Pyrolysis behaviors and kinetic studies on Eucalyptus residues using thermogravimetric analysis

Energy Conversion and Management 105 (2015) 251–259

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Energy Conversion and Management journal homepage: www.elsevier.com/locate/enconman

Pyrolysis behaviors and kinetic studies on Eucalyptus residues using thermogravimetric analysis Zhihua Chen a, Quanjie Zhu b, Xun Wang a, Bo Xiao a, Shiming Liu a,⇑ a b

School of Environmental Science & Engineering, Huazhong University of Science and Technology, Wuhan 430074, China Safety Engineering College, North China Institute of Science and Technology, Beijing 101601, China

a r t i c l e

i n f o

Article history: Received 11 May 2015 Accepted 30 July 2015

Keywords: Pyrolysis Eucalyptus residues Kinetic Thermogravimetric analysis

a b s t r a c t The pyrolysis behaviors and kinetics of Eucalyptus leaves (EL), Eucalyptus bark (EB) and Eucalyptus sawdust (ESD) were investigated by using thermogravimetric analysis (TGA) technique. Three stages for EL, EB and ESD pyrolysis have been divided using differential derivative thermogravimetric (DDTG) method and the second stage is the main pyrolysis process with approximately 86.93% (EL), 88.96% (EB) and 97.84% (ESD) weight loss percentages. Kinetic parameters of Gaussian distributed activation energy model (DAEM) for EL, EB and ESD pyrolysis are: distributed centers (E0) of 141.15 kJ/mol (EL), 149.21 kJ/mol (EB), 175.79 kJ/mol (ESD), standard deviations (r) of 18.35 kJ/mol (EL), 18.37 kJ/mol (EB), 14.41 kJ/mol (ESD) and pre-exponential factors (A) of 1.15E+10 s1 (EL), 4.34E+10 s1 (EB), 7.44E+12 s1 (ESD). A new modified discrete DAEM was performed and showed excellent fits to experimental data than Gaussian DAEM. According to the modified discrete DAEM, the activation energies are in ranges of 122.67–308.64 kJ/mol, 118.72–410.80 kJ/mol and 108.39–192.93 kJ/mol for EL, EB and ESD pyrolysis, respectively. The pre-exponential factors of discrete DAEM have wide ranges of 4.84E+13– 6.12E+22 s1 (EL), 1.91E+12–4.51E+25 s1 (EB) and 63.43–4.36E+11 s1 (ESD). The variation of activation energy versus conversion reveals the mechanism change during pyrolysis process. The kinetic data would be of immense benefit to model, design and develop suitable thermo-chemical systems for the application of Eucalyptus residues. Ó 2015 Elsevier Ltd. All rights reserved.

1. Introduction Due to the decrease in reservoir of fossil fuels and the increasing concerns on environmental protection, biomass is widely regarded as a clean, sustainable, renewable and alternative energy source in the future. In recent decades, special attentions have been paid to the conversion of residual biomass into biofuels through thermo-chemical conversion processes, such as pyrolysis, gasification and combustion [1]. Particularly in China, biomass residues had been widely used for electric power generation over last decade [2]. The Eucalyptus trees, which represent fast-growing species, have been planted extensively in many parts of the world to meet the increasing demand for paper pulp, timber and fiber [3]. Annually in China, a large amount of Eucalyptus residues such as the leaves, bark and sawdust are generated during the process of paper pulp production. Such abundant of these residues is an attractive energy source. Moreover, the accumulation and disposal of these residues also constituted a source of environmental ⇑ Corresponding author. E-mail address: [email protected] (S. Liu). http://dx.doi.org/10.1016/j.enconman.2015.07.077 0196-8904/Ó 2015 Elsevier Ltd. All rights reserved.

problems. Therefore, the reuse of Eucalyptus residues could solve pollution problems by means of thermo-chemical conversion processes. Pyrolysis process is a promising technology for biomass utilization, which converts the biomass to bio-oil, char and gasses depending on the pyrolysis conditions [4]. The pyrolysis process is preferred because of its lower temperature requirement, oxygen independent operational conditions and higher-quality oils features comparing to gasification and combustion [5]. In order to use the Eucalyptus residues for energy applications through thermo-chemical conversion processes, it is very important to investigate their thermal characteristic and kinetics. Furthermore, pyrolysis also represents the initial stage of gasification and combustion, its kinetic modeling is helpful in describing practical conversion processes and design more efficient reactors [6]. To date, thermogravimetric analysis (TGA) is considered as a powerful technique for pyrolysis behaviors characterization and kinetic study. In a TGA experiment, the pyrolysis behaviors (mass loss and mass loss rate) can be measured as functions of temperature and time in an inert atmosphere [7]. By using the TGA data, many mathematical methods such as model-fit and model-free

