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Characterisation of coal and biomass based on kinetic parameter distributions for pyrolysis
Fuel 114 (2013) 206–215
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Characterisation of coal and biomass based on kinetic parameter distributions for pyrolysis Nozomu Sonoyama a,⇑, Jun-ichiro Hayashi b a b
Coal & Environment Research Laboratory, Coal Business Office, Petroleum & Coal Marketing Department, Idemitsu Kosan Co., Ltd., 3-1 Nakasode, Sodegaura, Chiba 299-0267, Japan Division of Advanced Device Material, Institute for Materials Chemistry and Engineering, Kyushu University, 6-1, Kasuga Koen, Kasuga, Fukuoka 816-8580, Japan
a r t i c l e
i n f o
Article history: Received 12 December 2011 Received in revised form 16 April 2012 Accepted 17 April 2012 Available online 5 May 2012 Keywords: Distributed activation energy model Kinetic parameters Solid fuels Pyrolysis
a b s t r a c t We performed pyrolysis analyses of various biomasses and biomass-derived materials using a thermogravimetry device and a wire-mesh reactor. Kinetic parameters, which were a frequency factor and an activation energy, were derived based on a distributed activation energy model. We validated the prediction of thermogravimetric curves calculated using the kinetic parameters in the case of rapid pyrolysis of biomass having the least interaction between particles and volatiles. Unlike coal, the kinetic parameters of the samples changed markedly with progression of pyrolysis. We estimated that the low activation energy in the initial step was caused by hydration and the volatilisation of lighter components. Some biomass samples showed a decrease in both the frequency factor and activation energy during pyrolysis. Changes in the chemical structures of xylan and cellulose from cyclic aliphatic units to aromatic units, accompanying amorphism, softening, or melting of part of the solid phase in some cases, caused a decrease in kinetic parameters during pyrolysis. Both the frequency factor and activation energy of all biomass samples increased in the final stages, which was considered to be the result of char forming by carbonisation. Analysing in detail the changes in kinetic parameters provided information on the behaviour of the volatilisation, the change in solid state, and the extent of char structural development. Ó 2012 Elsevier Ltd. All rights reserved.
1. Introduction World energy consumption increases yearly. We need to meet the demand for increased energy and also cope with environmental problems such as SO2 and NOx emissions and global climate change. Addressing global climate change requires decreasing fossil fuel consumption and increasing the use of renewable energy resources. Thus, the use of a great variety of the renewable energy resources for energy generation has expanded. The operating conditions of an existing boiler can be altered to match the combustion characteristics of renewable energy fuels, and new better boilers have been designed specifically for such fuels. Coal and biomass co-combustion technology is also an effective combination. The combustion characteristics of various biomass products differ from those of coal. Thus, it is difficult to predict the behaviour of combustion and ash deposition in a boiler for co-combustion and biomass combustion. Many studies on the structure and analysis of coal and biomass have been conducted previously. However, there has been little work to establish a method for the comprehensive evaluation of widely varying fuels.
⇑ Corresponding author. Tel.: +81 438 62 9511; fax: +81 438 60 1177. E-mail address: [email protected] (N. Sonoyama). 0016-2361/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.fuel.2012.04.023
Pyrolysis is essential for combustion and gasification, which is the first step in decomposition by heating. Thus, understanding pyrolysis will help in developing new processes and resolving the mechanisms of combustion and gasification. Thermogravimetric analysis is widely used in physical chemistry, materials research, and thermal analysis. Thermogravimetric analysis provides findings on volatilisation behaviour over a wide range of temperatures. However, the pyrolysis of solid fuels is complex because it includes not only volatilisation but also secondary reactions, such as particle-volatile interactions and structural changes in particles due to softening and/or melting. Excluding such effects is difficult in thermogravimetry. We evaluated in detail the kinetics for pyrolysis of solid carbonaceous materials, based on a distributed activation energy model, after investigating experimentally the effects of heating rate and sample height. 2. Experimental 2.1. Samples Oat-spelt xylan was washed with ion-exchanged water after stirring in 0.1-N hydrochloric acid solution over 15 h at ambient temperature. Hydrolytic lignin was washed with ion-exchanged water after stirring in 5-N hydrochloric acid solution over 15 h at
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207
Notation Ci c E Ei Es f fi k0 k0i m
fraction of pseudo component i (–) initial cooling rate (K/s) activation energy (J/mol) activation energy of pseudo component i (J/mol) activation energy based on the step function (J/mol) distribution function parameter (mol/J) distribution function parameter of seudo component i (mol/J) frequency factor (1/s) frequency factor of pseudo component i (1/s) sample weight at a given time (kg)
ambient temperature. Cellulose, sugarcane trash, sugarcane bagasse, pine sawdust, cedar sawdust, coffee residue, and Loy Yang coal were also used. The elemental analysis of the samples is shown in Table 1. Xylan and lignin were sieved in the range of 125–250 lm, cellulose at 74–105 lm, and sugarcane trash, sugarcane bagasse, pine sawdust, cedar sawdust, coffee residue, and Loy Yang coal at 125–210 lm. 2.2. Thermogravimetry using a Thermogravimetry device and a wiremesh reactor Xylan and lignin were weighed in the range of 6.5–7.5 mg-dry, cellulose at 1.2–13.5 mg-dry, and sugarcane trash, sugarcane bagasse, pine sawdust, cedar sawdust, coffee residue, and Loy Yang coal at 3.5–6.5 mg-dry. Each sample was loaded in a platinum cup, of which the inner diameter and height were 4.8 mm and 2.6 mm, respectively. A sample loaded in a platinum cup was set in the thermogravimetry (TG) device, and kept for about 60 min for stabilisation of the TG. Drying the sample was carried out by heating at a rate of 0.167 K/s to 383 K with flowing nitrogen, and then maintaining at 383 K. After drying, the sample was pyrolysed by heating at a rate from 0.0833–1 K/s to 1273 K under a nitrogen atmosphere. The weight of the sample was recorded at that time. Pyrolysis of samples of cellulose, pine sawdust, coffee residue, and Loy Yang coal was performed in a wire-mesh reactor (WMR; Fig. 1). The WMR was used to minimise the interaction between particles and volatiles during pyrolysis. Less than 5 mg of particles was tightly sandwiched in a monolayer formed by two layers of the wire mesh made of SUS316 stainless steel. The sample-laden mesh was heated at a rate of 1 or 1000 K/s from ambient to a prescribed temperature in the range of 573–1173 K, and then cooled to ambient temperature at an initial cooling rate calculated by the follow-
Table 1 Elemental analysis of xylan, cellulose, lignin, sugarcane trash, sugarcane bagasse, pine sawdust, cedar sawdust, coffee residue, and Loy Yang coal (daf: dry ash-free, nd: not detected). Sample
C
H
N
S
Oa
5.9 5.9 5.6 5.9 5.7 5.7 5.6 6.8 5.2
nd nd 0.7 0.3 0.3 nd nd 2.1 0.7
nd nd nd 0.3 nd nd nd nd 0.3
49.3 50.4 29.8 47.0 45.7 44.6 43.9 37.0 26.6
(wt.%-daf) Xylan Cellulose Lignin Sugarcane trash Sugarcane bagasse Pine sawdust Cedar sawdust Coffee residue Loy Yang coal a
By difference.
44.8 43.7 63.9 46.5 48.3 49.7 50.5 54.1 67.2
mchar m0,db R t T Tp X Xi
a b /
char weight at 1173 K (kg) initial sample weight on a dry basis (kg) gas constant (J/(K s)) time (s) temperature (K) prescribed temperature (K) conversion of volatilisable compounds to volatiles (–) final conversion of each stage i (–) coefficient for the compensation effect (–) coefficient for the compensation effect (–) heating rate (K/s)
ing empirical equation, derived from the temperature profiles of WMR experiments:
c ¼ 2:6853T 2:6715 106 ; p
ð1Þ
where c and Tp are the initial cooling rate and the prescribed temperature, respectively. In the course of heating and cooling, a carrier gas, helium, was forced to pass through the sample-laden mesh at a velocity high enough to sweep nascent volatiles away from the vicinity of the wire mesh and pyrolysing particles. This forced flow minimised the extents of any secondary reactions of the volatiles over the particles. The mass of char after pyrolysis was measured. 2.3. Theoretical background We used a first-order reaction model, as follows:
dX=dt ¼ k0 exp½E=ðRTÞð1 XÞ;
ð2Þ
X ¼ ðm mchar Þ=ðm0;db mchar Þ;
ð3Þ
where X, t, k0, E, R, T, m, mchar, and m0,db are conversion, time, frequency factor, activation energy, gas constant, temperature, sample weight at a given time, char weight at 1173 K, and initial sample weight on a dry basis, respectively. Thermogravimetric curves during pyrolysis of many carbonaceous substances cannot be described by one kinetic constant. Friedman [1] proposed determining kinetic parameters for each conversion on the basis of a differentiation method. However, according to Miura and Mae [2], differentiation methods cannot determine a frequency factor for the decomposition of a substance consisting of multiple components, because the frequency factor depends on the fraction of the components. An integration method proposed by Ozawa [3] has the same problem as Friedmanfs method. Miura [7] proposed determining kinetic parameters using an approximation of an activation energy distribution with a step function. This model assumed a set of irreversible first-order reactions for the volatilisation of components. Each reaction occurs independently, so that thermogravimetric curves are predicted by kinetic parameters derived from thermogravimetric data obtained by sufficiently slow heating rates that volatilisation of components takes place in ascending order of activation energies. However, activation energies for a multi-component model are different from a differentiation form and an integration one. Thus, we used the kinetic parameters of Miurafs model, based on the integration form, given by the following Arrhenius-type equation:
lnð/=T 2 Þ ¼ ðk0i Ei Þ=R þ 0:6075 Ei =ðRTÞ;
ð4Þ
where i is the number of components. In the present study, a decrease in activation energies with progression of pyrolysis were observed. According to the increased
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Wire mesh holder putted sample in
He + Volatiles
Diffuser
He
Wire-mesh
He+Volatile Sample
He
He
Wire-mesh
(b) A monolayer form of a sample on a wire-mesh holder
(a)Wire-mesh reactor
Fig. 1. Schematic diagrames of a wire-mesh reactor (WMR) and a monolayer form of a sample on a wire-mesh holder.
