Non-isothermal Kinetics of Pyrolysis of Three Kinds of Fresh Biomass

Mar. 2007

Journal of China University of Mining & Technology

Vol.17

No.1

J China Univ Mining & Technol 2007, 17(1): 0105–0111

Non-isothermal Kinetics of Pyrolysis of Three Kinds of Fresh Biomass MIN Fan-fei1,2, ZHANG Ming-xu1,2, CHEN Qing-ru3 1

Department of Material Science and Engineering, Anhui University of Science and Technology, Huainan, Anhui 232001, China 2 Key Laboratory of Modern Mining Engineering of Anhui Province, Huainan, Anhui 232001, China 3 School of Chemical and Engineering Technology, China University of Mining & Technology, Xuzhou, Jiangsu 221008, China Abstract: The pyrolysis kinetics of three different kinds of fresh biomass (grass: triple A, wheat straw, corn straw) in nitrogen flow were studied by thermogravimetric analysis at five different heating rates. The kinetic parameters of the pyrolysis process were calculated using the method of Ozawa-Flynn-Wall and the mechanism of reactions were investigated using the method of Popescu. It was found that the values of activation energy varied in different temperature ranges. The pyrolysis processes are well described by the models of Zhuravlev (Zh) and valid for diffusion-controlled between 200 ℃ and 280 ℃, by Ginstling-Brounshtein (G-B), valid for diffusion-control between 280 ℃ and 310 ℃, for first-order chemical reaction between 310℃ and 350 ℃, by Zhuravlev (Zh) valid for diffusion-control between 350 ℃ and 430 ℃ and by the one-way transport model when temperatures are over 430 ℃. Key words: non-isothermal kinetics; fresh biomass; pyrolysis; thermogravimetric analysis CLC number: TQ 351.2

1

Introduction

Renewable energy is of growing importance in solving environmental concerns over fossil fuel usage. Biomass is potentially the most attractive renewable energy resource available because it is widely dispersed and could contribute zero net carbon dioxide emission to the atmosphere. Biomass is already the fourth largest source of energy in the world supplying about 14% of primary energy. It is considered the renewable energy source with the highest potential to meet the energy demand of modern society for both the developed and developing economies worldwide[1–3]. Biomass can be used as raw material to generate liquid, gaseous and solid fuels. To achieve this, thermochemical methods such as pyrolysis and gasification are the most appropriate and therefore, the most commercially used. The increased interest in the conversion of biomass for producing alternative fuels necessitates fundamental understanding of processes involving pyrolysis of biomass. Knowledge of the kinetics of thermal reactions is vital to predicting the behavior of biomass. Thermal decomposition mechaReceived 22 September 2006; accepted 28 November 2006 Project 50474056 supported by the National Natural Science Foundation of China Corresponding author. Tel: +86-554-6668649; E-mail address: [email protected]

nism must be known so that the design and control of these processes can be carried out [4]. Much experimental work has been done in order to study the kinetics of the thermal decomposition of biomass. Most of the research has focused on the kinetics of cellulose pyrolysis [5–8]. Most of the kinetic models in the literature for the pyrolysis of biomass are simple first or nth-order reaction models [9–14]. Vlaev et al. estimated kinetic parameters from the thermogravimetric analysis (TGA) of pyrolysis of rice husk by using the method of Coats-Redfern and fourteen equations. The pyrolysis process can be best described by the equation of Ginstling-Brounshtein, valid for diffusion-controlled reactions. Values of activation energy, frequency factor, change of entropy, enthalpy and Gibbs energy have been calculated [15]. Safi determined the kinetic parameters of thermal decomposition of pine needles in air by using several methods. Agrawal and Sivasubramanian’s method was found to be the most consistent. For the total degradation zone, the orders of reaction were found to be in the range of 0.00–2.50 by using Agrawal and Sivasubramanian’s method and the activation energy in the range of 34.60–85.34 kJ/mol [16]. However, the

