The two phase model is developed by using Nomenclature A Specific surface area of packed bed m1 Ac Specific surface area of char m1 Ad Specific surface area for gas diffusion m1 Ag Cross se
Trang 1A comprehensive two dimensional Computational Fluid Dynamics
Department of Chemical and Process Engineering, University of Moratuwa, Sri Lanka
a r t i c l e i n f o
Article history:
Received 27 June 2015
Received in revised form
19 July 2016
Accepted 21 July 2016
Keywords:
Gasification
Mathematical model
Computational Fluid Dynamics
Moving bed
a b s t r a c t
This study focuses on developing a dynamic two dimensional Computational Fluid Dynamics (CFD) model of a moving bed updraft biomass gasifier The model uses inlet air at room temperature as the gasifying medium and afixed batch of biomass The biomass batch is initially ignited by a heat source which is removed after a certain amount of time This model operates by the heat emitted by combustion reactions, until the fuel isfinished Since the operation is batch wise, model is transient and takes into consideration the effect of bed movement as a result of shrinkage The CFD model is capable of simu-lating the movement of interface between solid packed bed and gas free board and this motion is also presented The model is validated by comparing the simulation results with experimental data obtained from a laboratory scale updraft gasifier operated in batch mode with Gliricidia The developed model is used tofind the optimum air flow rate that maximizes the cumulative CO production It is found that from the simulation study for the particular experimental gasifier, a flow rate of 7 m3/h maximizes the
CO production The maximum cumulative CO production was 6.4 m3for a 28 kg batch of Gliricidia
© 2016 Elsevier Ltd All rights reserved
1 Introduction
With the depletion of fossil fuels, alternative, renewable energy
sources are promoted as possible ways of providing the world's
energy demand In this respect, biomass is a promising energy
source to produce green energy It is expected that biomass will
provide half of the present world's main energy consumption in
future [1] [2] However, the direct combustion of biomass has
several drawbacks to produce thermal energy These drawbacks
include; low heating value of biomass, unsuitability as a fuel for
high temperature applications, cannot be used directly as a fuel for
internal combustion engines and low versatility Therefore,
corre-sponding to industrial requirements, biomass is usually converted
into a more versatile secondary fuel by thermo-chemical, bio
echemical or extraction processes[3] [4] Gasification is a major
thermo-chemical process, which is being used worldwide to
convert biomass into a versatile, energy efficient fuel gas called
Syngas This gas is a mixture of carbon monoxide, carbon dioxide,
hydrogen, methane, small amount of light hydrocarbons and
ni-trogen[5] The gas produced is more versatile than original raw
biomass fuel and can be used for a variety of applications Examples are electricity generation, heat generation and hydrogen produc-tion[5] It can also be used as a raw material to produce liquid biofuels[6]
Gasification of biomass is carried out in a special reactor called a gasifier Number of other factors related to gasifier design and fuel properties significantly affect the produced gas quality These include; gasifying medium, properties of biomass, moisture con-tent, particle size, temperature of the gasification zone, operating pressure and equivalence ratio[5] [7]
The optimization of gasifiers based on these design factors can
be done in two main ways; through experimental approach and through computer aided simulations Experimental approach fol-lows a series of experiments, usually on scaled down laboratory scale gasifiers Parameters such as the optimum equivalence ratio can be determined by measuring the gas quality under various equivalence ratios until the best results are obtained However, experimental approach leads in to a series of difficulties and drawbacks It is very difficult to perform experimental analysis on pilot scales systems, especially when considering geometry opti-mization, therefore scaled down models have to be used for experimental analysis The results obtained on scaled down sys-tems may not fully work on the pilot scale system The scale down systems cannot be used to determine the effects of biomass particle
* Corresponding author.
E-mail addresses: kcniranjanfernando@gmail.com (N Fernando), mahinsasa@
uom.lk (M Narayana).
Contents lists available atScienceDirect Renewable Energy
j o u r n a l h o me p a g e : w w w e l s e v i e r c o m/ l o ca t e / r e n e n e
http://dx.doi.org/10.1016/j.renene.2016.07.057
0960-1481/© 2016 Elsevier Ltd All rights reserved.
Renewable Energy 99 (2016) 698e710
Trang 2sizes, as particle size relies on the diameter of the real system.
