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Kinetics of Perovskite Catalyzed Biomass Tar Combustion Studied by Thermogravimetry and Differential Thermal Analysis Chunshan Li* and Kenzi Suzuki EcoTopia Science Institute, Nagoya UniVersity, Nagoya, 464-8603, Japan ReceiVed August 1, 2008. ReVised Manuscript ReceiVed February 21, 2009
Biomass tar combustion behavior was investigated with perovskite (CaTiO3, Ca2Fe2O5) as catalysts using heating rates of 5, 10, 15, 20, and 30 °C min-1. The addition of 10 wt % of CaTiO3, Ca2Fe2O5 greatly improved the tar combustion characteristics. For the combustion process, a three stages model, volatilization, low-temperature oxidation (LTO), and high-temperature combustion (HTC), was proposed and applied. The starting temperature of HTC stage was lowered by 30-60 °C, and the combustion residue decreased by nearly 10 wt % resulting from the catalyst’s presence. Different reaction kinetics mechanisms using the classical Arrhenius equation were applied to treat TG data, the results showing the first-order combustion model fitted the data best. Volatilization and HTC stages had large apparent activation energy (E), and a uniform trend of decreasing E was observed in the presence of CaTiO3, Ca2Fe2O5.
Introduction Tar is an unavoidable waste or byproduct during biomass thermal conversion process such as biomass pyrolysis or gasification.1 For example, the concentration of tar in the product gases can be from 0.1% to 20% or greater for biomass gasification, which will condense at low temperature, then cause operational problems in downstream processes by blocking gas coolers, filter elements, and engine suction channels; in addition, the calculated low heating value of biomass tar stands for approximately 10% of the heat content in the product gases.2 To satisfy the quality of final gas, tar should be controlled and removed by many methods, among which scrubber, filter, cyclone, and electrostatic precipitator are conventionally used to collect tar and clean the hot product gas. So the collected tar decomposition and comprehensive utilization are the key factors for successful cleaner application of the above processes, which also has an important influence on the efficiency of the energy source. Thermo-analytical tools such as thermogravimetry (TG) and differential thermal analysis (DTA) can play an important role in combustion behavior research. The use of thermal methods for characterization of fuel such as coal, biomass, or crude oil is certainly not new, and much work has been carried out to study the combustion process.3-11 Nevertheless, only a few * To whom correspondence should be addressed. Tel/Fax: +81-52-7895845. E-mail:
[email protected]. (1) Li, C.; Suzuki, K. Renewable Sustainable Energy ReV. 2009, 13, 594-604. (2) Kirma, I.; Brandinb, J.; Sanatia, M. Top. Catal. 2007, 45, 31–37. (3) Stamatov, V.; Honnery, D.; Soria, J. Renewable Energy 2006, 31, 2108–2121. (4) Karaosmanoglu, F.; Tetik, E. Renewable Energy 1999, 16, 1090– 1093. (5) Dasa, P.; Sreelathab, T.; Ganesha, A. Biomass Bioenergy 2004, 27, 265–275. (6) Zhang, S. P.; Yan, Y. J.; Li, T. C.; Ren, Z. W. J. East China UniV.Sci. Technol. 2002, 28, 104–106. (7) Aprameya, A.; Nader, M.; Norman, F. Energy Fuels 2006, 20, 560– 565. (8) Shishkin, Y. L. Thermochim. Acta 2006, 440, 156–165. (9) Shishkin, Y. L. Thermochim. Acta 2006, 441, 162–167.