or decreased activation energies with the progression, we defined a conversion against the activation energy, Es, and a distribution function parameter, f(E), as follows:
8R R < 1 f ðEÞdE ¼ 1 Es f ðEÞdE; 0 Es 1X ffi : 1 R 1 f ðEÞdE ¼ R Es f ðEÞdE; Es 0 Z
ðE ! 1Þ
ð5Þ
ðE ! 0Þ;
1
f ðEÞdE ¼ 1:
ð6Þ
0
Consequently, a distribution function parameter against an activation energy provided by Eq. (4) was determined from the following:
f ðEs Þ ¼
dX=dE; ðE ! 1Þ dX=dE; ðE ! 0Þ:
ð7Þ
For some samples, the pyrolysis process was separated into three sections such that the distribution function was defined as follows:
-12
Conversion
2
ln ( φ /T ) [1/(sK)]
-13
-14
8 R1 f ðEÞdE; ð0 < X < X 1 Þ C > > < 1 REs 1 Es 1 X ffi C 2 0 f2 ðEÞdE; ðX 1 < X < X 2 Þ > > : C R 1 f ðEÞdE; ðX < X < 1Þ; 3 Es 3 2
ð8Þ
8 > < C1 ¼ X1 C2 ¼ X2 X1 > : C3 ¼ 1 X2
ð9Þ
Z 3 X Ci i¼1
1
fi ðEÞdE ¼ 1;
ð10Þ
0
where Ci and fi are the fraction of component i in a substance and the distribution function parameter of component i, respectively. 2.4. Thermogravimetric data processing Thermogravimetric curves were smoothed using a fast Fourier transform (FFT). Thermogravimetric analysis was then conducted on the basis of the smoothed curves. Using the smoothed curves at three heating rates, Arrhenius plots based on Eq. (4) were drawn at intervals of 0.001 for conversion. Arrhenius plots, as in Fig. 2, were obtained, and then the frequency factor and an activation energy for each conversion were determined from a straight line given by an Arrhenius plot. We used frequency factors and activation energies that had a multiple coefficient, R2, above 0.990 for the Arrhenius plot. A distribution function, defined by Eq. (7), was obtained from an FFT-fitting curve of an activation energy distribution against conversion, because it was very sensitive to small disturbance fluctuations in the activation energy.
-15
3. Results and discussion -3
1.4x10
-3
1.6x10
-3
1.8x10
-3
2.0x10
1/T [1/K] Fig. 2. Arrhenius plots for pyrolysis of sugarcane bagasse.
3.1. Particle-volatile interactions The TG data were compared with those from WMR in a preliminary investigation of the effects of secondary reactions during
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1.0
1.0 Heating rate: 1 K/s TG (6.75 mg-dry) WMR
0.8
m/m0,db [-]
m/m0,db [-]
0.8
0.6
0.4
0.2
0.6
0.4
0.2
0.0 400
600
800
0.0 400
1000
Temperature [K]
600
800
1000
Temperature [K]
1.0
1.0 Heating rate: 1 K/s TG (6.02 mg-dry) WMR
0.8
m/m0,db [-]
0.8
m/m0,db [-]
Heating rate: 1 K/s TG (5.78 mg-dry) WMR
0.6
0.4
0.2
0.6
0.4
0.2
0.0 400
600
800
1000
Temperature [K]
0.0 400
Heating rate: 1 K/s TG (5.90 mg-dry) WMR
600
800
1000
Temperature [K]
Fig. 3. Comparison of weight change between WMR and TG in slow pyrolysis.
pyrolysis in TG. The mass loss profiles of the samples during pyrolysis in TG and WMR were compared (Fig. 3). The profiles of Loy Yang coal and pine sawdust were the same by TG and WMR; however, there was a difference in the final char yield between TG and WMR for cellulose. Additionally, the mass loss profile of coffee residue was distinctly different between TG and WMR. Char forming as a result of an interaction between particles and volatiles might have occurred in the pyrolysis of cellulose and coffee residue in TG.
Final Char Yield [wt.%-dry]
40
Heating rate: 1 K/s Cellulose Sugarcane bagasse Pine sawdust Coffee residue
30
20
WMR 10
0
0
1
2
3
4
Height [mm] Fig. 4. Effect of particle bed height of a sample on the final char yield by TG and WMR.
The relationship between final char yield and the packed bed height was investigated (Fig. 4). For sugarcane bagasse and pine sawdust, the final char yield showed little change with increased packed bed height. This indicates that sugarcane bagasse and pine sawdust were little affected by particle-volatile interactions. We estimated that sugarcane trash and cedar sawdust had basically the same characteristics as sugarcane bagasse and pine sawdust. The final char yield of the monolayer form of coffee residue was less than that of a packed bed, because char forming by secondary reactions between particles and volatiles occurred shortly after volatiles were eliminated from the particles. The difference between coffee residue and the other samples may be caused by different characteristics of the volatiles and/or catalytic effects of inherent alkali and alkaline earth metal species. The pyrolysis of cellulose with WMR yielded little char, and the final char yield increased with increased packed bed height. Char forming by the secondary reactions was not negligible during cellulose pyrolysis in TG. The increase in final char yield was caused by large amounts of tar and/or char precursors in the tar. The increase in final char yield indicated that the partition of char and tar could be controlled during the pyrolysis of cellulose. Ultimately, cellulose has the potential to undergo char-free pyrolysis. However, attention must be paid to how a sample is loaded when characterising cellulose using TG. There was a difference in the degree to which a sample was affected by secondary reactions during pyrolysis in TG. A schematic of the interaction between particles and volatiles is shown in Fig. 5. Pine sawdust was little affected by secondary reactions, and had no particle-volatile interaction or strong intra-particle interaction with volatiles. Char deposition is caused by not only extra-particle
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Out of a packed bed Out of a packed bed Out of a packed bed Coking Volatile
Coking Coking
Pine Sawdust
Weak
Coffee Residue
Cellulose
Strong
Effect of a packed bed height
Fig. 5. Schematic diagrames of the secondary reaction of extra-particle interactions for a packed bed height during pyrolysis in TG.
reactions but intra-particle reactions. In our experiments, we were not able to determine whether intra-particle reactions occurred or not. But the volatile deposition within char particles at heating rates as slow as 1 K/s was typically known. The intra-particle interaction for pine sawdust pyrolysis was expected to be more important than the extra-particle interaction. Coffee residue had an interaction between the particle outer surfaces and volatiles shortly after eliminating the volatiles. Accordingly, when coffee residue is evaluated for pyrolysis characteristics in a packed-bed reactor, eliminating the effects of secondary reactions is difficult. Cellulose seemed to continue char forming from volatiles until the volatiles moved outside the packed bed; these effects were not negligible during pyrolysis of cellulose in TG. Cellulose had little effect of the intra-particle interaction, and the extra-particle interaction was very important for char formation of cellulose pyrolysis. Thus, in the following discussion, we used about 1 mg of cellulose to minimise the interaction between particles and volatiles. We calculated each conversion during pyrolysis of pine sawdust, which had the same profiles between TG and WMR, and coffee residue, which had different profiles, and tried to predict the mass loss profiles of pyrolysis at a fast heating rate. Fig. 6 shows the calculated curves and experimental data collected by WMR for pine sawdust and coffee residue. In the case of fast pyrolysis of pine sawdust, the calculated profile for conversion was in good agreement with the WMR data. Consequently, the Miura method was able to predict a conversion profile for fast pyrolysis. In contrast, the calculated profiles of both slow and fast pyrolysis for coffee residue did not agree with the experimental results. The evaluation of characteristics in a pulverised feeding system,
Fig. 7 shows the distributions of activation energy with conversion during pyrolysis of the biomass-derived materials and biomasses. The activation energy distribution depended on the samples. A decrease in activation energy with progression of pyrolysis was observed for xylan in the initial pyrolysis, cellulose, sugarcane trash, sugarcane bagasse, pine sawdust, and cedar sawdust. The activation energy in the case of many coals has been reported to increase with increased conversion [7,8]. The activation energy distributions of lignin and coffee residue were similar to those of coals, but the activation energy distributions of the other samples differed from those of coals. Sonobe and Worasuwannarak [9] investigated the pyrolysis characteristics of several agricultural residues, showing an increase in activation energy. Ferdous et al. [10] reported an increase in activation energy for the pyrolysis of Alcell and Kraft lignins, consistent with our data. The activation energies of sawdust, cellulose, hemicellulose, and lignin reported by Wang et al. [11] decreased with progress in the pyrolysis reaction. There was also a decrease in activation energy during pyrolysis in the present study. However, there have been a few studies showing a combination of an increase and a decrease in activation energy during solid fuel pyrolysis. According to Li and Suzuki [12], biomass tar pyrolysis showed increased activation energy in the initial and final stages and a decrease in activation energy in the
Heating rate 1 K/s 1000 K/s
1.4
Exp. Calc.