Scaling down the particle size will not produce equivalent results
because the packing factors will differ between the two systems
Also, taking measurements inside packed beds is a difficult task
considering the higher temperatures present in an operational
gasifier Because of these reasons, the experimental approach is
usually difficult, time consuming, costly and the accuracy of the
results are also low
Therefore many researchers use the computer based approach
to analyze packed bed processes A large number of research works
are available in literature where numerical models are used to
optimize packed bed processes [2] [8] [9] [10] Mathematical
models offer certain advantages over the conventional
experi-mental procedure Mathematical models can produce a large
number of data points as compared to fewer experimental data, for
example, when measuring temperature, experimental analysis can
provide temperatures at only afinite number of locations along the
packed bed, while numerical models can provide the complete
variation of the temperature profile over the region of interest
With the development of the computer hardware technology,
Computational Fluid Dynamics (CFD) is widely applied as a
nu-merical modeling tool[11] [12] [13] [14] CFD models can be made
to match the exact geometry of the real scale gasifier, as a result no
scaling down problems arise, in CFD simulations, any number of input parameters can be easily changed at will, including equiva-lence ratio, particle size, moisture content, feed properties, super-ficial velocity etc and system performance can be obtained accordingly CFD simulations are best suited to perform geometry optimization A large number of geometrical parameters can be optimized by simply changing the computational mesh Because of these advantages CFD models are now widely used by researchers around the world as a tool to study and optimize gasification process
In the present work a two dimensional dynamic twofluids CFD model has been developed for an updraft biomass moving bed gasifier This model uses inlet air at room temperature as the gasification medium and a fixed batch of biomass The biomass batch is initially ignited by a heat source, which is removed after a certain amount of time The mathematical model developed in this study is capable of maintaining the operation by the own heat emitted by combustion reactions, until the fuel isfinished, as in the real world scenario Since the operation is batch wise, model is transient and takes into consideration the effect of bed movement
as a result of shrinkage This model is capable to detect the movement of interface between solid packed bed and gas free board in time domain The two phase model is developed by using
Nomenclature
A Specific surface area of packed bed (m1)
Ac Specific surface area of char (m1)
Ad Specific surface area for gas diffusion (m1)
Ag Cross sectional area of gasifier (m2)
Aj pre-exponential factor for heterogeneous reactions (m
s1T1)
Ar Specific surface area available for radiation (m1)
a Absorption coefficient of gas phase (m1)
ap Absorption coefficient of solid phase (m1)
Cg Heat capacity of gas phase (J kg1K1)
Cs Heat capacity of solid phase (J kg1K1)
Di;g Diffusion coefficient of gas species i (m2
s1)
d Particle size of biomass (m)
Ei Activation energy of reaction i (J mol1)
fi Pre-exponential factor of reaction i (s1)
G Radiation intensity (W m2)
h Heat transfer coefficient (W m2K1)
k Turbulent kinetic energy (m2s2)
kg Thermal conductivity of gas phase (W m1K1)
ks Thermal conductivity of solid phase (W m1K1)
km;j Mass transfer coefficient of species j (m s1)
Mi Molecular weight of species i (kg mol1)
mi Specific mass of species i in a computational cell (kg
m3)
n Refractive index of gas phase
pin Inlet pressure (Pa)
Qrad Radiation heat source (W m3)
Qi Initial heat source (W m3)
Qsg Convective heat transfer rate (W m3)
qr Radiation heatflux (W m2)
Rg;pyro Rate of release of pyrolytic volatiles (Kg m3s1)
ri Rate of reaction i (Kg m3s1)
ri;hetero Rate of heterogeneous reaction i (Kg m3s1)
ri;homo Rate of homogenous reaction i (Kg m3s1)
rm;i Mass transfer limited reaction rate (Kg m3s1)
rk;i Kinetic reaction rate (Kg m3s1)
rt;i Turbulent mixing limited reaction rate (kg m3s1)
Shj Sherwood number for species j
S∅ Source term for property∅
Ss;∅ Source term for property∅ due to solid phase
Sg;∅ Source term for property∅ due to gas phase
sij Reynolds stress tensor (Pa)
Tg Gas phase temperature (K)
Tg;in Inlet gas temperature (K)
Ts Solid phase temperature (K)
Ug Gas phase velocity (m s1)
Ug;in Inlet gas velocity (m s1)
Us Shrinkage velocity (m s1)
vi Stoichiometric coefficient of species i
Yi;g Mole fraction of gas species i
Yi;air Mole fraction of i in air
Yi;s Mole fraction of solid species i
s Stefan constant (W m2K4)
sp Scattering coefficient of solid particles (m1)
ε Emissivity of solid particles
∅ A general transport property
εg Volume fraction of gas phase
εs Volume fraction of solid phase
rg Density of gas phase (Kg m3)
rs Density of solid phase (Kg m3)
rj Cell density of species j (Kg m3)
m Dynamic viscosity (Pa s)
si ;air Average collision diameter (A)
Ui ;air Diffusion collision integral
ε Turbulent dissipation rate (m2s3)
DHi Enthalpy of reaction i (J kg1)
Trang 3the Euler-Euler approach The overall model consists of several sub
models; including reaction models, which govern the reaction rates
and compositions of the products, turbulence model for packed bed
gas phase and free board, a radiation model for solid phase, a bed
shrinkage model, and interphase heat transfer model In this study,
the ultimate mathematical model for the gasifier is converted into a
