Simulation of biomass gasification in fluidized bed ...

ARTICLE IN PRESS BIOMASS AND BIOENERGY

32 (2008) 1245 – 1254

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Simulation of biomass gasification in fluidized bed reactor using ASPEN PLUS Mehrdokht B. Nikooa, Nader Mahinpeya,b, a

Environmental Systems Engineering, University of Regina, 3737 Wascana Parkway, Regina, Saskatchewan, Canada S4S 0A2 Process Systems Engineering, University of Regina, 3737 Wascana Parkway, Regina, Saskatchewan, Canada S4S 0A2

b

art i cle info

ab st rac t

Article history:

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

Received 10 February 2007

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

Received in revised form

hydrodynamic parameters and reaction kinetic modeling. Governing hydrodynamic

19 February 2008

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

Accepted 29 February 2008

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

Available online 11 April 2008

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

Keywords: Biomass Gasification Fluidized bed Simulation ASPEN PLUS

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 0.25–0.75 mm does not seem to contribute significantly 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 forestrelated 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

Corresponding author at: Environmental Systems Engineering, University of Regina, 3737 Wascana Parkway, Regina, Saskatchewan, Canada S4S 0A2. Tel.: +1 306 558 4490; fax: +1 306 585 4855. E-mail address: [email protected] (N. Mahinpey). 0961-9534/$ - see front matter & 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.biombioe.2008.02.020

ARTICLE IN PRESS 1246

BIOMASS AND BIOENERGY

Nomenclature a Ar dp E g k MC N P R rC T t u umf XCO XSG YC yi

decay constant of clusters in freeboard (m1) Archimedes number particle diameter (m) activation energy (kcal/mol) gravitational acceleration (m/s2) rate constant (s1 atm1) molecular weight of carbon (kg/kmol) total number of data points pressure (bar) universal gas constant (kcal/mol K) reaction rate of carbon (kmol/m3 s) temperature (K) time (s) superficial velocity (m/s) minimum fluidization velocity (m/s) carbon conversion due to combustion carbon conversion due to steam gasification volume fraction of carbon in solid mole fraction of i

material balance, energy balance, and chemical equilibrium 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 equilibrium. In a typical atmospheric fluidized bed gasifier, feed, together with bed material, are fluidized by the gasifying agents, 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 gas–solid 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 homogeneous 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.

32 (2008) 1245 – 1254

z

distance above the surface of the bed (m)

Greek letters kinetics parameter kinetics parameter volume fraction of bed occupied by bubble average voidage of bed average voidage of freeboard voidage in emulsion at minimum fluidization volume fraction of solid in bed carbon conversion efficiency density of carbon (kg/m3) density of gas (kg/m3) density of solid (kg/m3) viscosity (kg/m s)

a b eb ef efb emf es ZC rC rg rs m

Subscripts e p

experimental predicted

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 [4,14–16] All the gases are uniformly distributed within the emulsion phase Particles are spherical and of uniform size and the average diameter 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 following reactions occur: Combustion reaction [17]: C þ aO2 ! 2ð1 aÞCO þ ð2a 1ÞCO2

(1)

Steam-gasification reactions [18]:

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.

C þ H2 O ! CO þ H2

(2)

CO þ H2 O ! CO2 þ H2

(3)

C þ 2H2 O ! CO2 þ 2H2

(4)

C þ bH2 O ! ðb 1ÞCO2 þ ð2 bÞCO þ bH2

(5)


Here, a is a mechanism factor [19] that changes, in the range of 0.5–1, 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 steam consumed by reaction (2) and 2(b1)/b represents the fraction of steam consumed by reaction (4). Matsui et al. [18] experimentally determined b to be in the range of 1.1–1.5 at 750–900 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: dXCO ECO n ¼ kCO exp PO ð1 XCO Þ2=3 (6) 2 dt RT (7)

dXCO dXSG r s Y C þ C dt dt 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 0.25–0.8 atm. Kinetic parameters can be found in Table 1.

2.3.

Hydrodynamic assumptions

Fluidized bed reactor is divided into two regions: bed and freeboard

The fluidization state in the bed is maintained in the bubbling 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, char particles, and bed material, is perfect The reactor is divided into a finite number of equal elements with constant hydrodynamic parameters The fluidized bed is one-dimensional; any variations in conditions are considered to occur only in the axial direction.