1.2
Exp. Calc.
1.2
1.0
Conversion [-]
Conversion [-]
3.2. Characterisation of coal and biomass by kinetic parameter distributions
Heating rate 1 K/s 1000 K/s
1.4
0.8 0.6 0.4
1.0 0.8 0.6 0.4 0.2
0.2 0.0 400
such as pulverised combustion may be difficult for a sample like coffee residue. As shown in the pyrolysis of coffee residue, kinetic parameters obtained by TG were not suitable for evaluating samples with strong extra-article interaction in pulverised condition. But we believe that the kinetic parameters have potential to apply to packed-bed condition such as fixed bed reactors and moving bed reactors. Representative pyrolysis models called FLASHCHAIN [4], FG-DVC [5], and CPD [6] have been proposed based on coal structure and pyrolysis mechanism. While many models based on coal structure and pyrolysis mechanism can describe in detail for generating rates of gaseous products, tar, and char, it will take a fair amount of time to use the models due to requiring a great deal of analysis data, the description of cross-link formation accurately, and the adjustment of parameters for widely applying to fuel species except for coal. Although TG analysis provides kinetic parameters for volatilisation without understanding solid phase reactions, the Miura method is easy to use and understand in detail volatilisation behaviour during pyrolysis and gives rapidly practical information about pyrolysis for commercial use.
500
600
700
Temperature [K]
800
900
0.0 400
500
600
700
800
900
Temperature [K]
Fig. 6. Prediction of conversion of pine sawdust and coffee residue during slow and fast pyrolysis including a cooling process. Experimental data were collected by WMR.
211
400
400
350
350
Activation Energy [kJ/mol]
Activation Energy [kJ/mol]
N. Sonoyama, J.-i. Hayashi / Fuel 114 (2013) 206–215
300 250 200 150 100
Xylan Cellulose Lignin
50 0 0.0
0.5
1.0
Conversion [-]
300 250 200 150 100 50 0 0.0
Sugarcane trash Sugarcane bagasse Pine sawdust Cedar sawdust Coffee residue
0.5
1.0
Conversion [-]
Fig. 7. Activation energy distributions of material derived from biomass and biomasses with conversion.
middle stage. Sugarcane trash and sugarcane bagasse also showed changes in activation energy and biomass tar. Fig. 8 compares the change in activation energy with temperature between the biomass derived materials and biomasses for pyrolysis at 0.167 K/s. Xylan, which decomposed first amongst the biomass-derived materials, was not similar to the biomasses; we suggest that the difference was caused by the melting of xylan into liquid form, as reported by Hosoya et al. [13]. Sonobe and Worasuwannarak [9] detected CO, CO2, CH4, and H2O in the initial pyrolysis of agricultural residues, and Yang et al. [14] reported CO, CO2, CH4, H2O, and low-molecular-weight compounds including the functional bonds of aldehydes and ethers from the initial pyrolysis of biomass major components. The increase in activation energy in biomass pyrolysis in the low temperature range was derived from the decomposition of carboxyl, carbonyl, and alkyl groups and the release of the low-molecular-weight compounds. The decrease in activation energy in the biomass pyrolysis was caused by cellulose, indicating that the pyrolysis of cellulose was dominant. In the beginning of pyrolysis, volatilisation and decomposition by breaking the bonds are dominant. As increasing in temperature, the decomposition, the polymerisation of the breaking bonds and intra-particle deposition between char and tar occurs competitively. The polymerisation and the intra-particle deposition prevented the emission of components such as heavy tar having high activation energies for volatilisation. As a result, activation energies decreasing with an increase in temperature were obtained. No decrease in activation energy was observed with coffee residue, which contains only a small amount of cellulose. The rapid increase in activation energy in the high temperature range represented the process of the char forming and the end of pyrolysis. Kinetic parameters obtained by the Miura method represent the characteristics of volatile emission, including the results of solid phase reactions such as free-radical chain reactions, depolymerisation, and phase changes of the heavier reaction products. The kinetic parameters are not consistent with those of solid phase reactions because independent parallel first-order reactions cannot describe exactly solid phase reactions. But the kinetic parameters obtained by the Miura method relate closely to the solid phase reactions because volatilisation is the results of the solid phase reactions. Therefore, the following discussion linking the change of the kinetic parameters to the solid phase reactions is fruitful for understanding what occurs during pyrolysis. In the beginning of pyrolysis, the release of CO, CO2, and CH4, generated by the decomposition of functional groups, H2O, gener-
ated by dehydration condensation reactions derived from hydrogen bonds, and low-molecular-weight compounds, including the functional bonds of aldehydes and ethers, led to an increase in the activation energy of the samples, except xylan, to about 200 kJ/mol. In specific conditions, solid structure changes related to bond dissociation and polymerisation during pyrolysis are caused by part of the solid phase softening and/or melting and the aromatisation of the reconstructed carbonaceous structures through softening and/or melting. Xylan has a tendency to melt during heating, leading to formation of a saccharide [15,13]. In many studies on the pyrolysis of cellulose, the existence of intermediates, such as amorphous cellulose and active cellulose, has been reported and various models including the intermediates have been proposed [16–18]. Fisher et al. [15] observed melting cellulose by pyrolysis at a high heating rate over 1000 K/s. Cellulose seemed to have a tendency to form an amorphous solid or to soften during pyrolysis. Additionally, the aromatisation or formation of graphite-like layers of cellulose proceeded with chain scissions, or depolymerisation, and breaking C–C bonds within ring units [19]. We suggest that the decrease in activation energy indicated marked changes in the solid structure caused by the formation of cyclic aliphatic units to aromatic units with amorphism, softening, and/or melting. In the final process of pyrolysis, the activation energy of all samples increased at a rapid rate with an increase in conversion. This increasing activation energy was caused as a result of char forming by carbonisation. Although the solid structural changes are complex, we may be able to obtain information on the solid structural change from an activation energy distribution. We considered that each activation energy distribution reflects the effects of not only compositions in a sample but also the marked changes in solid structure. Pyrolysis of biomass samples was classified into three stages, based on Eq. (8). The first, second, and third stages were considered to be the volatilisation of lighter components and H2O from dehydration reaction, softening and/or melting of part of the solid organisation and reconstruction of solid structure following the softening and/or melting, and char forming. According to this classification, Fig. 9 presents representative examples of the distribution function of activation energy. The distribution of pine sawdust was not shown in the first stage, because pine sawdust has R2 < 0.990. The direction of the arrow indicates the progression in pyrolysis at slow heating rates. As shown in Fig. 7, C1, C2, and C3 for sugarcane bagasse and cedar sawdust were 0.51, 0.34, and 0.15, and 0.06, 0.74, and 0.20, respectively. Sugarcane bagasse and sugarcane trash include many low-molecular-weight components,
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400
Xylan
Activation Energy [kJ/mol]
Activation Energy [kJ/mol]
400
300
200
Lignin 100
0 400
Cellulose
Sugarcane trash
500
600
Xylan 300
200
Lignin 100
Sugarcane bagasse
0 400
700
400
600
700
400
Xylan 300
Cellulose 200
Lignin
Pine sawdust
100
500
600
Activation Energy [kJ/mol]
Activation Energy [kJ/mol]
500
Temperature [K]
Temperature [K]
0 400
Cellulose
Xylan 300
200
Lignin 100
0 400
700
Temperature [K]
Cellulose
Cedar sawdust
500
600
700
Temperature [K]
Activation Energy [kJ/mol]
400
Xylan 300
200
Lignin
Cellulose
100
Coffee residue 0 400
500
600
700
Temperature [K]
Fig. 8. Comparison of the activation energies between biomass-derived materials and biomasses for pyrolysis at a heating rate of 0.167 K/s.
