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Generation of hydrogen rich gas through fluidized bed gasification of biomass
M.K. Karmakar *, A.B. Datta
Thermal Engineering Group, Central Mechanical Engineering Research Institute (CSIR), Durgapur 713209, India
a r t i c l e i n f o
Article history:
Received 30 April 2010
Received in revised form 2 August 2010Accepted 4 August 2010
Available online 6 August 2010
Keywords:
Fluidized bed
Biomass
Steam gasification
Equilibrium modeling
Hydrogen
a b s t r a c t
The objective of this study was to investigate the process of generating hydrogen rich syngas through
thermo chemical fluidized bed gasification of biomass. The experiments were performed in a laboratory
scale externally heated biomass gasifier. Rice husk had been taken as a representative biomass and, steamhad been used as the fluidizing and gasifying media. A thermodynamic equilibrium model was used to
predict the gasification process. The work included the parametric study of process parameters such as
reactor temperature and steam biomass ratio which generally influence the percentage of hydrogen con-
tent in the product gas. Steam had been used here to generate nitrogen free product gas and also to
increase the hydrogen concentration in syngas with a medium range heating value of around 12 MJ/Nm 3.
2010 Elsevier Ltd. All rights reserved.
1. Introduction
Energy is an essential factor in both livelihoods and industrialactivities and, hydrogen has been identified as a clean energy
source and a potential alternative fuel. When hydrogen is com-
busted, it does not add any adverse effect to the environment.
However, to make the sustainable energy security based on hydro-
gen, renewable resource such as biomass instead of fossil fuels has
to be utilized.
Biomass gasification produces fuel gas or synthesis gas through
the thermo chemical conversion of biomass, usually involving par-
tial oxidation of feedstock in a reducing atmosphere in presence of
air, oxygen and/or steam (Li et al., 2004). The biomass materials
differ greatly in chemical, physical and morphological properties
and, therefore, this necessitates developing different methods of
gasification and consequently requires different reactor designs
or even gasification technologies. There is an excellent overview
concerning the characteristics of the various gasifiers, i.e. of up-
draft and downdraft fixed bed gasifiers and bubbling, circulating
fluidized and entrained beds (Bridgwater, 1995). Moving-bed gas-
ifiers suffer from high tar yields in the product gas (Beenackers,
1999). The inability to maintain uniform radial temperature pro-
files and to avoid local slagging problems makes the moving bed
unsuitable for large installations (Babu, 1995). Fluidized beds
now find wide application in biomass gasification (Corella et al.,
1998). Air-blown processes produce low calorific value gases with
a typical Higher Heating Value (HHV) of 4–7 MJ/Nm3, while oxygen
and steam blown processes result in gases with a HHV of 10–
18 MJ/Nm
3
(Schuster et al., 2001).Biomass gasification models are predominantly separated in
two groups: equilibrium approach and kinetic approach (Li et al.,
2001). Kinetic models take into account the chemical kinetics of
the main reactions and the transfer phenomena among phases,
estimating the composition of each species on any point of space
and time of a system. These models are specific in general for each
process, providing important considerations related to chemical
mechanisms and ways to increase reaction rates and process per-
formance. On the other hand, equilibrium models provide the
greatest possible conversion of each species regardless the system
size and the time needed to reach equilibrium. These models do
not require details of system geometry neither estimate the neces-
sary time to reach that equilibrium (Rodrigues et al., 2009).
Bilodeau et al. (1993) developed a mathematical model of bio-
mass gasification in a fluidized bed reactor considering axial vari-
ations of concentrations and temperature in bubble and emulsion
phases. The mass balance involved instantaneous oxidation and
equilibrium devolatilization of biomass, kinetics of solid–gas gasi-
fication reactions as well as gaseous phase reactions with inter-
phase mass transfer and gas convection. Constantineau et al.
(2007) proposed to predict the performance of fluidized bed reac-
tors operated wholly or partially in the slug flow regime. This mod-
el simulated the transition from bubbling to slugging within a
given reactor with operating variables such as superficial gas
velocity, bed inventory and change of height. Corella and Sanz
(2005) developed a one-dimensional model for an atmospheric
circulating fluidized bed biomass gasifier under stationary state.