Table 1 – Kinetic parameters

Combustion Steam gasification

Ar ¼

Bed hydrodynamics

d3p rg ðrs rg Þg m2

k (s1 atm1)

13,523 19,544

0.046 6474.7

(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 ¼ 1:0 þ

10:978ðu umf Þ0:738 r0:376 d1:006 p s u0:937 r0:126 g mf

(11) (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 varying gas velocity in obtaining results with higher precision in simulation. The bed void fraction [24] is then given by the following: f ¼ b þ ð1 b Þmf mf ¼ 0:4

(13)

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 ¼ ð1 f Þ expðazÞ

(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: u p 1:25 m=s dp p 800 mm The constant a for this simulation has been found from the graph as follows: a¼

1:8 . u

2.4.

(15)

ASPEN PLUS model

The different stages considered in ASPEN PLUS simulation, in order to show the overall gasification process, are decomposition of the feed, volatile reactions, char gasification, and gas–solid separation.

2.4.1.

E/R (K)

1247

Kunii and Levenspil [24] introduced the following equation to calculate the minimum fluidization velocity for fine particles: ffi 33:7m pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð 1 þ 3:59 105 Ar 1Þ (9) umf ¼ rg dp

2.3.2.

The following assumptions were made in simulating the hydrodynamics:

2.3.1.

b ¼ 1 1=B

dXSG ESG n ¼ kSG exp PH O ð1 XSG Þ2=3 2 dt RT rC ¼

32 (2008) 1245 – 1254

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.


2.4.2.


32 (2008) 1245 – 1254

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 gasification

The ASPEN PLUS CSTR reactor, RCSTR, performs char gasification by using reaction kinetics, as mentioned previously, written as an external FORTRAN code. The hydrodynamic 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 parameters can be considered constant. A description of the ASPEN PLUS reactor blocks and simulation diagram are given in Table 2 and Fig. 1, respectively.

Table 2 – Reactor blocks description utilized in the simulation [28] Reactor block

Description

RYIELD

Models a reactor by specifying reaction yields of each component. This model is useful when reaction 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

Fig. 1 – Comprehensive simulation diagram for the fluidized bed gasification process. Table 3 – Characteristics of pine sawdust Moisture content (wt%) Proximate analysis (wt% dry basis) Volatile matter Fixed carbon Ash

82.29 17.16 0.55

Ultimate analysis (wt% dry basis) C H O N S

50.54 7.08 41.11 0.15 0.57

Average particle size (mm) Char density (kg/m3) Flow rate (kg/h)

0.25–0.75 1300 0.445–0.512

3.

8

Model validation

In order to validate the simulation results, experimental data from gasification of pine in a lab-scale fluidized bed gasifier was 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 conversion 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 ¼

Weight oxygen ðairÞ=weight dry biomass Stoichiometric oxygen ðairÞ=biomass ratio

(16)


ZC ¼ 1

Total rate of carbon in the outlet stream Total rate of carbon in the feed stream

(17)

Simulation results were compared with all sets of experimental data. The sum squared deviation method was used to estimate the accuracy of simulation results [29]. N y y 2 X ie ip (18) RSS ¼ yie i¼1 MRSS ¼

RSS N

Mean error ¼

(19) pffiffiffiffiffiffiffiffiffiffiffiffiffiffi MRSS

1249

32 (2008) 1245 – 1254

temperatures higher than 800 1C. 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-equilibrium 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.

(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. 2–5 show the simulation results compared with experimental data for product gas composition versus five different temperatures in the range of 700–900 1C. Fig. 2 shows better agreement between simulation prediction and experimental data for hydrogen production in the

Table 4 – Experimental setup parameters used in the simulation Fluidized bed reactor Temperature (1C) Pressure (bar)

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.

700–900 1.05

Bed diameter (mm) Freeboard diameter (mm) Height (mm)

40 60 1400

Air Temperature (1C) Flow rate (N m3/h)

65 0.5–0.7

Steam Temperature (1C) Flow rate (kg/h)

145 0–1.8

Bed material Silica sand Average particle size (mm) Weight (g)

0.275 30

Fig. 3 – Effect of temperature on carbon monoxide. Biomass feed rate: 0.445 kg/h; air: 0.5 N m3/h; steam rate: 1.2 kg/h.

Table 5 – Analysis of data Mean error

Gas Gas Gas Gas

composition composition composition composition

versus versus versus versus

temperature ER particle size S/B ratio

H2

CO

CO2

CH4

0.36057 0.19811 0.1847 0.2045

0.10442 0.0939 0.0868 0.1143

0.3009 0.23079 0.2038 0.2382

0.21523 0.19974 0.1632 0.2712



Fig. 4 – Effect of temperature on carbon dioxide. Biomass feed rate: 0.445 kg/h; air: 0.5 N m3/h; steam rate: 1.2 kg/h.