numerical model by using open source CFD tool OpenFOAM CFD
code was developed using Cþþ language and available tools in
OpenFOAM package to include all the relevant differential
equa-tions and procedures in the mathematical model The code is
developed for two dimensional generic analyses, which is capable
to perform two dimensional geometrical optimizations, such as
inclusion of tapered sections in gasifier body To validate the CFD
model, simulation results are compared against experimental data
from an operational laboratory gasifier It is found that the model is
in good agreement with experimental data
2 Mathematical model
A two dimensional, transient, two-phase, Euler-Euler model was
developed in the present study The two phases consist of gas and
solid phases In the Euler-Euler approach, both solid and gas phases
are treated as continuums, as a result, motion of individual particles
in solid phase are not calculated based on forces acting on them
The motion of solid continuum is resolved using continuity
equa-tion Heat and mass transfer between two phases due to chemical
reactions are modelled through source terms of governing
equa-tions Radiation heat transfer is modelled in the solid phase It is
assumed that the optical thickness of gas phase is small and gas
does not absorb radiation energy [15] Gas phase turbulence is
modelled using standard k ε model [16]including the effects of
porosity Motion of the biomass bed and solid-freeboard interface is
considered in the present work A schematic diagram of the pre-sented mathematical model is shown inFig 1
2.1 Governing equations Conservation equations for momentum, energy and species are solved in the gas phase
v
vtrgεgUgþ V$rgεgUg5Ug
V$mεgVUg
¼ εgVp þ V$εg
VUgþVUgT
23rgkI
þ S (1)
With I the second order identity tensor
Gasesolid momentum exchange rate[17];
S¼ 150
1 εg
2
d2ε2 g
Ugþ 1:75rg
1 εgUg
dεg Ug (2)
vrgεgCv;gTg
vt þ V$rgεgCv;gTgUg V$εgkgVTg
¼ hATs Tg
þX
i
DHiri;homoþ Rg;pyroCv;g
Ts Tg
(3)
v vt
rgεgYi;g
þ V$rgεgYi;gU
V$εgDi;gVYi;g
¼X
i
ri;homoþX
i
Energy conservation and species conservation equations are solved in the solid phase,
N Fernando, M Narayana / Renewable Energy 99 (2016) 698e710 700
Trang 4vt þ V$rsεsCsTsUs V$ðεsksVTsÞ
¼ hATs Tg
þX
i
DHiri;heteroþ Qradþ Qi (5)
v
vt
rsεsYi;s
þ V$rsεsYi;sUs
V$εsDi;sVYi;s
¼X
i
ri;hetero (6)
2.2 Evaluation of source terms
Terms appearing on the right hand side of each governing
equation represent generation terms of transport property
described by the equation These consist of convective heat transfer
between phases, radiation heat transfer terms, heat generation due
to chemical reactions and species generation due to chemical
re-actions These source terms are evaluated by considering
correla-tions of parameters and kinetic models of chemical reaccorrela-tions
2.2.1 Inter-phase heat transfer
Two main processes are responsible for interphase heat transfer
These are;
1 Convective transfer of heat between two phases as a result of
temperature difference between gas and solid phases
2 During pyrolysis stage, hot volatile gases generated within the
porous structure of biomass release into gas phase These hot
volatile gases introduce an energyflow to the gas phase
Convective heat transfer is modelled by using an overall heat
transfer coefficient The heat transfer rate is evaluated by;
Qsg¼ hATs Tg
(7)
The specific surface area A of biomass is calculated by the
cor-relation of the following equation[18]
A¼ 6εs
The heat transfer coefficient h is evaluated using definition of
the Nusselt number[14]
h¼kgεgNu
The Nusselt number is evaluated using the following
relation-ship[14]
Nu¼7 10εgþ 5ε2
g
1þ 0:7Re0:2Pr0:33
þ1:33 2:4εg
þ 1:2ε2
g
Re0:7Pr0:33
(10)
During the process of pyrolysis, the solid biomass decomposes
into gas products, which is released into the gas phase It is
assumed that these gas products are of the temperature of the solid
phase The release of these higher temperature gases into the gas
phase results in an additional energy transfer term in gas phase
energy equation given by;
Hpyro ¼ Rg;pyroCv;g
Ts Tg
where, Hpyrois the heat generation rate due to biomass pyrolysis
2.2.2 Radiation heat transfer Radiation heat transfer plays a major role in transporting heat generated in combustion zone from combustion reactions, to top wood layers of the packed bed This heat provides the energy for thermal cracking of biomass and other endothermic solid phase reactions that take place in top layers In the present work, P1 ra-diation model is applied to model rara-diation in the packed bed with following assumptions[19] [20]
Biomass bed can be treated as an absorbing, emitting, scattering medium of dispersed solid particles
Combustion zone can be approximated by a hot emissive plate located at the bottom of the gasifier
The gas phase is optically thin and does not interact with radiation
A schematic diagram of the radiation model is shown inFig 2 The governing transport equation of P1 model for incident in-tensity, G, with a dispersed solid phase is given by equation(12) [20];
V$ðGVGÞ þ 4an2sTg4þ Ep
aþ ap
G¼ 0 (12)
whereGis given by,
3
The equivalent emission of particles, Ep, is calculated by;
With the simplifying assumptions of an optically thin gas phase (a¼ 0 and n ¼ 0), Eq.(12)can be reduced to;
V$ðGVGÞ þ 4Ep apG¼ 0 (15)
where,
3
The radiation heatflux in P1 model is given by Eq.(17) [19]
The radiation source term in energy equation is given byVqr, which is obtained by applying gradient operator to Eq.(17) and simplifying with the use of Eq.(15)
Vqr¼ apG 4Ep (18)
This term is substituted in the solid phase energy equation as the source term for thermal radiation
Fig 2 Schematic of Radiation model.
