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Kinetic study and syngas production from pyrolysis of forestry waste
Energy Conversion and Management 135 (2017) 453–462
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Kinetic study and syngas production from pyrolysis of forestry waste Mian Hu a, Xun Wang a, Jian Chen a, Ping Yang a, Cuixia Liu b, Bo Xiao a, Dabin Guo a,⇑ a b
School of Environmental Science & Engineering, Huazhong University of Science and Technology, Wuhan 430074, China School of Energy and Environmental Engineering, Zhongyuan University of Technology, Zhengzhou 450007, China
a r t i c l e
i n f o
Article history: Received 2 November 2016 Received in revised form 13 December 2016 Accepted 28 December 2016
Keywords: Forestry waste Pyrolysis Kinetic Syngas
a b s t r a c t Kinetic study and syngas production from pyrolysis of forestry waste (pine sawdust (PS)) were investigated using a thermogravimetric analyzer (TGA) and a fixed-bed reactor, respectively. In TGA, it was found that the pyrolysis of PS could be divided into three stages and stage II was the major mass reduction stage with mass loss of 73–74%. The discrete distributed activation energy model (DAEM) with discrete 200 first-order reactions was introduced to study the pyrolysis kinetic. The results indicated that the DAEM with 200 first-order reactions could approximate the pyrolysis process with an excellent fit between experimental and calculated data. The apparent activation energies of PS ranged from 147.86 kJmol1 to 395.76 kJmol1, with corresponding pre-exponential factors of 8.30 1013 s1 to 3.11 1025 s1. In the fixed-bed reactor, char supported iron catalyst was prepared for tar cracking. Compared with no catalyst which the gas yield and tar yield were 0.58 N m3/kg biomass and 201.23 g/ kg biomass, the gas yield was markedly increased to 1.02 N m3/kg biomass and the tar yield was decreased to only 26.37 g/kg biomass in the presence of char supported iron catalyst. These results indicated that char supported iron catalyst could potentially be used to catalytically decompose tar molecules in syngas generated via biomass pyrolysis. Ó 2017 Elsevier Ltd. All rights reserved.
1. Introduction Forestry wastes, in general, and all biomass residues, can be used as raw materials for the generation of liquid biofuels, syngas, chemicals, or charcoal via pyrolysis and liquefaction processes [1– 3]. Thermochemical conversion methods, i.e. pyrolysis, gasification and combustion, are the most commonly employed and the most appropriate for these purposes. Most of the biomass have a heterogeneous property attributes to the fact that the biomass itself composes of numerous components, such as hemicellulose, cellulose, lignin, and minor amounts of extractives. Such of these components, in fact, contribute to the actual reaction mechanism of biomass pyrolysis possibly is extremely complex [4]. The proportion and composition of pyrolysis products are crosswise affected by many factors such as biomass type, feedstock pretreatment, and pyrolysis conditions. During the biomass pyrolysis, a large number of reactions take place in parallel and series, including dehydration, depolymerisation, isomerization, aromatisation, decarboxylation, and charring [4,5]. Kinetic modeling of pyrolysis can help to describe practical conversion processes and optimize the design of efficient reactors [6]. ⇑ Corresponding author at: School of Environmental Science & Engineering, Huazhong University of Science and Technology, Wuhan 430074, China. E-mail address: [email protected] (D. Guo). http://dx.doi.org/10.1016/j.enconman.2016.12.086 0196-8904/Ó 2017 Elsevier Ltd. All rights reserved.
Thermogravimetric analysis (TGA) is a useful technique for studying the decomposition reactions of a solid and it has been widely used to study the apparent kinetics of biomass pyrolysis [7]. By using the TGA data, the kinetic parameters as well as pyrolysis mechanism can be determined according to different mathematical approaches. The single-step global model couples with different iso-conversional method, is the most used kinetic approach. However, as mentioned above, the biomass pyrolysis process is extremely complex due to the difference in decomposition of the biomass components. The iso-conversional method is considered to be conflicting rather than complementary when treating the kinetics of complex reaction system. According to the International Confederation for Thermal Analysis and Calorimetry (ICTAC) Kinetics Committee recommendations, multi-step reaction model is more suitable to simulate solid fuels (such as coal and biomass) pyrolysis kinetic [8]. Among many multi-step reaction models, distributed activation energy model (DAEM) is one of the commonly used in biomass pyrolysis kinetics studies [3,9]. Many methods such as model-fitting method, isoconversional method and discretization method can be used to treat the DAEM and between them the discrete DAEM is even better for DAEM calculation. During biomass pyrolysis, there are many obstacles need to be resolved before it further become a viable commercial renewable energy. The generation of tar in product gas is one of the major
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issues, which is known as energy waste, block pipeline, and even threat to human health. Methods of physical treatment, thermal cracking, plasma-assisted cracking, and catalytic reforming, etc. are considerable to eliminate the tar. Among these methods, catalytic reforming is considered the most promising approach for the tar removes as well as convert to combustible gas [10]. Various types of catalysts such as minerals (iron ores, clay minerals, olivine, calcined rocks) and synthetic catalysts (transition metals-based, activated alumina, alkali metal carbonates, FCC catalysts, and char/char-supported) have been studied on tar removal in biomass pyrolysis/gasification [11–16]. Among these catalysts, the char/ char-supported catalysts have shown low costs and adequate catalytic activities for tar reforming during the pyrolysis/gasification of biomass. In this study, behaviors and kinetics of a representative forestry waste (pine sawdust) pyrolysis were investigated using a discretion DAEM method via thermogravimetric analysis. Meanwhile, char supported iron catalyst used for syngas production was also investigated. 2. Experiment and methods 2.1. Materials Forestry waste (Pine Sawdust (PS), belong the species of macrophanerophytes) was obtained from a furniture factory Wuhan City, Hubei Province, China. The sawdust was naturally dried for a period of 7 days and then grinded and screened into a size of <0.107 mm (pass a Tyler standard screen scale of 100 mesh). Table 1 gives the results of proximate and ultimate analysis of pine sawdust feedstock. Ultimate analysis of the PS samples was measured by a CHNS/O analyzer (Vario Micro cube, Elementar). Such an analysis gave the weight percent of carbon, hydrogen, nitrogen and sulfur in the samples simultaneously. The weight percent of oxygen was determined by differences. The proximate analysis of the PS was conducted following ASTM standard test methods. The low calorific value (LHV) of PS was calculated Mendeleev formula: LHV (MJ/kg) = 339.1C + 1256H 108.8(O-S) 25.1 (9H + M), (where M is the moisture content). The chemical functional groups in PS were investigated by using FT-IR technique (Vertex 70, Germany). PS samples were ground to fine particles and mixed with KBr powder. The mass ratio of samples to KBr powder was 1:100. The spectral resolution was set at 4 cm1. 2.2. Thermogravimetric analysis (TGA) TG/DTA Synchronous analyzer (Diamond TG/DTA, PerkinElmer Instruments) was used to perform the pyrolysis of the samples with sweeping gas of high purity nitrogen (flow rate 100 mL/min). The test temperature was raised from room temperature to 800 °C with various heating rates of 20, 30 and 50 °C/min for samples. 2.3. Catalyst Biochar was obtained from a pilot-scale allothermal biomass gasification system which studied in our previous research [17].
The rice straw as a feedstock for gasification was from the farm in Wuhan City, Hubei province, China. Firstly, biochar was gently crushed and sieved into 0.5–1 mm size fraction. Secondly, the biochar sample was washed with deionized water for several times and oven dried (80 °C) as catalyst support. Fe/biochar catalyst, prepared by dissolving 21.6 g of Fe(NO3)39(H2O) in 200 mL of deionized water, was mixed with 47.0 g biochar for 12 h under continuous strong agitation using a magnetic stirrer, and then dried at 105 °C for 24 h. The resulting product is the Fe/biochar catalyst. 2.4. Apparatus and procedure The used facility in this experiment includes two stages which are pyrolysis zone and catalysis zone, shown in Fig. 1. The effective length of the pyrolysis furnace is 700 mm with an outer diameter (OD) of 80 mm (ID. 75 mm), and the fixed catalytic reactor is 800 mm in height and 65 mm in outer diameter (ID. 60 mm). Pyrolysis and catalytic bed are both made of quartz glass and externally heated by electrical ring furnaces. Before the experiments, 5.0 g catalyst was put into the catalytic furnace. Then, nitrogen (with a constant flow rate of 0.1 L/min) was injected into the reactor for 20 min to maintain an inert atmosphere. Subsequently, the pyrolysis and catalytic reactors were heated in a heating rate of 30 °C/min to achieve the set-point temperature (800 °C), respectively. 5.0 g PS was put into the stainless steel boat and placed into the middle of quartz tubular reactor. The set-point temperatures were considered as the pyrolytic and catalytic temperature which would be held for 15 min. During the experiment process, the volatile flowed out of the reactor and was condensed by ice bath. The condensable volatiles (i.e. bio-oil) were captured in the collector and the non-condensable gas whose volume was measured by a gas flow meter. The main composition of non-condensable gas was analyzed by GC 9800T which functioned based on a thermal conductivity detector (TCD) with TDX-01 columns. The temperatures of the injector, oven and detector were at 200 °C, 85 °C and 90 °C, respectively. The carrier gas in all analyses was argon. Standard gas mixtures were examined by the quantitative calibration. At the end of the pyrolysis, the furnace was cooled down to room temperature and the mass of the solid char as well as bio-oil yields could be calculated. 2.5. Distributed activation energy model (DAEM) kinetics Conversion (a) form of apparent reaction rate of solid-state material thermal decomposition in TGA process can be described as:
da=dt ¼ kðTÞf ðaÞ
ð1Þ
where t is the reaction time (s); T denotes absolute temperature (K); k (T), also is known as the reaction constant, is the temperature dependent function in apparent kinetic; and f(a), which is known as the reaction mechanism model, is depends on its independent variable conversion (a):
a ¼ ðm0 mt Þ=ðm0 mf Þ
ð2Þ
Table 1 Ultimate and proximate analysis of PS sample.b Ultimate Analysis (Wt.%)
Proximate Analysis (Wt.%)
LHV (MJ/kg)
C
H
Oa
N
S
M
V
F
A
46.36
5.75
43.62
2.26
0.32
4.54
82.18
16.13
1.69
M, Moisture Content; V, Volatile Matter; F, Fixed Carbon; A, Ash; LHV, Low Heating Value. a By difference. b Air dry basis.