0960-8524/$ - see front matter 2010 Elsevier Ltd. All rights reserved.doi:10.1016/j.biortech.2010.08.015
* Corresponding author. Tel.: +91 343 6452157; fax: +91 343 2546745.
E-mail addresses: [email protected], [email protected]
(M.K. Karmakar).
Bioresource Technology 102 (2011) 1907–1913
Contents lists available at ScienceDirect
Bioresource Technology
j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / b i o r t e c h
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The model was based on kinetic equations for the reaction network
solved together with mass and heat balances and, with several
hydrodynamic considerations. The reaction network used in the
model involved twelve different reactions. Schuster et al. (2001)
also developed a model for steam gasification of biomass applying
thermodynamic equilibrium calculations. With this model, the
simulation of a decentralized combined heat and power station
based on a dual fluidized bed steam gasifier was carried out. Fuelcomposition, temperature and amount of gasification agent were
varied over a wide range and it was shown that the accuracy of
an equilibrium model for the gas composition was sufficient for
thermodynamic considerations.
Kinetic models predict the progress and product composition at
different positions along a reactor, where as equilibrium model
predicts the maximum achievable yield of a desired product from
a reacting system (Li et al., 2004). Kinetic models always contain
parameters which make them hardly applicable to different plants
(Schuster et al., 2001). An accurate description of the chemical ki-
netic rate expression is a key issue. The choice of chemical kinetic
laws is difficult because there are as many kinetic laws as kinetic
studies. A large discrepancy can be observed between them and
it is highly hazardous to extrapolate literature results obtained un-
der different operating conditions (reactor, heating rate, tempera-
ture, biomass type) (Dupont et al., 2007). Although kinetic
models provide essential information on mechanisms and rates,
equilibrium models are valuable because they can predict thermo-
dynamic limits as a guide to process design, evaluation and
improvement. Equilibrium model also provides a useful design
aid in evaluating the limiting possible behavior of a complex react-
ing system which is difficult or unsafe to reproduce experimentally
or in commercial operation (Li et al., 2004).
The present work aimed to develop a fluidized bed biomass gas-
ifier using steam as the gasifying agent. A laboratory scale fluidized
bed gasifier had been developed for this purpose. The effect of
steam biomass ratio and gasifying temperature on the product
gas composition had been carried out. A thermodynamic equilib-
rium model had been used to predict the gas composition withthe assumptions that the principal reactions were at thermody-
namic equilibrium conditions. The model considered three mass
balance equations of elements C, H, O and two equilibrium con-
stants relations, which were solved for predicting the gas
compositions.
2. Methods
2.1. Materials
In this study, rice husk was taken as the raw material since it is
one of the important biomass resources in India. The proximate
and ultimate analyses of rice husk are presented in Table 1. The
steam, the gasification agent, had been used as the fluidizing mediaas it prevented the dilution of product gases with nitrogen and also
increased the hydrogen content in the product gas.
The superheated steam was supplied to the gasification cham-
ber at the bottom in order to enable better fluidization and thereby
attaining better gasification.
As per Geldart’s classification of particle distribution, the silica
sands are classified as the group B particles (Geldart, 1973). These
silica sand particles were used as bed materials in the present
investigation. The sand samples were prepared using various mesh
sieves and the mean diameter was found as 0.334 mm. The sandparticles ensured proper fluidization in the gasifier bed and main-
tained the uniform temperature throughout the gasification zone.
During the experiment, the gasifier temperature was varied in
the range of 650–800 C. The experiments showed that rice husk
was difficult to be fluidized due to its non-granular and flaky nat-
ure. However, this fluidization behavior of rice husk was improved
due to presence of silica sand as bed materials.
2.2. Experimental set up
A laboratory scale fluidized bed biomass gasifier had been set
up in the laboratory shed. The schematic diagram is shown in
Fig. 1. The gasifier vessel was made of stainless steel with inside
diameter of 50 mm and a height of 1200 mm. The distributor plate
was fitted with at the bottom and the product gas outlet pipe came
out from the top of gasifier. This gasifier was placed inside an elec-
tric furnace, which provided the heat for gasification reaction and
the temperature inside the gasifier was controlled using a thermo-
couple with a control panel system. There was an under-bed feed-
ing arrangement of biomass fuels into the reactor and a lock
hopper had been kept to dispose off the ash from the gasifier
periodically.
