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Numerical simulation of the pyrolysis zone in a downdraft gasification process
Bioresource Technology 100 (2009) 6052–6058
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Numerical simulation of the pyrolysis zone in a downdraft gasification process K. Jaojaruek *, S. Kumar Energy Field of Study, School of Environment, Resources and Development (SERD), Asian Institute of Technology, P.O. Box 4, Klongluang, Pathumthani 12120, Thailand
a r t i c l e
i n f o
Article history: Received 1 April 2009 Received in revised form 16 June 2009 Accepted 16 June 2009 Available online 23 July 2009 Keywords: Downdraft gasification Finite modeling of pyrolysis process Temperature profile in the pyrolysis zone Heat transfer in porous medium Feedstock consumption in downdraft gasifier
a b s t r a c t Models of the gasification process are mostly based on lumped analysis with distinct zones of the process treated as one entity. The study presented here was conducted to develop a more useful model specifically for the pyrolysis zone of the reactor of a downdraft gasifier based on finite computation method. Applying principles of energy and mass conservation, governing equations formed were solved by implicit finite difference method on the node of 100 throughout the length of the considered pyrolysis range (20 cm). Heat transfer considered convection, conduction, and the influence of solid radiation components. Chemical kinetics concept was also adopted to simultaneously solve the temperature profile and feedstock consumption rate on the pyrolysis zone. The convergence criteria were set at 106 and simulation used Fortran Power Station 4.0. Validation experiments were also conducted resulting in maximum deviation of 24 °C and 0.37 kg/h for temperature and feedstock feed rate, respectively. Ó 2009 Elsevier Ltd. All rights reserved.
1. Introduction Downdraft gasification is a process to convert biomass or solids carbonaceous fuel into useful combustible gas. This process is endothermic with four distinct zones that are arranged from top to bottom. These zones are: drying, pyrolysis also known as devolatilization, combustion or oxidation, and gasification or reduction. The pyrolysis gas formed when the volatile matter in the solid fuel is devolatilized in the combustion/oxidation zone undergoes partial oxidation. The combustion zone acts as heat source and the heat comes from partial combustion of mainly char. The combustion is controlled by the quantity of air supply. The pyrolysis gas in the pyrolysis zone is mainly composed of CO, CO2, H2, CH4 and higher hydrocarbons CmHn (m > 1). This gas then flows downward to the reduction/gasification zone to further react with the char leaving the combustion zone and becomes the producer gas that has mainly the same composition as pyrolysis gas but with improved percentage of CO and H2. It is at the gasification zone that the producer gas leaves the gasifier at high temperature laden with dust, tar, and water vapor. Cooling and cleaning of the gas is necessary depending on the intended application of the producer gas. There are several studies available in the literatures that discuss the downdraft gasification process. Examples of these studies ranged from the evaluation of the use of different types of feedstocks, gasifying agents like oxygen and/or steam, and performance evaluation of multi-stage gasifiers (Zainal et al., 2001; Wander et al., * Corresponding author. Tel.: +66 2 524 6074/6069; fax: +66 2 524 6071/5439. E-mail addresses: [email protected], [email protected] (K. Jaojaruek), kumar@ ait.ac.th (S. Kumar). 0960-8524/$ - see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.biortech.2009.06.052
2004; Dogru et al., 2002; Bhattacharya et al., 2001; Henriksen et al., 2006). Equilibrium modeling of a downdraft gasifier intended for different biomass materials was explored by Zainal et al. (2001) while Altafini et al. (2003) applied the concept of minimization of the Gibbs free energy on their numerical simulation of wood waste gasification. As for the pyrolysis process, studies on mathematical models that considered mass and heat transfer are available (Koufopanos et al., 1989), however most are for the pyrolysis process itself or pyrolysis reactors and not on the pyrolysis zone in a gasification process. A model for the co-pyrolysis behaviour of lignite coal–biomass blends was proposed by Sadhukan et al. (2008a) in which they proposed a parallel-series kinetic model for biomass and a distributed activation energy model for coal. In another study by Sadhukan et al. (2008b), the pyrolysis process was also modeled using transient analysis involving a kinetic model and a heat transfer model but this study only considered a single wood cylinder particle, which may not be able to fully represent the inter-reactions taking place in a reactor filled with numerous wood particles. It can be emphasized that most studies on gasification modeling have analyzed and developed models based on the whole gasification process itself, which can be called as lump analysis (Zainal et al., 2001; Radmanesh et al., 2006) that are not zone-specific analyses. Sheth and Babu (2009) performed experimental studies on wood waste and their work also focused on a general downdraft gasification process rather than a specific zone. Although, there are many studies done on the pyrolysis process (Orfao et al., 1999; Gronli et al., 2002; Branca and Di Blasi, 2003; Vamvuka et al., 2003; Meszaros et al., 2004; Park et al., 2009) these studies however were based on thermogravimetric analysis (TGA). Therefore,
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Nomenclature A Ac Ap cp cps D E H hfg,w hp L m_ f n np Qdry R T Ts T TGA
pre-exponential factor or collision factor, s1 reactor cross section area, m2 reactive surface area of particle, m2 gas specific heat, J kg1 K1 solid specific heat, J kg1 K1 gas diffusibility, m2 s1 activation energy, J g mol1 heat of reaction, J kg1 water latent heat, J kg1 gas convective heat transfer coefficient over particle, W m2 K1 considered length, m feedstock feed rate, kg s1 kinetic order of reaction particle equivalent number, m3 drying load, W m3 Universal gas constant, 1.987 cal g mol1 K1 gas temperature, K solid temperature, K absolute temperature, K thermo-gravimeter analysis