such as hemicellulose, and had a wide range of activation energies in the first stage, while the area of the distribution function in the third stage for cedar sawdust and pine sawdust were larger than that of sugarcane bagasse and sugarcane trash. This increased area for cedar sawdust and pine sawdust reflected the lignin content. Components such as hemicellulose, cellulose, and lignin are not important for characterising pyrolysis of various carbonaceous materials. We considered the kinetic parameters to be more important for the characterisation. Each stage represented the contribution to pyrolysis reactions such as volatilisation of lowmolecular-weight compounds, dehydration by hydrogen bonds,
volatilisation accompanied by softening, melting, and reconstructing of a solid phase, and, finally, carbonisation. According to the distribution function of activation energy for sugarcane bagasse, we estimated that volatilisation of low-molecular-weight compounds and dehydration contributed significantly to pyrolysis behaviour and softening/melting during pyrolysis. For cedar sawdust, little change in solid structure occurred and the reaction of the second stage was predominant during pyrolysis. Coffee residue was more like coal pyrolysis with no softening or melting. Volatilisation of lighter components and dehydration proceeded with lower activation energies. Carbonisation started primarily
213
300
350
1000C3f3(E) [mol/J]
0.04 0.03 0.02
Progress in pyrolysis
0.01
2nd stage
0.4
Progress in pyrolysis
0.2 0.0
1st stage 0.08 Progress in pyrolysis
0.04 0.02 0.00 150
250
300
350
0.04 0.03 0.02
Progress in pyrolysis
0.01
200
250
300
350
0.8 0.6 0.4
Progress in pyrolysis
0.2
Progress in pyrolysis
0.06 0.04 0.02 0.00 150
200
1000C3f3(E) [mol/J]
250
250
300
350
3rd stage 0.04 0.03 0.02
Progress in pyrolysis
0.01
350
Progress in pyrolysis
0.01
2nd stage 0.8 0.6 0.4
Progress in pyrolysis
0.2
300
350
1st stage 0.08
Progress in pyrolysis
0.06 0.04 0.02 0.00 150
200
250
300
350
Activation Energy [kJ/mol]
150 200 0.05 3rd stage 0.04
250
300
350
0.03 0.02
Progress in pyrolysis
0.01 0.00
1000C2f2(E) [mol/J]
2nd stage 0.8 0.6 0.4
Progress in pyrolysis
0.2 0.0
1st stage
1000C1f1(E) [mol/J]
1000C1f1(E) [mol/J]
1000C2f2(E) [mol/J]
0.00
0.08
0.02
Activation Energy [kJ/mol]
200
300
0.0
1st stage 0.08
250
0.03
0.0
Activation Energy [kJ/mol]
150 0.05
150 200 0.05 3rd stage 0.04
0.00
0.00
1000C1f1(E) [mol/J]
0.6
0.06
200
3rd stage
2nd stage
0.8
1000C2f2(E) [mol/J]
1000C2f2(E) [mol/J]
0.00
1000C1f1(E) [mol/J]
150 0.05
1000C3f3(E) [mol/J]
250
1000C2f2(E) [mol/J]
200
3rd stage
1000C3f3(E) [mol/J]
150 0.05
1000C1f1(E) [mol/J]
1000C3f3(E) [mol/J]
N. Sonoyama, J.-i. Hayashi / Fuel 114 (2013) 206–215
Progress in pyrolysis
0.06 0.04 0.02 0.00 150
200
250
300
350
Activation Energy [kJ/mol]
2nd stage 0.8 0.6 0.4
Progress in pyrolysis
0.2 0.0
1st stage 0.08
Progress in pyrolysis
0.06 0.04 0.02 0.00 150
200
250
300
350
Activation Energy [kJ/mol]
Fig. 9. Distribution function parameter of an activation energy for biomass-derived materials and biomasses. The direction of the arrow indicates the progression in pyrolysis at slow heating rates.
above about 250 kJ/mol, the second peak of the distribution function for coffee residue. The classified distribution function of activation energy provided detailed characteristics of the pyrolysis of the components in a fuel. The kinetic parameter distribution, showing the change in frequency factor against activation energy, is useful for understanding pyrolysis kinetics. All samples showed marked changes in kinetic parameters (Fig. 10). Additionally, for the all samples, except lignin and coffee residue, the activation energy and frequency factor decreased together. The kinetic parameter distribution gave information on the kinetics of each component i, corresponding to each activation energy, and on the solid structural changes from raw material to char.
The kinetic parameter distribution has been reported to present follow the equation, known as the compensation effect:
k ¼ a expðbEÞ:
ð11Þ
Cohrnet and Roy [20] and Agrawal [21] discussed the existence of the compensation effect for cellulosic materials, but their discussions were incomplete because they used only one set of kinetic parameters. Sonobe and Worasuwannarak [9] and Li et al. [22] concluded that there was an approximate linear relationship between kinetic parameters during the pyrolysis of coal and biomass. In the present study, no sample showed a compensation effect, such as a linear relationship between E and lnk0. However, we were able to observe each compensation effect of the initial
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Xylan
Frequency Factor [1/s]
Frequency Factor [1/s]
Xylan 1020 X=0.34
Lignin 1015
X=0.80
Cellulose
1020 X=0.53
Lignin 1015
X=0.87
Cellulose
Sugarcane trash 1010 100
200
Sugarcane bagasse 1010 100
300
Activation Energy [kJ/mol]
1020
Cellulose X=0.24
Lignin Pine sawdust X=0.73
1010 100
300
Xylan
200
Frequency Factor [1/s]
Frequency Factor [1/s]
Xylan
1015
200
Activation Energy [kJ/mol]
1020
Cellulose X=0.05
1015
Cedar sawdust Lignin
1010 100
300
Activation Energy [kJ/mol]
X=0.81
200
300
Activation Energy [kJ/mol]
Xylan
Frequency Factor [1/s]
X=0.30
20
10
X=0.70
15
10
Cellulose Lignin Coffee residue
10
10
100
200
300
Activation Energy [kJ/mol]
Fig. 10. Distributions of kinetic parameters for biomass-derived materials and biomasses.
volatilisation and char forming during pyroysis. We believe that the change in compensation effect suggested a change in reaction mechanism including the solid structural changes. We found that the reaction mechanism changed during pyrolysis, although the distribution function for coffee residue pyrolysis did not show an obvious change. The distribution function of activation energy and the kinetic parameter distribution provided more understanding of the process from the volatilisation of lighter components through carbonisation during pyrolysis.
Miura and Maki [8] reported that the distribution of the kinetic parameters differed, depending on coal rank. Kinetic parameters for pyrolysis of coal and biomass are shown in Fig. 11. The kinetic parameter distributions of coals were different from those of biomasses. The biomasses showed a greater changes in activation energy and frequency factor than coals, as shown in Fig. 11. With an activation energy below 200 kJ/mol, the higher the frequency factor a sample has, the lower the starting temperature of volatilisation is. An activation energy of 200–250 kJ/mol indicated
N. Sonoyama, J.-i. Hayashi / Fuel 114 (2013) 206–215
Frequency Factor [1/s]
1025
1020
References
Coal(C>85%) Coal(75%
1015
1010 100
150
200
250
215
300
350
Activation Energy [kJ/mol] Fig. 11. Comparison of distributions of kinetic parameters for coal and biomass. The coal data are from Miura and Maki [8].
the transition to forming char. A frequency factor with an activation energy of 350 kJ/mol represented the extent of carbonisation of the sample. We consider that the frequency factor can be used as an index of the extent of char decomposition. Additionally, although the index includes no catalytic effect of inherent metallic species, predicting combustion characteristics, such as char-burn out, is expected. 4. Conclusions We performed pyrolysis of various biomass and biomass-derived materials by thermogravimetry and investigated kinetic parameter distributions derived using the Miura method. Before the thermogravimetric analysis, preliminary experiments were carried out using TG and WMR; we showed that secondary reactions affected the mass-loss curves of some samples. Biomasses and biomass-derived materials showed marked changes in kinetic parameters. We were able to characterise pyrolysis for biomass samples by the kinetic parameter distribution and could predict some combustion characteristics, such as char-burn out and the temperature at which the emission of volatiles started.