The under-bed feeding system of biomass was comprised of two
screw feeders fitted with a hopper. The upper feeder connected to a
variable speed drive system that controlled the fuel feed rate and
the lower high-speed screw feeder was used to feed the fuel inside
the gasifier vessel instantaneously. The lower screw feeder also
prevented the pyrolysis of biomass outside the gasifier.
Steam for gasification, obtained from a small boiler, was furthersuperheated in an electric furnace to the temperature range of
200–250 C. The quantity of steam is very important for maintain-
ing fluidization conditions in the biomass gasifiers.
The gasifier had been operated at different process conditions to
evaluate the performance. The product gas was generated at vary-
ing reactor temperatures between 650 and 770 C at a fixed steam
biomass ratio of 1.32. In another set of tests, the steam biomass ra-
tios were varied in the range of 0.6–1.7 to produce the syngas
maintaining the gasifier temperature at 750 C.
The product gas from the gasifier was made dust-free and
cleaned before the sampling was done for analysis in a Gas Chro-
matograph. The hot product gas from the gasifier was passed
through a cyclone to remove the larger particles. After the cyclone,
the gas still contained dust particles and tar and hence, it was fur-ther de-dusted by passing it through a bag filter. A water cooler
and an ice trap were used in series for cooling of fuel gas and tar
capture. Finally, the product gas was passed through a silica gel
unit to remove the remaining moisture in the gas before taking
up the sampling process.
The sampling system was composed of gas sampling probes fit-
ted with septum. The stainless steel gas sampling probes were
12.5 mm in diameter and 50 mm in length and they were located
at the downstream of suction blower. A syringe of volume of
10 ml was used to collect the gas sample through the gas sampling
probe.
The analysis was done in a Gas Chromatograph (Make–Chemito,
model – GC 1000). Two detectors had been used – one was Ther-
mal Conductivity Detector (TCD) and another was Flame IonizationDetector (FID). The standard gas mixtures had been used for cali-
Table 1
Ultimate and proximate analysis of rice husk.
Ultimate analysis Proximate analysis
Components Percent Components Percent
Carbon 38.43 Volatile matter 55.54
Hydrogen 2.97 Fixed carbon 14.99
Sulfur 0.07 Moisture 9.95
Nitrogen 0.49 Ash 19.52
Oxygen 36.36
Ash 21.68
HHV = 15.68 MJ/kg
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brating the equipment. The GC showed the different picks for dif-
ferent constituents of product gas mixture.
An orifice meter was positioned on the pipe between the box
containing silica gel and the suction blower to measure the gas
yield. The pressure drop across this orifice plate was measured
using a micro manometer and this pressure drop was then used
to estimate the gas yield rate.
2.3. Mathematical modeling
At chemical equilibrium, a reacting system achieves its moststable composition when the entropy of the system is maximized
and its Gibbs free energy is minimized. Two approaches are de-
scribed for equilibrium modeling: stoichiometric and non-stoichi-
ometric (Smith and Missen, 1982). The stoichiometric approach
requires a clearly defined reaction mechanism incorporating all
chemical reactions and species involved. In a non-stoichiometric
formulation, on the other hand, no particular reaction mechanism
or species are involved in the numerical solution. The only input
required to specify is the feed elemental composition, which can
be readily obtained from ultimate analysis data. This method is
particularly suitable for problems with unclear reaction mecha-
nisms and feed streams like biomass whose precise chemical com-
positions are unknown.
The laboratory scale gasifier described above was operated un-der near-steady-state conditions. In the current equilibrium model
the reactor was implicitly considered to be zero-dimensional, i.e.
neither any spatial distribution of parameters is considered nor
were there any changes with time, because all forward and reverse
reactions had reached the chemical equilibrium state. Thus, the
molar inflow for any individual element involved in the chemical
reactions was written as the sum of moles of that element in the
various feed streams. In this study, tars had not been included in
the product stream.