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were widely used in corresponding studies [8–10]. However, these methods are limited to use in biomass pyrolytic kinetics investigation due to the high heterogeneous (such as catalysis of alkalis and secondary reactions) of the pyrolysis process [11,12]. And the distributed activation energy model (DAEM) to treat the biomass pyrolysis kinetics is even better. The DAEM assumes that infinity irreversible first-order reactions happen during pyrolysis process. Such activation energy is thought to be not a constant value but obeys a kind of distributed probability density function (PDF) such as Gaussian distribution [11–13]. Many attractive methods can be used to treat the DAEM calculation, such as fitting method [14,15], iso-conversional method [16] and discretization method [17–20]. Among these methods, the discrete DAEM which discretize the infinity first-order reaction as many (but finite) parallel first-order reactions is even better for the DAEM calculation [18–20]. So far, to our knowledge, there are few studies on the pyrolysis characteristics and kinetics of Eucalyptus residues, which is important to evaluate biomass feedstock for fuel or chemical production, reactor design and control of thermo-chemical processes. In this study, the pyrolysis behaviors and kinetics of Eucalyptus residues, Eucalyptus leaves (EL), Eucalyptus bark (EB) and Eucalyptus sawdust (ESD), were investigated by using thermogravimetric analysis. A new modified discretize DAEM method which based on Starink temperature integral [21] and Scott algorithm [19] was carried out for the corresponding kinetics studies. As for a comparative study, the Gaussian DAEM was also used in this study.

with Arrhenius equation [12]. The differential form of such reaction rate equation can be expressed as:

r¼

  da A E f ðaÞ ¼ exp  RT dT b

ð1Þ

where r is the reaction rate, 1/K; a is the conversion, dimensionless; T is absolute temperature, K; A is apparent pre-exponential factor, 1/s; E is the apparent activation energy, J/mol; R is ideal gas constant, 8.314 J/(mol K); b is the linear heating rate, K/s; f(a) is conversion dependence function (or reaction model) and its independent variable, a, can be defined as

a¼

m0  mt m0  mf

ð2Þ

where m0 (%), mt (%) and mf (%) is the initial mass, instant mass and final mass of the sample recorded by TGA instrument, respectively. Integral form of Eq. (1) is given as

gðaÞ ¼

Z 0

a

1 A da ¼ f ðaÞ b

Z

T

T0

  E A dT ¼ wðE; TÞ exp  RT b

ð3Þ

where g(a) is the integral form of f(a); w(E,T) is the temperature integral which has not an analytic solution but always can be replaced with approximable expressions. A high accuracy equation (Eq. (4)) to approximate the w(E,T) was given by Starink [21].

wðE; TÞ ¼

 1:92   E TR E exp 1:0008  0:312 R E RT

ð4Þ

2. Methods

According to first-order reaction, f(a) = 1  a, and Eq. (3) becomes

2.1. Material

  A x ¼ 1  a ¼ exp  wðE; TÞ b

ð5Þ

The used Eucalyptus residues Eucalyptus leaves (EL), Eucalyptus bark (EB) and Eucalyptus sawdust (ESD) were obtained from a sawmill which locates on Guiping City, Guangxi province, China. The fresh EL, EB and ESD were firstly dried under the sun for 30 days to gain the air-dried basis (ADB) samples. ADB samples were further crushed, milled and sieved to achieve a particle size below 0.074 mm. The undersize ADB samples were used to perform proximate analysis. The undersize ADB samples were then dried at 105 °C for 48 h to get dried basis (DB) samples. The DB samples were used to conduct ultimate and TGA analysis.

2.3.2. Gaussian DAEM The distributed activation energy model (DAEM) assumes infinite irreversible first-order reactions happen during the whole pyrolysis process [11,12,23]. All such reactions share a constant pre-exponential factor (A) and the activation energy (E) is further assumed as a continuous distribution [23]. Then the conversion function of DAEM during the whole temperature range is given as

2.2. Physicochemical characterization and thermogravimetric analysis

x¼1a¼

where x denotes the remaining conversion.

Z 0

The C, H, N, S and P elements weight contents of EL, EB and ESD were analyzed using an elemental analyser (Vario Micro cube, Elementar). The weight percent of oxygen was determined by difference. Poximate analysis of EL, EB and ESD were performed according to ASTM standards [22]. And the low heating values (LHV) of the samples (dried basis) were measured by using a bomb calorimeter. Thermogravimetric analysis (TGA) tests were conducted on a Pyris1 TGA instrument (Perkin Elmer Co., Ltd) with platinum crucibles. Approximately 5.0 mg of each sample was used for all TGA runs. Argon was used as sweeping gas and maintained at a constant flow rate of 100 ml/min. Heating rates of 10 °C/min and 40 °C/min were used for EL, EB and ESD samples from room temperature to 800 °C. Each TGA experiment was repeated three times to eliminate the errors.