this paper would like to address this research gap since a zonebase model on the pyrolysis zone would provide more details needed for an in-depth analysis of the downdraft gasification process. The study presented in this paper would like to present a numerical simulation of the pyrolysis zone in the reactor of a downdraft gasification process using a mathematical model that adapts thermo-chemical kinetic analysis, energy, and mass conservation. Differential equations by an approach of finite computation analysis were formed. The solution or output of the model is composed of feedstock feed rate, profile of temperature, and volatile gas generation (presented on the form of volatile gas fraction) throughout the pyrolysis zone. This study also provides the validation of model by actual gasification experiments of wood chips. 2. Development of the mathematical model on the pyrolysis zone The governing equations were developed based on basic concepts of energy and mass conservation under steady state condition. In order to form the equations aimed by this study, some assumptions were made. Heat loss at the reactor’s surface was considered to be negligible since in actual cases, the reactor is insulated with insulation cement. The pack bed in the system was considered as fixed bed, as it is an important assumption for steady state condition. The pack bed (solid fuel) inside the reactor was treated as porous media and was considered as heat transfer participant (Yang et al., 2004). The properties of materials/gas in the system were assumed to be constant at average temperature and the pack bed was assumed as a gray body. Steady supply of the solid fuel with a sufficient quantity to replace the solid that is consumed in the reactor was also assumed. The moisture in the solid fuel was also considered by including it as a drying load and it was assumed to be distributed evenly throughout the considered length. 2.1. Energy balance equations The considered control volume of the system included the drying and pyrolysis zones covering a distance L measured from the drying zone up to the location of air supply nozzle along the reactor height (z-axis). This L was taken as the height of the control volume and was divided into n elements of Dz-thickness. Heat and
average gas velocity, m s1 volatile gas at location z ultimate attainable yield of volatile gas weight of sample at time t weight of residue initial weight of sample humidity in solid fuel % rate of generated volatile gas, kg s1 m3 generated volatile gas fraction at z = L volatile gas fraction = VV volatile matter in solid fuel % gas conductive heat transfer coefficient, W m1 K1 gas density, kg m3 solid conductive heat transfer coefficient, W m1 K1 solid density, kg m3
u V V* w wf w0 wf w YL Y
vf k
q ks
qs
Subscripts s solid p particle w water f fuel, feedstock
mass balance were then applied into each element. Heat is generated at the combustion zone (the location of air supply nozzle) and as result, the highest temperature is naturally located here. The heat from this source transfers to both upstream (pyrolysis zone) and downstream (gasification zone) directions by conduction, convection, and radiation. In the pyrolysis zone (0 6 z 6 L), heat is absorbed by the pyrolysis process. The heat transfer mediums in the reactor consist of gas phase (volatile gas) and solid phase (biomass bed). The heat flowing in each Dz-thickness element in pyrolysis zone of gas consist of gas enthalpy, heat conduction, heat convection over the solid, heat accumulation or heat storage, and heat sink due to volatile matter (which is converted into volatile gas). For solid phase, the bed was considered as a fixed bed and a participant medium in radiation, thus the resulting energy balance consisted of heat conduction, heat convection over the solid, heat accumulation or heat storage, and radiation. The heat balance equations for the gas phase at the considered pyrolysis zone 0 6 z 6 L was defined by the governing equation below 2
qcp
dT dT d T þ qucp ¼ k 2 þ Hw þ Q dry hp np Ap ðT T s Þ dt dz dz
ð1Þ
where the first term qcp dT corresponds to the heat accumulation, dt qucp dT is for the gas enthalpy. The right side of the equation is comdz 2 posed of the heat conduction term k ddz2T , heat sink due to volatile matter that is converted into volatile gas Hw, drying load Qdry, and heat convection over the solid matter hpnpAp(TTs). The particle number equivalent np is estimated directly from the actual number of wood chips contained in a certain volume. The value of hp is estimated from the empirical formula of gas flow over sphere and the effect of gas velocity is also included (McAdams, 1954). The reactive surface area of particle Ap is estimated from the sphere diameter that has surface area equivalent to surface area of the wood chip. The average gas velocity was related to feedstock feed rate using the equation below
quAc ¼ mf m_ f Y L
ð2Þ
The drying load was evaluated as
Q dry ¼
wf m_ f hfg;w LAc
ð3Þ
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On the other hand, the heat balance equations for the solid phase at the considered pyrolysis zone 0 6 z 6 L was defined as 2
qs cps
dT s d T s dqr ¼ ks þ hp np Ap ðT T s Þ dt dz2 dz
ð4Þ
s where, the first term qs cps dT , takes care of the effect of heat accudt 2 r is for radiation, and mulation, ks ddzT2s is for the heat conduction, dq dz (hpnpAp(TTs) is heat convection over the solid. The radiation heat dqr flux (qþ r and qr ) and divergence of heat flux ( dz ) were taken from Jugjai et al. (1998), Echigo et al. (1987), Yoshizawa et al. (1988) as follows:
Z s qþr ðsÞ ¼ 2p I0 E3 ðsÞ þ Ib ðs0 ÞE2 ðs s0 Þds0 0 Z se Ib ðs0 ÞE2 ðs s0 Þds0 qr ðsÞ ¼ 2p Ie E3 ðse sÞ þ
ð5Þ ð6Þ
s
and,
dqr ðsÞ ¼ 2pj I0 E2 ðsÞ þ Ie E2 ðse sÞ 2Ib ðsÞ dz Z se Ib ðs0 ÞE1 ðjs0 sjÞds0 þ
TM
ð7Þ
0 4
here, Ib ðsÞ ¼ rpT s , s = optical thickness and En(s) = exponential integral function of nth order was taken from Necati Ozisik (1985)
En ðsÞ ¼
Z
1
ln2 expðs= lÞd l
did not show significant difference (within 1%). However, the calculation times of node numbers 150 and 200 were doubled than that of node number 100, thus this node number was considered as the optimum to use. The reactor diameter was taken as 8-inch. The unknowns were solid and gas temperature profiles and feedstock feed rate. Volatile gas generation is also an output of the model. The calculation flow chart is shown in the Fig. 1 and the convergence criteria were set at 106. The calculation was done by a program that was coded by Fortran Power Station 4.0 on a WindowsXP platform. The input parameters consisted of feedstock initial temperature, thermodynamic properties, heat transfer properties, physical properties and chemical kinetic properties of the feedstock. The varying input parameter for this study is the combustion temperature. The feedstock properties were referred to eucalyptus wood chip. The feedstock was sampled to evaluate its properties by proximate analysis following the standard procedure described in ASTM E871–82, ASTM E872–82 and ASTM E1534–93. The CHON compositions were evaluated by ultimate analysis. The ultimate analysis was done with ThermoFinnigan (FlashEA 1112) to find the composition percentage of C, H, O and N. The results were C = 45.12%, H = 4.62%, O = 50.26% and N = 0.0%. The results of the proximate analysis showed that %volatile mater, %fixed carbon, %humidity and %ash were 62%, 24%, 9% and 5%, respectively and bulk density was found to be 259 kg/m3. The wood chip cubical size is 2 cm. The kinetic parameters of wood were adopted from a previous work by Koufopanos et al. (1989).