Trang 52.2.3 Reaction sub models
2.2.3.1 Heterogeneous reactions Four main heterogeneous
re-actions are considered in the present work These are drying,
py-rolysis, reduction and combustion In mathematical modelling of a
gasifier, these processes are included in the mathematical model as
rate terms in governing equations Because of this, in modelling
view point, the most important parameter of these processes is the
rate of the process A number of different models are available for
describing the rate of each of these processes The reaction sub
models used in the present study are described in following
sections
2.2.3.1.1 Drying Drying is the process through which moisture
in the biomass transfers into the gas phase In the present work
drying process is represented by a one-step global reaction in
which moisture in the solid phase transfers to gas phase[21]
ðC2:85H3:69O:H2OÞs/ðC2:85H3:69OÞdry;sþ ðH2OÞg (R1)
Here ðC2:85H3:69O$H2OÞs represents the initial moist Gliricidia
biomass species and ðC2:85H3:69OÞdry;s represents dry Gliricidia
biomass species
The drying rate is calculated by an Arrhenius rate equation[22]
[18] [23]
rd¼ fdexp
Ed
RTs εsrsYH2O;s (19)
The values of pre exponential factor, fd, and activation energy,
Ed, for the drying rate are obtained from Ref.[22]and are listed in
Table 2
2.2.3.1.2 Pyrolysis Pyrolysis is the thermal decomposition of
biomass into volatile gases and char Pyrolysis is an important step
in gasification process because products of pyrolysis process are the
reactants of all the other chemical processes that take place in the
system The decomposition, which is a result of pyrolysis, is a
complex series of reactions in different pathways These pathways
may depend on heating conditions and biomass species [24]
Various researchers have developed different reaction schemes of
varying complexity[25] These models can be classified into three
classes, they are; one step global models, single stage multi reaction
models and multi stage semi global models[24] The applicability
of these models depends on the species of wood and heating
conditions In the present work, a one-step global reaction scheme
is used to model the pyrolysis processes[24],[26] The scheme is
presented in following equation[27]
C2:85H3:69O/aC þ bCO þ cCO2þ dH2þ eCH4þ fAsh (R2)
It is assumed that the stoichiometric coefficients are dependent
on the species of wood This gives the overall mathematical model
the ability to analyze different wood species
The coefficients are determined using experimental data
ob-tained by proximate analysis and an assumed distribution of
vol-atiles gases based on previous literature
The coefficients for carbon and ash are directly determined by
the fraction of free carbon and ash content given by proximate
analysis For gas species, each coefficient is determined by
following equation
Where, airepresents a, b, c etc for different values of i The factor
bdescribes the distribution of gases in the volatile fraction and VF is
the volatile fraction of wood species under interest For present
study values forbis estimated by using data given in previous
lit-eratures[15]
The pyrolysis rate is calculated by,
rp¼ fpexp
Ep
RTs εsrsYC2:85H3:69O (21)
2.2.3.1.3 Char combustion and gasification reactions Three main heterogeneous reactions of char are considered in the present study These are combustion, carbon dioxide gasification and water gasification as follows;
CþaO2/2ð1 aÞCO þ ð2a 1ÞCO2 (R3)
Cþ H2O/CO þ H2 (R5)
The parameterais dependent on the fuel temperature and is given by Eq.(22) [14] [18]
2þ 2512exp
6420
T s
2
1þ 2512exp
6420
T s
(22)
The actual reaction rates of these reactions depend on two factors The kinetic rate and mass transfer rate of the reactant gas into the surface of the porous char Usually the reaction rate is limited by the mass transfer process, because mass transfer rates are much slower than the kinetic rates at higher temperatures The kinetic rates of above reactions can be generally expressed as fol-lows[28];
rk;i¼ AcAiTsexp
Ei
RTs
Mc
viMiri (23)
As the solid phase is a collection of three components; un-reacted biomass, char and ash, only a fraction of the solid phase contains char Because of this fact, the entire specific surface area of solid phase, which is given by Eq.(8), is different to the specific surface area of char The specific surface area of char, Ac,is evalu-ated based on the ratio of char formation to the maximum amount
of possible char generation due to total pyrolysis process This is expressed in equation(24)