[1] Friedman HL. Kinetics of thermal degradation of char-forming plastics from thermogravimetry. J Poly Sci: Part C 1964;6:183–95. [2] Miura K, Mae K. Flash pyrolysis of coal as a means for obtaining valuable chemicals. Kagaku Kogaku Ronbunshu 1994;6:733–46. [3] Ozawa T. Critical investigation of methods for kinetic analysis of thermoanalytical data. J Therm Anal 1975;7:601–17. [4] Niksa S, Kerstein AR. FLASHCHAIN theory for rapid coal devolatilization kinetics. 1. Formulation. Energy & Fuels 1991;5:647–65. [5] Solomon PR, Hamblen DG, Carangelo RM, Serio MA, Deshpande GV. General model of coal devolatilization. Energy & Fuels 1988;2:405–22. [6] Grant DM, Pugmire RJ, Fletcher TH, Kerstein AR. Chemical model of coal devolatilization using percolation lattice statistics. Energy & Fuels 1989;3:175–86. [7] Miura K. A new and simple method to estimate f(E) and k0(E) in the distributed activation energy model from three sets of experimental data. Energy & Fuels 1995;9:302–7. [8] Miura K, Maki T. A simple method for estimating f(E) and k0(E) in the distributed activation energy model. Energy & Fuels 1998;12:864–9. [9] Sonobe T, Worasuwannarak N. Kinetic analyses of biomass pyrolysis using the distributed activation energy model. Fuel 2008;87:414–21. [10] Ferdous D, Dalai AK, Bej SK, Thring RW. Pyrolysis of lignins: experimental and kinetics studies. Energy & Fuels 2002;16:1405–12. [11] Wang G, Li W, Li B, Chen H. TG study on pyrolysis of biomass and its three components under syngas. Fuel 2008;87:552–8. [12] Li C, Suzuki K. Kinetic analyses of biomass tar pyrolysis using the distributed activation energy model by TG/DTA technique. J Therm Anal Calorim 2009;98:261–6. [13] Hosoya T, Kawamoto H, Saka S. Cellulose–hemicellulose and cellulose–lignin interactions in wood pyrolysis at gasification temperature. J Anal Appl Pyrolysis 2007;80:118–25. [14] Yang H, Yan R, Chen H, Lee DH, Zheng C. Characteristics of hemicellulose, cellulose and lignin pyrolysis. Fuel 2007;86:1781–8. [15] Fisher T, Hajaligol M, Waymack B, Kellogg D. Pyrolysis behaviour and kinetics of biomass derived materials. J Anal Appl Pyrolysis 2002;62: 331–49. [16] Diebold JP. A unified, global model for the pyrolysis of cellulose. Biomass and Bioenergy 1994;7:75–85. [17] Antal MJ, Varhegyi G. Cellulose pyrolysis kinetics: the current state of knowledge. Ind Eng Chem Res 1995;34:703–17. [18] Wooten JB, Seeman JI, Hajaligol MR. Observation and characterization of cellulose pyrolysis intermediates by 13C CPMAS NMR. A new mechanistic model. Energy & Fuels 2004;18:1–15. [19] Tang MM, Bacon R. Carbonization of cellulose fibers-I low temperature pyrolysis. Carbon 1964;2:211–20. [20] Chornet E, Roy C. Compensation effect in the thermal decomposition of cellulosic materials. Thermochmica Acta 1980;35:389–93. [21] Agrawal RK. Compensation effect in the pyrolysis of cellulosic materials. Thermochmica Acta 1985;90:347–51. [22] Li Z, Liu C, Chen Z, Qian J, Zhao W, Zhu Q. Analysis of coals and biomass pyrolysis using the distributed activation energy model. Bioresour Technol 2009;100:948–52.
Contents lists available at SciVerse ScienceDirect
Fuel journal homepage: www.elsevier.com/locate/fuel
Characterisation of coal and biomass based on kinetic parameter distributions for pyrolysis Nozomu Sonoyama a,⇑, Jun-ichiro Hayashi b a b
Coal & Environment Research Laboratory, Coal Business Office, Petroleum & Coal Marketing Department, Idemitsu Kosan Co., Ltd., 3-1 Nakasode, Sodegaura, Chiba 299-0267, Japan Division of Advanced Device Material, Institute for Materials Chemistry and Engineering, Kyushu University, 6-1, Kasuga Koen, Kasuga, Fukuoka 816-8580, Japan
a r t i c l e
i n f o
Article history: Received 12 December 2011 Received in revised form 16 April 2012 Accepted 17 April 2012 Available online 5 May 2012 Keywords: Distributed activation energy model Kinetic parameters Solid fuels Pyrolysis
a b s t r a c t We performed pyrolysis analyses of various biomasses and biomass-derived materials using a thermogravimetry device and a wire-mesh reactor. Kinetic parameters, which were a frequency factor and an activation energy, were derived based on a distributed activation energy model. We validated the prediction of thermogravimetric curves calculated using the kinetic parameters in the case of rapid pyrolysis of biomass having the least interaction between particles and volatiles. Unlike coal, the kinetic parameters of the samples changed markedly with progression of pyrolysis. We estimated that the low activation energy in the initial step was caused by hydration and the volatilisation of lighter components. Some biomass samples showed a decrease in both the frequency factor and activation energy during pyrolysis. Changes in the chemical structures of xylan and cellulose from cyclic aliphatic units to aromatic units, accompanying amorphism, softening, or melting of part of the solid phase in some cases, caused a decrease in kinetic parameters during pyrolysis. Both the frequency factor and activation energy of all biomass samples increased in the final stages, which was considered to be the result of char forming by carbonisation. Analysing in detail the changes in kinetic parameters provided information on the behaviour of the volatilisation, the change in solid state, and the extent of char structural development. Ó 2012 Elsevier Ltd. All rights reserved.
1. Introduction World energy consumption increases yearly. We need to meet the demand for increased energy and also cope with environmental problems such as SO2 and NOx emissions and global climate change. Addressing global climate change requires decreasing fossil fuel consumption and increasing the use of renewable energy resources. Thus, the use of a great variety of the renewable energy resources for energy generation has expanded. The operating conditions of an existing boiler can be altered to match the combustion characteristics of renewable energy fuels, and new better boilers have been designed specifically for such fuels. Coal and biomass co-combustion technology is also an effective combination. The combustion characteristics of various biomass products differ from those of coal. Thus, it is difficult to predict the behaviour of combustion and ash deposition in a boiler for co-combustion and biomass combustion. Many studies on the structure and analysis of coal and biomass have been conducted previously. However, there has been little work to establish a method for the comprehensive evaluation of widely varying fuels.
⇑ Corresponding author. Tel.: +81 438 62 9511; fax: +81 438 60 1177. E-mail address: [email protected] (N. Sonoyama). 0016-2361/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.fuel.2012.04.023
Pyrolysis is essential for combustion and gasification, which is the first step in decomposition by heating. Thus, understanding pyrolysis will help in developing new processes and resolving the mechanisms of combustion and gasification. Thermogravimetric analysis is widely used in physical chemistry, materials research, and thermal analysis. Thermogravimetric analysis provides findings on volatilisation behaviour over a wide range of temperatures. However, the pyrolysis of solid fuels is complex because it includes not only volatilisation but also secondary reactions, such as particle-volatile interactions and structural changes in particles due to softening and/or melting. Excluding such effects is difficult in thermogravimetry. We evaluated in detail the kinetics for pyrolysis of solid carbonaceous materials, based on a distributed activation energy model, after investigating experimentally the effects of heating rate and sample height. 2. Experimental 2.1. Samples Oat-spelt xylan was washed with ion-exchanged water after stirring in 0.1-N hydrochloric acid solution over 15 h at ambient temperature. Hydrolytic lignin was washed with ion-exchanged water after stirring in 5-N hydrochloric acid solution over 15 h at
N. Sonoyama, J.-i. Hayashi / Fuel 114 (2013) 206–215
207
Notation Ci c E Ei Es f fi k0 k0i m
fraction of pseudo component i (–) initial cooling rate (K/s) activation energy (J/mol) activation energy of pseudo component i (J/mol) activation energy based on the step function (J/mol) distribution function parameter (mol/J) distribution function parameter of seudo component i (mol/J) frequency factor (1/s) frequency factor of pseudo component i (1/s) sample weight at a given time (kg)
ambient temperature. Cellulose, sugarcane trash, sugarcane bagasse, pine sawdust, cedar sawdust, coffee residue, and Loy Yang coal were also used. The elemental analysis of the samples is shown in Table 1. Xylan and lignin were sieved in the range of 125–250 lm, cellulose at 74–105 lm, and sugarcane trash, sugarcane bagasse, pine sawdust, cedar sawdust, coffee residue, and Loy Yang coal at 125–210 lm. 2.2. Thermogravimetry using a Thermogravimetry device and a wiremesh reactor Xylan and lignin were weighed in the range of 6.5–7.5 mg-dry, cellulose at 1.2–13.5 mg-dry, and sugarcane trash, sugarcane bagasse, pine sawdust, cedar sawdust, coffee residue, and Loy Yang coal at 3.5–6.5 mg-dry. Each sample was loaded in a platinum cup, of which the inner diameter and height were 4.8 mm and 2.6 mm, respectively. A sample loaded in a platinum cup was set in the thermogravimetry (TG) device, and kept for about 60 min for stabilisation of the TG. Drying the sample was carried out by heating at a rate of 0.167 K/s to 383 K with flowing nitrogen, and then maintaining at 383 K. After drying, the sample was pyrolysed by heating at a rate from 0.0833–1 K/s to 1273 K under a nitrogen atmosphere. The weight of the sample was recorded at that time. Pyrolysis of samples of cellulose, pine sawdust, coffee residue, and Loy Yang coal was performed in a wire-mesh reactor (WMR; Fig. 1). The WMR was used to minimise the interaction between particles and volatiles during pyrolysis. Less than 5 mg of particles was tightly sandwiched in a monolayer formed by two layers of the wire mesh made of SUS316 stainless steel. The sample-laden mesh was heated at a rate of 1 or 1000 K/s from ambient to a prescribed temperature in the range of 573–1173 K, and then cooled to ambient temperature at an initial cooling rate calculated by the follow-
Table 1 Elemental analysis of xylan, cellulose, lignin, sugarcane trash, sugarcane bagasse, pine sawdust, cedar sawdust, coffee residue, and Loy Yang coal (daf: dry ash-free, nd: not detected). Sample
C
H
N
S
Oa
5.9 5.9 5.6 5.9 5.7 5.7 5.6 6.8 5.2
nd nd 0.7 0.3 0.3 nd nd 2.1 0.7
nd nd nd 0.3 nd nd nd nd 0.3
49.3 50.4 29.8 47.0 45.7 44.6 43.9 37.0 26.6
(wt.%-daf) Xylan Cellulose Lignin Sugarcane trash Sugarcane bagasse Pine sawdust Cedar sawdust Coffee residue Loy Yang coal a
By difference.