investigations are currently available on the thermochemical behavior of biomass tar especially in the presence of catalysts. Pyrolitic oil derived via a slow pyrolysis process blended with ethanol and burned in a circular jet spray at atmospheric pressure was studied by Stamatov.3 He showed that biomass pyrolitic oil flames were shorter, wider, and brighter than diesel fuel flames under similar conditions and could be regarded as a potential energy source. Karaosmanoglu4 studied the tar oil as a source of combustion energy from biomass pyrolysis. It was found that pyrolitic oil was a carbon- and oxygen-rich hydrocarbon mixture containing ash, sulfur, and nitrogen in very small quantities. Dasa5 studied the stability and combustion characteristics of cashew nut shell (CNS) pyrolysis oil. Zhang6 deduced the kinetic parameters of bio-oil from flash pyrolysis of biomass (wood chip). Aprameya7 studied the pyrolysis and combustion behavior of heavy oil and its asphaltenes by TGA. Shishkin8,9 proposed a method for determining the composition of crude oils and oil heavy residues by DSC and TG techniques. Ko¨k10 investigated the role of clay on the combustion and kinetic behavior of pyrolitic oil in limestone matrix by TG/DTA and proposed a four stage combustion kinetic model. Ma11 investigated the combustion behavior of coal incorporating different catalysts by TGA. To some degree, the property of biomass tar is similar to heavy oil and can be regarded as a potential combustion oil, so it is quite promising for its evaluation as a fuel from the view of kinetic process. However, biomass tar combustion is extremely complicated, and its combustion behavior and mechanism still require further study, especially in the presence of catalysts. In this study, thermogravimetric analyzer was used to obtain information on the combustion behavior of biomass tar in the absence and presence of CaTiO3 and Ca2Fe2O5. A three stage kinetic model was proposed and applied for combustion calculation. The apparent activation energy (E) and preexponential factor (A) for combustion kinetics were obtained. It was shown that biomass tar could be regarded as a potential (10) Ko¨k, M. V. J. Therm. Anal. Calorim. 2006, 84, 361–366. (11) Ma, B. G.; Li, X. G.; Xu, L.; Wang, K.; Wang, X. G. Thermochim. Acta 2006, 445, 19–22.
10.1021/ef800959f CCC: $40.75 2009 American Chemical Society Published on Web 03/20/2009
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Figure 1. Biomass tar residual pictures of after heating at different temperature. Table 1. Elemental Composition of Tar by CHN C H N ignition loss
45.21% 6.43% 6.20% 42.16%
Table 2. Physical Properties of CaTiO3 and Ca2Fe2O5 as,BET (m2 g-1) full pore volume (cm3 g-1) F/F0 ) 0.99 av diameter of hole (nm)
CaTiO3
Ca2Fe2O5
8.17 1.10 × 10-2 5.84
1.15 2.24 × 10-3 7.83
fuel, and CaTiO3, Ca2Fe2O5 could improve the tar combustion characteristics. 2. Experimental Section 2.1. Sample Preparation. The tar studied in this research was obtained from fowl droppings gasification process, it is a black liquid with high viscosity. The physics state at room temperature took by camera is shown in Figure 1. The chemical composition of the tar is shown in Table 1. It can found higher nitrogen concentration. For every sample, around 10% (wt) of catalyst was added to the tar and mixed uniformly to see the effect of the catalyst on the tar combustion. 2.1.1. Catalysts. CaTiO3 was prepared by mixing Ca(OH)2 and TiO2 in stoichiometric ratio, grinding, and then calcining at 1000 °C over 4 h in air environment, followed by crushing and sieving to obtain particle sizes between 212 and 425 µm. Ca2Fe2O5 was prepared by mixing Ca(OH)2 and Fe2O3 in stoichiometric ratio, then grinding, and calcining similar to CaTiO3. The XRD pattern is given in Figure 1 showing the calcined catalysts (CaTiO3 and Ca2Fe2O5) forming a crystalline phase of perovskite. A very attractive feature of perovskite related compounds is the formation of active oxygen adspecies on their surface. The Raman spectra of the powders showed that an intense absorption at 1093 cm-1 is observed, which has typically been assigned to the superoxide radical (O2-), an important factor for promoting tar combustion.12 Table 2 lists the basic physical properties of CaTiO3 and Ca2Fe2O5 showing them to have a low surface area, scanning electron microscopy (SEM) (12) Hirabayashi, D.; Yoshikawa, T.; Mochizuki, K.; Suzuki, K.; Sakai, Y. Catal. Lett. 2006, 110 (3-4), 269–274.