To develop the model, the chemical formula of feedstock was
defined as CH xOY . The global gasification reaction could be written
as follows:
CH X OY þ wH2O þ mH2O ¼ x1H2 þ x2CO þ x3CO2 þ x4H2O þ x5CH4
ð1Þ
where x and y were the number of atoms of hydrogen and oxygen
for each atom of carbon in the feedstock, respectively, w was the
amount of moisture per kmol of feedstock and m was the amount
of steam supplied per kmol of feedstock. On the right-hand side,
xi’s were the molar concentrations of species i that were also un-
known. The global steam gasification reaction in Eq. (1) represented
an overall reaction, but a number of competing intermediate reac-
tions took place during the process, of which the following were
predominant.
Steam gasification
C þ H2O ¼ CO þ H2 ðþ131:4 kJ=molÞ ð2Þ
Boudouard reaction
C þ CO2 ¼ 2CO ðþ172:6 kJ=molÞ ð3Þ
Methanation reaction
C þ 2H2 ¼ CH4 ð74:9 kJ=molÞ ð4Þ
Steam reforming reaction
CH4 þ H2O ¼ CO þ 3H2 ðþ206:2 kJ=molÞ ð5Þ
Water gas shift reaction
CO þ H2O ¼ CO2 þ H2 ð41:2 kJ=molÞ ð6Þ
According to Von Fredersdorff and Elliot (1963), the three reac-
tions namely, Boudouard, steam gasification and methanation
were in equilibrium and the water gas shift reaction was a combi-
nation of the Boudouard and steam gasification reactions.
To find the five unknown species i.e. x1, x2, x3, x4 and x5 of the
producer gas, five equations were required. Those equations were
generated using mass balance equations and equilibrium constants
relationships. Considering the global gasification reaction in Eq.
(1), the first three equations were formulated by balancing each
chemical element.
Carbon balance:
x2 þ x3 þ x5 1 ¼ 0 ð7Þ
Hydrogen balance:
2 x1 þ 2 x4 þ 4 x5 x 2w 2m ¼ 0 ð8Þ
Fig. 1. Schematic diagram of bubbling fluidized bed gasifier system.
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Oxygen balance:
x2 þ 2 x3 þ x4 w m Y ¼ 0: ð9Þ
Ultimate analysis of rice husk (dry and ash free basis) yielded a
typical mass composition of 49% carbon, 46.4% oxygen, and 3.8%
hydrogen with the balance comprised of traces of nitrogen and sul-
fur. Considering the major elements, the fuel was represented on a
molar basis as CH0.93
O0.71
.
Chemical equilibrium is usually explained either by minimiza-
tion of Gibbs free energy or by using an equilibrium constant. To
minimize the Gibbs free energy, constrained optimization methods
are generally used which requires an understanding of complex
mathematical theories. The present thermodynamic equilibrium
model, a more simplistic one, was developed based on the equilib-
rium constant method and not on the Gibbs free energy principle.
In this study, the other two equations were obtained from the
equilibrium constants of the reactions described in Eqs. (4) and (6).
For the present model study, the thermodynamic equilibrium
was assumed for all chemical reactions in the gasification zone.