ð8Þ
0
2.2. Mass balance equations
4. Results and discussions
The concept of mass conservation was applied only for the gas phase because the solid phase was treated as a fixed bed. Additionally, the gas phase in this model refers to the volatile gas. The model presents the development of volatile gas through the volatile gas fraction (Y) which is defined as the ratio between generated volatile gas and available volatile gas (volatile matter) residing in the biomass bed. Analysis was performed on each Dz-thickness element at z location along the reactor length of the pyrolysis zone. At the considered pyrolysis zone 0 6 z 6 L
4.1. Numerical analysis/simulation
q
dY dY dY 2 þ qu ¼ Dq 2 þ w dt dz dz
ð9Þ
where, w is the volatile gas generation rate which is also presented in the Arrhenius’s form as E w ¼ qAeðRT Þ ð1 YÞ
ð10Þ
2.3. Boundary conditions The boundary conditions set for the mathematical model of the pyrolysis zone of the downdraft gasification process are as follows: Ts (z = 0) = constant = feedstock temperature at entrance; Ts (z = L) = constant = combustion temperature; T (z = 0) = conðz ¼ LÞ = constant; Y stant = gas temperature at entrance; dT dz ðz ¼ LÞ = constant. (z = 0) = 0; dY dz 3. Numerical method The computation domain for the numerical analysis is covered on the zones of drying and pyrolysis. The governing equations were rearranged into different equation forms and were solved by implicit finite difference method on the node number of 100 throughout the length of 20 cm of the considered pyrolysis range. The node numbers of 150 and 200 were first tried but the results
Fig. 2 shows the result of the model calculation for the sample case with a combustion temperature of 700 °C. The temperature profile and the generated volatile gas fraction (V/V*) were plotted along the pyrolysis range. Referring to the figure, distance 0 cm represents the location of the air supply and combustion (flame), while the distance of 20 cm represents the feedstock entry location, with respect to the pyrolysis zone. It can be seen that from the viewpoint of the feedstock, the temperature start to increase significantly at distance of around 10 cm before the flame location. On the other hand, the onset of devolatilization (removal of volatile matter from the material) can be observed from the elevation of volatile gas fraction. It can be seen that volatile matter start to be liberated (point in the temperature curve where V/V* > 0) at temperature of around 320 °C and then became completely volatile gas at temperature of around 430 °C (V/V* = 1). This graph also shows that devolatilization took place on a narrow range of around 2.0 cm. The slight change in temperature is also consistent with change in the volatile gas fraction change. From Fig. 2, it can be observed that the temperature slightly dropped on the range that devolatilization have taken place. This is because heat was used up for the devolatilization process. Fig. 2 also illustrates the simulation results of the temperature profile and the volatile gas fraction (V/V*) on other combustion temperatures of 750 and 800 °C. It can be seen that the temperature along the profile started to change significantly at a distance of 10 cm before the flame location. At combustion temperature of 800 °C, the distance from the flame where temperature was observed to change significantly is closer than that for the combustion temperature of 700 °C. The onset of volatile gas generation was also observed to be at a location much nearer to the flame when the combustion temperature is higher. This is because the high temperature directly af-
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Fig. 1. The calculation flow chart.
fected the feedstock. High temperature means the feedstock is consumed at a faster rate therefore the volatile gas generation rate consequently increased. The volatile gas generated then flows towards the flame location and in effect quenched the heat transferred from the combustion zone. This identifies the location of the start of the significant temperature change as shown in the figure. It can also be observed that the points of complete devolatilization are at 4.8, 4.0 and 3.5 cm from the air supply location for combustion temperatures of 700, 750, and 800 °C, respectively. This point was also observed to move closer to the combustion zone location when combustion temperature is increased. The effect of higher combustion temperature on the volatile gas generation rate was found to be very obvious at temperature of 800 °C. Both the starting point of devolatilization and the point of complete devolatilization for this combustion temperature are very much closer to the combustion zone than those simulated from the other temperatures. The feedstock feed rate results are also show that the feed rate increased as combustion temperature increased. This is because at higher combustion temperature the volatile matter generation rate is much higher than at lower
temperature and that all the volatile matter is liberated at a faster rate resulting into a faster consumption of the feedstock. 4.2. Validation of the mathematical model In order to verify the mathematical model and numerical method used in this study, validation of the numerical simulation results were performed. The devolatilization rate was compared and found to fit quite well with the experimental data taken from a previous study made on kinetic modeling of pyrolysis of wood samples by TGA technique (Koufopanos et al., 1989). The numerical simulation results of the temperature profile and the feedstock consumption rate were also validated and compared with data obtained from actual experiments. The model was validated using data from the experiments conducted at the Energy Park Asian Institute of Technology, Thailand (AIT). The main apparatus is shown in Fig. 3 which also shows the apparatus with the necessary instrumentation set-up for data recording. The experimental set-up consisted of cylinder reactor, suction-type blower, cyclone, air tube and its associated accessory with rotameter, ex-
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1000
5 Temp. at Comb.Temp. 800 Temp. at Comb.Temp. 750 Temp. at Comb.Temp. 700 Volatile frac. at Comb.Temp. 800 Volatile frac. at Comb.Temp. 750 Volatile frac. at Comb.Temp. 700
600
3
400
2
200
1
Volatile gas fraction (V/V*)
4
o
Temperature ( C)
800
0
0 20
15
10
5
0
Distance from air supply location (cm.) Fig. 2. Temperature and gas generation rate from numerical simulations of three different combustion temperatures.
Fig. 3. Schematic diagram of the experimental set-up showing the instrumentations for data collection.
haust pipe set and stack. The reactor had an outside diameter of 10 inches. The inner wall was lined with a 1-inch thick insulation (refractory cement), giving an inner diameter of 8 inches. The height was 160 cm from the bottom to the top of hopper. Four air supply ports were situated 40 cm from the bottom and were connected to the air supply hub with same length of connection tube in order to balance air quantity supply from each port. The producer gas pipe was connected at 10 cm from the bottom and routed to the suction inlet of a suction-type blower with a cyclone installed to trap dust particles. The exhaust stack pipe was con-
nected to the blower outlet. The reactor had 10 service ports installed at every 10 cm along the reactor height with a special service port located at 5 cm above the air supply. The above configuration shows that the air supply system is a draw-through type. The feedstock is filled at the top of hopper. When the gasifier is filled up to its full capacity, the reactor can operate 3–4 h continuously. Type K thermocouples were used to measure the temperature along the reactor height. The temperature data from each location were logged into the HIOKI data-logger (model HiLogger-8420) as
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shown in Fig. 3. The measurement accuracy of temperature type K thermocouples of HiLogger-8420 is +/2.0 °C. Experiments were done on different air supplies of 70, 100 and 130 lpm using eucalyptus wood chip as feedstock with an average cubical size of 2 cm Fig. 4 shows the temperature along the height of the reactor at 70 lpm air supply. The measured temperature indicated that the combustion temperature is 660 °C at z = 20 cm. The figure shows that the temperature significantly changed after a distance of 10 cm (z = 30 cm) above the air supply location/flame location. This point also indicated the location where the onset of devolatilization took place. Using this initial result, it is indeed reasonable to assign the area 20 cm above the air supply location as the pyrolysis zone for the numerical simulation. Fig. 4 also shows the temperature profile of the drying and the pyrolysis zones (40 cm above air supply port) obtained from experiments as compared to that obtained from the model. Air supplies of 70, 100 and 130 lpm were used generating combustion temperatures of 705 °C, 756 °C and 819 °C, respectively. The results show that the temperature data from the model fitted well with experimental data but that it tends to deviate more if the combustion temperature is higher. The values from simulation results were a little bit lower than the values taken from experiments because the experiments had the layer of pack bed in the gasification zone that blocked the radiation from the combustion zone. This layer was not included in the model calculation because the control volume is up to the pyrolysis zone. So, the heat loss in the simulation is then more than in the experiment. The deviations are tabulated in Table 1. Another reason for this is that high combustion temperature from high air supply rate results to high feedstock consumption rate and high volatile gas flow rate (that is directed downward). This downward flow of the volatile gas quenched the heat transferred from the upstream direction. In effect, the combustion (flame) location moved closer
Table 1 Deviation between the results of the numerical simulation and the actual experiments. Combustion temperature, °C
Maximum deviation Temperature, °C
Feed rate, kg/h
819 756 705
24 23 20
0.37 0.34 0.14
to the air supply. In reality, the flame location is dynamic while in the model it is fixed, and because of this the deviation between the model and numerical simulation results tends to be higher when the combustion temperature is very high. However, it should be noted that downdraft gasifier applications mostly work on temperature range of 500–800 °C, thus this model is deemed satisfactory as it can predict well at this temperature range. The ability of the model to predict the feedstock feed rate was also looked into. Fig. 4 also shows the how the model measuredup with the results from actual experiments at different combustion temperatures. The results show good agreement between feedstock feed rate from the model and from the experimental data. Feedstock feed rate varied as the air supply is changed because the combustion temperature also changed. However, it can be observed that data from experiments are slightly higher than the data from the model. This is because the gasification reaction below the pyrolysis zone was not included in the model. The solid consumption rate in the gasification and combustion zone are higher, thus the solid above these zones moved downward at a faster rate causing a little faster consumption rate. Table 1 shows the deviation between measured and model-simulated temperature profile and feedstock feed rate. It is seen that Air supply Location
1000
1000
o
Temperature ( C)
800
Temperature (°C) 0
200
400
600
800
T8
400
400
200
200
T7
40
T6
30
T5
0 40
30 20 10 Distance from air supply location (cm.)