Figure 2. X-ray diffraction pattern of CaTiO3 and Ca2Fe2O5.
showed CaTiO3 and Ca2Fe2O5 to be porous with hole diameters being under 1 µm) (Figure 2). Thermogravimetry (TG) and differential thermal analysis (DTA) were performed with Rigaku Thermo Plus TG8120 thermogravimeteric analysis system. TG/DTA curves were obtained under the following experimental conditions: air flow rate 50 mL min-1; mass of tar sample 20-25 mg; heating rates 5, 10, 15, 20, and 30 °C min-1; temperature range 25-1000 °C. Prior to experiments, TG/ DTA equipment was calibrated. Each experiment was performed more than twice to check for repeatability. 2.2. Kinetic Theory. Various reaction models such as those of Arrheniusl,13-15 Coats-Redfern,16-20 and Horowitz-Metzger 21,22 were used to describe solid fuels (coal, lignin) combustion by different researchers. Our sample (biomass tar) is a black liquid of high viscosity and can be regarded as a material whose properties lie between a liquid such as a crude heavy oil and a solid such as coal or asphaltene, so the above reaction models are applicable. Theoretically, the combustion of tar can be initiated whenever oxygen comes into contact with the tar at a sufficiently high temperature. However, the reaction process is exceedingly complex and many competing reactions contribute to the thermal
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Li and Suzuki Because of the complex nature of the tar, its combustion or decomposition is often assumed to be a single reaction in which all the tar components are regarded as one group (a tar mixture) undergoing several simultaneous reactions: (steam-, dehydro-, thermal-,...) reforming, cracking, etc. The overall rate of tar disintegration is thus given by the sum of the rates of all the elementary individual reactions. This approach has been accepted by most researchers.1,24 In this study, we also adopt this method, that is, all the reactions involved in the combustion process are lumped into one. The Coats-Redfern method used by many other investigators16–20,25,26 was adopted in this study for determining the combustion kinetics. Thus, the tar volatilization/combustion rate equation can be expressed as
dR/dt ) kf(R)
(1)
where dR/dt is volatilization/combustion conversion rate, n is assuming reaction order, the correct order leading to the best linear plot (usually the first-order linear rate is assumed), k is reaction rate constant, and f(R)is a function that is reaction proceeds way characteristic of the samples. The extent of conversion, or fraction of the material reacted, can be defined as
R ) (W0 - Wt)/(W0 - W∞)
(2)
where W0 is the initial sample mass, Wt is sample mass at time t, and W∞ is sample mass at the end of reaction. The temperature dependence of the reaction rate constant k can be expressed by the Arrhenius equation
k ) Ae-E/RT
(3)
min-1,
where A is pre-exponential factor, and E is the apparent activation energy, J mol-1. Heating rate (β, min-1) can be expressed as
β ) dT/dt
(4)
Then eq 1 can rewritten as follows:
A 1 dR ) e-E/RTdT f(R) β
Figure 3. Scanning electron micrographs of (a) CaTiO3 and (b) Ca2Fe2O5.
analysis curves, and also biomass tars from different sources possess different combustion characteristics. DTA curves of the tar in the absence of catalysts were obtained with heating rates of 5, 10, and 20 °C min-1, Figure 3. It was found that an endothermic appeared for all the samples in the interval 100-200 °C, followed by a wide and gently exothermic peak. Then, when the temperature reached around 400 °C or higher, a sharp exothermic peak showed up. So, in conformity with the DTA curves, a three-stage kinetic model was proposed and used in this study. The first stage is volatilization process, the endothermic peak being the result of volatilization of water and some low molecular weight compounds contained in the tar, and then the low temperature oxidation stage (LTO) sets in generating small quantities of carbon dioxide and presumably acids, aldehydes, ketones, and peroxides typical of low temperature oxidation.23 As the temperature rises, the combustion process enters the high temperature combustion stage (HTC) concomitant with a sharp and big exothermic peak, in this period, the sharply combustion process instead of slowly oxidation process, the tar residual tar combusts in short time, and release lots of energy, and carbon dioxide being the major product. (13) Altun, N. E.; Ko¨k, M. V.; Hicyilmaz, C. Energy Fuels 2002, 16, 785–790. (14) Brance, C.; Iannace, A. Energy Fuels 2007, 21, 1078–1084. (15) Yagmur, S.; Durusoy, T. J. Therm. Anal. Calorim. 2006, 86, 479– 482. (16) Coats, A.; Redfern, J. Nature 1964, 201, 68–68. (17) Ko¨k, M. V. J. Therm. Anal. Calorim. 2007, 88, 663–668.