Ac¼ mchar
a:mðC2:85H 3:69 OÞ initial
In Eq.(24), mcharis the mass of char per unit volume in a given position, a is the stoichiometric coefficient of char in pyrolysis re-action (Eq.R2) and mðC2:85H3:69OÞ
initial is the initial mass of wood per unit volume at the same position A is the total specific surface area
of solid phase given by Eq.(8) Mass transfer rate of a reactant gas to the surface of the char particle was calculated by the following equation[29]
rm;i¼ km;iAd
riri;s
(25)
Two simplifying assumptions are used to derive Eq.(25) First, it
is noted that diffusion is anisotropic in the vicinity of biomass particle This effect is due to the airflow around the biomass par-ticle Diffusion is stronger in parallel to the airflow and minimum in the direction of perpendicular to the airflow In order to account for this anisotropy with an isotropic mass transfer coefficient, it is assumed that mass diffusion occurs only parallel to theflow, and based on a cubic biomass particle This assumption reduces the specific diffusive surface area to 1/6th of total specific surface area,
as given in Eq.(26)
N Fernando, M Narayana / Renewable Energy 99 (2016) 698e710 702
Trang 6Ad¼ 1
Second assumption is used to evaluateri ;s , the density of the
gasifying agent at the surface of the biomass particle After the
gasifying agent reaches the surface of char particle, diffusion
pro-cess is completed and reaction between gasifying agent and char
progresses at the kinetic rate given by Eq.(23) At the temperatures
prevailing in combustion processes, this rate is higher compared
with diffusion rate Hence it is assumed that once the gasifying
agent reaches the particle surface, it undergoes immediate
con-version and as a result ri;s¼ 0 can be used in evaluating the
diffusion rate using Eq.(25)
The mass transfer coefficient of ith gas, km;i, is evaluated using
following correlation[18]
Shi¼ 2 þ 0:1Sc1
The overall reaction rates of heterogeneous reactions are
ob-tained by evaluating the equivalent parallel resistance of the kinetic
and mass transfer rates, this is given by Eq.(28) [18];
ri¼ rk;irm;i
2.2.3.2 Homogenous reactions Following homogenous reactions
taking place between gas phase components are considered in this
study
H2þ 0:5O2/H2O (R7)
CH4þ 2O2/CO2þ 2H2O (R8)
COþ H2O#H2þ CO2 (R9)
Expressions for kinetic reaction raterk, of these reactions are
obtained from literature[19]and are listed inTable 1
Kinetic data used for evaluation of reaction rates are
summa-rized inTable 2
The kinetic reaction rate is limited by the turbulent mixing rate
of the gas species The turbulent mixing rate is calculated according
to the eddy dissipation model, which is given by Eq.(29) [14];
rt;i¼ 4rg
ε
kmin
Yj
vjMj; Yk
vkMk
!
(29)
where; j and k represents the reactants of reaction i
The reaction rate for each gas phase reaction is taken to be equal
to the minimum value of kinetic rate and turbulent mixing rate [14].There are certain areas of flow, especially near walls where turbulence is low, in such areas reactants are not well mixed together for reactions to proceed at kinetic rates Limiting assumption in Eq (30)is used to account for this effect Kinetic rates play a major role in free board area and away from the walls, where turbulence is well developed
ri¼ minrk;i; rt;i (30)
2.3 Modelling of bed shrinkage
As heterogeneous reactions of char progresses, the volume of char particles reduces As a result, top layers of the biomass bed moves downwards This motion is important to keep the combus-tion zone stable When fuel is consumed in combuscombus-tion zone, new char particles from pyrolysis zone enters to the combustion zone as
a result of this bed motion If particle movement is not there, the combustion zone tends to propagate along the height of the gasifier, reducing the quality of the producer gas So it is important that the model should be capable of predicting the bed motion The effect of bed motion is included into solid species equations
as a convectiveflow term It is assumed that the bed motion can be represented by a continuous velocityfield of the solid phase and this velocity, called shrinkage velocity is applied to all solid species
as in Eq.(31) Shrinkage velocity is calculated by equating the downward volumetricflow rate of solid phase to total reduction rate of volume caused as a result of heterogeneous reactions of char Shrinkage velocity in Eqs.(5) and (6)is evaluated using following equation
Us¼rs1Ag
Z XR3;R4;R5 i
ri
!