44.8 43.7 63.9 46.5 48.3 49.7 50.5 54.1 67.2
mchar m0,db R t T Tp X Xi
a b /
char weight at 1173 K (kg) initial sample weight on a dry basis (kg) gas constant (J/(K s)) time (s) temperature (K) prescribed temperature (K) conversion of volatilisable compounds to volatiles (–) final conversion of each stage i (–) coefficient for the compensation effect (–) coefficient for the compensation effect (–) heating rate (K/s)
ing empirical equation, derived from the temperature profiles of WMR experiments:
c ¼ 2:6853T 2:6715 106 ; p
ð1Þ
where c and Tp are the initial cooling rate and the prescribed temperature, respectively. In the course of heating and cooling, a carrier gas, helium, was forced to pass through the sample-laden mesh at a velocity high enough to sweep nascent volatiles away from the vicinity of the wire mesh and pyrolysing particles. This forced flow minimised the extents of any secondary reactions of the volatiles over the particles. The mass of char after pyrolysis was measured. 2.3. Theoretical background We used a first-order reaction model, as follows:
dX=dt ¼ k0 exp½E=ðRTÞð1 XÞ;
ð2Þ
X ¼ ðm mchar Þ=ðm0;db mchar Þ;
ð3Þ
where X, t, k0, E, R, T, m, mchar, and m0,db are conversion, time, frequency factor, activation energy, gas constant, temperature, sample weight at a given time, char weight at 1173 K, and initial sample weight on a dry basis, respectively. Thermogravimetric curves during pyrolysis of many carbonaceous substances cannot be described by one kinetic constant. Friedman [1] proposed determining kinetic parameters for each conversion on the basis of a differentiation method. However, according to Miura and Mae [2], differentiation methods cannot determine a frequency factor for the decomposition of a substance consisting of multiple components, because the frequency factor depends on the fraction of the components. An integration method proposed by Ozawa [3] has the same problem as Friedmanfs method. Miura [7] proposed determining kinetic parameters using an approximation of an activation energy distribution with a step function. This model assumed a set of irreversible first-order reactions for the volatilisation of components. Each reaction occurs independently, so that thermogravimetric curves are predicted by kinetic parameters derived from thermogravimetric data obtained by sufficiently slow heating rates that volatilisation of components takes place in ascending order of activation energies. However, activation energies for a multi-component model are different from a differentiation form and an integration one. Thus, we used the kinetic parameters of Miurafs model, based on the integration form, given by the following Arrhenius-type equation:
lnð/=T 2 Þ ¼ ðk0i Ei Þ=R þ 0:6075 Ei =ðRTÞ;
ð4Þ
where i is the number of components. In the present study, a decrease in activation energies with progression of pyrolysis were observed. According to the increased
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N. Sonoyama, J.-i. Hayashi / Fuel 114 (2013) 206–215 N2-Cooling trap or Gas bag
Wire mesh holder putted sample in
He + Volatiles
Diffuser
He
Wire-mesh
He+Volatile Sample
He
He
Wire-mesh
(b) A monolayer form of a sample on a wire-mesh holder
(a)Wire-mesh reactor
Fig. 1. Schematic diagrames of a wire-mesh reactor (WMR) and a monolayer form of a sample on a wire-mesh holder.
or decreased activation energies with the progression, we defined a conversion against the activation energy, Es, and a distribution function parameter, f(E), as follows:
8R R < 1 f ðEÞdE ¼ 1 Es f ðEÞdE; 0 Es 1X ffi : 1 R 1 f ðEÞdE ¼ R Es f ðEÞdE; Es 0 Z
ðE ! 1Þ
ð5Þ
ðE ! 0Þ;
1
f ðEÞdE ¼ 1:
ð6Þ
0
Consequently, a distribution function parameter against an activation energy provided by Eq. (4) was determined from the following:
f ðEs Þ ¼
dX=dE; ðE ! 1Þ dX=dE; ðE ! 0Þ:
ð7Þ
For some samples, the pyrolysis process was separated into three sections such that the distribution function was defined as follows:
-12
Conversion
2
ln ( φ /T ) [1/(sK)]
-13
-14
8 R1 f ðEÞdE; ð0 < X < X 1 Þ C > > < 1 REs 1 Es 1 X ffi C 2 0 f2 ðEÞdE; ðX 1 < X < X 2 Þ > > : C R 1 f ðEÞdE; ðX < X < 1Þ; 3 Es 3 2
ð8Þ
8 > < C1 ¼ X1 C2 ¼ X2 X1 > : C3 ¼ 1 X2
ð9Þ
Z 3 X Ci i¼1
1
fi ðEÞdE ¼ 1;
ð10Þ
0
where Ci and fi are the fraction of component i in a substance and the distribution function parameter of component i, respectively. 2.4. Thermogravimetric data processing Thermogravimetric curves were smoothed using a fast Fourier transform (FFT). Thermogravimetric analysis was then conducted on the basis of the smoothed curves. Using the smoothed curves at three heating rates, Arrhenius plots based on Eq. (4) were drawn at intervals of 0.001 for conversion. Arrhenius plots, as in Fig. 2, were obtained, and then the frequency factor and an activation energy for each conversion were determined from a straight line given by an Arrhenius plot. We used frequency factors and activation energies that had a multiple coefficient, R2, above 0.990 for the Arrhenius plot. A distribution function, defined by Eq. (7), was obtained from an FFT-fitting curve of an activation energy distribution against conversion, because it was very sensitive to small disturbance fluctuations in the activation energy.
-15
3. Results and discussion -3
1.4x10
-3
1.6x10
-3
1.8x10
-3
2.0x10
1/T [1/K] Fig. 2. Arrhenius plots for pyrolysis of sugarcane bagasse.
3.1. Particle-volatile interactions The TG data were compared with those from WMR in a preliminary investigation of the effects of secondary reactions during
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1.0
1.0 Heating rate: 1 K/s TG (6.75 mg-dry) WMR
0.8
m/m0,db [-]
m/m0,db [-]
0.8
0.6
0.4
0.2
0.6
0.4
0.2
0.0 400
600
800
0.0 400
1000
Temperature [K]
600
800
1000
Temperature [K]
1.0
1.0 Heating rate: 1 K/s TG (6.02 mg-dry) WMR
0.8
m/m0,db [-]
0.8
m/m0,db [-]
Heating rate: 1 K/s TG (5.78 mg-dry) WMR
0.6
0.4
0.2
0.6
0.4
0.2
0.0 400
600
800
1000
Temperature [K]
0.0 400
Heating rate: 1 K/s TG (5.90 mg-dry) WMR
600
800
1000
Temperature [K]
Fig. 3. Comparison of weight change between WMR and TG in slow pyrolysis.
pyrolysis in TG. The mass loss profiles of the samples during pyrolysis in TG and WMR were compared (Fig. 3). The profiles of Loy Yang coal and pine sawdust were the same by TG and WMR; however, there was a difference in the final char yield between TG and WMR for cellulose. Additionally, the mass loss profile of coffee residue was distinctly different between TG and WMR. Char forming as a result of an interaction between particles and volatiles might have occurred in the pyrolysis of cellulose and coffee residue in TG.
Final Char Yield [wt.%-dry]
40
Heating rate: 1 K/s Cellulose Sugarcane bagasse Pine sawdust Coffee residue
30
20
WMR 10
0
0
1
2
3
4
Height [mm] Fig. 4. Effect of particle bed height of a sample on the final char yield by TG and WMR.
The relationship between final char yield and the packed bed height was investigated (Fig. 4). For sugarcane bagasse and pine sawdust, the final char yield showed little change with increased packed bed height. This indicates that sugarcane bagasse and pine sawdust were little affected by particle-volatile interactions. We estimated that sugarcane trash and cedar sawdust had basically the same characteristics as sugarcane bagasse and pine sawdust. The final char yield of the monolayer form of coffee residue was less than that of a packed bed, because char forming by secondary reactions between particles and volatiles occurred shortly after volatiles were eliminated from the particles. The difference between coffee residue and the other samples may be caused by different characteristics of the volatiles and/or catalytic effects of inherent alkali and alkaline earth metal species. The pyrolysis of cellulose with WMR yielded little char, and the final char yield increased with increased packed bed height. Char forming by the secondary reactions was not negligible during cellulose pyrolysis in TG. The increase in final char yield was caused by large amounts of tar and/or char precursors in the tar. The increase in final char yield indicated that the partition of char and tar could be controlled during the pyrolysis of cellulose. Ultimately, cellulose has the potential to undergo char-free pyrolysis. However, attention must be paid to how a sample is loaded when characterising cellulose using TG. There was a difference in the degree to which a sample was affected by secondary reactions during pyrolysis in TG. A schematic of the interaction between particles and volatiles is shown in Fig. 5. Pine sawdust was little affected by secondary reactions, and had no particle-volatile interaction or strong intra-particle interaction with volatiles. Char deposition is caused by not only extra-particle
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Out of a packed bed Out of a packed bed Out of a packed bed Coking Volatile
Coking Coking
Pine Sawdust
Weak
Coffee Residue
Cellulose
Strong
Effect of a packed bed height
Fig. 5. Schematic diagrames of the secondary reaction of extra-particle interactions for a packed bed height during pyrolysis in TG.
