simulation of biomass gasification in fluidized bed reactor

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http://www.elsevier.com/locate/biombioe

Simulation of biomass gasification in fluidized bed reactor

using ASPEN PLUS

Mehrdokht B. Nikooa, Nader Mahinpeya,b,

aEnvironmental Systems Engineering, University of Regina, 3737 Wascana Parkway, Regina, Saskatchewan, Canada S4S 0A2bProcess Systems Engineering, University of Regina, 3737 Wascana Parkway, Regina, Saskatchewan, Canada S4S 0A2

a r t i c l e i n f o

Article history:

Received 10 February 2007

Received in revised form

19 February 2008

Accepted 29 February 2008

Keywords:

Biomass

Gasification

Fluidized bed

Simulation

ASPEN PLUS

a b s t r a c t

A comprehensive process model is developed for biomass gasification in an atmospheric

fluidized bed gasifier using the ASPEN PLUS simulator. The proposed model addresses both

hydrodynamic parameters and reaction kinetic modeling. Governing hydrodynamic

equations for a bubbling bed and kinetic expressions for the char combustion are adopted

from the literature. Four ASPEN PLUS reactor models and external FORTRAN subroutines

for hydrodynamics and kinetics nested in ASPEN PLUS simulate the gasification process.

Different sets of operating conditions for a lab-scale pine gasifier are used to demonstrate

validation of the model.

Temperature increases the production of hydrogen and enhances carbon conversion

efficiency. Equivalence ratio is directly proportional to carbon dioxide production and

carbon conversion efficiency. Increasing steam-to-biomass ratio increases hydrogen and

carbon monoxide production and decreases carbon dioxide and carbon conversion

efficiency. Particle average size in the range of 0.250.75 mm does not seem to contributesignificantly to the composition of product gases.

& 2008 Elsevier Ltd. All rights reserved.

1. Introduction

Biomass, fuel derived from organic matter on a renewable

basis, is among the largest sources of energy in the world, third

only to coal and oil [1]. Biomass adsorbs CO2 from the

atmosphere during photosynthesis, and the CO2 is then

returned to the environment after combustion. Because of this

cycle, biomass is CO2 neutral, making it an advantageous fuel

source and a dominant choice for replacement of fossil fuels as

the concern of global warming increases. Biomass materials

known as potential sources of energy are agricultural residues

such as straw, bagasse, and husk and residues from forest-

related industries such as wood chips, sawdust, and bark [2,3].

Fluidized bed gasifiers are advantageous for transforming

biomass, particularly agricultural residues, into energy.

Perfect contact between gas and solid, along with a

high degree of turbulence, improves heat and mass

transfer characteristics, enhances the ability to control

temperature, and increases heat storage and volumetric

capacity [4].

The ASPEN PLUS process simulator has been used by

different investigators to simulate coal conversion; examples

include methanol synthesis [5,6], indirect coal liquefaction

processes [7], integrated coal gasification combined cycle

(IGCC) power plants [8], atmospheric fluidized bed combustor

processes [9], compartmented fluidized bed coal gasifiers [10],

coal hydrogasification processes [11], and coal gasification

simulation [12]. However, the work that has been done on

biomass gasification is limited. Mansaray et al. [13] used

ASPEN PLUS to simulate rice husk gasification based on

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0961-9534/$ - see front matter&

2008 Elsevier Ltd. All rights reserved.doi:10.1016/j.biombioe.2008.02.020

Corresponding author at: Environmental Systems Engineering, University of Regina, 3737 Wascana Parkway, Regina, Saskatchewan,Canada S4S 0A2. Tel.: +1 306558 4490; fax: +1 306585 4855.

E-mail address: [email protected] (N. Mahinpey).

B I O M A S S A N D B I O E N E R G Y ] ( ] ] ] ] ) ] ] ] ] ] ]

Please cite this article as: Nikoo MB, Mahinpey N. Simulation of biomass gasification in fluidized bed reactor using ASPENPLUS. Biomass and Bioenergy (2008), doi:10.1016/j.biombioe.2008.02.020
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material balance, energy balance, and chemical equi-

librium relations. Because of the high amount of volatile

material in biomass and the complexity of biomass

reaction rate kinetics in fluidized beds, they ignored the char

gasification and simulated the gasification process by the

assumption that biomass gasification follows Gibbs equili-

brium.