32 (2008) 1245 – 1254

Fig. 6 – Effect of temperature on carbon conversion efficiency. Biomass feed rate: 0.445 kg/h; air: 0.5 N m3/h; steam rate: 1.2 kg/h.

Fig. 5 – Effect of temperature on methane. Biomass feed rate: 0.445 kg/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: 800 1C; steam rate: 0.8 kg/h. 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 700–900 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)

3.2.1.

Gas composition

Simulation results and experimental data for gas composition versus five different equivalence ratios in the range of 0.19–0.27 are shown in Figs. 7–10. 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.

3.2.2.


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 0.19–0.27.


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.

32 (2008) 1245 – 1254

1251

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: 0.5 N m3/h.

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 monoxide and carbon dioxide in the product gas, and as a result, simulation predicts the increasing trend for carbon conversion efficiency in the whole range. Fig. 10 – Effect of ER on methane. Biomass feed rate: 0.512 kg/h; temperature: 800 1C; steam rate: 0.8 kg/h.

The oxidation reaction for carbon monoxide production is C þ 12O2 ! CO:

(21)

The oxidation reaction for carbon dioxide production is C þ O2 ! CO2

(22)

3.3.

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

3.3.1.

Gas composition

Comparisons of simulation predictions with experimental results of gas composition versus steam-to-biomass ratio in five points in the range of 0–4 are shown in Figs. 12–15. 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



32 (2008) 1245 – 1254

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.

3.3.2.


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.

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.

3.4.

Effect of biomass particle size

3.4.1.

Gas composition

Figs. 17–20 show the results of the simulation compared with experimental data for gas composition versus four biomass average particle diameters in the range of 0.25–0.75 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 percentage of carbon monoxide compared with the experimental

Fig. 14 – Effect of steam-to-biomass ratio on carbon dioxide. Biomass feed rate: 0.445 kg/h; temperature: 800 1C, air: 0.5 N m3/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. 15 – Effect of steam-to-biomass ratio on methane. Biomass feed rate: 0.445 kg/h; temperature: 800 1C, air: 0.5 N m3/h.

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-

Fig. 17 – Effect of biomass particle size on hydrogen. Biomass feed rate: 0.512 kg/h; temperature: 800 1C, air: 0.6 N m3/h.


32 (2008) 1245 – 1254

3.4.2.

1253


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 0.25–0.75 mm. 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 experimental data is due to the higher mass transfer resistance for larger particles in real processes.

4. Fig. 18 – Effect of biomass particle size on carbon monoxide. Biomass feed rate: 0.512 kg/h; temperature: 800 1C, air: 0.6 N m3/h.

Fig. 19 – Effect of biomass particle size on carbon dioxide. Biomass feed rate: 0.512 kg/h; temperature: 800 1C, air: 0.6 N m3/h.

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 composition (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 nonequilibrium products in the RGIBBS reactor. Mass transfer inside solid particles is an important parameter in gas–solid 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.

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.

data. For methane, in Fig. 20, there is an overestimation in biomass with average size equal to 0.75 mm, but the simulation predicts experimental data with acceptable accuracy for other points.

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.


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 developed 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 temperature, equivalence ratio (ER), steam-to-biomass ratio, and biomass average particle size were compared with experimental 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 monoxide 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 (SaskPower) for providing funding for this study and also Petroleum 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. R E F E R E N C E S

[1] Bapat DW, Kulkarni SV, Bhandarkar VP. Design and operating experience on fluidized bed boiler burning biomass fuels with high alkali ash. In: Preto FDS, editor. Proceedings of the 14th international conference on fluidized bed combustion. Vancouver, New York, NY: ASME; 1997. p. 165–74. [2] Strehler A, Stuetzle W. Biomass residues. In: Hall DO, editor. Biomass. New York: Wiley; 1987. p. 75–102. [3] Werther J, Saenger M, Hartge E-U, Ogada T, Siagi Z. Combustion of agricultural residues. Progress in Energy and Combustion Science 2000;26:1–27. [4] Sadaka S, Ghaly AE, Sabbah MA. Two phase biomass air– steam gasification model for fluidized bed reactors: Part I— model development. Biomass and Bioenergy 2002;22:439–62. [5] Kundsen RA, Bailey T, Fabiano LA. Experience with ASPEN while simulating a new methanol plant. AIChE Symposium Series 1982;78:214. [6] Schwint KT. Great plains ASPEN model development, methanol synthesis flowsheet. Final topical report, Scientific Design Co., Inc., USA, 1985.