2.3.1. Theory of distributed activation energy model Reaction rate of solid-state pyrolysis in linear non-isothermal TGA could be interpreted as the homogeneous kinetic combined

  A exp  wðE; TÞ f ðEÞdE b

ð6Þ

where f(E) is the probability density function (PDF) of the activation energy distribution. Several PDFs such as Gaussian, Weibull, and Gamma etc. were reported in the literature [12]. Among these PDFs, the Gaussian PDF according to Eq. (7) is the most widely used.

1 ðE  E0 Þ2 f ðEÞ ¼ pffiffiffiffiffiffiffi exp  2r2 r 2p

! ð7Þ

where E0 and r of Eq. (7) is the mean value and standard deviation of Gaussian PDF, respectively. Then unknown kinetic parameters (i.e. E0, r and A) of Gaussian DAEM for Eucalyptus residues pyrolysis can be estimated by using optimization algorithms to fit the experimental data. In this study, the minimizing of the following objective function (Eq. (8)) is used to perform the optimization.

rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi X2 XNd . 2 ðx Np c  xe Þ j¼1 i¼1

Fitð%Þ ¼ 100 2.3. Kinetics

1

ð8Þ

where j (j = 1, 2) denotes j-th heating rate TGA set; i denotes the data points used; Nd denotes the number of experimental data; Np denotes the number of unknown parameters; xc represents the calculated x from Eq. (6); xe represents the experimental x; Fit(%) is

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fitting quality parameter between calculated and experimental x. And a lower Fit value indicates a better quality of fit. 2.3.3. Discrete DAEM According to Scott et al. [19], the infinite reactions can be discretized as n parallel first-order reactions. Each of these reactions has its characteristic activation energy and pre-exponential factor. For the pyrolysis process of Eucalyptus residues under non-isothermal condition, the reaction rate of one such first-order reaction can be given as



  dmk Ak Ek mk ¼ exp dT bj RT

ð9Þ

where mk, Ak and Ek is the instantaneous mass, pre-exponential factor and activation energy of k-th reaction, respectively. The integral form of Eq. (9) is given as:

" M k ¼ M k;0 exp 

Ak wðEk ; TÞ bj

# ð10Þ

where Mk is the mass of k-th reaction and Mk,0 is the initial value of Mk. The mass of sample is the sum of mass of n reactions. And then the conversion function during pyrolysis process can be expressed as:

" # Pn n n X X Ak k¼1 M k x ¼ Pn  wchar;k ¼ f k;0 exp  wðEk ; T k Þ bj k¼1 M k;0 i¼1 k¼1

ð11Þ

where fk,0 = Mk,0 and fk = Mk; n is number of first-order reactions; bj denotes the j-th (j = 1,2) heating rate; Tk is the corresponding temperature; and wchar,k is char mass of k-th reaction. If the value of Ek and Ak were known, Eq. (11) would be solved linearly by using Eq. (4), as:

2

 Ab0 w1 ðE0 ; T 0 Þ

 Ab0 w2 ðE0 ; T 0 Þ

. . .  Ab0 wn ðE0 ; T 0 Þ

1

3

7  Ab1 w2 ðE1 ; T 1 Þ . . .  Ab1 wn ðE1 ; T 1 Þ 1 7 7 7 : ... : : 7 7 : ... : : 7 7 7 : ... : : 5 An An An  b w1 ðEn ; T n Þ  b w2 ðEn ; T n Þ . . .  b wn ðEn ; T n Þ 1 |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}

6 A1 6  w1 ðE1 ; T 1 Þ 6 b 6 6: x ¼ exp 6 6: 6 6 4:

2

3

f 1;0

WðA;E;TÞ

7 6f 7 6 2;0 7 6 7 6 f 3;0 7 6 6 7 : 7 6 7 6 5 4: Pn i¼0 wchar;k |fflfflfflfflfflfflfflfflfflfflffl ffl{zfflfflfflfflfflfflfflfflfflfflfflffl}

ð12Þ

ð15Þ

i.e.

"

 1:92  #

Ak Ek T k R Ek

exp  exp 1:0008  0:312

b1 R Ek RT k b1 "  1:92  #

Ak E k T k R Ek

 exp  exp 1:0008  0:312

b1 R Ek RT k

ð16Þ b2

Once the Ek and Ak are known, Eq. (12) can be solved by using Nonnegative Linear Least Square Method (NLLSM) to minimize an objective function (Eq. (17)) and the initial fraction fk,0 can be estimated.