In a typical atmospheric fluidized bed gasifier, feed,

together with bed material, are fluidized by the gasifyingagents, such as air and/or steam, entering at the bottom of

the bed. The product gas resulting from the gasification

process is fed to a gassolid separator (i.e., cyclone) to

separate solid particles carried by exhaust gas.

The objective of this study is to develop simulation capable

of predicting the steady-state performance of an atmospheric

fluidized bed gasifier by considering the hydrodynamic and

reaction rate kinetics simultaneously. The products of homo-

geneous reactions are defined by Gibbs equilibrium, and

reaction rate kinetics are used to determine the products of

char gasification. A drawback in using ASPEN PLUS is the lack

of a library model to simulate fluidized bed unit operation.

However, it is possible for users to input their own models,using FORTRAN codes nested within the ASPEN PLUS input

file, to simulate operation of a fluidized bed. This paper

presents the details of the modeling approaches taken to

obtain a process simulation program for biomass gasification

in a fluidized bed reactor.

2. Modeling approach

Because of the influence of hydrodynamic parameters on

biomass gasification in fluidized beds, both hydrodynamic

and reaction kinetics must be treated simultaneously.

2.1. Assumptions

The following assumptions were considered in modeling the

gasification process:

Process is steady state and isothermal Biomass devolatilization takes place instantaneously and

volatile products mainly consist of H2, CO, CO2, CH4, and

H2O [4,1416]

All the gases are uniformly distributed within the emul-sion phase

Particles are spherical and of uniform size and the averagediameter remains constant during the gasification, based

on the shrinking core model

Char only contains carbon and ash Char gasification starts in the bed and completes in the

freeboard.

2.2. Reaction kinetics

The gasification process begins with pyrolysis and continues

with combustion and steam gasification, wherein the follow-

ing reactions occur:

Combustion reaction [17]:

C aO2 ! 21 aCO 2a 1CO2 (1)Steam-gasification reactions [18]:

C H2O ! CO H2 (2)

CO H2O ! CO2 H2 (3)

C 2H2O ! CO2 2H2 (4)

C bH2O ! b 1CO2 2 bCO bH2 (5)

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Nomenclature

a decay constant of clusters in freeboard (m1)

Ar Archimedes number

dp particle diameter (m)

E activation energy (kcal/mol)

g gravitational acceleration (m/s2)k rate constant (s1atm1)

MC molecular weight of carbon (kg/kmol)

N total number of data points

P pressure (bar)

R universal gas constant (kcal/molK)

rC reaction rate of carbon (kmol/m3 s)

T temperature (K)

t time (s)

u superficial velocity (m/s)

umf minimum fluidization velocity (m/s)

XCO carbon conversion due to combustion

XSG carbon conversion due to steam gasification

YC volume fraction of carbon in solid

yi mole fraction of i

z distance above the surface of the bed (m)

Greek letters

a kinetics parameter

b kinetics parameter

eb volume fraction of bed occupied by bubble

ef average voidage of bed

efb average voidage of freeboard

emf voidage in emulsion at minimum fluidization

es volume fraction of solid in bed

ZC carbon conversion efficiency

rC density of carbon (kg/m3)

rg density of gas (kg/m3)

rs density of solid (kg/m3)

m viscosity (kg/ms)

Subscripts

e experimental

p predicted

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Here, a is a mechanism factor [19] that changes, in the

range of 0.51, when CO or CO2, is carried away from the char

particle during char combustion. The factor, a, is a function of

the temperature and average diameter of the char particles.

In reaction (5), (2b)/b represents the fraction of the steamconsumed by reaction (2) and 2(b1)/b represents the fractionof steam consumed by reaction (4). Matsui et al. [18]

experimentally determined b to be in the range of 1.11.5 at

750900 1C. For the proposed model, the values of a and b

equal 0.9 and 1.4, respectively, and show the best agreement

with experimental data.