16.81
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Fig. 1. Schematic of the PS catalytic pyrolysis facility.
where m0 (%), mt (%) and mf (%) corresponds to the mass of initial, instantaneous and final of pyrolysis state. In order to use the homogeneous kinetic theory in apparent kinetics, Arrhenius equation (Eq. (3)) can approximable express the k (T) term of Eq. (1).
E kðTÞ ¼ A exp RT
ð3Þ
In Eq. (3), E is the apparent activation energy (Jmol1); A is the apparent pre-exponential factor (min1); R = 8.3145 Jmol1K1 is the ideal gas constant. Then, reaction rate equation for PS pyrolysis according to Eqs. (1) and (3) by means of a linear non-isothermal heating rate, b (b = dT/dt, K/s), gives:
A E f ðaÞ da=dT ¼ exp b RT
ð4Þ
After variables separation and boundary value integral (a 2 [0, a], T 2 [T0, T]), the integral form of Eq. (4) is obtained as:
GðaÞ ¼
Z
0
a
da A ¼ f ðaÞ b
Z
T
T0
expðEa =RTÞdT
A wðE; TÞ b
ð5Þ
where G(a) is integral of f (a); w(E, T) is the temperature integral who has not an analytical solution but can be replaced with approximable expressions. For the simplest first-order reaction f(a) = 1 a, Eq. (5) becomes
A 1 a ¼ exp wðE; TÞ b
ð6Þ
The distributed activation energy model (DAEM) is a mechanism that is combined by an infinite irreversible first order reaction. The apparent activation energy in DAEM is a continuous R distribution f(E) with f(E)dE = 1. Then, the conversion function according to DAEM is given as:
x¼1a¼
Z 0
1
Z A T E dT f ðEÞdE exp exp b T0 RT
ð7Þ
Many forms of f(E) such as Gaussian, Weibull, Logistic, Gamma distribution, etc. were used in biomass pyrolysis kinetics [18]. By substituting different forms of f(E) into Eq. (7), the kinetic parameters E, A can be obtained by using optimization algorithms.
However, such method will leads to a large calculation difficulty due to the improper integral. And pre-exponential factor (A) is a constant, which can no reflect the complexity of reaction process. Scott et al. [19] presented an algorithm providing a detailed description of the kinetics of devolatilization of complex fuels from TGA. This algorithm discrete the infinitely number into limited many of first-order reaction, each of which are characterized uniquely pair of activation energy and pre-exponential factor. According to Scott et al. [19], remaining conversion (x = 1 a) contributed by n number of first-order reactions during pyrolysis process can be expressed as:
" # n X Ai xj ¼ 1 aj ¼ f i exp wj ðE; TÞ bj i¼1 |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}
ð8Þ
W
where subscript j denotes j-th heating rate; n is a hypothetical number of first-order reactions. fi is the contributed weight of i-th firstorder reaction. In order to solve Eq. (8) in matrix form, Eq. (8) can be transferred to:
2
xðT 0 Þ
3
3 f 1;0 6f 7 6 2;0 7 7 6 6 f 3;0 7 7 6 7 6 7 6 7 7 7 6 7 5 4 5
W1 ðT 0 Þ W2 ðT 0 Þ Wn ðT 0 Þ 1 6 W ðT Þ W ðT Þ W ðT Þ 1 7 2 1 n 1 7 6 1 1 7 6 6 W1 ðT 2 Þ W2 ðT 2 Þ Wn ðT 1 Þ 1 7 7 6
6 xðT Þ 7 1 7 6 7 6 6 7 7 6 6 7¼6 7 6 6 7 6 6 4 5 4 xðT n Þ |fflfflfflfflfflffl{zfflfflfflfflffl ffl}
3
2
1 |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}
X
W
2
ð9Þ
|fflfflfflffl{zfflfflfflffl} F
i.e. X = W F. Scott et al. [19] indicated that it is possible to calculate each fi,0 from Eq. (9) by using Nonnegative Linear Least Square Method with constraints:
(
f i;0 2 ½0; 1 Pn i¼1 f i;0 ¼ 1
ð10Þ
Further, for i-th reaction, its contributed remaining mass fraction is given as:
xi ðTÞ ¼ f i WðEi ; TÞ
ð11Þ
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Under two different heating rates runs (b1 and b2) TGA experiments, the same remaining conversion (x) at different heating rate is considered to have a same pair of Ei and Ai, i.e. fi(b1, T1) = fi(b2, T2). Then, Wi ðb1 ; T 1 Þ ¼ Wi ðb2 ; T 2 Þ can be deduced from Eq. (7), as:
" Z 1 Ei Ei 1 expðuÞ Ei du T 1 exp T 0;b1 exp b1 u RT 0;b1 R E=ðRT 0;b Þ RT 1 1 # Z 1 Ei expðuÞ du þ u R E=ðRT 1 Þ " Z 1 Ei Ei 1 expðuÞ Ei ¼ du T 2 exp T 0;b2 exp b2 u RT 0;b2 R E=ðRT 0;b Þ RT 2 2 # Z 1 Ei expðuÞ du ð12Þ þ u R E=ðRT 2 Þ
where T 0;b1 , T 0;b2 corresponds to the initial temperature of b1 and b2; u = E/(RT). Activation energy of i-th reaction Ei can be calculated from Eq. (12). After the Ei is obtained, since x = 1 e1 and Wi = e1, pre-exponential factors Ai will be estimated by solving the following equation under given two heating rates:
" Z Ai Ei Ei þ1 expðuÞ du T 0;b1 exp u b1 RT 0;b1 R Ei =ðRT 0;b Þ 1 # Z Ei Ei þ1 expðuÞ þ du T 2 exp u RT 2 R Ei =ðRT 2 Þ
50 °C/min. It is noticeable that the DTG curves can be divided into three stages. Stage I take place in lower temperature range. In this stage, the weight loss is mainly contributed to the evaporation of adsorbed water and light volatiles. Stage II, which covers a wide temperature range and manifests high mass loss percentage, is caused by the devolatilization. The weight loss rate is very slow in Stage III. Such slow rate is attributed to slow decomposition of lignin and even the secondary decomposition of solid residues. Table 2 lists the mass loss and characteristic temperatures at different heating rates. It can be observed that Stage II, accounted for about 73–74% mass loss, is the main stage during the PS pyrolysis process. In addition, the initial temperature (Ti), final temperature (Tf), and peak temperature (Tp) increase with the increasing in heating rate. However, increasing of heating rate only shift the peaks temperature to higher value and do not change the thermal profile of decomposition (Fig. 3). The lateral shift of DTG curves to higher temperature and the increase of characteristic temperatures attribute to the combined effects of heat transfer and mass transfer, which render as thermal lag. 3.3. DAEM kinetic
lnðwi Þ ¼
ð13Þ
3. Results and discussion 3.1. Characterization of pine sawdust The proximate and ultimate analysis results, as well as the low calorific value (LHV) of pine sawdust (PS) are listed in Table 1. As seen in Table 1, proximate analysis results for PS have a high amount of volatile matter content (82.18%) which could be considered suitable for combustion, pyrolysis or gasification process. Another important feature for PS is that the ash content (1.69%) is very low than previous studied biomass in literatures, such as hazelnut husk (5.27%) [6], rice husk (4.57%) [20], orange waste (3.02%) [21]. Ash can limit heat and mass transfer while producing all kinds of problems such as agglomeration, slagging, and fouling in boilers. The lower heating value of PS is 16.81 MJ/kg that can be considered as an attractive biomass to be used as an alternative fuel. To clarify the internal chemical structure of PS, FTIR analysis was conducted and the result is displayed in Fig. 2. It is obvious that there are two peaks at 3341 cm1 and 1032 cm1 corresponding to the free of OH and primary alcohol CAOH functional groups, respectively. It indicates that there are a large number of hydroxyl groups in the PS. The peaks at 2931 cm1 and 1461 cm1 are clearly display the CAH absorption peaks, which are corresponding to the saturated hydrocarbon ACH2- and ACH-, respectively. However, the CAH bond could be easily broken and form alkane gas (H2, CH4, C2H6). The peak at 1650 cm1 is likely indicative of C@O amide stretching (R-CONH2), which easily to form the main components of tar (ketones, aldehydes, esters, amides, and acrylic) during the pyrolysis process. Moreover, the existence of the R-CONH2 is the main source of release NOX during the biomass combustion process. Furthermore, there are plenty of weak peaks appeared in spanning 800–1500 cm1, which indicated benzene ring exists. 3.2. Thermal characteristics of PS Fig. 3 gives the TG/DTG profiles pine sawdust (PS) pyrolysis obtained from heating rates of 20 °C/min, 30 °C/min and
The kinetic of the stage II of PS is studied which attribute to the stage II is the main stage in the pyrolysis process. In this stage, PS is discretized as 200 first-order reactions between remaining conversion X from 0.999 to 0.001 in this study. Two different heating rates (20 °C/min, 30 °C/min) were chosen to calculate the activation energy (Ei) by solving Eq. (12). When the Ei is known, the preexponential factor (Ai) can be calculated by solving Eq. (13). After the Ei and Ai were obtained, the initial mass fraction (fi,0) of the given conversion could be calculated from Eq. (9) using the Nonnegative Linear Least Square Method with constraint Eq. (10). Each conversion corresponding to a first-order reaction contains a characteristic activation energy, a pre-exponential factor and an initial mass fraction. The variation of activation energy along with conversion (a) for PS is shown in Fig. 4a which reflects the complexity of PS during the pyrolysis processed due to the big variation of activation energy with conversion. And the activation energy is in range of 147.86–395.76 kJmol1. It can also be seen from the compensatory effect between activation energy and preexponential factor Fig. 4b that the stage II of PS can be divided into multi-parts. The main reason may be that exist in the interaction between pseudo components (hemicellulose, cellulose and lignin) in PS, mineral catalysis and secondary reactions between pyrolysis products. Moreover, the values of lnAx changes in the range of 32.05–58.70 which means the pre-exponential factor have wide range of 8.30 1013 s1–3.11 1025 s1. It can be obviously instructed that the activation energy increases very fast in the late reaction. However, the activation energy denotes the energy barrier which pyrolysis reaction has to be overcome. By discretizing the pyrolysis process into 200 first-order reactions, the weights (fi,0) of these reactions contributed to the whole pyrolysis is obtained from Nonnegative Linear Least Square method. As depicts in Fig. 4c, 200 reactions are actually not all contributed to the pyrolysis process. At the conversion ranges of about 0.2–0.6 and 0.62–0.86, the weights of corresponding reactions are zero and can be considered as non-effective contributions to the pyrolysis. However, the distribution of such effective weights is quite complex and is possibly attributed to the complex mechanism of lignocellulosic biomass (forestry residue in this study) pyrolysis process, which is caused by the interaction of devolatilization, diffusion effect, catalyst and secondary reactions. Although the complexity of fi,0 vs. a distribution, a feature can be observed from Fig. 4c, i.e. the weights of low conversion (0–0.2), 0.6, and high conversion (greater than 0.86) are obvious. Such a
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0.65 0.60 C=O
0.55 C-H
0.50
Transmittance
C-H C-OH
0.45 0.40
O-H
0.35 0.30 0.25 0.20 4000
3500
3000
2500
2000
1500
1000
500
-1
Wavenumber (cm ) Fig. 2. FTIR spectra profiles of PS sample.