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[7] Barker RE. ASPEN modeling of the tri-state indirect-liquefaction process. Oak Ridge, USA: Oak Ridge National Laboratory; 1983. [8] Phillips JN, Erbes MR, Eustis RH. Study of the off-design performance of integrated coal gasification. In: Combined cycle power plants, computer-aided engineering of energy systems, vol. 2—analysis and simulation, winter annual meeting of the American Society of Mechanical Engineers, Anaheim, CA, USA conference, 1986. [9] Douglas PL, Young BE. Modelling and simulation of an AFBC steam heating plant using ASPEN/SP. Fuel 1990;70:145–54. [10] Yan HM, Rudolph V. Modeling a compartmented fluidized bed coal gasifier process using ASPEN PLUS. Chemical Engineering Communication 2000;183:1–38. [11] Backham L, Croiset E, Douglas PL. Simulation of a coal hydrogasification process with integrated CO2 capture. Combustion Canada 2003;3A(4). [12] Lee HG, Chung KM, Kim C, Han SH, Kim HT. Coal gasification simulation using ASPEN PLUS. In: US–Korea joint workshop on coal utilization technology, 1992, p. 447–74. [13] Mansaray KG, Al-Taweel AM, Ghaly AE, Hamdullahpur F, Ugursal VI. Mathematical modeling of a fluidized bed rice husk gasifier. Energy Sources 2000:83–98. [14] Lv PM, Xiong ZH, Chang J, Wu CZ, Chen Y, Zhu JX. An experimental study on biomass air–steam gasification in a fluidized bed. Bioresource Technology 2004;95:95–101. [15] Buekens AG, Schoeters JG. Modelling of biomass gasification. In: Overend RP, Milne TA, Mudge KL, editors. Fundamentals of thermochemical biomass conversion. London, UK: Elsevier Applied Science Publishers; 1985. p. 619–89. [16] Ergudenler A. Gasification of wheat straw in a dual-distributor type fluidized bed reactor. Unpublished PhD thesis, Technical University of Nova Scotia, NS, Canada, 1993. [17] Lee JM, Kim YJ, Lee WJ, Kim SD. Coal gasification kinetics derived from pyrolysis in a fluidized bed reactor. Energy 1998;23(6):475–88. [18] Matsui I, Kunii D, Furusawa T. Study of fluidized bed steam gasification of char by thermogravimetrically obtained kinetics. J Chem Eng Japan 1985;18:105–13. [19] Rajan RR, Wen CY. A comprehensive model for fluidized bed coal combustors. AIChE Journal 1980;26:642–55. [20] Walker PLJ, Rusinko FJ, Austin LG. Gas reactions of carbon. Advances in Catalysis 1959;11:133–221. [21] Dutta S, Wen CY. Reactivity of coal and char 2. In oxygen–nitrogen atmosphere. Industrial & Engineering Chemistry Process Design and Development 1977;16(1):31–6. [22] Kasaoka S, Skata Y, Tong C. Kinetic evaluation of the reactivity of various coal chars for gasification with carbon dioxide in comparison with steam. Industrial & Engineering Chemistry 1985;25:160. [23] Chin G, Kimura S, Tone S, Otake T. Gasification of coal char with steam. Industrial & Engineering Chemistry 1983;25:105. [24] Kunii D, Levenspiel O. Fluidization engineering, 2nd ed. 1991. [25] Babu SP, Shah B, Talwalker A. Fluidization correlations for coal gasification materials—minimum fluidization velocity and bed expansion ratio. AICHE Symposium Series 1978;74:176–86. [26] Yan HM, Heidenreichayb C, Zhanga DK. Mathematical modelling of a bubbling fluidized-bed coal gasifier and the significance of ‘net flow’. Fuel 1998;77:1067–79. [27] Lewis WK, Gilliland ER, Lang PM. Entrainment from fluidized beds. Chemical Engineering Progress Symposium Series 1962;58:65–78. [28] Aspen Technology. Aspen Plus 12.1 user guide. Cambridge, MA, 2003. [29] Gururajan VS, Agarwal PK, Agnew JB. Mathematical modeling of fluidized bed coal gasifier. Chemical Engineering Research and Design 1992;70a:211–38.