Lee et al. [17] defines the reaction rate equations for the

mentioned reactions as follows:

dXCOdt

kCO expECO

RT

PnO2

1 XCO2=3 (6)

dXSGdt

kSG expESG

RT

PnH2O

1 XSG2=3 (7)

rC

dXCO

dt dXSG

dt

rCsYC

MC(8)

Previous studies [20,21] considered parameter n to be equal

to 1.0 in Eqs. (6) and (7). For the steam-gasification reaction,

some studies [22,23] reported different numbers for n, but it is

actually 1.0 in the steam partial pressure range of

0.250.8 atm. Kinetic parameters can be found in Table 1.

2.3. Hydrodynamic assumptions

The following assumptions were made in simulating the

hydrodynamics:

Fluidized bed reactor is divided into two regions: bed and

freeboard

The fluidization state in the bed is maintained in thebubbling regime

The volume fraction of solids decreases as height in-creases, corresponding to the coalescence of bubbles in

the bed and the returning of solid particles to the bed in

the TDH zone

Volumetric flow rate of gas increases along with height,corresponding to the production of gaseous products

The mixing of solid particles, consisting of ash, charparticles, and bed material, is perfect

The reactor is divided into a finite number of equalelements with constant hydrodynamic parameters

The fluidized bed is one-dimensional; any variations inconditions are considered to occur only in the axial

direction.

2.3.1. Bed hydrodynamics

Kunii and Levenspil [24] introduced the following equation to

calculate the minimum fluidization velocity for fine particles:

umf 33:7mrgdp

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

1 3:59 105Arp

1 (9)

Ar d3

pr

gr

s r

ggm2

(10)

The following correlations, developed by Babu et al. [25,26],

are used to determine the volume fraction occupied by

bubbles in a fluidized bed

B 1:0 10:978u umf0:738r0:376s d1:006p

u0:937mf r0:126g

(11)

b 1 1=B (12)where u the superficial gas velocity, is not a constant

parameter, due to the gas production resulting from

homogeneous and heterogeneous reactions. Yan et al. [26]

demonstrated the importance of considering varyinggas velocity in obtaining results with higher precision in

simulation.

The bed void fraction [24] is then given by the following:

f b 1 bmfmf 0:4 (13)

2.3.2. Freeboard hydrodynamics

According to Lewis et al. [27] the volume fraction of solids at

various levels z in the freeboard falls off exponentially from

the value at the bed surface, or

1

fb

1

f

exp

az

(14)

Kunii and Levenspiel [24] prepared a graph from reported

data that correlates the constant a with particle size and

superficial gas velocity. This graph can be used in the

following range:

up 1:25 m=s

dp p 800 mm

The constant a for this simulation has been found from the

graph as follows:

a 1:8u

. (15)

2.4. ASPEN PLUS model

The different stages considered in ASPEN PLUS simulation, in

order to show the overall gasification process, are decom-

position of the feed, volatile reactions, char gasification, and

gassolid separation.

2.4.1. Biomass decomposition

The ASPEN PLUS yield reactor, RYIELD, was used to simulate

the decomposition of the feed. In this step, biomass is

converted into its constituting components including

carbon, hydrogen, oxygen, sulfur, nitrogen, and ash, by

specifying the yield distribution according to the biomass

ultimate analysis.

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Table 1 Kinetic parameters

E/R (K) k (s1atm1)

Combustion 13,523 0.046

Steam gasification 19,544 6474.7

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2.4.2. Volatile reactions

The ASPEN PLUS Gibbs reactor, RGIBBS, was used for volatile

combustion, in conformity with the assumption that volatile

reactions follow the Gibbs equilibrium. Biomass consists of

mainly C, H, N, O, S, Cl, ash, and moisture. Carbon will partly

constitute the gas phase, which takes part in devolatilization,

and the remaining carbon comprises part of the solid phase

(char) and subsequently results in char gasification.