20
354.76
80
TG:
348.79
TG (%)
DTG:
20oC/min 30oC/min 50oC/min
20oC/min 30oC/min 50oC/min Stage
60
Stage 40
15
Stage
10
DTG (%/min)
100
329.74 5
20
0 0
100
200
300
400
500
600
700
0 800
Temperature (oC) Fig. 3. TG/DTG curves at different heating rate for Pine Sawdust (PS). Table 2 Characters of PS in TGA process under different heating rates. HR (°C/min)
Ti (°C)
Tf (°C)
ML (%)
Stage I
20 30 50
163.24 174.31 191.65
3.18 3.45 3.67
Stage II
20 30 50
163.24 174.31 191.65
614.73 635.90 638.22
73.04 73.34 74.46
Stage III
20 30 50
614.73 635.90 638.22
798.90 787.45 780.91
2.94 1.05 1.96
Residue (%)
20 30 50
20.84 22.16 19.91
where ‘‘” is room temperature, Ti and Tf is the initial temperature and final temperature of each stage, respectively; ML is the mass loss in each stage.
feature considers that the pyrolysis process can be contributed to main four reactions range and similar to the components independent parallel reaction (IPR) model, in which the main components (extractives, pseudo hemicellulose, cellulose, lignin) decompose independently. At low conversion range of 0–0.07, the distribution of fi,0 vs. a is unordered and weak. In such range, the mass loss of sample is mainly contributed to the precipitation of extractives such as formic acid, acetic acid, alcohol matter, and even combined water. At the conversion of about 0.2, the weight is very obvious and is contributed to the pyrolysis of pseudo hemicelluloses. The weight fraction of pseudo cellulose is the highest corresponding to conversion of 0.6. The weight distribution at conversion range of 0.86–1 is quite unordered, which is possibly contributed to the lignin decomposition at high temperature or the secondary reaction between volatiles and char. In general, pseudo hemicellulose is a mixture of many kinds of sugar monomers such as xylan,
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arabino-xylan, arabino-glucurono-xylan, glucurono-xylan and galacto-arabino-glucorono-xylan [22]. The sugar monomers are far from the further formation of long chains. The sugar monomers are weakly restrained by long chains and their decomposition temperature is about 200–400 °C (473–673 K). Thus, the AC@O, AOH, AC@C bonds are easily to be broken down under relatively low temperature and generate CO2, CH4, CO gas and low molecular organic acids. Pseudo cellulose can be seen as a crystal which is a 400
(a)
350
linearly extending structure of (C6H10O5)n unit. Its terminated temperature of decomposition is a higher than hemicelluloses due to the long chain structure. Account for the crystal properties, the cellulose completes its decomposition quickly once temperature reaches its characteristic temperature about 623–673 K. As can be seen from Fig. 3, when the temperature is higher than the peak temperature, a nearly vertical down steep slope can be observed on the right hand side of DTG curves. Such slope is mainly attributed to the fast decomposition contribution of cellulose, and contributes to the rapid decline of whole DTG curves. It is believed that the lignin is a mixture of varieties of compounds which contains benzene ring. The benzene ring is extremely strong and leads to
1.0
(a)
250
0.8
Remaining Conversion X
Ea(kJ/mol)
300
200
150
100
0.0
0.2
0.4
0.6
0.8
1.0
0.6 Experiment: HR20 HR30 HR50
DAEM Simulation: HR20 HR30 HR50
0.4
0.2
Conversion α 70
0.0
(b)
60
400
600
700
800
900
Temperature(oC) 0.000
50
lnAx
500
(b)
-0.002
40
-0.004
dX/dT
30
20
10 100
150
200
250
300
350
400
Ex (kJ/mol)
-0.006
Experimental data: HR20 HR30 HR50
-0.008
Calculated data: HR20 HR30 HR50
-0.010 -0.012
0.40
-0.014
(c)
0.35
400
500
700
800
900
Temperature(oC)
0.30
Fig. 5. Comparison between experimental results and DAEM simulation results. (a) TG of PS; (b) DTG of PS.
0.25
f0,i
600
0.20 Table 3 Effect of catalysts on product yields and gas composition
0.15 0.10 0.05 0.00 0.0
0.2
0.4
0.6
0.8
1.0
Conversion α Fig. 4. DAEM kinetic parameters calculated for PS pyrolysis. (a) Change of activation energy with conversion a; (b) relationship between the activation energy and pre-exponential factor; (c) initial mass fraction of each component.
Without Catalyst
Biochar
Fe/biochar
Pyrolysis temperature (°C) Catalytic temperature (°C) Gas yield (N m3/kg biomass) Tar yield (g/kg biomass) Char yield (kg/kg biomass)
800 800 0.58 201.23 0.25
800 800 0.72 147.25 0.22
800 800 1.02 26.37 0.11
Gas composition H2 (vol.%) CO (vol.%) CO2 (vol.%) CH4 (vol.%)
19.43 25.34 20.15 13.56
25.36 16.08 27.01 10.67
39.88 23.34 14.27 5.89
M. Hu et al. / Energy Conversion and Management 135 (2017) 453–462
a high thermal stability of lignin. Thus, reaction rate of lignin is quite slow and covers the whole pyrolysis temperature range, even completes its decomposition nearly 900 °C. The calculated of Ex, Ax and f0,i were substituted into Eq. (8) and the X of different heating rates sets of PS was calculated by Lobatto Quadrature method (the quadl solver of MATLABÒ). The calculated and experimental X of PS at different heating rates were compared and given in Fig. 5. As can be seen from Fig. 5a, the discrete DAEM can fit the experimental data very well. Similar fits also can be seen from the DTG curves (Fig. 5b). These results indicate that the DAEM with 200 first-order reactions is suitable for PS pyrolysis kinetic study. 3.4. Catalytic reforming 3.4.1. Effect of catalyst Compared with the case of without catalyst, the Fe/biochar or biochar as catalyst for tar cracking and gas quality improving are investigated. The temperature of pyrolysis furnace and catalytic furnace are set as 800 °C. The test results are presented in Table 3. It can be seen that the gas yield and tar yield are 0.58 N m3/kg biomass and 201.23 g/kg biomass in pyrolysis of PS without catalyst. However, when the catalytic bed loads with Fe/biochar catalyst, the gas yield is markedly increased to 1.02 N m3/kg biomass, but the tar yield is decreased to only 26.37 g/kg biomass. The significant increase in gas yield is predominantly due to the secondary
459
cracking of vapors over catalysts. Moreover, the char yield decrease from 0.25 kg/kg biomass to 0.11 kg/kg biomass may be another reason to increase the gas yield (Boudouard reaction and/or Water gas (primary) reaction). With biochar catalyst, the gas yield is lower than that with Fe/biochar catalyst, while the tar yield trends to be opposite. This indicates that the Fe/biochar catalyst is more effective for the tar reduction and gas production in the pyrolysis of PS. Table 3 also shows that the catalyst had a significant effect on produced gas composition, and observably increase of H2 content, and decrease of CH4; however, CO and CO2 contents changed slightly. Those changes of gas composition might be attributed to several important reactions (water-gas (primary) reaction, steam reforming methane reaction, water-gas shift reaction and secondary cracking tar reaction) that occur simultaneously in catalytic reactor and are favorable for H2 production. Therefore, the content of H2 in pyrolysis of PS with the Fe/biochar catalyst increases roughly by 20 vol.% compared with that without catalyst. Meanwhile, the Fe/biochar catalyst is more effective than biochar catalyst in promoting the hydrocarbons reforming and the water– gas shift reaction, which results in a significant increase of the content H2 and a decrease of the content CH4. In comparison with the case in the absence of catalyst, CO and CO2 contents exhibit a dropping trend with the Fe/biochar catalyst. Hu et al. [17] also found the same trend with a variation of gas composition for CO and CO2 after the catalytic reactor. However, in the study of Cabballero et al. [23], CO content increases while CO2 content decreases after the catalytic
Fig. 6. SEM-EDS of (a) biochar and (b) Fe/biochar catalyst (before reaction).