Use of this velocity in convective terms of solid species equa-tions cause the solid speciesfields of the gasifier to move down-wards at the shrinking rate This causes the solid phase to move downwards and extend the free board region But mathematical equations used in free board region and solid phase region are different Therefore when shrinkage modelling is used there has to
be a procedure to track the interface and change the mathematical equations above and below the interface to obtain an accurate so-lution The changes of the equations are presented in graphical form inFig 3
Gas phase equations differ in two regions with respect to the gas phase porosity, which is defined as the volume fraction of gas phase
in each computational cell The porosityfield is initialized in the beginning of the simulation through initial conditions Gas phase porosity is equal to one in free board region and a variable (<1) in packed bed The source terms that arise as interactions with solid
Table 1
Rate expressions for homogenous reactions.
R6
2:32 10 12 exp
167
RT g ½CO½O 2 0:25½H 2 O0:5 R7
1:08 10 13 exp
125
RT g ½H 2 ½O 2 R8
5:16 10 13 Tg1exp
130
RT g ½CH 4 ½O 2 R9
12:6exp
2:78
RT g @ CO H2O
½CO2 ½H 2 0:0265exp 3968 Tg
1 A
Table 2 Kinetic data for evaluation of reaction rates.
Reaction Pre-exponential factor Activation energy Source
R6 2.32 10 12 (kmol/m 3 )0.75s1 167 kJ mol1 [19]
R7 1.08 10 13 (kmol/m 3 )1s1 125 kJ mol1 [19]
R8 5.16 10 13 (kmol/m 3 )1s1K 130 kJ mol1 [19]
R9 12.6 (kmol/m 3 )1s1 2.78 kJ mol1 [19]
Trang 7phase are not present in free board region Solid phase equations
are different entirely in two regions In free board, a solid phase
does not exist and values of solid phase quantities should be zero
The CFD solver should consider these changes as shrinkage
progresses
This is achieved by multiplying certain terms of the general
transport equation by a newfield variablec, which is a unit step
function moving along with shrinkage velocity, as illustrated in
Fig 4
The value ofcis evaluated based on gas phase porosity; it is
assumed that a certain point (i.e a computational cell) in the
so-lution domain belongs to free board when gas phase porosity
ex-ceeds 95% Classification of computational cells based on porosity is
discussed in literature[15] Henceccan be written as,
c¼ 10; if εg< 0:95
; if εg> 0:95 (32)
This produces a movingcfield along with the packed bed as
expected
The transport equations for gas and solid phases indicated in
Fig 3can be then generalized as,
vðεsrs∅Þ
vt þcV$ðεsrs∅UsÞ ¼cV$ðGV∅Þ þcS∅þcSg;∅ (33)
vεgrg∅
vt þ V$
εgrg∅Ug
¼ V$ðGV∅Þ þ S∅þcSs;∅ (34)
Depending on the value of cthe solver will selectively apply
equations in packed bed and free board region as shrinkage
progresses
2.4 Physical properties
Gas phase thermal conductivity is evaluated by following
equation[18]
kg¼ 4:8 104Tg0:717: (35)
The thermal conductivity of solid phase is evaluated using a correlation developed for thermal conductivity of a quiescent bed, with a correction for the effect of gasflow, as proposed in the literature[18] This correlation is presented by Eq.(36)
ks¼ 0:8kgþ 0:5Re:Pr:kg (36)
Biomass particle diameter is used as the characteristic length in evaluating the Reynolds number Two approaches are used to calculate the evolution of particle diameter in literature These are shrinking core model and volumetric shrinking density model[28]
In shrinking core model, particle diameter gradually reduces with conversion In volumetric shrinking density model particle size is heldfixed while density reduces[28] In the present case, shrinking density model is used and particle diameter is held constant while density of solid phase is reduced according to Eq (37) This assumption is used, as the developed model is a twofluid model, which considers the solid phase as a continuum rather than an assembly of solid particles and the motion of the solid phase is affected by its density rather than the particle size
rs;t¼rs;tΔtYðC2:85H3:69OÞ;tΔtþrs;tΔtYchar;tΔtþrs;tΔtYash;tΔt
(37)
ðC2:85H3 :69OÞ Density value is updated explicitly using Eq (37) during each temporal iteration
Heat capacities of solid and gas phases are assumed to vary according to the relations given in Eqs.(38) and (39) The correla-tion for gas phase heat capacity is taken from the literature[18] Solid phase heat capacity is modelled by using Eq.(38) [22]
Cs¼ 420 þ 2:09Tsþ 6:85 104Ts2 (38)
Cg¼ 990 þ 0:122Tg 5680 103Tg2 (39)
Volume fractions of solid and gas phases are calculated using Eqs.(40) and (41)
εs¼mC 2:85 H 3:69 O
rC 2:85 H 3:69 O þmchar
rchar þmash
rash
(40)
The binary diffusion coefficients based on diffusion of a specific component in air, are calculated using Eq.(42) [30]
Fig 3 Changes of governing equations as a result of bed shrinkage.
Fig 4 Motion of unit step variablecin the direction of bed shrinkage.