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Journal of China University of Mining & Technology

kinetic parameters of pyrolysis of biomass, especially fresh biomass, have rarely been studied using the methods based on experiments under various heating rates. Pyrolysis gasification of fresh biomass, which contains more moisture in its original state and is stored for brief periods, can produce hydrogen rich gas. This work aims to determine the kinetic parameters for the thermal decomposition of three different types of fresh biomass. The kinetic parameters of the process were calculated using the method of OzawaFlynn-Wall and the mechanisms of reactions were investigated using the method of Popescu. It is considered that methods based on experiments carried out under various heating rates give more reliable results than those based on data from a single heating rate. Moreover, the integral methods are subjected to fewer experimental errors. The methods of OzawaFlynn-Wall and Popescu have both virtues mentioned above [17–18].

2

Experimental

A SDT2960 thermal analyzer was used in the present study to monitor continuously the weight change Table 1

Vol.17

No.1

of fresh biomass samples during drying and thermal decomposition as the samples went through a linear heating program. The instrument also provided continuous recording of the DTG and DSC curves. In the present study, the flow rate of the purge nitrogen was fixed at 100 mL/min and the heating rate was varied from 2 to 10 K/min. Temperatures were raised from 25 ℃ to 800 ℃. The uniformity of the samples was maintained by using 5 mg samples and spreading them uniformly over the alumina crucible base in all experiments. 2.1

Materials and measurement

In the experiments, three fresh biomass samples, Grass: Triple A (GR), Wheat Straw (WS) and Corn Straw (CS) were used. Samples were grounded and sieved into a powder with a particle size of less than 0.2 mm. An Elementar Vario EL-III elemental analyzer and a thermogravimetric method calibrated to ASTM standards were used to carry out ultimate and proximate analysis, respectively. The results of proximate and ultimate analyses of three fresh biomass samples are given in Table 1.

Results of proximate and ultimate analyses of three fresh biomass samples

Proximate analysis (air dried basis) (%)

Biomass sample

Moisture

VM

FC

GR

8.64

68.74

13.08

Ultimate analysis (dry ash free basis) (%)

Ash

Qnet,ad (kJ/g)

C

H

N

O

S

9.54

14.70

49.45

7.57

2.37

40.52

0.09

WS

9.42

67.04

13.25

10.29

14.04

49.69

7.50

0.26

42.42

0.12

CS

9.51

72.54

13.11

4.84

14.95

48.77

7.94

0.41

42.85

0.04

Note: VM: volatile matter; FC: fixed carbon; Qnet,ad: air dried basis, low heating value

2.2

By using Doyle’s rule and

Theoretical

Data from TG curves were used to determine the kinetic parameters. Mathematical analysis was performed by using the integral method of OzawaFlynn-Wall [19]. The biomass pyrolysis process may be represented by the following reaction scheme: Biomass→Solid residue +Volatiles The common kinetic equation of can be written as follows dα A ⎛ E ⎞ = exp⎜ − ⎟ f (α ) dT β ⎝ RT ⎠

(1)

where f (α ) is a function, such as that given in Table 2 [6,15,17,19], the type of which depends on the reaction mechanism, T is the absolute temperature, α the degree of conversion, A the pre-exponential or frequency factor, E the activation energy, R the universal gas constant 8.314 J/(mol·K) and β the heating rate. The integral form of Eq. (1) is, then

∫

α 0

dα A T = ∫ exp(− E / RT )dT f (α ) β 0

(2)

g (α ) = ∫

α 0

dα f (α )

(3)

Eq. (2) can be transformed to ⎛ AE ⎞ E lg β = lg ⎜ ⎟ − 2.313 − 0.4567 Rg RT ( α ) ⎝ ⎠

(4)

Since the degrees of conversion α are the same for different heating rates β , according to Eq. (4) it fol⎛ AE ⎞ lows that lg ⎜ ⎟ is constant and a plot of lg β ⎝ Rg (α ) ⎠ against 1/T should be a straight line with slope E . −0.4567 RT The mechanisms of reactions were investigated using the method of Popescu, which is elaborated in the literature [17].