0
10
T4 Pyrolysis
20
T3
Gasification
Air 70 LPM
Air Supply
10
T2
0
T1 0
200
400
600
Temperature (°C)
10 Experiment Model
Feedstock Feed Rate (kg/h)
Distance (cm)
0 50
600
8
8
6
6
4
4
2
2
Feedstock Feed Rate (kg/h)
60
600
800 Temperature (°C)
Feedstock
Model at temperature 819 °C Model at temperature 756 °C Model at temperature 705 °C Experiment at temperature 819 °C Experiment at temperature 756 °C Experiment at temperature 705 °C
800
Producer Gas
0 640
660
680
700
720
740
760
780
800
820
0 840
Combustion Temperture (°C)
Fig. 4. Actual experiment results and numerical simulations for air supply = 70 lpm, combustion temperature = 660 °C; and temperature profile and feedstock feed rates at combustion temperatures of 660, 705, 756, and 812 °C.
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the numerical results agree very well with the measured values. The maximum deviation for temperature measurement was only at 24 °C, while the model was able to predict the feedstock feed rate well with maximum deviation of just 0.37 kg/h. Therefore, it is reasonable to use the mathematical model developed in Section 2 to predict the temperature profiles and feedstock feed rates in downdraft gasification process. The experimental errors are mainly related to the experimental system capability and the accuracy of instruments. For this work the system repeatability is within 10% and the temperature accuracy is +/2 °C while the feedstock feed rate accuracy is within 5%. 5. Conclusions The developed model, which used the chemical kinetic and heat transfer approach, can predict the temperature profile, feedstock feed rate, and generated volatile gas fraction well, especially when the combustion is <800 °C. Maximum deviation for temperature was only at 24 °C, for the feedstock feed rate it was just 0.37 kg/ h. More importantly, the authors see that the model is a relevant contribution as it focused specifically on the pyrolysis zone. Unlike most gasification models that are based on lump analysis, the model here is able to provide more detailed thermal and kinetic analysis of the pyrolysis zone. The outputs can then be very useful for detailed analysis of downstream zones. Additionally, in lump analysis such as the minimization of Gibbs free energy method (a method used to predict the gas composition percentage of producer gas), feedstock feed rate, which is an output of the model here is required as a parameter in the input. Therefore, this study can serve and fulfill a Gibbs free energy method weakness. Finally, if similar approach will be applied to other zones, then future studies on integration of all individual zone models is possible and can provide more realistic and sturdy overall downdraft gasification model. References Altafini, C.R., Wander, P.R., Barreto, R.M., 2003. Prediction of the working parameters of a wood waste gasifier through an equilibrium model. Energy Conversion and Management 44, 2763–2777. Bhattacharya, S.C., Hla, S.S., Pham, H.-L., 2001. A study on a multi-stage hybrid gasifier-engine system. Biomass and Bioenergy 21, 445–460. Branca, C., Di blasi, C., 2003. Kinetics of the isothermal degradation of wood in the temperature range 528–708 K. Journal of Analytical and Applied Pyrolysis 67, 207–219.