O:F ¼

n X jxc  xe j2

ð17Þ

k¼1

The calculation of NLLSM must guarantee the constraints of:

8 f 2 ½1 > < k;0 n X > f i;0 ¼ 1 :

ð18Þ

k¼1

2.3.4. Calculation details All the calculation of DAEM kinetic parameters were performed on MATLABÒ software (version R2012a). The correlation coefficients (R2) between calculated date and experimental data were obtained using corrcoef command of MATLABÒ. 3. Results and discussion 3.1. Characterization of samples The proximate and ultimate analysis results of Eucalyptus leaves (EL), Eucalyptus bark (EB) and Eucalyptus sawdust (ESD) are listed in Table 1. The three samples are rich in carbon and oxygen contents (41.82–52.76%). Except of the EB, the carbon contents of EL

Ultimate analysesa (wt. %)

EL EB ESD

f–

i.e. M(T) = exp[W(A,E,T)]  f–, where M(T)–, W(A,E,T) and f– are in matrix forms. The same x is thought to have the same Ek and Ak under two different heating rates (such b1 and b2) TGA runs. And one characteristic x represents one first-order reaction. Then, from w(E,T) expression in Eq. (4) one can get:

wk ðEk ; T 1 Þjb1 ¼ wk ðEk ; T 2 Þjb2

EL EB ESD

 1:92  

Ek T k R Ek

 exp 1:0008  0:312

Ek R RT k

b2

ð14Þ

H

N

S

P

Oc

47.71 41.82 49.75

4.85 5.01 5.77

3.49 0.28 0.17

0.45 0.07 0.03

0.13 0.06 0.02

43.37 52.76 44.26

MC

VMC

FC

AC

LHV(MJ/kg)

4.26 10.89 6.12

77.73 69.89 74.38

12.12 14.85 18.79

5.89 4.37 0.81

14.98 15.04 17.64

Chemical componentsa (wt. %)

EL EB ESD

b1

C

Proximate analysesb (wt. %)

ð13Þ

i.e.

¼

xjb1 ¼ xjb1

Table 1 Ultimate, proximate analyses results of Eucalyptus waste.

–

 1:92  

Ek T k R Ek

 exp 1:0008  0:312

Ek R RT k

The activation energy Ek can be calculated by solving Eq. (14). According to Eq. (5), the pre-exponential factor Ak can be calculated by solving following equations (Eqs. (15) and (16)) with the known Ek:

Hemicellulose

Cellulose

Lignin

11.28 12.90 24.74

17.93 27.48 33.89

9.25 32.09 20.77

MC, moisture content (ASTM D 3173-87); VMC, volatile matter content (ASTM D 3175-89); AC, ash content (ASTM D 3174-89); FC, fixed carbon content (100-MCVMC-AC); LHV, low heating value. a Dried basis (DB). b Air-dried basis (ADB). c Calculated by difference.

Z. Chen et al. / Energy Conversion and Management 105 (2015) 251–259

3.2. Thermal behaviors of samples The TG/DTG curves obtained at a heating rate of 10 °C/min of EL, EB and ESD are compared in Fig. 1(A). Similar curves at heating rate of 40 °C/min were obtained for all samples. The DTG curves show that the pyrolysis process of EL, EB and ESD be further divided into three stages. As shown in Fig. 1(A), stage 1 takes place in low temperature region and results in low mass loss (TG curves). It is believed to be attributed to the evaporation of physically adsorbed water and light volatiles [17]. Stage 2, which covers a wide temperature range and manifests high mass loss percentage, is caused by the devolatilization. The pyrolysis behavior in stage 3 is characterized as little mass is probably attributed to the decomposition of carbonaceous materials retained in char residues. However, the thermal behaviors of EL, EB and ESD of stage 2 are complex due to the overlapping peaks and corresponding shoulders.

0.7

90

3

(A)

6

2

80 70 5

Stage 1

60

Solid line: 1-TG of EL 2-TG of EB 3- TG of ESD

0.6

Dash line 4- DTG of EL 5- DTG of EP 6- DTG of ESD

0.4

Stage 3

0.2

0.5

0.3

50 1

40

DTG (−%⋅oC-1)

100

TG (%)

0.1 4

30

Stage 2

0.0 0

100

200

300

400

500

600

700

800

Temperatue (oC) 0.06

4.0

(B)

3.5

0.04

DDTG=0

DDTG

3.0 2.5

P

H

0.02 0.00

S

-0.02 2.0 -0.04 1.5

-0.06

1.0

DTG Ti

0.5

Tp

J

L

-0.08

Tf

DDTG (- %⋅min-1⋅oC-1)