A SEPARATION COLUMN model was used before the RGIBBS

reactor to separate the volatile materials and solids in order to

perform the volatile reactions. Within the ASPEN PLUS

environment, the separation column is the most appropriate

unit operation to achieve this goal. The amount of volatile

material can be specified from the biomass approximate

analysis. Also considering the assumption that char contains

only carbon and ash, the amount of carbon in the volatile

portion can be calculated by deducting the total amount of

carbon in char from the total carbon in biomass.

2.4.3. Char gasificationThe ASPEN PLUS CSTR reactor, RCSTR, performs char

gasification by using reaction kinetics, as mentioned pre-

viously, written as an external FORTRAN code. The hydro-

dynamic parameters divide the reactor into two regions, bed

and freeboard, and each region is simulated by one RCSTR.

Using FORTRAN code, each RCSTR is divided into a series of

CSTR reactors with equal volume. The hydrodynamic and

kinetic parameters, such as superficial velocity, voidage, and

fractional pressure of oxygen and steam, are constant in

these small reactors. The number of the elemental reactors

depends on the residence time, the reactor dimensions, and

the operational conditions wherein the mentioned para-

meters can be considered constant.A description of the ASPEN PLUS reactor blocks and

simulation diagram are given in Table 2 and Fig. 1, respec-

tively.

3. Model validation

In order to validate the simulation results, experimental data

from gasification of pine in a lab-scale fluidized bed gasifierwas used; details of the setup can be found elsewhere [14].

Tables 3 and 4 show feed material and reactor characteristics

used in the simulation.

Lv et al. [14] studied the influence of temperature,

equivalence ratio (ER), steam-to-biomass ratio, and biomass

average particle size on gas composition and carbon conver-

sion efficiency. They considered four main gases (i.e. H2, CO,

CO2, CH4) to study gas production.

Equivalence ratio and carbon conversion efficiency are

defined, respectively, as follows:

ER Weight oxygen

air=weight dry biomass

Stoichiometric oxygen air=biomass ratio (16)

ARTICLE IN PRESS

Fig. 1 Comprehensive simulation diagram for the fluidized

bed gasification process.

Table 2 Reactor blocks description utilized in thesimulation [28]

Reactorblock

Description

RYIELD Models a reactor by specifying reaction yields of

each component. This model is useful whenreaction stoichiometry and kinetics are unknown

and yield distribution data or correlations are

available

RGIBBS Models single-phase chemical equilibrium, or

simultaneous phase and chemical equilibrium by

minimizing Gibbs free energy, subject to atom

balance constraints. This model is useful when

temperature and pressure are known and reaction

stoichiometry is unknown

RCSTR Models a continuous-stirred tank reactor. This

model is useful when reaction kinetics is known.

This model is useful when solids, such as char, are

participating in the reactions

Table 3 Characteristics of pine sawdust

Moisture content (wt%) 8

Proximate analysis (wt% dry basis)

Volatile matter 82.29

Fixed carbon 17.16

Ash 0.55

Ultimate analysis (wt% dry basis)

C 50.54

H 7.08

O 41.11

N 0.15

S 0.57

Average particle size (mm) 0.250.75

Char density (kg/m3) 1300

Flow rate (kg/h) 0.4450.512




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ZC 1 Total rate of carbon in the outlet streamTotal rate of carbon in the feed stream

(17)

Simulation results were compared with all sets of experi-

mental data. The sum squared deviation method was used to

estimate the accuracy of simulation results [29].

RSSX

N

i1

yie yipyie

2

(18)

MRSS RSSN

(19)

Mean error ffiffiffiffiffiffiffiffiffiffiffiffiffiffi

MRSSp

(20)

The analysis of data for product gases is shown in Table 5.

Carbon monoxide and carbon dioxide show the lowest and

highest error, respectively, in all sets of experiments.

3.1. Effect of temperature

3.1.1. Gas composition

Figs. 25 show the simulation results compared with experi-mental data for product gas composition versus five different

temperatures in the range of 700900 1C.