MIN Fan-fei et al

Table 2

Algebraic expressions of functions of the most common reaction mechanisms for gas solid reactions

Symbol

Diffusion One-way transport

D2

Two-way transport

G-B Zh

3 3.1

g(α )

Mechanism

D1

D3

107

Non-isothermal Kinetics of Pyrolysis of Three Kinds of Fresh Biomass

α

f (α )

1 / 2α

2

α + (1− α )ln(1 − α ) 1 3

[1 − (1 − α ) ]

Three-way transport

[(1 − α )

Zhuravlev equation

1

−3

2 3

(3 / 2)[(1 − α )

Random nucleation and nuclei growth Bi-dimensional

[ − ln(1 − α )]

A3

Tree-dimensional

[ − ln(1 − α )] 3

P-T1

Prout-Tompkins (m =0.5)

P-T2

Prout-Tompkins (m =1)

(1 − α )α (1 − α )α

− ln(1 − α )

Second-order

(1 − α )

R1

Limiting surface reaction between both phase One dimension

R2

Two dimension

1 − (1 − α )

R3

Three dimension

1 − (1 − α ) 3

−1

Calculation of the values of activation energy

The TG and DTG curves of GR, WS and CS at heating rates of the 2, 4, 6, 8 and 10 K/min heating rates are shown in Fig. 1. The TG curves have three plateaus each. The first and smaller plateau show that there is an initial loss of moisture from the samples starting at around 25 ˚C and continuing up to about 180 ˚C. Higher temperature drying (>100 ˚C) occurs due to the loss of surface tension bound water of the ground sample particles. The second plateau occurs around 180–370 ˚C which largely reflects the thermal

(a) Curves of GR degradation

Fig. 1

−1

1 2

1−α (1 − α )

−1

α

2

1 1 2

1

2(1 − α ) 2

1

Results and Discussion

− 1] 1 2 2

ln[α /(1 − α )]

Chemical reaction First-order

− 13

3(1 − α )[− ln(1 − α )] 3

ln[(1 + α ) /(1 − α )]

F2

−1

−1

2(1 − α )[− ln(1 − α )]

1 2

F1

− 1]

(3 / 2)(1 − α ) [(1 − α )

1

1 2

− 13

4 3

2

1 2

A2

1

(3 / 2)(1 − α ) [1 − (1 − α ) 3 ]

2

− 1]

−1

2 3

1 − ( 2 / 3)α − (1 − α )

Ginstling-Brounshtein equation

[ − ln(1 − α )]

2

3(1 − α ) 3

decomposition of cellulose and hemicelluloses. The third plateau starts around >370 ˚C which largely reflects the thermal decomposition of lignin[20–21]. With the heating rate increases the thermal decomposition temperature zone also increases. The nature of a TG curve with the corresponding DTG peaks gives a clear indication of the number of stages of the thermal degradation. The kinetic studies in this work are devoted to the second stage and third one stage. The conversion degrees of the two stages are calculated. The largest conversion degree of the third stage is 0.5 for the 10 K/min heating rate.

(b) Curves of WS degradation

(c) Curves of CS degradation

TG and DTG curves in nitrogen at different heating rates

According to Eq. (4), based on the experimental data, the plot of lg β against 1/T leads to a straight E line whose slope is the value of −0.4567 . Values R of the activation energy calculated this way are presented in Table 3. The values of activation energy are in the range of 35–207 kJ/mol for the entire temperature range. These values are in the range of values

found by other investigators for various biomass materials and their components. The values of E are different in some reaction zones. The values of E corresponding to the conversion degrees ranges of 0.2–0.9 in the second reaction zone are in good agreement with the values of cellulose thermal degradation from the literature [6, 10–11, 15–16, 20]. The values of E corresponding to the conversion degrees ranges of >0.3 in the third reaction zone are in excellent

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Journal of China University of Mining & Technology

agreement with the values of lignin thermal degradation from the literature [6, 21]. In other words, the two reaction zones reflect the thermal decomposition of cellulose and lignin, respectively. The values of E Table 3

Second stage of CS degradation Third stage of CS degradation

3.2

No.1

for three different fresh biomass samples show little difference in the range of thermal decomposition, the average value of E of GR is the largest and that of CS is the least.