and ESD are higher than oxygen contents. The nitrogen (0.17– 3.49%) and sulfur (0.03–0.45%) contents of EL, EB and ESD samples are lower than some microalgae biomasses which are usually used via pyrolysis: Nannochloropis oculata (N: 5.02% and S: 0.64%) [24], Chlorella pyrenoidosa (N: 8.39% and S: 1.76%) [17], bloom-forming Cyanobacteria (N: 8.41% and S: 0.84%) [17]. It can be even more confirmed that the nitrogen and sulfur percentages of EL are quite higher than that of EB and ESD. However, the N content of EL (3.49%) is higher than some lignocellulosic biomass: alfalfa (2.39%) [25], wheat straw (0.38%) [25], giant reedgrass (2.24%) [26], cotton cocoon shell (1.3%) [27], cotton stalk (1.12%) [28], sugarcane bagasse (0.15%) [28]. And the sulfur content of EL (0.45%) is also higher than some other lignocellulosic biomass, such as palm kernel shell (0.38%) [29], sugarcane bagasse (0.24%) [30], cotton stalk (0.17%) [30]. In addition, the phosphorus contents of these three feedstocks are lower than N and S contents. Biomass with low nitrogen and sulfur contents are attractive to thermo-chemical conversion processes because high N and S contents will lead to more toxic NOx and SO2 emission [31]. As depicted in Table 1, the three feedstocks are in high volatile contents (69.89–77.73%) that could be considered suitable for pyrolysis, gasification or combustion processes [4,31]. The ash contents of EL, EB and ESD are low, about 0.81–5.89%. Low ash content features of EL, EB and ESD suggest that they can be regarded as suitable feedstocks for thermo-chemical conversion processes, since lower ash content of feedstock takes the advantages to lower fouling or aggregation on reactors. Furthermore, processing costs, poor combustion, disposal problems and low energy conversion are resulted from high ash content [32]. Biomass conversion technologies are also influenced by the moisture content of the feedstock. The moisture content of EL (4.26%) and ESD (6.12%) are higher than that of EB (10.89%) and some reported biomass feedstocks, such as cotton stalk (9.13%) [30], wheat husk (13.90%) [33], microalgae bloom-forming Cyanobacteria (9.59%) [17], microalgae Chlorella vulgaris (9.10%) [34]. Low moisture content is favorable to thermal conversion technique [32].

DTG (- %⋅min-1)

254

-0.10

0.0

-0.12 0

100

200

300

400

500

600

700

800

Temperature (oC) Fig. 1. (A) TG/DTG curves of Eucalyptus leaves (EL), Eucalyptus bark (EB) and Eucalyptus sawdust (ESD) at heating rate of 10 °C/min. (B) DTG/DDTG curves of Eucalyptus leaves pyrolysis at of 10 °C /min heating rate.

To obtain the characteristic temperatures of stage 2, the curve of differential DTG (DDTG) is used in this study. The function of differential DTG, i.e. second derivative of TG, is given as: 2

f ðmu ; TÞDDTG ¼ 

d mu dT

ð19Þ

2

where mu is the mass of samples, %. The peaks or valleys of DTG curves represent the fastest or slowest mass change rate. And then the variation rate of DTG v.s. temperature (i.e. DDTG curve), will be obtained as f (mu, T)DDTG = 0 at the peaks and valleys. DTG/DDTG curves of EL pyrolysis at heating rate of 10 °C/min are shown in Fig. 1(B). As can be seen, point H of DDTG curve corresponds to point J of DTG curve. And the corresponding temperature Ti is obtained and denotes the initial temperature of stage 2. Point S of DDTG curve corresponds to point L of DTG curve corresponds the final of stage 2 its final temperature, Tf, is obtained. Similar

Table 2 Initial, peaks and final temperatures and weigh loss percent of pyrolysis Stage 2 for EL, EB and ESD. Biomass

EL EB ESD

HR (°C/min)

10 40 10 40 10 40

Ti (°C)

143.8 161.4 174.3 203.3 141.5 173.1

Tp (°C) Peak 1

Peak 2

Peak 3

277.8 297.4 295.1 323.5 / /

373.6 384.8 338.6 364.7 344.5 355.5

/ / 490.6 522.5 / /

Tf (°C)

WLP (%)

546.3 578.5 582.7 601.9 573.5 639.2

86.93 87.35 88.96 89.48 95.20 97.84

HR is the heating rate; Ti is the initial temperature; Tp is the peak temperature; Tf is the final temperature; WLP is the weight loss percentage.

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Z. Chen et al. / Energy Conversion and Management 105 (2015) 251–259

(A) 2

TG (%)

TG-10oC/min TG-40oC/min DTG-10oC/min DTG-40oC/min

1: 2: 3: 4:

80

60

EL

3.3. Effect of heating rate

0.6

As shown in Table 2 and Fig. 2, the EL, EB and ESD pyrolysis characteristic temperatures of stage 2 increases with the heating rate. It is obvious that the increasing of heating rate shifts the characteristic temperature to higher value but do not change DTG profile (according to Fig. 2). The effect of heating rate on the characteristic temperature mainly attributes to the limited rate of heat conduction into the sample due to the thermal resistance of biomass. The increasing of heating rate leads to a simultaneous decrease of the effect temperature and an increase of heat effect [35]. Increasing of heating rate signify higher temperature is required to set off the decomposition process. One the other hand, the pyrolysis reaction can affect the heat transfer in particle. In fact, the release of volatiles leads to the formation of char which is in poor heat transfer performance and prevent the heat transfer to particle core to some extent. Moreover, the thermal decomposition reaction of biomass and the secondary reaction between the volatiles and char are endothermic processes. Such endothermic reactions absorb the heat that transfers along the radial direction before transfer to the core.