Dogru, M., Howarth, C.R., Akay, G., Keskinler, B., Malik, A.A., 2002. Gasification of hazelnut shells in a downdraft gasifier. Energy 27, 415–427. Echigo, R., Hanamura, K., Yoshizawa, Y., Tomimura, T., 1987. Radiative heat transfer enhancement to a water tube by combustion gases in porous media. In: Wang, B.X. (Ed.), Heat Transfer Science and Technology. Hemisphere Publishing Company, pp. 1–99. Gronli, M.G., Varhegyi, G., Di Blasi, C., 2002. Thermogravimetric analysis and devolatilization kinetics of wood. Industrial and Engineering Chemical Research 41, 4201–4208. Henriksen, U., Ahrenfeldt, J., Jensen, T.K., Gøbel, B., Bentzen, J.D., Hindsgaul, C., Sørensen, L.H., 2006. The design, construction and operation of a 75 kW twostage gasifier. Energy 31, 1542–1553. Jugjai, S., Sanitjai, S., Aimmanee, S., Boonprasert, T., 1998. Heat transfer enhancement to cooling water pipe by a surface combustor heater equipped with a convection-radiation converter. RERIC International Energy Journal 20, 91–105. Koufopanos, C.A., Mashio, G., Lucchesi, A., 1989. Kinetic modeling of the pyrolysis of biomass and biomass components. Canadian Journal of Chemical Engineering 67 (1), 75–84. McAdams, W.H., 1954. Heat Transmission, third ed. McGraw-Hill, New York. Meszaros, E., Varhegyi, G., Jakab, E., Marosvolgyi, B., 2004. Thermogravimetric and reaction kinetic analysis of biomass samples from an energy plantation. Energy Fuels 18 (2), 497–507. Necati Ozisik, M., 1985. Heat Transfer: A Basic Approach, international ed. McGrawHill, New York. pp. 307–375. Orfao, J.J.M., Antunes, F.J.A., Figueiredo, J.L., 1999. Pyrolysis kinetics of lignocellulosic materials – three independent reaction models. Fuel 78, 349– 358. Park, Y.H., Kim, S., Kim, J., Park, Y.K., 2009. Pyrolysis characteristics and kinetics of oak trees using thermogravimetric analyzer and micro-tubing reactor. Bioresource Technology 100, 400–405. Radmanesh, R., Courbariaux, Y., Chaouki, J., Guy, C.A., 2006. A unified lumped approach in kinetic modeling of biomass pyrolysis. Fuel 85, 1211–1220. Sadhukan, A.K., Gupta, P., Goyal, T., Saha, R.K., 2008a. Modelling of pyrolysis of coal– biomass blends using thermogravimetric analysis. Bioresource Technology 99, 8022–8026. Sadhukan, A.K., Gupta, P., Goyal, T., Saha, R.K., 2008b. Modelling and experimental studies on pyrolysis of biomass particles. Journal of Analytical and Applied Pyrolysis 81, 183–192. Sheth, N.P., Babu, B.V., 2009. Experimental studies on producer gas generation from wood waste in a downdraft biomass gasifier. Bioresource Technology 100, 3127–3133. Vamvuka, D., Kakaras, E., Kastanaki, E., Grammelis, P., 2003. Pyrolysis characteristics and kinetics of biomass residuals mixtures with lignite. Fuel 82, 1949–1960. Wander, P.R., Altafini, C.R., Barreto, R.M., 2004. Assessment of a small sawdust gasification unit. Biomass and Bioenergy 27, 467–476. Yang, Y.B., Sharifi, V.N., Swithenbank, J., 2004. Effect of air flow rate and fuel moisture on the burning behaviours of biomass and simulated municipal solid wastes in packed beds. Fuel 83, 1553–1562. Yoshizawa, Y., Sasaki, K., Echigo, R., 1988. Analytical study of the structure of radiation controlled flame. International Journal of Heat and Mass Transfer 31 (2), 311–319. Zainal, Z.A., Ali, R., Lean, C.H., Seetharamu, K.N., 2001. Prediction of performance of a downdraft gasifier using equilibrium modeling for different biomass materials. Energy Conversion and Management 42, 1499–1515.
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Numerical simulation of the pyrolysis zone in a downdraft gasification process K. Jaojaruek *, S. Kumar Energy Field of Study, School of Environment, Resources and Development (SERD), Asian Institute of Technology, P.O. Box 4, Klongluang, Pathumthani 12120, Thailand
a r t i c l e
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Article history: Received 1 April 2009 Received in revised form 16 June 2009 Accepted 16 June 2009 Available online 23 July 2009 Keywords: Downdraft gasification Finite modeling of pyrolysis process Temperature profile in the pyrolysis zone Heat transfer in porous medium Feedstock consumption in downdraft gasifier
a b s t r a c t Models of the gasification process are mostly based on lumped analysis with distinct zones of the process treated as one entity. The study presented here was conducted to develop a more useful model specifically for the pyrolysis zone of the reactor of a downdraft gasifier based on finite computation method. Applying principles of energy and mass conservation, governing equations formed were solved by implicit finite difference method on the node of 100 throughout the length of the considered pyrolysis range (20 cm). Heat transfer considered convection, conduction, and the influence of solid radiation components. Chemical kinetics concept was also adopted to simultaneously solve the temperature profile and feedstock consumption rate on the pyrolysis zone. The convergence criteria were set at 106 and simulation used Fortran Power Station 4.0. Validation experiments were also conducted resulting in maximum deviation of 24 °C and 0.37 kg/h for temperature and feedstock feed rate, respectively. Ó 2009 Elsevier Ltd. All rights reserved.
1. Introduction Downdraft gasification is a process to convert biomass or solids carbonaceous fuel into useful combustible gas. This process is endothermic with four distinct zones that are arranged from top to bottom. These zones are: drying, pyrolysis also known as devolatilization, combustion or oxidation, and gasification or reduction. The pyrolysis gas formed when the volatile matter in the solid fuel is devolatilized in the combustion/oxidation zone undergoes partial oxidation. The combustion zone acts as heat source and the heat comes from partial combustion of mainly char. The combustion is controlled by the quantity of air supply. The pyrolysis gas in the pyrolysis zone is mainly composed of CO, CO2, H2, CH4 and higher hydrocarbons CmHn (m > 1). This gas then flows downward to the reduction/gasification zone to further react with the char leaving the combustion zone and becomes the producer gas that has mainly the same composition as pyrolysis gas but with improved percentage of CO and H2. It is at the gasification zone that the producer gas leaves the gasifier at high temperature laden with dust, tar, and water vapor. Cooling and cleaning of the gas is necessary depending on the intended application of the producer gas. There are several studies available in the literatures that discuss the downdraft gasification process. Examples of these studies ranged from the evaluation of the use of different types of feedstocks, gasifying agents like oxygen and/or steam, and performance evaluation of multi-stage gasifiers (Zainal et al., 2001; Wander et al., * Corresponding author. Tel.: +66 2 524 6074/6069; fax: +66 2 524 6071/5439. E-mail addresses: [email protected], [email protected] (K. Jaojaruek), kumar@ ait.ac.th (S. Kumar). 0960-8524/$ - see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.biortech.2009.06.052
2004; Dogru et al., 2002; Bhattacharya et al., 2001; Henriksen et al., 2006). Equilibrium modeling of a downdraft gasifier intended for different biomass materials was explored by Zainal et al. (2001) while Altafini et al. (2003) applied the concept of minimization of the Gibbs free energy on their numerical simulation of wood waste gasification. As for the pyrolysis process, studies on mathematical models that considered mass and heat transfer are available (Koufopanos et al., 1989), however most are for the pyrolysis process itself or pyrolysis reactors and not on the pyrolysis zone in a gasification process. A model for the co-pyrolysis behaviour of lignite coal–biomass blends was proposed by Sadhukan et al. (2008a) in which they proposed a parallel-series kinetic model for biomass and a distributed activation energy model for coal. In another study by Sadhukan et al. (2008b), the pyrolysis process was also modeled using transient analysis involving a kinetic model and a heat transfer model but this study only considered a single wood cylinder particle, which may not be able to fully represent the inter-reactions taking place in a reactor filled with numerous wood particles. It can be emphasized that most studies on gasification modeling have analyzed and developed models based on the whole gasification process itself, which can be called as lump analysis (Zainal et al., 2001; Radmanesh et al., 2006) that are not zone-specific analyses. Sheth and Babu (2009) performed experimental studies on wood waste and their work also focused on a general downdraft gasification process rather than a specific zone. Although, there are many studies done on the pyrolysis process (Orfao et al., 1999; Gronli et al., 2002; Branca and Di Blasi, 2003; Vamvuka et al., 2003; Meszaros et al., 2004; Park et al., 2009) these studies however were based on thermogravimetric analysis (TGA). Therefore,