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Usually, the ionic states of Fe determines the peak position of Fe 2p1/2 and Fe 2p3/2. According to the previous studies [25,26], the binding energy of FeO, Fe2O3, Fe3O4, and Fe0 distribute, respectively, at 109.4 eV, 710.9 eV, (708.2 eV, 710.4 eV) and (706.6 eV, 719.3 eV). It is clearly observed that from the comparison of the two figures (Fig. 7b and c), a peak at the binding energy of 719.32 eV of Fe 2p1/2 appears after catalytic reforming, which associated with Fe0. In other words, the iron oxide could be reduced by carbon and reducing gas (CO, H2) which comes from biomass pyrolysis. The oxidation-reduction processes might be described by the following reactions as shown in Eqs. (14)–(17):
reactor; opposite trends are reported in the study of Lv et al. [24]. This difference possibly results from the different reactor, operating conditions, and gasifying agents used.
3.4.2. SEM-EDS and XPS analysis Fig. 6(a) and (b) illustrates the SEM-EDS images of biochar and Fe/biochar catalyst (before reaction), respectively. It is clear from the comparison (EDS results) of the two figures that the occurrence of Fe in the surface of the Fe/biochar, suggesting that iron is successfully impregnated into the biochar. In order to investigate the valence state of Fe during the catalytic reforming process, the Fe/biochars (before reaction and after reaction) are analyzed be XPS (Fig. 7). Surface of biochar is enriched with only carbon and oxygen (not given the atomic % in the experiment, but really exist in the binding energy of about 529 eV) and a small amount of silicon while Fe appear in the biochars after impregnation and reaction, respectively (Fig. 7a). These results are consistent with the consequences of EDS analyses.
Carbon reduction : C þ Me2þ ! Me þ C2þ nC þ Mem On ! mMe þ nC Gas reduction : nH2 þ Mem On ! mMe þ nH2 O
ð16Þ
nCO þ Mem On ! mMe þ nCO2
ð17Þ
where Me2+ and MemOn corresponds to metal ions and metallic oxides, respectively.
Fe/biochar (after reaction)
(a) Fe LMM
97.44
Si 2p
0.43
Fe 2p
2.13
Fe 2p1 Fe 2p3
Fe LMM1
Fe LMM2
Fe 3s Fe 3p
Fe/biochar (before reaction) Atomic % 88.63 0.68
Fe 2p
10.69
biochar Atomic % C 1s
99.87
Si 2p
0.13
Fe 3s Fe 3p
Si 2p
Si 2p
Intensity (cps)
C 1s
Atomic % C 1s
C 1s
ð14Þ ð15Þ
1200
1000
800
600
400
200
0
Binding Energy (eV)
Fe2p
2p3/2
2p1/2
Fe2p
2p3/2
2p1/2
Intensity
Intensity
2p1/2
(c)
(b) 730
725
720
715
Binding Energy (eV)
710
705
730
725
720
715
710
705
Binding Energy (eV)
Fig. 7. (a) XPS analysis of biochar and Fe/biochar (before and after reaction). (b) High-resolution XPS spectrum of Fe 2p from Fe/biochar before reaction. (c) High-resolution XPS spectrum of Fe 2p from Fe/biochar after reaction.
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Fig. 8. Mechanism of tar reforming using char/char-supported catalyst. Note: the red dotted line represents the simple cracking reactions. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
3.4.3. Mechanism of tar reforming using char/char-supported catalyst The main mechanism during tar conversion over char/charsupported catalyst can be illustrated in Fig. 8. The char particle is considered as a sphere surrounded by a pyrolysis gas film which comes from the front-end pyrolysis furnace. The tar is adsorbed on the active sites of the char particle surface, while it has two parallel pathways to decompose. The first way is a catalytic conversion of tar to CO and H2 by steam and dry gasification reactions. The second way is a decomposition of tar to form free radicals which are further recombined into low molecular compounds and form coke deposited on the surface of char/charsupported catalyst. It has been noted that the tendency toward coke formation depends linearly on the number of aromatic rings in the tar component [27]. The catalytic reactions accompanied by coke formations were classified as coke-sensitive or cokeinsensitive [28]. Coke-insensitive reactions, e.g. catalytic reforming, hydrotreating, hydrocracking, steam reforming, are encountered whenever there is a strong hydrogenation component in the catalyst. Therefore, the strong hydrogenation activity of the catalyst keeps the surface produce relatively reactive coke precursors form on active sites which readily removed by pyrolysis agent. In contrast to the above are the coke-sensitive reactions, in which the coke is deposited directly in the active sites on the catalyst. This coke is unreactive (because there is no hydrogen, or no strongly hydrogenating catalyst function in the system) and can be compared to typical catalyst poisoning, in which the activity falls rapidly with increasing quantity of coke on the surface of catalyst. Tar is expected to be decomposed over char/char-supported catalyst by the steam and dry reforming reactions. These reactions probably produce insensitive coke which can be effectively controlled by adapting the operating conditions in such a way that the gasification rate of coke is higher than the production rate. 4. Conclusions Pyrolysis behaviors and kinetics of forestry waste (pine sawdust) were studied. The decomposition process of pine sawdust could be divided into three stages. The kinetics of the main stage II was modeled in modified discretize distributed activation energy model (DAEM). The DAEM with 200 first-order reactions showed
an excellent fit between experimental results and simulated data. With the Fe/biochar catalyst, the tar yield obviously decreased, and the gas yield significantly increased. Under the condition of the experiment, the gas yield is 1.02 N m3/kg biomass and the tar yield is 26.37 g/kg biomass. The results indicated that there was a strong potential for producing syngas from pine sawdust which are essentially waste materials by pyrolysis process with inexpensive char/char-supported catalyst. Acknowledgment This research was supported by the China Postdoctoral Science Foundation – China (2015M580644 and 2016M592339), Wuhan International Science and Technology Cooperation Project (2016030409020221) and Ministry of Housing and Urban-Rural Development – China (No. 2016K4032). Authors would also like to thank the Analytical and Test Center of Huazhong University of Science and Technology for carrying out the analysis of the characterization of the samples. References [1] Abomohra AE, Jin W, El-Sheekh M. Enhancement of lipid extraction for improved biodiesel recovery from the biodiesel promising microalga Scenedesmus obliquus. Energ Convers Manage 2016;108:23–9. [2] Chan YH, Yusup S, Quitain AT, Tan RR, Sasaki M, Lam HL, et al. Effect of process parameters on hydrothermal liquefaction of oil palm biomass for bio-oil production and its life cycle assessment. Energ Convers Manage 2015;104:180–8. [3] Soria-Verdugo A, Garcia-Hernando N, Garcia-Gutierrez LM, Ruiz-Rivas U. Analysis of biomass and sewage sludge devolatilization using the distributed activation energy model. Energ Convers Manage 2013;65:239–44. [4] Collard F, Blin J. A review on pyrolysis of biomass constituents: mechanisms and composition of the products obtained from the conversion of cellulose, hemicelluloses and lignin. Renew Sust Energ Rev 2014;38:594–608. [5] Vamvuka D. Bio-oil, solid and gaseous biofuels from biomass pyrolysis processes—an overview. Int J Energ Res 2011;35:835–62. [6] Ceylan S, Topçu Y. Pyrolysis kinetics of hazelnut husk using thermogravimetric analysis. Bioresource Technol 2014;156:182–8. [7] Sharma R, Sheth PN, Gujrathi AM. Kinetic modeling and simulation: pyrolysis of Jatropha residue de-oiled cake. Renew Energ 2016;86:554–62. [8] Vyazovkin S, Burnham AK, Criado JM, Pérez-Maqueda LA, Popescu C, Sbirrazzuoli N. ICTAC Kinetics Committee recommendations for performing kinetic computations on thermal analysis data. Thermochim Acta 2011;520:1–19.
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[9] Bai F, Sun Y, Liu Y, Li Q, Guo M. Thermal and kinetic characteristics of pyrolysis and combustion of three oil shales. Energ Convers Manage 2015;97:374–81. [10] Zhang R, Brown RC, Suby A, Cummer K. Catalytic destruction of tar in biomass derived producer gas. Energ Convers Manage 2004;45:995–1014. [11] El-Rub ZA, Bramer EA, Brem G. Experimental comparison of biomass chars with other catalysts for tar reduction. Fuel 2008;87:2243–52. [12] Kang K, Azargohar R, Dalai AK, Wang H. Hydrogen production from lignin, cellulose and waste biomass via supercritical water gasification: catalyst activity and process optimization study. Energ Convers Manage 2016;117:528–37. [13] Asadieraghi M, Daud WMAW. In-situ catalytic upgrading of biomass pyrolysis vapor: co-feeding with methanol in a multi-zone fixed bed reactor. Energ Convers Manage 2015;92:448–58. [14] Galadima A, Muraza O. In situ fast pyrolysis of biomass with zeolite catalysts for bioaromatics/gasoline production: a review. Energ Convers Manage 2015;105:338–54. [15] Wang Y, Hu X, Song Y, Min Z, Mourant D, Li T, et al. Catalytic steam reforming of cellulose-derived compounds using a char-supported iron catalyst. Fuel Process Technol 2013;116:234–40. [16] He M, Xiao B, Hu Z, Liu S, Guo X, Luo S. Syngas production from catalytic gasification of waste polyethylene: influence of temperature on gas yield and composition. Int J Hydrogen Energ 2009;34:1342–8. [17] Hu M, Guo D, Ma C, Luo S, Chen X, Cheng Q, et al. A novel pilot-scale production of fuel gas by allothermal biomass gasification using biomass micron fuel (BMF) as external heat source. Clean Technol Environ 2016;18:743–51. [18] Cai J, Wu W, Liu R. An overview of distributed activation energy model and its application in the pyrolysis of lignocellulosic biomass. Renew Sust Energ Rev 2014;36:236–46.