0.5 0.4

peak 1 peak 2

1

4

40

0.7

0.3

3

0.2

DTG (−% ⋅ oC-1)

100

20 0.1 0

0.0 100

200

300

400

500

600

Temperature (oC) 100

0.8

o

TG (%)

3: 4:

60

DTG-10 C/min DTG-40oC/min

1

40

EB

3

0.6 0.5

peak 1 peak 2 peak 3

2

0.7

0.4

4

0.3

DTG ( − % ⋅oC-1)

(B)

80

TG-10 oC/min TG-40 oC/min

1: 2:

0.2

20

0.1 0

0.0 200

300

400

500

600

Temperature (oC) 100

TG (%)

80

1: 2:

TG-10 oC/min TG-40 oC/min

3: 4:

DTG-10 oC/min DTG- 40 oC/min

60

peak 2

3

40

1.0

0.8

0.6

1

ESD

0.4

DTG ( −% ⋅ oC-1)

2

(C)

4

20

0.2

0

0.0 100

200

300

400

500

600

Temperature (oC) Fig. 2. TG/DTG curves of Eucalyptus leaves (EL), Eucalyptus bark (EB) and Eucalyptus sawdust (ESD) pyrolysis of Stage 2 at 10 and 40 °C/min heating rates: (A) for EL, (B) for EB and (C) ESD.

processes have been done for the EB and ESD. And the characteristic temperatures together with the weight loss percentage (WLP) of EL, EB and ESD are listed in Table 2. It is obvious that stage 2 is the main pyrolysis stage with high WLP of 86.93–97.84%. The TG/DTG curves of stage 2 for all samples at heating rates of 10 °C/min and 40 °C/min are exhibited in Fig. 2. Fig. 2(A) shows that there are two peaks locates on the DTG curve of EL. However, three peaks and one peak are observed for EB and ESD, respectively. Peak 2 which appears at temperature range of 338.6–384.8 °C is the main peak of the DTG curves and it is believed to be mainly contributed to the decomposition of cellulose.

3.4. Gaussian DAEM kinetics To perform the calculation of Gaussian DAEM, the Lobatto Quadrature method (i.e. quadl solver that built-in MATLABÒ) was employed to deal with the improper integral (dE integral from 0 to infinity) in Eq. (6). The effect limit of dE integral referenced to literature [15] with a range of 0 – E0 + 30r. For the fitting program, 100 points of experimental data (xe = 0.999–0.001) was obtained using linspace command and the corresponding temperature Tx was obtained by using cubic spline interpolation method (interp1 and spline commands). The kinetics of EL, EB and ESD for Gaussian DAEM were investigated by minimizing objective function of Eq. (8) through patternsearch algorithm (pattrensearch Global optimization tool built-in MATLABÒ). E0 = 125 kJ/mol, A = 1E+3 s1 and r = 10 kJ/mol were used as the initial guess values for kinetic parameters referenced to the literature [23]. The trust region for the parameters are E0 2 (0, 350 kJ/mol), r 2 (0, 50 kJ/mol) and A 2 (0, 10E+20 s1), which were similar to literature [13]. The optimized kinetic parameters and final objective function values of Gaussian DAEM for EL, EB and ESD pyrolysis are listed Table 3. Gaussian DAEM fits to the experimental data of the three biomass feedstocks are shown in Fig. 3. The fitting qualities are good with low Fit (Table 3) and high correlation coefficients (R2). According to Gaussian DAEM, parameter E0 represents the distributed center of activation energy and parameter r represents the distributed width. It is obvious that activation energy of ESD pyrolysis has a narrower (r = 14.41 kJ/mol) distribution than that of EL (r = 18.35 kJ/mol) and EB (r = 18.37 kJ/mol). The distributions of EL and EB are similar to each other. However, the distribution center (E0) of ESD is higher (E0 = 175.79 kJ/mol) than that of EL (E0 = 141.15 kJ/mol) and EB (E0 = 149.21 kJ/mol). The mean activation energy of woody ESD is similar to some kinds of lignocellulosic biomass such as rice straw (170 kJ/mol), rice husk (174 kJ/mol) [36]. The activation energy denotes the energy barrier which Table 3 Gaussian parameters and fitting qualities of continuous DAEM simulation for Eucalyptus residues pyrolysis.

EL EB ESD

E0 (kJ/mol)

A (s1)

r (kJ/mol)

Fit (%)

141.15 149.21 175.79

1.15E+10 4.34 E+10 7.44E+12

18.35 18.37 14.41

4.61 3.38 3.29

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1.0

0.020

(A)

f (E) (mol/kJ)

0.8

x=1-α

0.6

0.4

0.2

biomass. Thus, the Gaussian DAEM kinetic parameters of EL, EB and ESD pyrolysis should not be compared.