Fig. 2 shows better agreement between simulation predic-

tion and experimental data for hydrogen production in the

temperatures higher than 8001C. Simulation results for

carbon monoxide in Fig. 3 display good qualitative prediction

of experimental data in the whole range, and carbon dioxide

production is underestimated in Fig. 4. Also, simulation

results in Fig. 5 show good accuracy for methane production.

Gases with a CnHm formula are the result of non-equili-

brium processes. Thus, because of the assumption in this

study that homogeneous reactions follow Gibbs equilibrium,

methane is the only possible hydrocarbon in the gasification

products.

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Table 4 Experimental setup parameters used in thesimulation

Fluidized bed reactorTemperature (1C) 700900Pressure (bar) 1.05

Bed diameter (mm) 40Freeboard diameter (mm) 60

Height (mm) 1400

Air

Temperature (1C) 65

Flow rate (N m3/h) 0.50.7

Steam

Temperature (1C) 145

Flow rate (kg/h) 01.8

Bed material

Silica sand

Average particle size (mm) 0.275

Weight (g) 30

Table 5 Analysis of data

Mean error

H2 CO CO2 CH4

Gas composition versus temperature 0.36057 0.10442 0.3009 0.21523

Gas composition versus ER 0.19811 0.0939 0.23079 0.19974

Gas composition versus particle size 0.1847 0.0868 0.2038 0.1632

Gas composition versus S/B ratio 0.2045 0.1143 0.2382 0.2712

Fig. 2 Effect of temperature on hydrogen. Biomass feed

rate: 0.445 kg/h; air: 0.5 N m3/h; steam rate: 1.2 kg/h.

Fig. 3 Effect of temperature on carbon monoxide. Biomass

feed rate: 0.445 kg/h; air: 0.5N m3/h; steam rate: 1.2 kg/h.




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Biomass produces more tar and unburned hydrocarbon in

lower temperatures, which decreases hydrogen production.

The error related to the prediction of hydrogen, especially in

lower temperatures, is the result of ignoring tar production in

the simulation, as shown in Fig. 2. Corresponding to reaction

(5) in Fig. 3, the higher amount of hydrogen favors the

backward reaction and causes prediction of lower carbon

dioxide production in simulation. Also, the backward reaction

(5) dominates the prediction of carbon monoxide, and it

shows slight underestimation in temperatures lower than

800 1C.

The equilibrium assumption substitutes the methane for all

other possible hydrocarbons. An amount of less than 10%

methane in product gas results in a negligible difference

between experimental and simulation results, as observed in

Fig. 5.

3.1.2. Carbon conversion efficiency

Fig. 6 shows the comparison of the simulation results with

the experimental data for carbon conversion efficiency versus

temperature in the range of 700900 1C. Higher temperature

improves the gasification process and increases the carbon

conversion. Increasing trends of carbon conversion efficiency

can be seen for both simulation and experimental results.

The high accuracy of the simulation results is depicted in

Fig. 6.

3.2. Effect of equivalence ratio (ER)


Simulation results and experimental data for gas composition

versus five different equivalence ratios in the range of

0.190.27 are shown in Figs. 710.The equivalence ratio shows two opposing effects on the

gasification process. Increasing the amount of air favors

gasification by increasing the temperature but, at the same

time, produces more carbon dioxide [14]. Gasification with a

better level of efficiency produces more carbon monoxide and

less carbon dioxide. Thus, the trends in Figs. 8 and 9 show

domination of the each opposing effects for ER of less and

more than 0.23, respectively.


Fig. 11 shows the predicted results from simulation and

measured data from experiments for carbon conversion

efficiency in five different ER in the range of 0.190.27.

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Fig. 4 Effect of temperature on carbon dioxide. Biomass

feed rate: 0.445 kg/h; air: 0.5N m3/h; steam rate: 1.2 kg/h.

Fig. 5 Effect of temperature on methane. Biomass feed rate:

0.445kg/h; air: 0.5 N m3/h; steam rate: 1.2 kg/h.

Fig. 6 Effect of temperature on carbon conversion

efficiency. Biomass feed rate: 0.445kg/h; air: 0.5 N m3/h;

steam rate: 1.2 kg/h.