(5)
Integrating eq 5, the following expression can be obtained:
h(R) )
∫
R
0
A 1 dR ) f(R) β
∫
T -E/RT
0
e
dT
(6)
For eq 6, many forms of the alpha functions (kinetic laws) can be envisioned, such as nucleation and growth, power law, or diffusion-controlled process.7,16,21,26 These commonly used for decomposition process calculation are listed in Table 3. They can be classified into four categories: nucleation and growth process, power law, diffusion-controlled process, and reaction interface growth process. (18) Santos, J. C. O.; Oliveira, A. D.; Silva, C. C. J. Therm. Anal. Calorim. 2007, 87, 823–829. ¨ nal, M. M.; Saikaya, Y. J. Therm. Anal. Calorim. 2007, 91, 261– (19) O 265. (20) Dantas, M. B.; Concei, M. M.; Fernandes, V. J.; Santos, N. A. J. Therm. Anal. Calorim. 2007, 87, 835. (21) Ko¨k, M. V.; Acar, C. J. Therm. Anal. Calorim. 2006, 83, 445– 449. (22) Beg, M. A. A.; Qaiser, M. A. Thermochim. Acta 1990, 173, 281– 294. (23) Corella, J.; Toledo, J. M.; Aznar, M. Ind. Eng. Chem. Res. 2002, 41, 3351–3356. (24) Ko¨k, M. V.; Keskin, C. J. Therm. Anal. Calorim. 1997, 49, 617– 625. (25) Elbeyli, I. Y.; Pis¸kin, S. J. Therm. Anal. Calorim. 2006, 83, 721– 726. (26) Reading, M.; Dollimore, D.; Whitehead, R. J. Therm. Anal. Calorim. 1991, 37, 2165–2188.
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Energy & Fuels, Vol. 23, 2009 2367
Table 3. Frequently-Used r Function for Solid-State Decomposition Reactions no.
model
1
nucleation and growth process
2
power law
3
diffusion controlled process
4
reaction interface growth process.
The right-hand side of eq 6 has no exact integral, but eq 7 can be obtained when this term is expanded into an asymptotic series, higher order terms being ignored by numerical approach.
h(R) )
∫
R
0
1 RT E dR ) ART2 1 - 2 exp /(βE) (7) f(R) E RT
(
) ( )
Taking natural logarithms of both sides of eq 7 and assuming that 2RT/E , 1, one gets
( ) ( )
h(R) AR E ) ln βE RT T2
ln
(8)
Then, a plot of ln[(h(R))/T2] versus 1/T should give a straight line of slope E/R and an intercept of ln(AR/βE) for an appropriate form of h(R). The criterion used for accepted values of E and A is that thef(R) should yield the best linear correlation coefficient (close to unity). In this study, only the power law is considered with assumed power values (reaction orders) n ) 0.5, 1, 1.5, 2; the final form of eq 8 for different reaction orders is shown in Table 4. A suitable reaction order can be selected taking account of correlation coefficient. Then from the slope (-E/R) and intercept (y) of the regression line obtained using the TG data, the apparent activation energy (Ea) and the pre-exponential factor (A) are calculated.