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Nomenclature A Ac Ap cp cps D E H hfg,w hp L m_ f n np Qdry R T Ts T TGA
pre-exponential factor or collision factor, s1 reactor cross section area, m2 reactive surface area of particle, m2 gas specific heat, J kg1 K1 solid specific heat, J kg1 K1 gas diffusibility, m2 s1 activation energy, J g mol1 heat of reaction, J kg1 water latent heat, J kg1 gas convective heat transfer coefficient over particle, W m2 K1 considered length, m feedstock feed rate, kg s1 kinetic order of reaction particle equivalent number, m3 drying load, W m3 Universal gas constant, 1.987 cal g mol1 K1 gas temperature, K solid temperature, K absolute temperature, K thermo-gravimeter analysis
this paper would like to address this research gap since a zonebase model on the pyrolysis zone would provide more details needed for an in-depth analysis of the downdraft gasification process. The study presented in this paper would like to present a numerical simulation of the pyrolysis zone in the reactor of a downdraft gasification process using a mathematical model that adapts thermo-chemical kinetic analysis, energy, and mass conservation. Differential equations by an approach of finite computation analysis were formed. The solution or output of the model is composed of feedstock feed rate, profile of temperature, and volatile gas generation (presented on the form of volatile gas fraction) throughout the pyrolysis zone. This study also provides the validation of model by actual gasification experiments of wood chips. 2. Development of the mathematical model on the pyrolysis zone The governing equations were developed based on basic concepts of energy and mass conservation under steady state condition. In order to form the equations aimed by this study, some assumptions were made. Heat loss at the reactor’s surface was considered to be negligible since in actual cases, the reactor is insulated with insulation cement. The pack bed in the system was considered as fixed bed, as it is an important assumption for steady state condition. The pack bed (solid fuel) inside the reactor was treated as porous media and was considered as heat transfer participant (Yang et al., 2004). The properties of materials/gas in the system were assumed to be constant at average temperature and the pack bed was assumed as a gray body. Steady supply of the solid fuel with a sufficient quantity to replace the solid that is consumed in the reactor was also assumed. The moisture in the solid fuel was also considered by including it as a drying load and it was assumed to be distributed evenly throughout the considered length. 2.1. Energy balance equations The considered control volume of the system included the drying and pyrolysis zones covering a distance L measured from the drying zone up to the location of air supply nozzle along the reactor height (z-axis). This L was taken as the height of the control volume and was divided into n elements of Dz-thickness. Heat and
average gas velocity, m s1 volatile gas at location z ultimate attainable yield of volatile gas weight of sample at time t weight of residue initial weight of sample humidity in solid fuel % rate of generated volatile gas, kg s1 m3 generated volatile gas fraction at z = L volatile gas fraction = VV volatile matter in solid fuel % gas conductive heat transfer coefficient, W m1 K1 gas density, kg m3 solid conductive heat transfer coefficient, W m1 K1 solid density, kg m3
u V V* w wf w0 wf w YL Y
vf k
q ks
qs
Subscripts s solid p particle w water f fuel, feedstock
mass balance were then applied into each element. Heat is generated at the combustion zone (the location of air supply nozzle) and as result, the highest temperature is naturally located here. The heat from this source transfers to both upstream (pyrolysis zone) and downstream (gasification zone) directions by conduction, convection, and radiation. In the pyrolysis zone (0 6 z 6 L), heat is absorbed by the pyrolysis process. The heat transfer mediums in the reactor consist of gas phase (volatile gas) and solid phase (biomass bed). The heat flowing in each Dz-thickness element in pyrolysis zone of gas consist of gas enthalpy, heat conduction, heat convection over the solid, heat accumulation or heat storage, and heat sink due to volatile matter (which is converted into volatile gas). For solid phase, the bed was considered as a fixed bed and a participant medium in radiation, thus the resulting energy balance consisted of heat conduction, heat convection over the solid, heat accumulation or heat storage, and radiation. The heat balance equations for the gas phase at the considered pyrolysis zone 0 6 z 6 L was defined by the governing equation below 2
qcp
dT dT d T þ qucp ¼ k 2 þ Hw þ Q dry hp np Ap ðT T s Þ dt dz dz
ð1Þ
where the first term qcp dT corresponds to the heat accumulation, dt qucp dT is for the gas enthalpy. The right side of the equation is comdz 2 posed of the heat conduction term k ddz2T , heat sink due to volatile matter that is converted into volatile gas Hw, drying load Qdry, and heat convection over the solid matter hpnpAp(TTs). The particle number equivalent np is estimated directly from the actual number of wood chips contained in a certain volume. The value of hp is estimated from the empirical formula of gas flow over sphere and the effect of gas velocity is also included (McAdams, 1954). The reactive surface area of particle Ap is estimated from the sphere diameter that has surface area equivalent to surface area of the wood chip. The average gas velocity was related to feedstock feed rate using the equation below
quAc ¼ mf m_ f Y L
ð2Þ
The drying load was evaluated as
Q dry ¼
wf m_ f hfg;w LAc
ð3Þ
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On the other hand, the heat balance equations for the solid phase at the considered pyrolysis zone 0 6 z 6 L was defined as 2
qs cps
dT s d T s dqr ¼ ks þ hp np Ap ðT T s Þ dt dz2 dz
ð4Þ
s where, the first term qs cps dT , takes care of the effect of heat accudt 2 r is for radiation, and mulation, ks ddzT2s is for the heat conduction, dq dz (hpnpAp(TTs) is heat convection over the solid. The radiation heat dqr flux (qþ r and qr ) and divergence of heat flux ( dz ) were taken from Jugjai et al. (1998), Echigo et al. (1987), Yoshizawa et al. (1988) as follows:
Z s qþr ðsÞ ¼ 2p I0 E3 ðsÞ þ Ib ðs0 ÞE2 ðs s0 Þds0 0 Z se Ib ðs0 ÞE2 ðs s0 Þds0 qr ðsÞ ¼ 2p Ie E3 ðse sÞ þ
ð5Þ ð6Þ
s
and,
dqr ðsÞ ¼ 2pj I0 E2 ðsÞ þ Ie E2 ðse sÞ 2Ib ðsÞ dz Z se Ib ðs0 ÞE1 ðjs0 sjÞds0 þ
TM
ð7Þ
0 4
here, Ib ðsÞ ¼ rpT s , s = optical thickness and En(s) = exponential integral function of nth order was taken from Necati Ozisik (1985)
En ðsÞ ¼
Z
1
ln2 expðs= lÞd l
did not show significant difference (within 1%). However, the calculation times of node numbers 150 and 200 were doubled than that of node number 100, thus this node number was considered as the optimum to use. The reactor diameter was taken as 8-inch. The unknowns were solid and gas temperature profiles and feedstock feed rate. Volatile gas generation is also an output of the model. The calculation flow chart is shown in the Fig. 1 and the convergence criteria were set at 106. The calculation was done by a program that was coded by Fortran Power Station 4.0 on a WindowsXP platform. The input parameters consisted of feedstock initial temperature, thermodynamic properties, heat transfer properties, physical properties and chemical kinetic properties of the feedstock. The varying input parameter for this study is the combustion temperature. The feedstock properties were referred to eucalyptus wood chip. The feedstock was sampled to evaluate its properties by proximate analysis following the standard procedure described in ASTM E871–82, ASTM E872–82 and ASTM E1534–93. The CHON compositions were evaluated by ultimate analysis. The ultimate analysis was done with ThermoFinnigan (FlashEA 1112) to find the composition percentage of C, H, O and N. The results were C = 45.12%, H = 4.62%, O = 50.26% and N = 0.0%. The results of the proximate analysis showed that %volatile mater, %fixed carbon, %humidity and %ash were 62%, 24%, 9% and 5%, respectively and bulk density was found to be 259 kg/m3. The wood chip cubical size is 2 cm. The kinetic parameters of wood were adopted from a previous work by Koufopanos et al. (1989).