All gases were assumed to be ideal and all reactions form at 1
atmospheric pressure. Therefore, the equilibrium constants, which
were the functions of temperature for the methanation reaction
and the water gas shift reaction, are given below:The equilibrium constant k1 for methanation reaction
k1 ¼ x5
ð x1Þ2
ð10Þ
The equilibrium constant k2 for water–gas shift reaction
k2 ¼ x3 x1
x2 x4
ð11Þ
Heat balancing of the reactants and products of the global reac-
tion in Eq. (1) results in the equation shown below:
H 0f RH þ wðH 0fH2O þ H vapÞ þ mH vap þ Q IN
¼ x1H 0fH2
þ x2H 0fCO þ x3H
0fCO2
þ þ x4H 0fH 2O þ x5H
0fCH4
þ DT ð x1C pH2 þ x2C pCO þ x3CpCO2
þ x4CpH2O þ x5CpCH4Þ ð12Þ
where C p is the specific heat of gas species. The heating value of
biomass, H 0fRH, was determined experimentally. The heat balance
Eq. (12) contained a term Q IN which stands for the external heat
addition required for endothermic reactions to occur. The heat of
formation, DH , of various gases could be sourced from the JANAF
Thermochemical Tables (Stull and Prophet, 1971). The dependence
of specific heat on temperature was given by various empirical
equations and according to Perry and Green (1997), the most sim-
plified version of specific heat, Cpam, at arithmetic mean tempera-
ture is expressed as
Cpam ¼ R½ A þ BT am þ C ð4T 2am T 1T 2Þ=3 þ D=ðT 1T 2Þ ð13Þ
where T am = (T 1 + T 2)/2, T 1 and T 2 are the reference temperature andthe reactor temperature, respectively. The values of the heat capac-
ity constants A, B, C and D are given by Perry and Green (1997) and R
is the universal gas constant in J/mol-K.
Following Zainal et al. (2001), the equilibrium constant k can be
written as
RTlnk ¼ DG0 ð14Þ
where DG0 is the standard Gibbs function of formation.
dlnk
dT ¼
DH 0
RT 2 ð15Þ
Integrating Eq. (15) the heat of formation can be calculated as
lnk ¼Z DH 0
RT 2 dT þ I ð16Þ
where I is a constant of integration.
DH 0 were also calculated as given by Perry and Green (1997)
DH 0
R ¼
J
Rþ D A:T þ
DB:T 2
2 þ
DC :T 3
3
DD
T ð17Þ
where D A, DB, DC , DD are the coefficients for determining specific
heats of gases and J is a constant.
Substituting Eq. (17) into Eq. (16), one gets
ln k ¼ J
RT þ D A: ln T þ
DB:T
2 þ
DC :T 2
6 þ
DD
2T 2 þ I ð18Þ
The dependence of DG0 on temperature was analyzed as
DG0 ¼ J RT ½D A: ln T þ
DB:T
2 þ
DC :T 2
6 þ
DD
2T 2 þ I ð19Þ
The values of standard Gibbs function of formation for various
gas compositions were obtained from Perry and Green (1997).
Both J and I were calculated, respectively, from Eqs. (17) and
(19) at temperature 298 K. Two equilibrium equations were re-
quired to determine the equilibrium constants k1 and k2. Equilib-
rium constant k1 for the reaction in Eq. (4) was solved as follows:
D ¼ CH4 C 2H2 ð20Þ
D A, DB, DC and DD were obtained from the data on heat capac-
ity. The equations to determine the values of D A, DB, DC and DD
are expressed as
D A ¼ ACH4 AC 2 AH2
ð21Þ
DB ¼ BCH4 BC 2BH2
ð22Þ
DC ¼ C CH4 C C 2C H2
ð23Þ
DD ¼ DCH4 DC 2DH2
ð24Þ
Application of these equations revealed the equilibrium con-
stant k1.
Similarly, the equilibrium constant k2 for the water gas shift
reaction in Eq. (6) could be solved by adopting the same procedure.
The molar concentrations x1, x2, x3, x4 and x5 were obtained by
solving Eqs. (7)–(11) simultaneously using Newton Raphson
method.
3. Results and discussion
The experimental tests were carried out to determine the effect
of steam biomass ratio and reactor temperature on gas composi-
tion and yield. The experimental values were compared with the
equilibrium model predictions.
3.1. Model validation
The equilibrium model, described in Section 2.3, was needed for
validation by comparing the model results and the experimental
data. In the following, the present experimental data and data re-
ported by Rapanga et al. (2000) were compared with the calcula-
tions from the currently developed model. The comparison is
shown in Table 2a. The error had been estimated using the statis-
tical parameter of root mean square (RMS) error, where
RMS ¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPð X e X pÞ2
N
s
where, X e and X p were the experimental data and the predicted va-
lue of product gas species from present model, respectively, and N
was the number of observations. The RMS error for the presentexperimental runs had been presented in Table 2b. It was observed
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from these tables that the predicted results generally agreed well
with the experimental data.