3. Results and Discussion 3.1. Selection of Reaction Order. Using the data of Table 4, we obtained the least straight line by the best squares method, linear correlations for the identified rectilinear portions of different reaction orders are shown in Table 5 for the heating rate 5 °C min-1, air flow rate 50 mL min-1, and catalyst amount of 10% (wt). From Table 5, one can conclude that the regression coefficient for the combustion of tar in the presence of catalyst is in the range of 0.96-0.99 for n ) 1, which is better than that for n ) 0.5, 1.5, and 2. The results reveal this to be the best reaction order for the three assumed selection, thus the firstorder model for the tar combustion was adopted in this study. Comparing tar combustion behavior in the absence and presence of catalysts, the combustion kinetic was not affected by the presence of catalysts. About the reaction mechanism, it is very hard to conclude because tar combustion process are very complex process, which proceed in several stages, that these can be the delocalization or transfer of electrons in chemical
f(R)
h(R) ) ∫R0 [1/f(R)]dR
2(1 - R)[-ln(1 - R)]1/2 3(1 - R)[-ln(1 - R)]2/3 n ) 1: (1 - R) n * 1: (1 - R)n 1/(2R) [-ln(1 - R)]- 1 3/2(1 - R)2/3[1 - (1 - R)1/3]-1 2(1 - R)1/2 3(1 - R)2/3
[-ln(1 - R)]1/2 [-ln(1 - R)]1/3 -ln(1 - R) (1 - (1 - R)1-n)/(1 - n) R2 R + (1 - R)ln(1 - R) [-ln(1 - R)1/3]2 1 - (1 - R)1/2 1 - (1 - R)1/3
Table 5. Correlation Coefficient for Different Reaction Orders (Heating Rate, 5°C min-1; Flow Rate of Air, 50 mL min-1; Amount of Catalysts, 10 wt % in Total Weight) correlation coefficient tar without catalyst tar + CaTiO3 (10 wt %) tar + Ca2Fe2O5 (10 wt %)
stages
n ) 0.5
n)1
n ) 1.5
n)2
volatilization
0.9760
0.9755
0.9778
0.9187
LTO HTC volatilization
0.9766 0.9225 0.9950
0.9880 0.9963 0.9951
0.9751 0.9803 0.9947
0.6510 0.8033 0.9051
LTO HTC volatilization
0.9992 0.9476 0.9823
0.9993 0.9651 0.9816
0.9982 0.9628 0.9809
0.8023 0.9566 0.9412
LTO HTC
0.9986 0.9108
0.9985 0.9715
0.9983 0.9647
0.9981 0.9661
bonds, the diffusion of atoms, free radicals or ions, the heat transfer to the reaction zone in the case of endothermic reaction, and the formation of new solid phase such as carbon, impurities and catalysts. For tar combustion process, the rate-limiting step is not confined to nor does not only occur at the reactant surface. In this condition, all molecules whether on the surface or in the bulk may be have an equal probability per unit time of reacting. This is the case when the change from the reactant to the solid product phase involves little rearrangement of the reactant atoms. The reaction has homogeneous mechanism and thus an approximate reaction order of one, that is, f(R) )(1 - R). 3.2. Combustion Process. 3.2.1. TG/DTA CurVes. In the course of the present research, TG-DTA curves were obtained for biomass tar with 10% (wt) of CaTiO3 and Ca2Fe2O5, respectively. Nonisothermal experiments were conducted from room temperature to 1000 °C. An integrative plot of TG-DTA curves in air environment with different heating rates is shown in Figures 3-5, respectively, from these curves, three main stages of transition can be observed. They are (I) volatilization, (II) low-temperature oxidation (LTO), and (III) high temperature combustion (HTC). Analysis of the DTA curve of the tars shows that the reaction behavior for the three cases: tar in the absence and presence of
Table 4. Approximate Solution versus Different Reaction Order by Numerical Approach reaction order (n)
approximate solution
[ [
1
ln -
1/2, 3/2, 2
ln
] [ ( ] [ (
ln(1 - R) 2RT AR ) ln 1βE E T2
)] - RTE (9)
1 - (1 - R ) 2RT AR ) ln 1βE E T2(1 - n)
)] - RTE (10)
Figure 4. DTA curves of tar in the absence of catalyst with different heating rates.
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Figure 7. Analysis of biomass tar combustion without catalysts by Coats-Redfern method (ln[-ln(1 - R)/T2] vs 1/T).
Figure 5. TG-DTA curves of tar in the absence and presence of CaTiO3 and Ca2Fe2O5 (heating rate: 10 °C min-1).
Figure 6. TG-DTA curves of tar in the absence and presence of CaTiO3 and Ca2Fe2O5 (heating rate 30 °C min-1).
CaTiO3 and Ca2Fe2O5 is similar at low temperature (