ð8Þ
0
2.2. Mass balance equations
4. Results and discussions
The concept of mass conservation was applied only for the gas phase because the solid phase was treated as a fixed bed. Additionally, the gas phase in this model refers to the volatile gas. The model presents the development of volatile gas through the volatile gas fraction (Y) which is defined as the ratio between generated volatile gas and available volatile gas (volatile matter) residing in the biomass bed. Analysis was performed on each Dz-thickness element at z location along the reactor length of the pyrolysis zone. At the considered pyrolysis zone 0 6 z 6 L
4.1. Numerical analysis/simulation
q
dY dY dY 2 þ qu ¼ Dq 2 þ w dt dz dz
ð9Þ
where, w is the volatile gas generation rate which is also presented in the Arrhenius’s form as E w ¼ qAeðRT Þ ð1 YÞ
ð10Þ
2.3. Boundary conditions The boundary conditions set for the mathematical model of the pyrolysis zone of the downdraft gasification process are as follows: Ts (z = 0) = constant = feedstock temperature at entrance; Ts (z = L) = constant = combustion temperature; T (z = 0) = conðz ¼ LÞ = constant; Y stant = gas temperature at entrance; dT dz ðz ¼ LÞ = constant. (z = 0) = 0; dY dz 3. Numerical method The computation domain for the numerical analysis is covered on the zones of drying and pyrolysis. The governing equations were rearranged into different equation forms and were solved by implicit finite difference method on the node number of 100 throughout the length of 20 cm of the considered pyrolysis range. The node numbers of 150 and 200 were first tried but the results
Fig. 2 shows the result of the model calculation for the sample case with a combustion temperature of 700 °C. The temperature profile and the generated volatile gas fraction (V/V*) were plotted along the pyrolysis range. Referring to the figure, distance 0 cm represents the location of the air supply and combustion (flame), while the distance of 20 cm represents the feedstock entry location, with respect to the pyrolysis zone. It can be seen that from the viewpoint of the feedstock, the temperature start to increase significantly at distance of around 10 cm before the flame location. On the other hand, the onset of devolatilization (removal of volatile matter from the material) can be observed from the elevation of volatile gas fraction. It can be seen that volatile matter start to be liberated (point in the temperature curve where V/V* > 0) at temperature of around 320 °C and then became completely volatile gas at temperature of around 430 °C (V/V* = 1). This graph also shows that devolatilization took place on a narrow range of around 2.0 cm. The slight change in temperature is also consistent with change in the volatile gas fraction change. From Fig. 2, it can be observed that the temperature slightly dropped on the range that devolatilization have taken place. This is because heat was used up for the devolatilization process. Fig. 2 also illustrates the simulation results of the temperature profile and the volatile gas fraction (V/V*) on other combustion temperatures of 750 and 800 °C. It can be seen that the temperature along the profile started to change significantly at a distance of 10 cm before the flame location. At combustion temperature of 800 °C, the distance from the flame where temperature was observed to change significantly is closer than that for the combustion temperature of 700 °C. The onset of volatile gas generation was also observed to be at a location much nearer to the flame when the combustion temperature is higher. This is because the high temperature directly af-
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Fig. 1. The calculation flow chart.
fected the feedstock. High temperature means the feedstock is consumed at a faster rate therefore the volatile gas generation rate consequently increased. The volatile gas generated then flows towards the flame location and in effect quenched the heat transferred from the combustion zone. This identifies the location of the start of the significant temperature change as shown in the figure. It can also be observed that the points of complete devolatilization are at 4.8, 4.0 and 3.5 cm from the air supply location for combustion temperatures of 700, 750, and 800 °C, respectively. This point was also observed to move closer to the combustion zone location when combustion temperature is increased. The effect of higher combustion temperature on the volatile gas generation rate was found to be very obvious at temperature of 800 °C. Both the starting point of devolatilization and the point of complete devolatilization for this combustion temperature are very much closer to the combustion zone than those simulated from the other temperatures. The feedstock feed rate results are also show that the feed rate increased as combustion temperature increased. This is because at higher combustion temperature the volatile matter generation rate is much higher than at lower
temperature and that all the volatile matter is liberated at a faster rate resulting into a faster consumption of the feedstock. 4.2. Validation of the mathematical model In order to verify the mathematical model and numerical method used in this study, validation of the numerical simulation results were performed. The devolatilization rate was compared and found to fit quite well with the experimental data taken from a previous study made on kinetic modeling of pyrolysis of wood samples by TGA technique (Koufopanos et al., 1989). The numerical simulation results of the temperature profile and the feedstock consumption rate were also validated and compared with data obtained from actual experiments. The model was validated using data from the experiments conducted at the Energy Park Asian Institute of Technology, Thailand (AIT). The main apparatus is shown in Fig. 3 which also shows the apparatus with the necessary instrumentation set-up for data recording. The experimental set-up consisted of cylinder reactor, suction-type blower, cyclone, air tube and its associated accessory with rotameter, ex-
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1000
5 Temp. at Comb.Temp. 800 Temp. at Comb.Temp. 750 Temp. at Comb.Temp. 700 Volatile frac. at Comb.Temp. 800 Volatile frac. at Comb.Temp. 750 Volatile frac. at Comb.Temp. 700
600
3
400
2
200
1
Volatile gas fraction (V/V*)
4
o
Temperature ( C)
800
0
0 20
15
10
5
0
Distance from air supply location (cm.) Fig. 2. Temperature and gas generation rate from numerical simulations of three different combustion temperatures.