[19] Scott SA, Dennis JS, Davidson JF, Hayhurst AN. An algorithm for determining the kinetics of devolatilisation of complex solid fuels from thermogravimetric experiments. Chem Eng Sci 2006;61:2339–48. [20] Mythili R, Venkatachalam P, Subramanian P, Uma D. Characterization of bioresidues for biooil production through pyrolysis. Bioresource Technol 2013;138:71–8. [21] Lopez-Velazquez MA, Santes V, Balmaseda J, Torres-Garcia E. Pyrolysis of orange waste: a thermo-kinetic study. J Anal Appl Pyrol 2013;99:170–7. [22] Foyle T, Jennings L, Mulcahy P. Compositional analysis of lignocellulosic materials: evaluation of methods used for sugar analysis of waste paper and straw. Bioresour Technol 2007;98:3026–36. [23] Caballero MA, Aznar MP, Gil J, Martín JA, Francés E, Corella J. Commercial steam reforming catalysts to improve biomass gasification with steam-oxygen mixtures. 1. Hot gas upgrading by the catalytic reactor. Ind Eng Chem Res 1997;36:5227–39. [24] Lv P, Chang J, Wang T, Fu Y, Chen Y, Zhu J. Hydrogen-rich gas production from biomass catalytic gasification. Energ Fuel 2004;18:228–33. [25] Yamashita T, Hayes P. Analysis of XPS spectra of Fe2+ and Fe3+ ions in oxide materials. Appl Surf Sci 2008;254:2441–9. [26] Lacˇnjevac U, Jovic´ BM, Jovic´ VD. Morphology and composition of the Fe–Ni powders electrodeposited from citrate containing electrolytes. Electrochim Acta 2009;55:535–43. [27] Shurupov SV, Tesner PA. Soot formation in isothermal pyrolysis of carbon tetrachloride and its mixture with methane. Combust Explo Shock 1999;35:386–92. [28] Menon PG. Coke on catalysts-harmful, harmless, invisible and beneficial types. J Mol Catal 1990;59:207–20.
Contents lists available at ScienceDirect
Energy Conversion and Management journal homepage: www.elsevier.com/locate/enconman
Kinetic study and syngas production from pyrolysis of forestry waste Mian Hu a, Xun Wang a, Jian Chen a, Ping Yang a, Cuixia Liu b, Bo Xiao a, Dabin Guo a,⇑ a b
School of Environmental Science & Engineering, Huazhong University of Science and Technology, Wuhan 430074, China School of Energy and Environmental Engineering, Zhongyuan University of Technology, Zhengzhou 450007, China
a r t i c l e
i n f o
Article history: Received 2 November 2016 Received in revised form 13 December 2016 Accepted 28 December 2016
Keywords: Forestry waste Pyrolysis Kinetic Syngas
a b s t r a c t Kinetic study and syngas production from pyrolysis of forestry waste (pine sawdust (PS)) were investigated using a thermogravimetric analyzer (TGA) and a fixed-bed reactor, respectively. In TGA, it was found that the pyrolysis of PS could be divided into three stages and stage II was the major mass reduction stage with mass loss of 73–74%. The discrete distributed activation energy model (DAEM) with discrete 200 first-order reactions was introduced to study the pyrolysis kinetic. The results indicated that the DAEM with 200 first-order reactions could approximate the pyrolysis process with an excellent fit between experimental and calculated data. The apparent activation energies of PS ranged from 147.86 kJmol1 to 395.76 kJmol1, with corresponding pre-exponential factors of 8.30 1013 s1 to 3.11 1025 s1. In the fixed-bed reactor, char supported iron catalyst was prepared for tar cracking. Compared with no catalyst which the gas yield and tar yield were 0.58 N m3/kg biomass and 201.23 g/ kg biomass, the gas yield was markedly increased to 1.02 N m3/kg biomass and the tar yield was decreased to only 26.37 g/kg biomass in the presence of char supported iron catalyst. These results indicated that char supported iron catalyst could potentially be used to catalytically decompose tar molecules in syngas generated via biomass pyrolysis. Ó 2017 Elsevier Ltd. All rights reserved.
1. Introduction Forestry wastes, in general, and all biomass residues, can be used as raw materials for the generation of liquid biofuels, syngas, chemicals, or charcoal via pyrolysis and liquefaction processes [1– 3]. Thermochemical conversion methods, i.e. pyrolysis, gasification and combustion, are the most commonly employed and the most appropriate for these purposes. Most of the biomass have a heterogeneous property attributes to the fact that the biomass itself composes of numerous components, such as hemicellulose, cellulose, lignin, and minor amounts of extractives. Such of these components, in fact, contribute to the actual reaction mechanism of biomass pyrolysis possibly is extremely complex [4]. The proportion and composition of pyrolysis products are crosswise affected by many factors such as biomass type, feedstock pretreatment, and pyrolysis conditions. During the biomass pyrolysis, a large number of reactions take place in parallel and series, including dehydration, depolymerisation, isomerization, aromatisation, decarboxylation, and charring [4,5]. Kinetic modeling of pyrolysis can help to describe practical conversion processes and optimize the design of efficient reactors [6]. ⇑ Corresponding author at: School of Environmental Science & Engineering, Huazhong University of Science and Technology, Wuhan 430074, China. E-mail address: [email protected] (D. Guo). http://dx.doi.org/10.1016/j.enconman.2016.12.086 0196-8904/Ó 2017 Elsevier Ltd. All rights reserved.
Thermogravimetric analysis (TGA) is a useful technique for studying the decomposition reactions of a solid and it has been widely used to study the apparent kinetics of biomass pyrolysis [7]. By using the TGA data, the kinetic parameters as well as pyrolysis mechanism can be determined according to different mathematical approaches. The single-step global model couples with different iso-conversional method, is the most used kinetic approach. However, as mentioned above, the biomass pyrolysis process is extremely complex due to the difference in decomposition of the biomass components. The iso-conversional method is considered to be conflicting rather than complementary when treating the kinetics of complex reaction system. According to the International Confederation for Thermal Analysis and Calorimetry (ICTAC) Kinetics Committee recommendations, multi-step reaction model is more suitable to simulate solid fuels (such as coal and biomass) pyrolysis kinetic [8]. Among many multi-step reaction models, distributed activation energy model (DAEM) is one of the commonly used in biomass pyrolysis kinetics studies [3,9]. Many methods such as model-fitting method, isoconversional method and discretization method can be used to treat the DAEM and between them the discrete DAEM is even better for DAEM calculation. During biomass pyrolysis, there are many obstacles need to be resolved before it further become a viable commercial renewable energy. The generation of tar in product gas is one of the major
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issues, which is known as energy waste, block pipeline, and even threat to human health. Methods of physical treatment, thermal cracking, plasma-assisted cracking, and catalytic reforming, etc. are considerable to eliminate the tar. Among these methods, catalytic reforming is considered the most promising approach for the tar removes as well as convert to combustible gas [10]. Various types of catalysts such as minerals (iron ores, clay minerals, olivine, calcined rocks) and synthetic catalysts (transition metals-based, activated alumina, alkali metal carbonates, FCC catalysts, and char/char-supported) have been studied on tar removal in biomass pyrolysis/gasification [11–16]. Among these catalysts, the char/ char-supported catalysts have shown low costs and adequate catalytic activities for tar reforming during the pyrolysis/gasification of biomass. In this study, behaviors and kinetics of a representative forestry waste (pine sawdust) pyrolysis were investigated using a discretion DAEM method via thermogravimetric analysis. Meanwhile, char supported iron catalyst used for syngas production was also investigated. 2. Experiment and methods 2.1. Materials Forestry waste (Pine Sawdust (PS), belong the species of macrophanerophytes) was obtained from a furniture factory Wuhan City, Hubei Province, China. The sawdust was naturally dried for a period of 7 days and then grinded and screened into a size of <0.107 mm (pass a Tyler standard screen scale of 100 mesh). Table 1 gives the results of proximate and ultimate analysis of pine sawdust feedstock. Ultimate analysis of the PS samples was measured by a CHNS/O analyzer (Vario Micro cube, Elementar). Such an analysis gave the weight percent of carbon, hydrogen, nitrogen and sulfur in the samples simultaneously. The weight percent of oxygen was determined by differences. The proximate analysis of the PS was conducted following ASTM standard test methods. The low calorific value (LHV) of PS was calculated Mendeleev formula: LHV (MJ/kg) = 339.1C + 1256H 108.8(O-S) 25.1 (9H + M), (where M is the moisture content). The chemical functional groups in PS were investigated by using FT-IR technique (Vertex 70, Germany). PS samples were ground to fine particles and mixed with KBr powder. The mass ratio of samples to KBr powder was 1:100. The spectral resolution was set at 4 cm1. 2.2. Thermogravimetric analysis (TGA) TG/DTA Synchronous analyzer (Diamond TG/DTA, PerkinElmer Instruments) was used to perform the pyrolysis of the samples with sweeping gas of high purity nitrogen (flow rate 100 mL/min). The test temperature was raised from room temperature to 800 °C with various heating rates of 20, 30 and 50 °C/min for samples. 2.3. Catalyst Biochar was obtained from a pilot-scale allothermal biomass gasification system which studied in our previous research [17].