N Fernando, M Narayana / Renewable Energy 99 (2016) 698e710 704
Trang 8Di;air¼ 0:0018583
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
T3g
1
Miþ 1
Mair
s
1
ps2 i;airUi;air
(42)
3 Numerical solution
The open source CFD software OpenFOAM was used to develop a
numerical solution to mathematical model presented in the
pre-vious section The equations are numerically solved using finite
volume method Required code was developed by using Cþþ
lan-guage in OpenFOAM package, including all the relevant differential
equations and procedures in the CFD model using built in tools of
OpenFOAM The solution domain is assumed to be two dimensional
and consists of radial (xe direction) and axial (y edirection)
di-mensions only The computational domain of CFD model is
pre-sented in Fig 5 Discretization schemes used to discretize
convective and divergence terms are listed inTable 3 A schematic
of solution algorithm is presented inFig 6 Grid size is determined
based on the value of non-dimensional turbulent wall distance
(yþ) A value of y þ approximately equal to one is used This results
inDx¼ 0:0094 m for near wall cell layer.Dy¼ 0:0188 m is used
based on a cell aspect ratio of 1:2 All cells in the domain were set
uniform in sizeðDx¼ 0:0094 m; Dy¼ 0:0188 mÞ to get a better
numerical resolution Simulations were performed using a 32 core
High Performance Computer with 2.2 GHz processor speed and
64 Gb RAM To simulate a single batch wise run, approximately 2 h
were needed in parallel mode using 32 processors for above mesh
resolution Mesh independence is investigated by increasing the
cell number by 50% and 75% from initial value Solid phase
tem-perature at the same location in mesh at same time was compared
under refined meshes It is found that deviation is less than 1% As a
result, initial mesh is used for subsequent simulations and results
are validated through experiment
3.1 Initial and boundary conditions
It is assumed that the gasification process is carried out in a
cylindrical reactor using air at room temperature as the gasifying
medium This air stream is supplied at constantflow rate from
bottom of the reactor To model the initial ignition process, a
distributed heat source similar to magnitude of heat generated by a
combustion reaction is applied over a bed region of 0.2 m above the grate and removed after model is capable of continuing operation
by own heat emitted by its combustion reactions The initial heat source is responsible for pyrolysing a small region of packed to generate char necessary to initiate combustion reactions This start
up method was chosen as it closely resembles the real world operation of a gasifier The required time for initial ignition was found by trial and error by simulating the system Initially 5 min time was applied and it was increased gradually until simulation was successfully progressed
The initial velocity field within the reactor is taken as zero Pressure is set to atmospheric pressure The initial temperatures of gas and solid phases are taken as 300 K Initial compositions of product gases are taken as zero and the inlet gas composition is taken to be equal to that of air at room temperature and atmo-spheric pressure Boundary conditions for velocity, pressure, tem-perature and species mole fractions are indicated in following equations
Inlet boundary conditions
Air inlet
Porous biomass packed bed
Free board Producer gas outlet
Insulated Wall (Wall boundary condiƟons applied)
Insulated Wall
(Wall boundary
condiƟons applied)
(Outlet boundary condiƟons applied)
(Inlet boundary condiƟons applied)
Fig 6 Solution algorithm.
Table 3 Discretization schemes.
V$rg ε g C v;g T g U g Upwind
Trang 9U¼ ð0; Uin; 0Þ (43)
vTs
Wall boundary conditions
U¼vP
vr¼
vTg
vr ¼
vTs
vr ¼
vYi
Outlet boundary conditions;
vU
vz ¼
vP
vz¼
vTg
vz ¼
vTs
vz ¼
vYi
4 Model validation
Model is validated by comparing the simulation results with
data obtained from a laboratory scale updraft gasifier The gasifier
consists of a vertical cylinder with a grate at the bottom Biomass is
fed from the top of the gasifier through a lid, which is closed after
loading one batch of biomass The loaded batch is ignited at the
bottom of the gasifier Air at room temperature is supplied through
the grate by using an air blower In this experimental facility, four
thermocouples, which record the temperature along the centre
line, were installed along the height of the gasifier A schematic
diagram of the experimental facility is shown inFig 7 Simulation
results were compared against experimental data for gasification of
Gliricidia under an airflow rate of 6 m3/hr The physical and
chemical properties of fuel are listed inTable 4 The comparison of
temperature profiles obtained from simulations with
experimen-tally measured temperature values using four thermocouples are
presented inFig 8 Exit gas temperatures predicted by simulation
and experimental exit gas temperatures are displayed in Fig 9
Theoretical and experimental outlet gas compositions are
pre-sented inFig 10 It can be observed from thesefigures that the
model is in good agreement with experimental data
InFig 8(c), which presents the temperature profiles after 2.5 h
of initial ignition, the biomass bed has reduced as a result of fuel
consumption due to heterogeneous reactions The thermocouples
located at 60 cm and 90 cm positions do not encounter any solid
phase There readings comply with gas phase temperatures at the points, as evident from thefigure The results indicate that tem-perature in the combustion zone rises to a value about 1300 K, with
a peak value resulting in few centimetres above the grate A similar behaviour of temperature variation can be observed in experi-mental work of Wei Chen at el [31] for updraft gasification of mesquite and juniper wood Their results indicate a combustion zone temperature of nearly 1300 K
Top lid
Air blower
Thermocouples
Gas outlet Outlet pipe
Cyclone separator
Grate
Ash collecƟng chamber Fig 7 Schematic diagram of experimental laboratory scale gasification system.