Fig. 7 Effect of ER on hydrogen. Biomass feed rate:0.512 kg/h; temperature: 8001C; steam rate: 0.8 kg/h.




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The oxidation reaction for carbon monoxide production is

C 12O2 ! CO: (21)

The oxidation reaction for carbon dioxide production is

C O2 ! CO2 (22)

Based on the oxidation reactions, Eqs. (21) and (22), carbon

monoxide production consumes more carbon for the same

amount of oxygen. Therefore, for ER of less than the optimum

point, equal to 0.23, the increasing trend of carbon monoxide

increases the carbon conversion efficiency, and it is the

reverse for ER of greater than the optimum point.

The constant amount of kinetic parameters, a and b, does

not reflect the change of proportion between carbon mon-

oxide and carbon dioxide in the product gas, and as a result,simulation predicts the increasing trend for carbon conver-

sion efficiency in the whole range.

3.3. Effect of steam-to-biomass ratio (S/B)


Comparisons of simulation predictions with experimental

results of gas composition versus steam-to-biomass ratio in

five points in the range of 04 are shown in Figs. 1215.

Introducing low-temperature steam to the gasification

process reduces the temperature of the process and increases

the amount of tar. Simulation (Fig. 12) predicts the percentage

of hydrogen in product gas with the best precision for

ARTICLE IN PRESS

Fig. 8 Effect of ER on carbon monoxide. Biomass feed rate:

0.512 kg/h; temperature: 800 1C; steam rate: 0.8 kg/h.

Fig. 9 Effect of ER on carbon dioxide. Biomass feed rate:0.512 kg/h; temperature: 800 1C; steam rate: 0.8 kg/h.

Fig. 10 Effect of ER on methane. Biomass feed rate:

0.512 kg/h; temperature: 800 1C; steam rate: 0.8 kg/h.

Fig. 11 Effect of ER on carbon conversion efficiency.

Biomass feed rate: 0.512 kg/h; temperature: 800 1C; steam

rate: 0.8 kg/h.

Fig. 12 Effect of steam-to-biomass ratio on hydrogen.

Biomass feed rate: 0.445 kg/h; temperature: 800 1C, air:

0.5Nm3/h.




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gasification without steam because of the low amount of tar

in the process. As seen in Figs. 13 and 14, a higher flow rate of

steam decreases carbon monoxide and increases carbon

dioxide in the product gas. However, simulation cannot

predict the real trends because the effect of varying tempera-

ture resulting from the entering steam is ignored. Also,

overestimation of the amount of methane is caused when

there is no steam in the process, as is shown in Fig. 15.


As shown in Fig. 16, carbon conversion efficiency decreases

over the S/B range from 0 to 4, which can be explained by the

excess amount of low-temperature steam in the gasification

process.

3.4. Effect of biomass particle size


Figs. 1720 show the results of the simulation compared with

experimental data for gas composition versus four biomass

average particle diameters in the range of 0.250.75 mm.

Simulation shows good agreement with experimental data,

especially in the qualitative view, regarding the production of

hydrogen and carbon dioxide, as can be seen in Figs. 17 and

19. Fig. 18 demonstrates very good prediction of the percen-tage of carbon monoxide compared with the experimental

ARTICLE IN PRESS

Fig. 13 Effect of steam-to-biomass ratio on carbon

monoxide. Biomass feed rate: 0.445 kg/h; temperature:

800 1C, air: 0.5 N m3/h.

Fig. 14 Effect of steam-to-biomass ratio on carbon dioxide.


0.5Nm3/h.

Fig. 15 Effect of steam-to-biomass ratio on methane.


0.5Nm3/h.

Fig. 16 Effect of steam-to-biomass ratio on carbon

conversion efficiency. Biomass feed rate: 0.445 kg/h;

temperature: 800 1C, air: 0.5 N m3/h.

Fig. 17 Effect of biomass particle size on hydrogen.


0.6Nm3/h.




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data. For methane, in Fig. 20, there is an overestimation in

biomass with average size equal to 0.75 mm, but the simula-

tion predicts experimental data with acceptable accuracy for

other points.