The rice straw as a feedstock for gasification was from the farm in Wuhan City, Hubei province, China. Firstly, biochar was gently crushed and sieved into 0.5–1 mm size fraction. Secondly, the biochar sample was washed with deionized water for several times and oven dried (80 °C) as catalyst support. Fe/biochar catalyst, prepared by dissolving 21.6 g of Fe(NO3)39(H2O) in 200 mL of deionized water, was mixed with 47.0 g biochar for 12 h under continuous strong agitation using a magnetic stirrer, and then dried at 105 °C for 24 h. The resulting product is the Fe/biochar catalyst. 2.4. Apparatus and procedure The used facility in this experiment includes two stages which are pyrolysis zone and catalysis zone, shown in Fig. 1. The effective length of the pyrolysis furnace is 700 mm with an outer diameter (OD) of 80 mm (ID. 75 mm), and the fixed catalytic reactor is 800 mm in height and 65 mm in outer diameter (ID. 60 mm). Pyrolysis and catalytic bed are both made of quartz glass and externally heated by electrical ring furnaces. Before the experiments, 5.0 g catalyst was put into the catalytic furnace. Then, nitrogen (with a constant flow rate of 0.1 L/min) was injected into the reactor for 20 min to maintain an inert atmosphere. Subsequently, the pyrolysis and catalytic reactors were heated in a heating rate of 30 °C/min to achieve the set-point temperature (800 °C), respectively. 5.0 g PS was put into the stainless steel boat and placed into the middle of quartz tubular reactor. The set-point temperatures were considered as the pyrolytic and catalytic temperature which would be held for 15 min. During the experiment process, the volatile flowed out of the reactor and was condensed by ice bath. The condensable volatiles (i.e. bio-oil) were captured in the collector and the non-condensable gas whose volume was measured by a gas flow meter. The main composition of non-condensable gas was analyzed by GC 9800T which functioned based on a thermal conductivity detector (TCD) with TDX-01 columns. The temperatures of the injector, oven and detector were at 200 °C, 85 °C and 90 °C, respectively. The carrier gas in all analyses was argon. Standard gas mixtures were examined by the quantitative calibration. At the end of the pyrolysis, the furnace was cooled down to room temperature and the mass of the solid char as well as bio-oil yields could be calculated. 2.5. Distributed activation energy model (DAEM) kinetics Conversion (a) form of apparent reaction rate of solid-state material thermal decomposition in TGA process can be described as:
da=dt ¼ kðTÞf ðaÞ
ð1Þ
where t is the reaction time (s); T denotes absolute temperature (K); k (T), also is known as the reaction constant, is the temperature dependent function in apparent kinetic; and f(a), which is known as the reaction mechanism model, is depends on its independent variable conversion (a):
a ¼ ðm0 mt Þ=ðm0 mf Þ
ð2Þ
Table 1 Ultimate and proximate analysis of PS sample.b Ultimate Analysis (Wt.%)
Proximate Analysis (Wt.%)
LHV (MJ/kg)
C
H
Oa
N
S
M
V
F
A
46.36
5.75
43.62
2.26
0.32
4.54
82.18
16.13
1.69
M, Moisture Content; V, Volatile Matter; F, Fixed Carbon; A, Ash; LHV, Low Heating Value. a By difference. b Air dry basis.
16.81
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Fig. 1. Schematic of the PS catalytic pyrolysis facility.
where m0 (%), mt (%) and mf (%) corresponds to the mass of initial, instantaneous and final of pyrolysis state. In order to use the homogeneous kinetic theory in apparent kinetics, Arrhenius equation (Eq. (3)) can approximable express the k (T) term of Eq. (1).
E kðTÞ ¼ A exp RT
ð3Þ
In Eq. (3), E is the apparent activation energy (Jmol1); A is the apparent pre-exponential factor (min1); R = 8.3145 Jmol1K1 is the ideal gas constant. Then, reaction rate equation for PS pyrolysis according to Eqs. (1) and (3) by means of a linear non-isothermal heating rate, b (b = dT/dt, K/s), gives:
A E f ðaÞ da=dT ¼ exp b RT
ð4Þ
After variables separation and boundary value integral (a 2 [0, a], T 2 [T0, T]), the integral form of Eq. (4) is obtained as:
GðaÞ ¼
Z
0
a
da A ¼ f ðaÞ b
Z
T
T0
expðEa =RTÞdT
A wðE; TÞ b
ð5Þ
where G(a) is integral of f (a); w(E, T) is the temperature integral who has not an analytical solution but can be replaced with approximable expressions. For the simplest first-order reaction f(a) = 1 a, Eq. (5) becomes
A 1 a ¼ exp wðE; TÞ b
ð6Þ
The distributed activation energy model (DAEM) is a mechanism that is combined by an infinite irreversible first order reaction. The apparent activation energy in DAEM is a continuous R distribution f(E) with f(E)dE = 1. Then, the conversion function according to DAEM is given as:
x¼1a¼
Z 0
1
Z A T E dT f ðEÞdE exp exp b T0 RT
ð7Þ
Many forms of f(E) such as Gaussian, Weibull, Logistic, Gamma distribution, etc. were used in biomass pyrolysis kinetics [18]. By substituting different forms of f(E) into Eq. (7), the kinetic parameters E, A can be obtained by using optimization algorithms.
However, such method will leads to a large calculation difficulty due to the improper integral. And pre-exponential factor (A) is a constant, which can no reflect the complexity of reaction process. Scott et al. [19] presented an algorithm providing a detailed description of the kinetics of devolatilization of complex fuels from TGA. This algorithm discrete the infinitely number into limited many of first-order reaction, each of which are characterized uniquely pair of activation energy and pre-exponential factor. According to Scott et al. [19], remaining conversion (x = 1 a) contributed by n number of first-order reactions during pyrolysis process can be expressed as:
" # n X Ai xj ¼ 1 aj ¼ f i exp wj ðE; TÞ bj i¼1 |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}
ð8Þ
W
where subscript j denotes j-th heating rate; n is a hypothetical number of first-order reactions. fi is the contributed weight of i-th firstorder reaction. In order to solve Eq. (8) in matrix form, Eq. (8) can be transferred to:
2
xðT 0 Þ
3
3 f 1;0 6f 7 6 2;0 7 7 6 6 f 3;0 7 7 6 7 6 7 6 7 7 7 6 7 5 4 5
W1 ðT 0 Þ W2 ðT 0 Þ Wn ðT 0 Þ 1 6 W ðT Þ W ðT Þ W ðT Þ 1 7 2 1 n 1 7 6 1 1 7 6 6 W1 ðT 2 Þ W2 ðT 2 Þ Wn ðT 1 Þ 1 7 7 6
6 xðT Þ 7 1 7 6 7 6 6 7 7 6 6 7¼6 7 6 6 7 6 6 4 5 4 xðT n Þ |fflfflfflfflfflffl{zfflfflfflfflffl ffl}
3
2
1 |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}
X
W
2
ð9Þ
|fflfflfflffl{zfflfflfflffl} F
i.e. X = W F. Scott et al. [19] indicated that it is possible to calculate each fi,0 from Eq. (9) by using Nonnegative Linear Least Square Method with constraints:
(
f i;0 2 ½0; 1 Pn i¼1 f i;0 ¼ 1
ð10Þ
Further, for i-th reaction, its contributed remaining mass fraction is given as:
xi ðTÞ ¼ f i WðEi ; TÞ
ð11Þ
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Under two different heating rates runs (b1 and b2) TGA experiments, the same remaining conversion (x) at different heating rate is considered to have a same pair of Ei and Ai, i.e. fi(b1, T1) = fi(b2, T2). Then, Wi ðb1 ; T 1 Þ ¼ Wi ðb2 ; T 2 Þ can be deduced from Eq. (7), as:
" Z 1 Ei Ei 1 expðuÞ Ei du T 1 exp T 0;b1 exp b1 u RT 0;b1 R E=ðRT 0;b Þ RT 1 1 # Z 1 Ei expðuÞ du þ u R E=ðRT 1 Þ " Z 1 Ei Ei 1 expðuÞ Ei ¼ du T 2 exp T 0;b2 exp b2 u RT 0;b2 R E=ðRT 0;b Þ RT 2 2 # Z 1 Ei expðuÞ du ð12Þ þ u R E=ðRT 2 Þ
where T 0;b1 , T 0;b2 corresponds to the initial temperature of b1 and b2; u = E/(RT). Activation energy of i-th reaction Ei can be calculated from Eq. (12). After the Ei is obtained, since x = 1 e1 and Wi = e1, pre-exponential factors Ai will be estimated by solving the following equation under given two heating rates:
" Z Ai Ei Ei þ1 expðuÞ du T 0;b1 exp u b1 RT 0;b1 R Ei =ðRT 0;b Þ 1 # Z Ei Ei þ1 expðuÞ þ du T 2 exp u RT 2 R Ei =ðRT 2 Þ
50 °C/min. It is noticeable that the DTG curves can be divided into three stages. Stage I take place in lower temperature range. In this stage, the weight loss is mainly contributed to the evaporation of adsorbed water and light volatiles. Stage II, which covers a wide temperature range and manifests high mass loss percentage, is caused by the devolatilization. The weight loss rate is very slow in Stage III. Such slow rate is attributed to slow decomposition of lignin and even the secondary decomposition of solid residues. Table 2 lists the mass loss and characteristic temperatures at different heating rates. It can be observed that Stage II, accounted for about 73–74% mass loss, is the main stage during the PS pyrolysis process. In addition, the initial temperature (Ti), final temperature (Tf), and peak temperature (Tp) increase with the increasing in heating rate. However, increasing of heating rate only shift the peaks temperature to higher value and do not change the thermal profile of decomposition (Fig. 3). The lateral shift of DTG curves to higher temperature and the increase of characteristic temperatures attribute to the combined effects of heat transfer and mass transfer, which render as thermal lag. 3.3. DAEM kinetic
lnðwi Þ ¼
ð13Þ