reactions but intra-particle reactions. In our experiments, we were not able to determine whether intra-particle reactions occurred or not. But the volatile deposition within char particles at heating rates as slow as 1 K/s was typically known. The intra-particle interaction for pine sawdust pyrolysis was expected to be more important than the extra-particle interaction. Coffee residue had an interaction between the particle outer surfaces and volatiles shortly after eliminating the volatiles. Accordingly, when coffee residue is evaluated for pyrolysis characteristics in a packed-bed reactor, eliminating the effects of secondary reactions is difficult. Cellulose seemed to continue char forming from volatiles until the volatiles moved outside the packed bed; these effects were not negligible during pyrolysis of cellulose in TG. Cellulose had little effect of the intra-particle interaction, and the extra-particle interaction was very important for char formation of cellulose pyrolysis. Thus, in the following discussion, we used about 1 mg of cellulose to minimise the interaction between particles and volatiles. We calculated each conversion during pyrolysis of pine sawdust, which had the same profiles between TG and WMR, and coffee residue, which had different profiles, and tried to predict the mass loss profiles of pyrolysis at a fast heating rate. Fig. 6 shows the calculated curves and experimental data collected by WMR for pine sawdust and coffee residue. In the case of fast pyrolysis of pine sawdust, the calculated profile for conversion was in good agreement with the WMR data. Consequently, the Miura method was able to predict a conversion profile for fast pyrolysis. In contrast, the calculated profiles of both slow and fast pyrolysis for coffee residue did not agree with the experimental results. The evaluation of characteristics in a pulverised feeding system,
Fig. 7 shows the distributions of activation energy with conversion during pyrolysis of the biomass-derived materials and biomasses. The activation energy distribution depended on the samples. A decrease in activation energy with progression of pyrolysis was observed for xylan in the initial pyrolysis, cellulose, sugarcane trash, sugarcane bagasse, pine sawdust, and cedar sawdust. The activation energy in the case of many coals has been reported to increase with increased conversion [7,8]. The activation energy distributions of lignin and coffee residue were similar to those of coals, but the activation energy distributions of the other samples differed from those of coals. Sonobe and Worasuwannarak [9] investigated the pyrolysis characteristics of several agricultural residues, showing an increase in activation energy. Ferdous et al. [10] reported an increase in activation energy for the pyrolysis of Alcell and Kraft lignins, consistent with our data. The activation energies of sawdust, cellulose, hemicellulose, and lignin reported by Wang et al. [11] decreased with progress in the pyrolysis reaction. There was also a decrease in activation energy during pyrolysis in the present study. However, there have been a few studies showing a combination of an increase and a decrease in activation energy during solid fuel pyrolysis. According to Li and Suzuki [12], biomass tar pyrolysis showed increased activation energy in the initial and final stages and a decrease in activation energy in the
Heating rate 1 K/s 1000 K/s
1.4
Exp. Calc.
1.2
Exp. Calc.
1.2
1.0
Conversion [-]
Conversion [-]
3.2. Characterisation of coal and biomass by kinetic parameter distributions
Heating rate 1 K/s 1000 K/s
1.4
0.8 0.6 0.4
1.0 0.8 0.6 0.4 0.2
0.2 0.0 400
such as pulverised combustion may be difficult for a sample like coffee residue. As shown in the pyrolysis of coffee residue, kinetic parameters obtained by TG were not suitable for evaluating samples with strong extra-article interaction in pulverised condition. But we believe that the kinetic parameters have potential to apply to packed-bed condition such as fixed bed reactors and moving bed reactors. Representative pyrolysis models called FLASHCHAIN [4], FG-DVC [5], and CPD [6] have been proposed based on coal structure and pyrolysis mechanism. While many models based on coal structure and pyrolysis mechanism can describe in detail for generating rates of gaseous products, tar, and char, it will take a fair amount of time to use the models due to requiring a great deal of analysis data, the description of cross-link formation accurately, and the adjustment of parameters for widely applying to fuel species except for coal. Although TG analysis provides kinetic parameters for volatilisation without understanding solid phase reactions, the Miura method is easy to use and understand in detail volatilisation behaviour during pyrolysis and gives rapidly practical information about pyrolysis for commercial use.
500
600
700
Temperature [K]
800
900
0.0 400
500
600
700
800
900
Temperature [K]
Fig. 6. Prediction of conversion of pine sawdust and coffee residue during slow and fast pyrolysis including a cooling process. Experimental data were collected by WMR.
211
400
400
350
350
Activation Energy [kJ/mol]
Activation Energy [kJ/mol]
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300 250 200 150 100
Xylan Cellulose Lignin
50 0 0.0
0.5
1.0
Conversion [-]
300 250 200 150 100 50 0 0.0
Sugarcane trash Sugarcane bagasse Pine sawdust Cedar sawdust Coffee residue
0.5
1.0
Conversion [-]
Fig. 7. Activation energy distributions of material derived from biomass and biomasses with conversion.
middle stage. Sugarcane trash and sugarcane bagasse also showed changes in activation energy and biomass tar. Fig. 8 compares the change in activation energy with temperature between the biomass derived materials and biomasses for pyrolysis at 0.167 K/s. Xylan, which decomposed first amongst the biomass-derived materials, was not similar to the biomasses; we suggest that the difference was caused by the melting of xylan into liquid form, as reported by Hosoya et al. [13]. Sonobe and Worasuwannarak [9] detected CO, CO2, CH4, and H2O in the initial pyrolysis of agricultural residues, and Yang et al. [14] reported CO, CO2, CH4, H2O, and low-molecular-weight compounds including the functional bonds of aldehydes and ethers from the initial pyrolysis of biomass major components. The increase in activation energy in biomass pyrolysis in the low temperature range was derived from the decomposition of carboxyl, carbonyl, and alkyl groups and the release of the low-molecular-weight compounds. The decrease in activation energy in the biomass pyrolysis was caused by cellulose, indicating that the pyrolysis of cellulose was dominant. In the beginning of pyrolysis, volatilisation and decomposition by breaking the bonds are dominant. As increasing in temperature, the decomposition, the polymerisation of the breaking bonds and intra-particle deposition between char and tar occurs competitively. The polymerisation and the intra-particle deposition prevented the emission of components such as heavy tar having high activation energies for volatilisation. As a result, activation energies decreasing with an increase in temperature were obtained. No decrease in activation energy was observed with coffee residue, which contains only a small amount of cellulose. The rapid increase in activation energy in the high temperature range represented the process of the char forming and the end of pyrolysis. Kinetic parameters obtained by the Miura method represent the characteristics of volatile emission, including the results of solid phase reactions such as free-radical chain reactions, depolymerisation, and phase changes of the heavier reaction products. The kinetic parameters are not consistent with those of solid phase reactions because independent parallel first-order reactions cannot describe exactly solid phase reactions. But the kinetic parameters obtained by the Miura method relate closely to the solid phase reactions because volatilisation is the results of the solid phase reactions. Therefore, the following discussion linking the change of the kinetic parameters to the solid phase reactions is fruitful for understanding what occurs during pyrolysis. In the beginning of pyrolysis, the release of CO, CO2, and CH4, generated by the decomposition of functional groups, H2O, gener-
ated by dehydration condensation reactions derived from hydrogen bonds, and low-molecular-weight compounds, including the functional bonds of aldehydes and ethers, led to an increase in the activation energy of the samples, except xylan, to about 200 kJ/mol. In specific conditions, solid structure changes related to bond dissociation and polymerisation during pyrolysis are caused by part of the solid phase softening and/or melting and the aromatisation of the reconstructed carbonaceous structures through softening and/or melting. Xylan has a tendency to melt during heating, leading to formation of a saccharide [15,13]. In many studies on the pyrolysis of cellulose, the existence of intermediates, such as amorphous cellulose and active cellulose, has been reported and various models including the intermediates have been proposed [16–18]. Fisher et al. [15] observed melting cellulose by pyrolysis at a high heating rate over 1000 K/s. Cellulose seemed to have a tendency to form an amorphous solid or to soften during pyrolysis. Additionally, the aromatisation or formation of graphite-like layers of cellulose proceeded with chain scissions, or depolymerisation, and breaking C–C bonds within ring units [19]. We suggest that the decrease in activation energy indicated marked changes in the solid structure caused by the formation of cyclic aliphatic units to aromatic units with amorphism, softening, and/or melting. In the final process of pyrolysis, the activation energy of all samples increased at a rapid rate with an increase in conversion. This increasing activation energy was caused as a result of char forming by carbonisation. Although the solid structural changes are complex, we may be able to obtain information on the solid structural change from an activation energy distribution. We considered that each activation energy distribution reflects the effects of not only compositions in a sample but also the marked changes in solid structure. Pyrolysis of biomass samples was classified into three stages, based on Eq. (8). The first, second, and third stages were considered to be the volatilisation of lighter components and H2O from dehydration reaction, softening and/or melting of part of the solid organisation and reconstruction of solid structure following the softening and/or melting, and char forming. According to this classification, Fig. 9 presents representative examples of the distribution function of activation energy. The distribution of pine sawdust was not shown in the first stage, because pine sawdust has R2 < 0.990. The direction of the arrow indicates the progression in pyrolysis at slow heating rates. As shown in Fig. 7, C1, C2, and C3 for sugarcane bagasse and cedar sawdust were 0.51, 0.34, and 0.15, and 0.06, 0.74, and 0.20, respectively. Sugarcane bagasse and sugarcane trash include many low-molecular-weight components,
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400
Xylan
Activation Energy [kJ/mol]
Activation Energy [kJ/mol]
400
300
200
Lignin 100
0 400
Cellulose
Sugarcane trash
500
600
Xylan 300
200
Lignin 100
Sugarcane bagasse
0 400
700
400
600
700
400
Xylan 300
Cellulose 200
Lignin
Pine sawdust
100
500
600
Activation Energy [kJ/mol]
Activation Energy [kJ/mol]
500
Temperature [K]
Temperature [K]
0 400
Cellulose
Xylan 300
200
Lignin 100
0 400
700
Temperature [K]
Cellulose
Cedar sawdust
500
600
700
Temperature [K]
Activation Energy [kJ/mol]
400
Xylan 300
200
Lignin
Cellulose
100
Coffee residue 0 400
500
600
700
Temperature [K]
Fig. 8. Comparison of the activation energies between biomass-derived materials and biomasses for pyrolysis at a heating rate of 0.167 K/s.