Table 4 Physical and chemical properties of fuel.
Initial moisture content (dry basis) 20%
200 400 600 800 1000 1200
Height from grate (m)
Ts-Simulation Tg-Simulation Experimental gas temperature
200 400 600 800 1000 1200 1400
Height from grate (m)
Ts-Simulation Tg-Simulation Experimental gas temperature
200 400 600 800 1000 1200 1400
Height from grate (m)
Ts-Simulation Tg-Simulation Exerimental gas temperature
Fig 8 Theoretical and experimental temperature profiles; (a) 45 min after ignition (b)
75 min after ignition (c) 150 min after ignition.
N Fernando, M Narayana / Renewable Energy 99 (2016) 698e710 706
Trang 10The followingfigure compares the experimental and theoretical
exit gas temperatures of the gasifier
It can be observed that at higher temperatures, the difference
between experimental value and theoretical prediction is higher
The CFD model predicts a higher outlet gas temperature than the
observed value This is because the radiation losses from the gas
phase through walls and the top lid of the gasifier are not accounted
in the model And the radiation losses become higher at higher
temperatures
During the simulations, it is found that composition of produced
gas varies with time, during initial period, lot of raw biomass is
present in the bed and moisture levels are higher This introduces
moisture into gas phase Pyrolysis in top layers is not complete and
as a result low amount of char is available on the top layers to react
with carbon dioxide produced in the combustion zone The initial
gas is therefore higher in carbon dioxide Experimental data and
simulation results for gas composition after 30 min of initial
igni-tion are presented inFig 10
The values for gas compositions are also comparable with
experimental observations of C.Mandlet et al.[22] Their
experi-mental data for afixed bed updraft gasifier operated with softwood
pellets indicate afinal CO volume percentage of 22.6%, a CO2
per-centage of 4.8%, H2percentage of 4.3% and a CH4percentage of 2.7%
Experimentally it is found that during the process of gasi
fica-tion, packed bed can be separated into four zones; drying, pyrolysis,
reduction and combustion, depending on the main processes
tak-ing place in these zones It is possible to identify the development
of these zones in the present CFD model by observing the carbon
dioxide mass fraction along the height of the gasifier This is
pre-sented inFig 11
The two CO2hot spots inFig 11can be attributed to near wall
flow stagnation The dark blue and green interface just below the
hot spots marks the pyrolysis reaction front Pyrolysis reactions
take place in region above this interface which generates CO2 The
produced CO2is transported to higher regions of the bed through
convection due to gasflow In near wall region, flow velocity is very low This reduces the convective transport and tends to accumulate CO2in near wall cells, increasing its concentration in comparison with centre cells
During a batch process the quality of the produced gas varies with the time, mainly as a result of downward motion of the fuel bed During experiments it is observed that a stableflame cannot be maintained approximately after 4 h of operation.Fig 12present the variation of outlet gas mass fractions andFig 13present the bed movement The packed bed location is identified by viewing the solid phase temperature profile
Velocity distributions within the gasifier at different times are presented inFigs 14 and 15
An increase inflow velocity can be observed in free board region according toFig 14 This increase is due to the release of gases from packed bed to free board region, especially during pyrolysis Vola-tiles are released to gas phase increasing its velocity and pyrolysis zone is located in top layers of the packed bed, which can be observed inFig 11 A span wise variation of velocity can be observed
in free board region, which can be clearly noticed inFig 15 This variation is reduced in packed bed, mainly due to the effect of porosity In a batch wise simulation as in the present case, free board region extends with time and span wise velocity variation becomes significant Even within the packed bed, a reduction of flow velocity near walls can be noticed, this effect is reflected in CO2 hot spots inFig 11, where CO2is accumulated due to low convective
0
100
200
300
400
500
600
700
800
900
45
minutes
75 minutes
120 minutes
150 minutes
180 minutes
SimulaƟon Experimental data
Fig 9 Theoretical and Experimental exit gas temperatures.
0
5
10
15
20
25
SimulaƟon Experimental data
Fig 10 Theoretical and Experimental gas compositions after 30 min of ignition.
Fig 11 Development of reaction zones in the solution domain.
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35
Time (s)000 10000 12000 14000
CO2 H2 CH4 CO