0.025

3.5. Discrete DAEM kinetics

0.015 0.010 0.005 0.000 80 100 120 140 160 180 200 220 240

Experiment: 10 oC/min 40 oC/min

E (kJ/mol)

Simulation: 10 oC/min 40 oC/min

EL

0.0 200

300

400

500

600

Temperature (oC) 1.0

0.025

(B)

f (E) (mol/kJ)

0.020

0.8

x=1-α

0.6

0.4

Experiment: 10 oC/min 40 oC/min

0.2

Simulation: 10 oC/min 40 oC/min

0.015 0.010 0.005 0.000 80

120

160

E (kJ/mol)

200

240

EB

0.0 200

300

400

500

600

Temperature (oC) 1.0

0.030

(C) f (E) (mol/kJ)

0.025

0.8

x=1-α

0.6

0.4

0.2

0.020 0.015 0.010 0.005

Experiment: 10 oC/min 40 oC/min

0.000 120

Simulation: 10 oC/min 40 oC/min

150

180

210

240

E (kJ/mol)

ESD

0.0 200

300

400

500

600

Temperature (oC) Fig. 3. Gaussian distributions (sub-graphs) and the comparisons between simulations and experiments: (A) Eucalyptus leaves (EL), (B) Eucalyptus bark (EB) and (C) Eucalyptus sawdust (ESD).

pyrolysis reaction has to be overcome. And it seems that the pyrolysis activities of EL, EB and ESD follow an order of EL > EB > ESD. However, the pyrolysis activities between EL, EB and ESD cannot be compared directly because the activation energy also depends on the temperature. As shown in Table 2, pyrolysis temperature ranges for EL, EB and ESD are different from to each other. Furthermore, the activation energy mainly depends on species of

3.5.1. Kinetics parameters According to discrete DAEM, each characteristic remaining conversion (x) represents an irreversible first-order reaction, which has a characteristic activation energy (Ex) and pre-exponential factor (Ax). Furthermore, such reaction represents a pseudo component which contains in biomass. These components react through parallel reaction regime [18,19]. At the same time, a characteristic remaining conversion at different heating rate has the same activation energy. By choosing a batch of x in spite of at different heating rate, the Ex and Ax can be estimated from Eq. (14) and Eq. (16) respectively. And then the initial contributed fraction (fk,0) of each hypothetical pseudo component can be estimated from Eq. (12) by minimizing the objective function of Eq. (17). 100 characteristic remaining conversion points of experimental data (xe = 0.999–0.001) have been chosen the same with Gaussian DAEM. The fzero solver built-in MATLABÒ has been used to solve Eq. (14) and Eq. (16) to obtain the Ex and Ax for EL, EB and ESD pyrolysis. fk,0 have been calculated from Eq. (12) and Eq. (17) using lsqnonneg optimization tool built-in MATLABÒ. The variations of activation energies, pre-exponential factors (in ln(Aa) form) and fk,0 along with conversion for EL, EB and ESD pyrolysis are shown in Fig. 4. The variations of activation energy versus conversion (a) are complex. The activation energy of EB almost increases with the increasing of conversion. But a downtrend can be seen for EL and ESD at conversion range of 0.87–0.99 and 0.79–0.99, respectively. Platforms with very little activation energy changes also can be seen in dependencies of Ea v.s. a for EL, EB and ESD pyrolysis. The platform of EL is narrow at about 0.45–0.60 conversion range. However, the platforms for the EB and ESD are wider than EL, about range of 0.06–0.78 for EB and 0.06–0.61 for ESD. Because the discrete DAEM used in this study assumes the Ex is the same at two different heating rates. It is similar to the iso-conversional methods [12,34]. As indicated by [12,37] and [12], the first-order reaction model can be seen as an alternative for different mechanism model thought it is simple. The Ea v.s. a dependencies can used to identify the change of decomposition mechanisms. In fact, the nuclei, diffusion and catalysis effects should be account for the activation energy variation during solid-state thermal decomposition [12]. The platform and downtrend of Ea v.s. a dependencies in EL, EB and ESD pyrolysis illustrate the continuous change of decomposition mechanisms during their pyrolysis process. [38] have reported that under low linear non-isothermal heating rate conditions (10 °C/min and 40 °C/min in the present study), the decomposition of solid-state was limited by nuclei growth which leads to a rising trend in activation energies. However, the diffusion effect leads to a downtrend for activation energies. It is believed that more porous char will be formed with progresses of the biomass pyrolysis [39]. This porous char enhance the diffusion of the volatiles and then reduce the activation energy. In case of non-isothermal decomposition, the two opposite effects (nuclei growth and gas diffusion) may finally make the activation energy as a weak function of conversion, which also leads to downtrend of activation energy. Thus, the platform of Ea v.s. a dependencies may attribute to the weak balances between nuclei growth and gas diffusion effects. Additionally, the in-situ catalysis of alkalis metals also leads to the decrease of activation energy especially in high temperature or high conversion [40,41]. The values of ln (Aa) were not constant for all reactions but have ranges of 31.51–52.50 (EB), 28.28–59.07 (EB) and 4.15–26.80 (ESD), i.e. the pre-exponential factors have wide ranges of 4.84E+13–6.12E+22 s1 (EL), 1.91E+12–4.51E+25 s1 (EB) and