3. Results and discussion 3.1. Characterization of pine sawdust The proximate and ultimate analysis results, as well as the low calorific value (LHV) of pine sawdust (PS) are listed in Table 1. As seen in Table 1, proximate analysis results for PS have a high amount of volatile matter content (82.18%) which could be considered suitable for combustion, pyrolysis or gasification process. Another important feature for PS is that the ash content (1.69%) is very low than previous studied biomass in literatures, such as hazelnut husk (5.27%) [6], rice husk (4.57%) [20], orange waste (3.02%) [21]. Ash can limit heat and mass transfer while producing all kinds of problems such as agglomeration, slagging, and fouling in boilers. The lower heating value of PS is 16.81 MJ/kg that can be considered as an attractive biomass to be used as an alternative fuel. To clarify the internal chemical structure of PS, FTIR analysis was conducted and the result is displayed in Fig. 2. It is obvious that there are two peaks at 3341 cm1 and 1032 cm1 corresponding to the free of OH and primary alcohol CAOH functional groups, respectively. It indicates that there are a large number of hydroxyl groups in the PS. The peaks at 2931 cm1 and 1461 cm1 are clearly display the CAH absorption peaks, which are corresponding to the saturated hydrocarbon ACH2- and ACH-, respectively. However, the CAH bond could be easily broken and form alkane gas (H2, CH4, C2H6). The peak at 1650 cm1 is likely indicative of C@O amide stretching (R-CONH2), which easily to form the main components of tar (ketones, aldehydes, esters, amides, and acrylic) during the pyrolysis process. Moreover, the existence of the R-CONH2 is the main source of release NOX during the biomass combustion process. Furthermore, there are plenty of weak peaks appeared in spanning 800–1500 cm1, which indicated benzene ring exists. 3.2. Thermal characteristics of PS Fig. 3 gives the TG/DTG profiles pine sawdust (PS) pyrolysis obtained from heating rates of 20 °C/min, 30 °C/min and
The kinetic of the stage II of PS is studied which attribute to the stage II is the main stage in the pyrolysis process. In this stage, PS is discretized as 200 first-order reactions between remaining conversion X from 0.999 to 0.001 in this study. Two different heating rates (20 °C/min, 30 °C/min) were chosen to calculate the activation energy (Ei) by solving Eq. (12). When the Ei is known, the preexponential factor (Ai) can be calculated by solving Eq. (13). After the Ei and Ai were obtained, the initial mass fraction (fi,0) of the given conversion could be calculated from Eq. (9) using the Nonnegative Linear Least Square Method with constraint Eq. (10). Each conversion corresponding to a first-order reaction contains a characteristic activation energy, a pre-exponential factor and an initial mass fraction. The variation of activation energy along with conversion (a) for PS is shown in Fig. 4a which reflects the complexity of PS during the pyrolysis processed due to the big variation of activation energy with conversion. And the activation energy is in range of 147.86–395.76 kJmol1. It can also be seen from the compensatory effect between activation energy and preexponential factor Fig. 4b that the stage II of PS can be divided into multi-parts. The main reason may be that exist in the interaction between pseudo components (hemicellulose, cellulose and lignin) in PS, mineral catalysis and secondary reactions between pyrolysis products. Moreover, the values of lnAx changes in the range of 32.05–58.70 which means the pre-exponential factor have wide range of 8.30 1013 s1–3.11 1025 s1. It can be obviously instructed that the activation energy increases very fast in the late reaction. However, the activation energy denotes the energy barrier which pyrolysis reaction has to be overcome. By discretizing the pyrolysis process into 200 first-order reactions, the weights (fi,0) of these reactions contributed to the whole pyrolysis is obtained from Nonnegative Linear Least Square method. As depicts in Fig. 4c, 200 reactions are actually not all contributed to the pyrolysis process. At the conversion ranges of about 0.2–0.6 and 0.62–0.86, the weights of corresponding reactions are zero and can be considered as non-effective contributions to the pyrolysis. However, the distribution of such effective weights is quite complex and is possibly attributed to the complex mechanism of lignocellulosic biomass (forestry residue in this study) pyrolysis process, which is caused by the interaction of devolatilization, diffusion effect, catalyst and secondary reactions. Although the complexity of fi,0 vs. a distribution, a feature can be observed from Fig. 4c, i.e. the weights of low conversion (0–0.2), 0.6, and high conversion (greater than 0.86) are obvious. Such a
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0.65 0.60 C=O
0.55 C-H
0.50
Transmittance
C-H C-OH
0.45 0.40
O-H
0.35 0.30 0.25 0.20 4000
3500
3000
2500
2000
1500
1000
500
-1
Wavenumber (cm ) Fig. 2. FTIR spectra profiles of PS sample.
20
354.76
80
TG:
348.79
TG (%)
DTG:
20oC/min 30oC/min 50oC/min
20oC/min 30oC/min 50oC/min Stage
60
Stage 40
15
Stage
10
DTG (%/min)
100
329.74 5
20
0 0
100
200
300
400
500
600
700
0 800
Temperature (oC) Fig. 3. TG/DTG curves at different heating rate for Pine Sawdust (PS). Table 2 Characters of PS in TGA process under different heating rates. HR (°C/min)
Ti (°C)
Tf (°C)
ML (%)
Stage I
20 30 50
163.24 174.31 191.65
3.18 3.45 3.67
Stage II
20 30 50
163.24 174.31 191.65
614.73 635.90 638.22
73.04 73.34 74.46
Stage III
20 30 50
614.73 635.90 638.22
798.90 787.45 780.91
2.94 1.05 1.96
Residue (%)
20 30 50
20.84 22.16 19.91
where ‘‘” is room temperature, Ti and Tf is the initial temperature and final temperature of each stage, respectively; ML is the mass loss in each stage.
feature considers that the pyrolysis process can be contributed to main four reactions range and similar to the components independent parallel reaction (IPR) model, in which the main components (extractives, pseudo hemicellulose, cellulose, lignin) decompose independently. At low conversion range of 0–0.07, the distribution of fi,0 vs. a is unordered and weak. In such range, the mass loss of sample is mainly contributed to the precipitation of extractives such as formic acid, acetic acid, alcohol matter, and even combined water. At the conversion of about 0.2, the weight is very obvious and is contributed to the pyrolysis of pseudo hemicelluloses. The weight fraction of pseudo cellulose is the highest corresponding to conversion of 0.6. The weight distribution at conversion range of 0.86–1 is quite unordered, which is possibly contributed to the lignin decomposition at high temperature or the secondary reaction between volatiles and char. In general, pseudo hemicellulose is a mixture of many kinds of sugar monomers such as xylan,
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arabino-xylan, arabino-glucurono-xylan, glucurono-xylan and galacto-arabino-glucorono-xylan [22]. The sugar monomers are far from the further formation of long chains. The sugar monomers are weakly restrained by long chains and their decomposition temperature is about 200–400 °C (473–673 K). Thus, the AC@O, AOH, AC@C bonds are easily to be broken down under relatively low temperature and generate CO2, CH4, CO gas and low molecular organic acids. Pseudo cellulose can be seen as a crystal which is a 400
(a)
350
linearly extending structure of (C6H10O5)n unit. Its terminated temperature of decomposition is a higher than hemicelluloses due to the long chain structure. Account for the crystal properties, the cellulose completes its decomposition quickly once temperature reaches its characteristic temperature about 623–673 K. As can be seen from Fig. 3, when the temperature is higher than the peak temperature, a nearly vertical down steep slope can be observed on the right hand side of DTG curves. Such slope is mainly attributed to the fast decomposition contribution of cellulose, and contributes to the rapid decline of whole DTG curves. It is believed that the lignin is a mixture of varieties of compounds which contains benzene ring. The benzene ring is extremely strong and leads to
1.0
(a)
250
0.8
Remaining Conversion X
Ea(kJ/mol)
300
200
150
100
0.0
0.2
0.4
0.6
0.8
1.0
0.6 Experiment: HR20 HR30 HR50
DAEM Simulation: HR20 HR30 HR50
0.4
0.2
Conversion α 70
0.0
(b)
60
400
600
700
800
900
Temperature(oC) 0.000
50
lnAx
500
(b)
-0.002
40
-0.004
dX/dT
30
20
10 100
150
200
250
300
350
400
Ex (kJ/mol)
-0.006
Experimental data: HR20 HR30 HR50
-0.008
Calculated data: HR20 HR30 HR50
-0.010 -0.012
0.40
-0.014
(c)
0.35
400
500
700
800
900
Temperature(oC)
0.30
Fig. 5. Comparison between experimental results and DAEM simulation results. (a) TG of PS; (b) DTG of PS.
0.25
f0,i
600
0.20 Table 3 Effect of catalysts on product yields and gas composition
0.15 0.10 0.05 0.00 0.0
0.2
0.4
0.6
0.8
1.0
Conversion α Fig. 4. DAEM kinetic parameters calculated for PS pyrolysis. (a) Change of activation energy with conversion a; (b) relationship between the activation energy and pre-exponential factor; (c) initial mass fraction of each component.