Fig. 3. Schematic diagram of the experimental set-up showing the instrumentations for data collection.
haust pipe set and stack. The reactor had an outside diameter of 10 inches. The inner wall was lined with a 1-inch thick insulation (refractory cement), giving an inner diameter of 8 inches. The height was 160 cm from the bottom to the top of hopper. Four air supply ports were situated 40 cm from the bottom and were connected to the air supply hub with same length of connection tube in order to balance air quantity supply from each port. The producer gas pipe was connected at 10 cm from the bottom and routed to the suction inlet of a suction-type blower with a cyclone installed to trap dust particles. The exhaust stack pipe was con-
nected to the blower outlet. The reactor had 10 service ports installed at every 10 cm along the reactor height with a special service port located at 5 cm above the air supply. The above configuration shows that the air supply system is a draw-through type. The feedstock is filled at the top of hopper. When the gasifier is filled up to its full capacity, the reactor can operate 3–4 h continuously. Type K thermocouples were used to measure the temperature along the reactor height. The temperature data from each location were logged into the HIOKI data-logger (model HiLogger-8420) as
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shown in Fig. 3. The measurement accuracy of temperature type K thermocouples of HiLogger-8420 is +/2.0 °C. Experiments were done on different air supplies of 70, 100 and 130 lpm using eucalyptus wood chip as feedstock with an average cubical size of 2 cm Fig. 4 shows the temperature along the height of the reactor at 70 lpm air supply. The measured temperature indicated that the combustion temperature is 660 °C at z = 20 cm. The figure shows that the temperature significantly changed after a distance of 10 cm (z = 30 cm) above the air supply location/flame location. This point also indicated the location where the onset of devolatilization took place. Using this initial result, it is indeed reasonable to assign the area 20 cm above the air supply location as the pyrolysis zone for the numerical simulation. Fig. 4 also shows the temperature profile of the drying and the pyrolysis zones (40 cm above air supply port) obtained from experiments as compared to that obtained from the model. Air supplies of 70, 100 and 130 lpm were used generating combustion temperatures of 705 °C, 756 °C and 819 °C, respectively. The results show that the temperature data from the model fitted well with experimental data but that it tends to deviate more if the combustion temperature is higher. The values from simulation results were a little bit lower than the values taken from experiments because the experiments had the layer of pack bed in the gasification zone that blocked the radiation from the combustion zone. This layer was not included in the model calculation because the control volume is up to the pyrolysis zone. So, the heat loss in the simulation is then more than in the experiment. The deviations are tabulated in Table 1. Another reason for this is that high combustion temperature from high air supply rate results to high feedstock consumption rate and high volatile gas flow rate (that is directed downward). This downward flow of the volatile gas quenched the heat transferred from the upstream direction. In effect, the combustion (flame) location moved closer
Table 1 Deviation between the results of the numerical simulation and the actual experiments. Combustion temperature, °C
Maximum deviation Temperature, °C
Feed rate, kg/h
819 756 705
24 23 20
0.37 0.34 0.14
to the air supply. In reality, the flame location is dynamic while in the model it is fixed, and because of this the deviation between the model and numerical simulation results tends to be higher when the combustion temperature is very high. However, it should be noted that downdraft gasifier applications mostly work on temperature range of 500–800 °C, thus this model is deemed satisfactory as it can predict well at this temperature range. The ability of the model to predict the feedstock feed rate was also looked into. Fig. 4 also shows the how the model measuredup with the results from actual experiments at different combustion temperatures. The results show good agreement between feedstock feed rate from the model and from the experimental data. Feedstock feed rate varied as the air supply is changed because the combustion temperature also changed. However, it can be observed that data from experiments are slightly higher than the data from the model. This is because the gasification reaction below the pyrolysis zone was not included in the model. The solid consumption rate in the gasification and combustion zone are higher, thus the solid above these zones moved downward at a faster rate causing a little faster consumption rate. Table 1 shows the deviation between measured and model-simulated temperature profile and feedstock feed rate. It is seen that Air supply Location
1000
1000
o
Temperature ( C)
800
Temperature (°C) 0
200
400
600
800
T8
400
400
200
200
T7
40
T6
30
T5
0 40
30 20 10 Distance from air supply location (cm.)
0
10
T4 Pyrolysis
20
T3
Gasification
Air 70 LPM
Air Supply
10
T2
0
T1 0
200
400
600
Temperature (°C)
10 Experiment Model
Feedstock Feed Rate (kg/h)
Distance (cm)
0 50
600
8
8
6
6
4
4
2
2
Feedstock Feed Rate (kg/h)
60
600
800 Temperature (°C)
Feedstock
Model at temperature 819 °C Model at temperature 756 °C Model at temperature 705 °C Experiment at temperature 819 °C Experiment at temperature 756 °C Experiment at temperature 705 °C
800
Producer Gas
0 640
660
680
700
720
740
760
780
800
820
0 840
Combustion Temperture (°C)
Fig. 4. Actual experiment results and numerical simulations for air supply = 70 lpm, combustion temperature = 660 °C; and temperature profile and feedstock feed rates at combustion temperatures of 660, 705, 756, and 812 °C.
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the numerical results agree very well with the measured values. The maximum deviation for temperature measurement was only at 24 °C, while the model was able to predict the feedstock feed rate well with maximum deviation of just 0.37 kg/h. Therefore, it is reasonable to use the mathematical model developed in Section 2 to predict the temperature profiles and feedstock feed rates in downdraft gasification process. The experimental errors are mainly related to the experimental system capability and the accuracy of instruments. For this work the system repeatability is within 10% and the temperature accuracy is +/2 °C while the feedstock feed rate accuracy is within 5%. 5. Conclusions The developed model, which used the chemical kinetic and heat transfer approach, can predict the temperature profile, feedstock feed rate, and generated volatile gas fraction well, especially when the combustion is <800 °C. Maximum deviation for temperature was only at 24 °C, for the feedstock feed rate it was just 0.37 kg/ h. More importantly, the authors see that the model is a relevant contribution as it focused specifically on the pyrolysis zone. Unlike most gasification models that are based on lump analysis, the model here is able to provide more detailed thermal and kinetic analysis of the pyrolysis zone. The outputs can then be very useful for detailed analysis of downstream zones. Additionally, in lump analysis such as the minimization of Gibbs free energy method (a method used to predict the gas composition percentage of producer gas), feedstock feed rate, which is an output of the model here is required as a parameter in the input. Therefore, this study can serve and fulfill a Gibbs free energy method weakness. Finally, if similar approach will be applied to other zones, then future studies on integration of all individual zone models is possible and can provide more realistic and sturdy overall downdraft gasification model. References Altafini, C.R., Wander, P.R., Barreto, R.M., 2003. Prediction of the working parameters of a wood waste gasifier through an equilibrium model. Energy Conversion and Management 44, 2763–2777. Bhattacharya, S.C., Hla, S.S., Pham, H.-L., 2001. A study on a multi-stage hybrid gasifier-engine system. Biomass and Bioenergy 21, 445–460. Branca, C., Di blasi, C., 2003. Kinetics of the isothermal degradation of wood in the temperature range 528–708 K. Journal of Analytical and Applied Pyrolysis 67, 207–219.
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