Based on the hydrodynamic model used in this simulation,

larger biomass particle size results in a higher volume

fraction of solid that improves the carbon conversion

efficiency in the range of 0.250.75mm. This is the reason

for the increasing trend of simulation results for carbon

conversion versus particle size in Fig. 21. However, the

decreasing trend of carbon conversion efficiency in experi-

mental data is due to the higher mass transfer resistance for

larger particles in real processes.

4. Future work

Good qualitative agreement between model prediction and

experimental data was achieved. However, to improve the

simulation results, some modifications should be considered.

The present paper intended to present the simulation results

of parametric study of the effects of temperature, equivalence

ratio, steam-to-biomass ratio, and particle size on gas compo-

sition (i.e., H2, CO, CO2, and CH4) and carbon conversion. Tar

formation will improve the predicted results in the simulation.

Detailed experimental data about the influence of operating

conditions on the formation of tar along with the kinetics

studies is needed to obtain a thorough evaluation. The

chemical formula of tar is CxHyOz. The parameters (x, y, z)

are temperature and heating rate dependent. Such study is

being carried out in our lab and results will be communicated

very soon. Once these results are analyzed, the tar production

can be implemented in the current model by defining non-

equilibrium products in the RGIBBS reactor.

Mass transfer inside solid particles is an important para-

meter in gassolid reactions, and heat transfer inside

particles, between phases, and between material and wall is

another feature that should be included in order to achieve

better simulation prediction. Radial dispersion inside the

reactor helps to see wall effects on the hydrodynamics of the

fluidized bed reactor. Additional modeling studies with more

detailed assumptions are underway, and results of such

studies will be communicated upon their completion.

ARTICLE IN PRESS

Fig. 18 Effect of biomass particle size on carbon monoxide.


0.6Nm3/h.

Fig. 19 Effect of biomass particle size on carbon dioxide.


0.6Nm3/h.

Fig. 20 Effect of biomass particle size on methane. Biomass

feed rate: 0.512 kg/h; temperature: 800 1C, air: 0.6 N m3/h.

Fig. 21 Effect of biomass particle size on carbon conversion

efficiency. Biomass feed rate: 0.512 kg/h; temperature:

800 1C, air: 0.6 N m3/h.




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5. Conclusion

A model was developed for the gasification of biomass in an

atmospheric fluidized bed gasifier using the ASPEN PLUS

simulator. To provide the model, several ASPEN PLUS unit

operation blocks were combined and, where necessary,

kinetic expressions and hydrodynamic models were devel-oped using data and models from the literature. The model

was used to predict the results of lab-scale gasification of pine

with air and steam. The simulation results for the product gas

composition and carbon conversion efficiency versus tem-

perature, equivalence ratio (ER), steam-to-biomass ratio, and

biomass average particle size were compared with experi-

mental results.

Higher temperature improves the gasification process. It

increases both the production of hydrogen and the carbon

conversion efficiency. Carbon monoxide and methane show

decreasing trends with increasing temperature. Carbon

dioxide production and carbon conversion efficiency increase

by increasing the ER. Although, hydrogen, carbon monoxide,and methane decrease when ER is increased, increasing

steam-to-biomass ratio increases hydrogen and carbon mon-

oxide production and decreases carbon dioxide and carbon

conversion efficiency. Particle average size does not show a

significant influence on the composition of product gases.

Acknowledgments

The authors express their gratitude to Communities of

Tomorrow (CT) and Saskatchewan Power Corporation (Sask-

Power) for providing funding for this study and also Petro-leum Technology Research Centre (PTRC) for providing

computational resources. Special thanks are also extended

to Dr. Malcolm Wilson for his instrumental support and

valuable comments provided toward accomplishing this

study.

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Please cite this article as: Nikoo MB, Mahinpey N. Simulation of biomass gasification in fluidized bed reactor using ASPEN
http://localhost/var/www/apps/conversion/tmp/scratch_2/dx.doi.org/10.1016/j.biombioe.2008.02.020

simulation of biomass gasification in fluidized bed reactor

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