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0.14

55

(A)

45

40

200

fk ,0

0.08

fk , 0

Eα (kJ/mol)

0.10

Eα

250

0.12

50

ln (Aα)

ln (Aα )

300

0.06 0.04

35

150 0.02 30 0.00

100 0.0

0.2

0.4

0.6

0.8

1.0

α 450

(B)

60 0.20

400

Eα

300

ln (Aα )

fk,0

0.15

50 45

250 40

0.10

f k, 0

350

ln (Aα)

Eα (kJ/mol)

55

200 35

0.05

150 30 100 0.0

0.2

0.4

0.6

0.8

25

0.00

40

0.6

1.0

α 200

(C)

35

0.5

180

Eα

f k,0

ln(Aα )

140

20 15

0.3

f k,0

0.4

25

160

ln(Aα )

Eα (kJ/mol)

30

0.2

10 120 5 100

0 0.0

0.2

0.4

0.6

0.8

0.1

0.0

1.0

α Fig. 4. Discrete DAEM kinetic parameters (activation energy, pre-exponential factor (lnAa) and initial mass fraction of Eucalyptus waste pyrolysis: (A) Eucalyptus leaves; (B) Eucalyptus bark and (C) Eucalyptus sawdust.

63.43–4.36E+11 s1 (ESD). The ln(Aa) v.s. a dependencies for EL, EB and ESD are very similar to that of Ea v.s. a. It may be attributed to the so-called energy compensation effect [12,42]. Although 100 reactions are assumed for the discrete DAEM, actually not all the reactions show effective contribution to the pyrolysis process. As can be seen from Fig. 4, some fk,0 value is zero but some are not zero. The corresponding reactions of fk,0 – 0 can be seen as the effective contribution to the pyrolysis [17].

3.5.2. Discrete DAEM simulation The comparsion of DAEM simulation and experiments of EL, EB and ESD pyrolysis are shows in Fig. 5. As can be seen, excellent fits have been obtained by using discrete DAEM with even higher correlation coefficient (R2) than Gaussian DAEM (as listed in Table 4). It indicates that the discrete DAEM is better than single Gaussian DAEM for pyrolysis studies.

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4. Conclusion

1.0

Experiment: 10 oC/min 40 oC/min

0.8

The pyrolysis behaviors of Eucalyptus leaves (EL), Eucalyptus bark (EB) and Eucalyptus sawdust (ESD) are different from each other. The pyrolysis processes can be divided into three stages where Stage 2 contributes to the main pyrolysis part with 86.93– 97.84% weight loss percentages. Pyrolytic temperature ranges of EL, EB and ESD is 143.8–578.5 °C, 174.3–601.9 °C and 141.5– 639.2 °C, respectively. The pyrolytic kinetics of Gaussian DAEM showed good fits to experimental data. And the discrete DAEM with 100 hypothetical first-order reactions is even better than Gaussian DAEM for biomass kinetic studies. For biomass pyrolysis, the variation of activation energy versus conversion reveals the reaction mechanism changes.

Simulation: 10 oC/min 40 oC/min

1-α

0.6

EL

0.4

0.2

(A)

0.0 100

200

300

400

500

600

Temperature ( oC)

Acknowledgements This research was supported by the National High Technology Research and Development Program (863 Program) of China (No. 2012AA101809), National Natural Science Foundation of China (No. 21276100) and class General Financial Grant from the China Postdoctoral Science Foundation (Grant No.: 2014M560892). The first author wants to acknowledge the Analytical and Testing Center of Huazhong University of Science and Technology for carrying out the analysis of feedstock samples.

1.0

Experiment: 10 oC/min 40 oC/min

0.8

Calculation: 10 oC/min 40 oC/min

1-α

0.6

EB

0.4

References 0.2

(B)

0.0 100

200

300

400

500

600

Temperature (oC) 1.0

Simulation: 10 oC/min 40 oC/min

0.8

Experiment: 10 oC/min) 40 oC/min

1-α

0.6

ESD

0.4

0.2

(C) 0.0 100

200

300

400

500

600

700

Temperature ( oC) Fig. 5. Comparisons between the discrete DAEM simulated data and experimental data for Eucalyptus waste pyrolysis: (A) Eucalyptus leaves (EL); (B) Eucalyptus bark (EB) and (C) Eucalyptus sawdust (ESD).

Table 4 Correlation coefficients (R2) between the experimental data and calculated data. Biomass

HR

Gaussian DAEM

Discrete DAEM

EL

10 40 10 40 10 40

0.9892 0.9891 0.9907 0.9911 0.9886 0.9889

0.9987 0.9976 0.9994 0.9987 0.9991 0.9992

EB ESD

HR, heating rate (°C/min).

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