β
(
1 3
)]
(1 β π) (1 β π)πππ₯
β1
Granular temperature: 3 1 , , 2Ξπ = 2β©π’π π’π βͺ with: 2π 0.5 π’π , = π 3 π is a Gauss distribution random number 0 β€ π β€ 1.
(
)
60
]2
(38)
ACCEPTED MANUSCRIPT 1
Table 10 - Granular temperature model [74] β βπ‘
2
(ππ (1 β π)Ξπ ) + β.(1 β π)ππ π’π Ξπ = 3[( β ππ πΌ + ππ ) :βπ’π + β.(ΞΞβΞ) β πΎπ + π·ππ + ππ ]
πΎπ = 3(1 β π2)(1 β π)2ππ π0Ξ
(
)
4 Ξ β β.π’π ππ π
ππ =β 3π½Ξ
( ) |π’
ππππ 18ππ
π·ππ = 4 ΞΞ =
2
πΞ π2π π π
150ππ ππ πΞ
|2
π β π’π
[
]
6 Ξ 1 + (1 + π)π0(1 β π) 2 + 2(1 β π)2ππ πππ0(1 + π) 384(1 + π)π0 5 π
2
3
4
5
6
7
8
9
10
11
12
13 61
ACCEPTED MANUSCRIPT Table 11 - Energy conservation Gas phase: β((1 β π)πππππππ) βπ‘
+ β(ππππ’ππππππ) = β(ππ.βππ) + π΄π βπ , (ππ β ππ ) + ππ
π
Solid phase: β((1 β π)ππ πππ ππ ) βπ‘
+ β((1 β π)ππ π’π πππ ππ ) = β(ππππ.βππ ) + (βππ) β π΄π βπ , (ππ β ππ ) + ππ
Radiative flux density [75]: βππ =β
16ππ2
16ππ3
πΎ
3πΎ
(βπ)2 +
(β2π)
Thermal dispersion coefficient ππ [76]: ππ = ππππ,0 + 0.5 Γ ππ Γ ππ Γ ππ Γ πΆππ ππππ,0 = π(ππ + βππ£βπ) +
(1 β π)βπ ππ
1/( π + βππ ) + ππ /ππ π£
Where: ππ =
( )
2ππ 3
; ππ£ = 0.151912βπ
ππ
ππππ
ππ
( )( )π;
βππ = 0.1952 Γ ππ
π 2βπ
100
(
π(1 β π)
; βππ£ = 0.1952 1 + 2(1 β π)π
ππ
) β 1( )π 100
βπ = 0.96795 ππ(1 β π) β 1/3;
(
ππππ(ππ) = 5.66 Γ 10 β 5ππ + 1.1 Γ 10 β 2; π = 1.93 + 0.67exp β Source term: πππ = β π
ππ£π Γ βπ,πΆπ ππΆπ
πππ = β π
ππ£π Γ π
πΆπ2
Γ [βπ,πΆπ2 β βπ,πΆπ] Γ
[
ππΆπ 2
]
β1
1
2
3
4
5
62
(ππ. β 0.39) 0.054
)
π
ACCEPTED MANUSCRIPT Table 12 - Species conservation Gas phase: β(ππππππ) βπ‘
+ β(ππππ’ππππ) = β(π·ππβ(ππππππ)) + ππ
π
Solid phase: β((1 β π)ππ πππ ) βπ‘
+ β((1 β π)ππ π’π πππ ) = ππ
π
1
2
3
4
5
6
7
8
9
10
11
12
13
63
ACCEPTED MANUSCRIPT 1
Table 13 - Boundary conditions Inlet
π = πππ ; ππ = πππ,π ; π’ = π’ππ ; ππ = ππ, πππ
Outlet βππ βπ¦
βπ’ βππ βππ βππ βπ = = = = =0 βπ¦ βπ¦ βπ¦ βπ¦ βπ¦
=0
2
3
4
5
6
7
8
9
10
11
12
13
14
15
64
ACCEPTED MANUSCRIPT 1 2
Table 14 - Mesh characteristics Mesh type
Number of cells
Coarse
100,000
Medium
200,000
Fine
250,000
3
4
65