such as hemicellulose, and had a wide range of activation energies in the first stage, while the area of the distribution function in the third stage for cedar sawdust and pine sawdust were larger than that of sugarcane bagasse and sugarcane trash. This increased area for cedar sawdust and pine sawdust reflected the lignin content. Components such as hemicellulose, cellulose, and lignin are not important for characterising pyrolysis of various carbonaceous materials. We considered the kinetic parameters to be more important for the characterisation. Each stage represented the contribution to pyrolysis reactions such as volatilisation of lowmolecular-weight compounds, dehydration by hydrogen bonds,
volatilisation accompanied by softening, melting, and reconstructing of a solid phase, and, finally, carbonisation. According to the distribution function of activation energy for sugarcane bagasse, we estimated that volatilisation of low-molecular-weight compounds and dehydration contributed significantly to pyrolysis behaviour and softening/melting during pyrolysis. For cedar sawdust, little change in solid structure occurred and the reaction of the second stage was predominant during pyrolysis. Coffee residue was more like coal pyrolysis with no softening or melting. Volatilisation of lighter components and dehydration proceeded with lower activation energies. Carbonisation started primarily
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1000C3f3(E) [mol/J]
0.04 0.03 0.02
Progress in pyrolysis
0.01
2nd stage
0.4
Progress in pyrolysis
0.2 0.0
1st stage 0.08 Progress in pyrolysis
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0.04 0.03 0.02
Progress in pyrolysis
0.01
200
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300
350
0.8 0.6 0.4
Progress in pyrolysis
0.2
Progress in pyrolysis
0.06 0.04 0.02 0.00 150
200
1000C3f3(E) [mol/J]
250
250
300
350
3rd stage 0.04 0.03 0.02
Progress in pyrolysis
0.01
350
Progress in pyrolysis
0.01
2nd stage 0.8 0.6 0.4
Progress in pyrolysis
0.2
300
350
1st stage 0.08
Progress in pyrolysis
0.06 0.04 0.02 0.00 150
200
250
300
350
Activation Energy [kJ/mol]
150 200 0.05 3rd stage 0.04
250
300
350
0.03 0.02
Progress in pyrolysis
0.01 0.00
1000C2f2(E) [mol/J]
2nd stage 0.8 0.6 0.4
Progress in pyrolysis
0.2 0.0
1st stage
1000C1f1(E) [mol/J]
1000C1f1(E) [mol/J]
1000C2f2(E) [mol/J]
0.00
0.08
0.02
Activation Energy [kJ/mol]
200
300
0.0
1st stage 0.08
250
0.03
0.0
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150 0.05
150 200 0.05 3rd stage 0.04
0.00
0.00
1000C1f1(E) [mol/J]
0.6
0.06
200
3rd stage
2nd stage
0.8
1000C2f2(E) [mol/J]
1000C2f2(E) [mol/J]
0.00
1000C1f1(E) [mol/J]
150 0.05
1000C3f3(E) [mol/J]
250
1000C2f2(E) [mol/J]
200
3rd stage
1000C3f3(E) [mol/J]
150 0.05
1000C1f1(E) [mol/J]
1000C3f3(E) [mol/J]
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Progress in pyrolysis
0.06 0.04 0.02 0.00 150
200
250
300
350
Activation Energy [kJ/mol]
2nd stage 0.8 0.6 0.4
Progress in pyrolysis
0.2 0.0
1st stage 0.08
Progress in pyrolysis
0.06 0.04 0.02 0.00 150
200
250
300
350
Activation Energy [kJ/mol]
Fig. 9. Distribution function parameter of an activation energy for biomass-derived materials and biomasses. The direction of the arrow indicates the progression in pyrolysis at slow heating rates.
above about 250 kJ/mol, the second peak of the distribution function for coffee residue. The classified distribution function of activation energy provided detailed characteristics of the pyrolysis of the components in a fuel. The kinetic parameter distribution, showing the change in frequency factor against activation energy, is useful for understanding pyrolysis kinetics. All samples showed marked changes in kinetic parameters (Fig. 10). Additionally, for the all samples, except lignin and coffee residue, the activation energy and frequency factor decreased together. The kinetic parameter distribution gave information on the kinetics of each component i, corresponding to each activation energy, and on the solid structural changes from raw material to char.
The kinetic parameter distribution has been reported to present follow the equation, known as the compensation effect:
k ¼ a expðbEÞ:
ð11Þ
Cohrnet and Roy [20] and Agrawal [21] discussed the existence of the compensation effect for cellulosic materials, but their discussions were incomplete because they used only one set of kinetic parameters. Sonobe and Worasuwannarak [9] and Li et al. [22] concluded that there was an approximate linear relationship between kinetic parameters during the pyrolysis of coal and biomass. In the present study, no sample showed a compensation effect, such as a linear relationship between E and lnk0. However, we were able to observe each compensation effect of the initial
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Xylan
Frequency Factor [1/s]
Frequency Factor [1/s]
Xylan 1020 X=0.34
Lignin 1015
X=0.80
Cellulose
1020 X=0.53
Lignin 1015
X=0.87
Cellulose
Sugarcane trash 1010 100
200
Sugarcane bagasse 1010 100
300
Activation Energy [kJ/mol]
1020
Cellulose X=0.24
Lignin Pine sawdust X=0.73
1010 100
300
Xylan
200
Frequency Factor [1/s]
Frequency Factor [1/s]
Xylan
1015
200
Activation Energy [kJ/mol]
1020
Cellulose X=0.05
1015
Cedar sawdust Lignin
1010 100
300
Activation Energy [kJ/mol]
X=0.81
200
300
Activation Energy [kJ/mol]
Xylan
Frequency Factor [1/s]
X=0.30
20
10
X=0.70
15
10
Cellulose Lignin Coffee residue
10
10
100
200
300
Activation Energy [kJ/mol]
Fig. 10. Distributions of kinetic parameters for biomass-derived materials and biomasses.
volatilisation and char forming during pyroysis. We believe that the change in compensation effect suggested a change in reaction mechanism including the solid structural changes. We found that the reaction mechanism changed during pyrolysis, although the distribution function for coffee residue pyrolysis did not show an obvious change. The distribution function of activation energy and the kinetic parameter distribution provided more understanding of the process from the volatilisation of lighter components through carbonisation during pyrolysis.
Miura and Maki [8] reported that the distribution of the kinetic parameters differed, depending on coal rank. Kinetic parameters for pyrolysis of coal and biomass are shown in Fig. 11. The kinetic parameter distributions of coals were different from those of biomasses. The biomasses showed a greater changes in activation energy and frequency factor than coals, as shown in Fig. 11. With an activation energy below 200 kJ/mol, the higher the frequency factor a sample has, the lower the starting temperature of volatilisation is. An activation energy of 200–250 kJ/mol indicated
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Frequency Factor [1/s]
1025
1020
References
Coal(C>85%) Coal(75%
1015
1010 100
150
200
250
215
300
350
Activation Energy [kJ/mol] Fig. 11. Comparison of distributions of kinetic parameters for coal and biomass. The coal data are from Miura and Maki [8].
the transition to forming char. A frequency factor with an activation energy of 350 kJ/mol represented the extent of carbonisation of the sample. We consider that the frequency factor can be used as an index of the extent of char decomposition. Additionally, although the index includes no catalytic effect of inherent metallic species, predicting combustion characteristics, such as char-burn out, is expected. 4. Conclusions We performed pyrolysis of various biomass and biomass-derived materials by thermogravimetry and investigated kinetic parameter distributions derived using the Miura method. Before the thermogravimetric analysis, preliminary experiments were carried out using TG and WMR; we showed that secondary reactions affected the mass-loss curves of some samples. Biomasses and biomass-derived materials showed marked changes in kinetic parameters. We were able to characterise pyrolysis for biomass samples by the kinetic parameter distribution and could predict some combustion characteristics, such as char-burn out and the temperature at which the emission of volatiles started.
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