Without Catalyst
Biochar
Fe/biochar
Pyrolysis temperature (°C) Catalytic temperature (°C) Gas yield (N m3/kg biomass) Tar yield (g/kg biomass) Char yield (kg/kg biomass)
800 800 0.58 201.23 0.25
800 800 0.72 147.25 0.22
800 800 1.02 26.37 0.11
Gas composition H2 (vol.%) CO (vol.%) CO2 (vol.%) CH4 (vol.%)
19.43 25.34 20.15 13.56
25.36 16.08 27.01 10.67
39.88 23.34 14.27 5.89
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a high thermal stability of lignin. Thus, reaction rate of lignin is quite slow and covers the whole pyrolysis temperature range, even completes its decomposition nearly 900 °C. The calculated of Ex, Ax and f0,i were substituted into Eq. (8) and the X of different heating rates sets of PS was calculated by Lobatto Quadrature method (the quadl solver of MATLABÒ). The calculated and experimental X of PS at different heating rates were compared and given in Fig. 5. As can be seen from Fig. 5a, the discrete DAEM can fit the experimental data very well. Similar fits also can be seen from the DTG curves (Fig. 5b). These results indicate that the DAEM with 200 first-order reactions is suitable for PS pyrolysis kinetic study. 3.4. Catalytic reforming 3.4.1. Effect of catalyst Compared with the case of without catalyst, the Fe/biochar or biochar as catalyst for tar cracking and gas quality improving are investigated. The temperature of pyrolysis furnace and catalytic furnace are set as 800 °C. The test results are presented in Table 3. It can be seen that the gas yield and tar yield are 0.58 N m3/kg biomass and 201.23 g/kg biomass in pyrolysis of PS without catalyst. However, when the catalytic bed loads with Fe/biochar catalyst, the gas yield is markedly increased to 1.02 N m3/kg biomass, but the tar yield is decreased to only 26.37 g/kg biomass. The significant increase in gas yield is predominantly due to the secondary
459
cracking of vapors over catalysts. Moreover, the char yield decrease from 0.25 kg/kg biomass to 0.11 kg/kg biomass may be another reason to increase the gas yield (Boudouard reaction and/or Water gas (primary) reaction). With biochar catalyst, the gas yield is lower than that with Fe/biochar catalyst, while the tar yield trends to be opposite. This indicates that the Fe/biochar catalyst is more effective for the tar reduction and gas production in the pyrolysis of PS. Table 3 also shows that the catalyst had a significant effect on produced gas composition, and observably increase of H2 content, and decrease of CH4; however, CO and CO2 contents changed slightly. Those changes of gas composition might be attributed to several important reactions (water-gas (primary) reaction, steam reforming methane reaction, water-gas shift reaction and secondary cracking tar reaction) that occur simultaneously in catalytic reactor and are favorable for H2 production. Therefore, the content of H2 in pyrolysis of PS with the Fe/biochar catalyst increases roughly by 20 vol.% compared with that without catalyst. Meanwhile, the Fe/biochar catalyst is more effective than biochar catalyst in promoting the hydrocarbons reforming and the water– gas shift reaction, which results in a significant increase of the content H2 and a decrease of the content CH4. In comparison with the case in the absence of catalyst, CO and CO2 contents exhibit a dropping trend with the Fe/biochar catalyst. Hu et al. [17] also found the same trend with a variation of gas composition for CO and CO2 after the catalytic reactor. However, in the study of Cabballero et al. [23], CO content increases while CO2 content decreases after the catalytic
Fig. 6. SEM-EDS of (a) biochar and (b) Fe/biochar catalyst (before reaction).
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Usually, the ionic states of Fe determines the peak position of Fe 2p1/2 and Fe 2p3/2. According to the previous studies [25,26], the binding energy of FeO, Fe2O3, Fe3O4, and Fe0 distribute, respectively, at 109.4 eV, 710.9 eV, (708.2 eV, 710.4 eV) and (706.6 eV, 719.3 eV). It is clearly observed that from the comparison of the two figures (Fig. 7b and c), a peak at the binding energy of 719.32 eV of Fe 2p1/2 appears after catalytic reforming, which associated with Fe0. In other words, the iron oxide could be reduced by carbon and reducing gas (CO, H2) which comes from biomass pyrolysis. The oxidation-reduction processes might be described by the following reactions as shown in Eqs. (14)–(17):
reactor; opposite trends are reported in the study of Lv et al. [24]. This difference possibly results from the different reactor, operating conditions, and gasifying agents used.
3.4.2. SEM-EDS and XPS analysis Fig. 6(a) and (b) illustrates the SEM-EDS images of biochar and Fe/biochar catalyst (before reaction), respectively. It is clear from the comparison (EDS results) of the two figures that the occurrence of Fe in the surface of the Fe/biochar, suggesting that iron is successfully impregnated into the biochar. In order to investigate the valence state of Fe during the catalytic reforming process, the Fe/biochars (before reaction and after reaction) are analyzed be XPS (Fig. 7). Surface of biochar is enriched with only carbon and oxygen (not given the atomic % in the experiment, but really exist in the binding energy of about 529 eV) and a small amount of silicon while Fe appear in the biochars after impregnation and reaction, respectively (Fig. 7a). These results are consistent with the consequences of EDS analyses.
Carbon reduction : C þ Me2þ ! Me þ C2þ nC þ Mem On ! mMe þ nC Gas reduction : nH2 þ Mem On ! mMe þ nH2 O
ð16Þ
nCO þ Mem On ! mMe þ nCO2
ð17Þ
where Me2+ and MemOn corresponds to metal ions and metallic oxides, respectively.
Fe/biochar (after reaction)
(a) Fe LMM
97.44
Si 2p
0.43
Fe 2p
2.13
Fe 2p1 Fe 2p3
Fe LMM1
Fe LMM2
Fe 3s Fe 3p
Fe/biochar (before reaction) Atomic % 88.63 0.68
Fe 2p
10.69
biochar Atomic % C 1s
99.87
Si 2p
0.13
Fe 3s Fe 3p
Si 2p
Si 2p
Intensity (cps)
C 1s
Atomic % C 1s
C 1s
ð14Þ ð15Þ
1200
1000
800
600
400
200
0
Binding Energy (eV)
Fe2p
2p3/2
2p1/2
Fe2p
2p3/2
2p1/2
Intensity
Intensity
2p1/2
(c)
(b) 730
725
720
715
Binding Energy (eV)
710
705
730
725
720
715
710
705
Binding Energy (eV)
Fig. 7. (a) XPS analysis of biochar and Fe/biochar (before and after reaction). (b) High-resolution XPS spectrum of Fe 2p from Fe/biochar before reaction. (c) High-resolution XPS spectrum of Fe 2p from Fe/biochar after reaction.
M. Hu et al. / Energy Conversion and Management 135 (2017) 453–462
461
Fig. 8. Mechanism of tar reforming using char/char-supported catalyst. Note: the red dotted line represents the simple cracking reactions. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
3.4.3. Mechanism of tar reforming using char/char-supported catalyst The main mechanism during tar conversion over char/charsupported catalyst can be illustrated in Fig. 8. The char particle is considered as a sphere surrounded by a pyrolysis gas film which comes from the front-end pyrolysis furnace. The tar is adsorbed on the active sites of the char particle surface, while it has two parallel pathways to decompose. The first way is a catalytic conversion of tar to CO and H2 by steam and dry gasification reactions. The second way is a decomposition of tar to form free radicals which are further recombined into low molecular compounds and form coke deposited on the surface of char/charsupported catalyst. It has been noted that the tendency toward coke formation depends linearly on the number of aromatic rings in the tar component [27]. The catalytic reactions accompanied by coke formations were classified as coke-sensitive or cokeinsensitive [28]. Coke-insensitive reactions, e.g. catalytic reforming, hydrotreating, hydrocracking, steam reforming, are encountered whenever there is a strong hydrogenation component in the catalyst. Therefore, the strong hydrogenation activity of the catalyst keeps the surface produce relatively reactive coke precursors form on active sites which readily removed by pyrolysis agent. In contrast to the above are the coke-sensitive reactions, in which the coke is deposited directly in the active sites on the catalyst. This coke is unreactive (because there is no hydrogen, or no strongly hydrogenating catalyst function in the system) and can be compared to typical catalyst poisoning, in which the activity falls rapidly with increasing quantity of coke on the surface of catalyst. Tar is expected to be decomposed over char/char-supported catalyst by the steam and dry reforming reactions. These reactions probably produce insensitive coke which can be effectively controlled by adapting the operating conditions in such a way that the gasification rate of coke is higher than the production rate. 4. Conclusions Pyrolysis behaviors and kinetics of forestry waste (pine sawdust) were studied. The decomposition process of pine sawdust could be divided into three stages. The kinetics of the main stage II was modeled in modified discretize distributed activation energy model (DAEM). The DAEM with 200 first-order reactions showed
an excellent fit between experimental results and simulated data. With the Fe/biochar catalyst, the tar yield obviously decreased, and the gas yield significantly increased. Under the condition of the experiment, the gas yield is 1.02 N m3/kg biomass and the tar yield is 26.37 g/kg biomass. The results indicated that there was a strong potential for producing syngas from pine sawdust which are essentially waste materials by pyrolysis process with inexpensive char/char-supported catalyst. Acknowledgment This research was supported by the China Postdoctoral Science Foundation – China (2015M580644 and 2016M592339), Wuhan International Science and Technology Cooperation Project (2016030409020221) and Ministry of Housing and Urban-Rural Development – China (No. 2016K4032). Authors would also like to thank the Analytical and Test Center of Huazhong University of Science and Technology for carrying out the analysis of the characterization of the samples. References [1] Abomohra AE, Jin W, El-Sheekh M. Enhancement of lipid extraction for improved biodiesel recovery from the biodiesel promising microalga Scenedesmus obliquus. Energ Convers Manage 2016;108:23–9. [2] Chan YH, Yusup S, Quitain AT, Tan RR, Sasaki M, Lam HL, et al. Effect of process parameters on hydrothermal liquefaction of oil palm biomass for bio-oil production and its life cycle assessment. Energ Convers Manage 2015;104:180–8. [3] Soria-Verdugo A, Garcia-Hernando N, Garcia-Gutierrez LM, Ruiz-Rivas U. Analysis of biomass and sewage sludge devolatilization using the distributed activation energy model. Energ Convers Manage 2013;65:239–44. [4] Collard F, Blin J. A review on pyrolysis of biomass constituents: mechanisms and composition of the products obtained from the conversion of cellulose, hemicelluloses and lignin. Renew Sust Energ Rev 2014;38:594–608. [5] Vamvuka D. Bio-oil, solid and gaseous biofuels from biomass pyrolysis processes—an overview. Int J Energ Res 2011;35:835–62. [6] Ceylan S, Topçu Y. Pyrolysis kinetics of hazelnut husk using thermogravimetric analysis. Bioresource Technol 2014;156:182–8. [7] Sharma R, Sheth PN, Gujrathi AM. Kinetic modeling and simulation: pyrolysis of Jatropha residue de-oiled cake. Renew Energ 2016;86:554–62. [8] Vyazovkin S, Burnham AK, Criado JM, Pérez-Maqueda LA, Popescu C, Sbirrazzuoli N. ICTAC Kinetics Committee recommendations for performing kinetic computations on thermal analysis data. Thermochim Acta 2011;520:1–19.
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