3.2. Effect of reactor temperature
Fig. 2 shows both theoretical and experimental composition of
the product gas at different reactor temperatures. It was observed
that H2 and CO concentrations increased with rise in temperature
and the concentrations of CH4 and CO2 showed an opposite trend.
According to Le Chatelier’s principle, higher temperatures favour
the reactants in exothermic reactions and the products in endo-
thermic reactions. Therefore, the endothermic reaction in Eq. (5)was strengthened with increasing temperature, which resulted in
an increase of H2 concentration and a decrease of CH4 concentra-
tion. It could be seen that a good agreement existed between the
predicted values and the experimental data, indicating that the
equilibrium model predicted reasonably well about the gas compo-
sitions for the fluidized bed gasifier.
3.3. Effect of steam biomass ratio
The product gas composition varying with steam biomass ratio
is presented in Fig. 3. It is seen from the graphs that, with the in-
crease of steam biomass ratio, the experimental values of H2 and
CO2 concentrations showed a gradual increased trend, while those
of CO and CH4 decreased. The predicted results of H2 agreed fairly
well with the experimental data, but those of CO2 and CO had some
differences with the experimental values although the changes
showed similar trends. Also, the model values of CH 4 concentra-
tion, which increased with rise in steam flow rate, contradicted
the experimental trend. The possible explanation to this observa-
tion was that the equilibrium state might not have reached for
not having enough bed temperature in gasifier. The devolatiliza-
tion of biomass might have contributed higher content of methane
in product gas which did not react completely with the concentra-
tions of CO, CO2 and H2. Furthermore, the tar, actually produced in
the gasification process, was not considered in the present equilib-
rium model. Some of these facts might have caused the model val-
ues for CO and CO2 concentrations to differ from experimental
data. The gasification process involving thermal pyrolysis, homo-
geneous and heterogeneous reactions were complicated to deter-
mine the kinetics of the chemical reactions. A simple but
adequate kinetically-modified equilibrium model might have re-
sulted in better prediction of the gas composition.
The experimental data of bubbling fluidized bed gasifier againstvarious steam biomass (S /B) ratios at fixed temperature of 750 C
are shown in Table 3. Steam being the major source of hydrogen
during steam gasification, an increase in steam biomass ratio re-
sulted in higher production of H2. Due to higher production of
hydrogen, the product gas volume was also increased as indicated
Table 2a
Comparison of present model result with the experimental data.
Gas composition % mol dry basis Temperature = 750 C,
steam biomass ratio = 1.0
Temperature = 770 C,
steam biomass ratio = 1.0
Present Model Present exp. data RMS error Present model Literature exp. data (Rapanga et al., 2000) RMS error
H2 50.76 48.88 2.66 55.0 52.2 2.517
CO 19.78 22.7 22.3 23.0
CO2 25.52 22.2 18.5 16.9
CH4 3.96 6.22 4.2 7.9
Table 2b
RMS error for different experimental runs of the present work.
Expt. run No. Operating parameters RMS error
Gasifier temperature (C) Steam biomass ratio
1. 650 Const. at 1.32 1.44
2. 690 1.80
3. 730 2.04
4. 770 1.91
5. Const at 750 0.6 1.90
6. 1.0 2.65
7. 1.32 3.27
8. 1.70 3.62
640 660 680 700 720 740 760 780
0
10
20
30
40
50
60
G a s c o m p o s i t i o n ( % b
y m
o l e )
Temperature, deg C
H2-Model value
H2-Exp value
CO-Model value
CO-Exp value
CO2-Model value
CO2-Exp value
CH4-Model value
CH4-Exp value
Fig. 2. Gas composition vs. gasification temperature with steam biomass ratio of 1.32.
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in Table 3. The proportion of CO in the product gas became low
when it was produced by steam reforming of CH4. With the in-
crease in steam biomass ratio, the steam reforming of methane be-
came significant and as a result, the proportion of CO was
decreased. The higher heating value (HHV) of product gas de-
creased with rise in steam biomass ratio from 12.21 MJ/Nm3 for
S /B ratio of 0.6–11.18 MJ/Nm3 for S/B ratio of 1.7 at temperature
of 750 C.
3.4. Carbon conversion and gasification efficiency
The carbon conversion is defined as the fraction of carbon in thefeed converted to gaseous products. The results of carbon conver-
sion and gasification efficiency are presented in Table 3. Higher
temperature and higher steam biomass ratio favoured more carbon
conversion. For all the runs in the present study, the overall carbon
conversion was within 84–90% closure. Analysis of tar and the frac-
tion of unconverted solid carbon had not been included in the
study and, these components might account for the rest.
Cold gas efficiency (CGE) was used to evaluate the gasification
performance. The cold gas efficiency is defined as the percentage
of the heating value of biomass converted into the heating value
of the product gas. The system ran on steam only and no oxygen
was supplied. Thus the endothermic heat was required to be sup-
plied externally for gasification reactions to happen. This heat was
considered to be added to the denominator to calculate gasificationefficiency.
CGE ¼ Gas yield ðNm3 kg
1fuelÞ HHV of productgas ðMJNm3 Þ
HHV of fuel ðMJkg1
Þ þ Heat addition for endothermic reaction ðMJkg1
Þ
Calculated gasification efficiencies for different experimental
runs are given in Table 3. Experimental cold gas efficiencies for
the present runs were in the range of 63–66%. The cold gas effi-
ciency, taking account of the external heat supply, measured the
gasification performance.
4. Conclusion
During the study of steam gasification of rice husk in a fluidizedbed gasifier, it was seen that hydrogen content in product gas in-
creased with rise in temperature and increased in steam biomass
ratio. Hydrogen content in product gas had reached as high as
53.08%. Carbon monoxide content increased with rise in tempera-
ture, but decreased with steam biomass ratio. The percentage of
methane decreased with rise in temperature and steam biomass
ratio. Increase in steam biomass ratio generally gave rise to higher
gas yields. Higher temperature also yielded the higher quantity of
gas. The proposed mathematical model closely predicted the
experimental results.
Acknowledgements
The authors thankfully acknowledge Prof. Gautam Biswas,Director, Central Mechanical Engineering Research Institute
0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.80
10
20
30
40
50
60
G a s c o m p o s i t i o n ( %
b y m o l e )
Steam Biomass ratio
H2-Model value
H2-Exp value
CO-Model value
CO-Exp value
CO2-Model value
CO2-Exp value CH4-Model value
CH4-Exp value
Fig. 3. Gas composition vs. steam biomass ratio at reactor temperature of 750 C.
Table 3
Carbon balance and gasification efficiency of experimental results of fluidized bed gasifier.
Exp. run no. 1 2 3 4 5 6 7 8
Steam biomass ratio Constant at 1.32 0.60 1.00 1.32 1.70
Gasifier temp. (C) 650 690 730 770 Constant at 750
Gas Composition (% of mole)
H2 47.25 50.5 52.2 53.08 47.81 48.88 51.17 51.89
CO 11.25 12.83 15.9 17.85 27.48 22.70 19.65 17.38
CO2 31.9 28.51 25.65 23.9 18.09 22.20 23.15 24.81
CH4 9.6 8.16 6.25 5.17 6.62 6.22 6.03 5.92
HHV of prod. gas (MJ/Nm3) 11.28 11.23 11.16 11.09 12.21 11.59 11.42 11.18
Gas yield (Nm3/kg of raw biomass) 1.03 1.12 1.16 1.21 1.05 1.09 1.16 1.21
Carbon conversion (%) 84.10 85.82 85.83 87.88 84.83 86.25 87.68 90.11
Cold gas efficiency (%) 62.99 64.78 65.58 66.06 65.75 65.96 66.10 66.15
1912 M.K. Karmakar, A.B. Datta/ Bioresource Technology 102 (2011) 1907–1913
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(CMERI), Durgapur, India and Dr. P.K. Chatterjee, Scientist G and
Head, Thermal Engineering Group, CMERI for their continuous sup-
port, enthusiasm and encouragement.
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M.K. Karmakar, A.B. Datta / Bioresource Technology 102 (2011) 1907–1913 1913