Values of activation energy computed by Eq. (4)

GR

α

Vol.17

WS

CS

E (kJ/mol)

Correlation coeff.

E (kJ/mol)

Correlation coeff.

E (kJ/mol)

Correlation coeff.

0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90

162.29 177.30 187.54 196.89 205.51 208.63 205.33 201.12 206.58 194.08 194.85 187.35 185.42 186.46 186.95 188.11 190.10

－0.998 91 －0.999 62 －0.999 08 －0.999 44 －0.999 07 －0.999 15 －0.999 32 －0.999 31 －0.999 87 －0.999 28 －0.999 4 －0.999 24 －0.999 38 －0.999 30 －0.999 37 －0.999 28 －0.999 59

181.82 182.36 183.28 185.40 187.11 186.99 184.99 182.96 180.36 178.90 178.32 178.77 180.03 182.39 186.06 192.43 203.99

－0.999 46 －0.999 83 －0.999 87 －0.999 84 －0.999 84 －0.999 91 －0.999 87 －0.999 88 －0.999 87 －0.999 95 －0.999 95 －0.999 97 －0.999 96 －0.999 98 －0.999 98 －0.999 93 －0.999 81

169.39 177.83 182.14 181.42 185.62 186.79 186.97 179.38 173.58 172.06 168.49 169.69 169.45 172.44 173.82 178.31 180.39

－0.999 15 －0.999 28 －0.999 14 －0.999 30 －0.999 30 －0.999 70 －0.999 36 －0.999 25 －0.999 67 －0.999 60 －0.999 52 －0.999 95 －0.999 85 －0.999 19 －0.999 57 －0.999 57 －0.999 27

0.10

143.35

－0.999 24

146.56

－0.999 07

184.77

－0.999 39

0.15

110.93

－0.999 78

126.26

－0.999 09

140.51

－0.999 42

0.20

122.06

－0.999 67

104.80

－0.999 67

105.59

－0.999 39

0.25

110.93

－0.999 78

85.35

－0.999 97

82.94

－0.999 09

0.30

89.63

－0.999 01

69.26

－0.999 67

65.56

－0.998 87

0.35

68.80

－0.999 14

57.01

－0.998 93

52.73

－0.998 27

0.40

54.71

－0.999 10

48.39

－0.998 49

44.05

－0.998 39

0.45

46.66

－0.999 27

42.18

－0.998 31

38.46

－0.997 92

0.50

42.12

－0.999 26

39.13

－0.998 76

35.70

－0.998 67

Mechanism of thermal decomposition

The formal expressions of the functions f(α) and g(α) depend on the conversion mechanisms and their mathematical models. These models represent the limiting stages of the reactions which include chemical reactions, random nucleation and nuclei growth and phase boundary reactions or diffusion. Table 2 shows the most common kinetic models and their algebraic expressions for solid thermal decomposition reaction. With the help of the functions from Table 2 and by using the least squares method, Tables 4–6 are obtained. It can be observed in Tables 4–6 that the highest correlation coefficients of the three samples are obtained with the equation of Zhuravlev between 180 ˚C and 280 ˚C and the equation of Ginstling-Brounshtein around 280–310˚C. It indicates that pyrolysis of three types of fresh biomass takes place under typical diffusion control in the studied temperature range. The higher values of E (Table 3) are observed in the reaction zone. Thermal decomposition in this temperature range is subjected to kinetic diffusion control due to the release of large amounts of volatile matter. This

can be confirmed by TG and DTG curves (Fig. 1). As can be seen in Tables 4–6 the highest correlation coefficients of the three samples are obtained with the first-order chemical reaction around 310–350 ˚C. As the thermal decomposition of hemicelluloses and cellulose are nearly completed, the release of volatile matter decreases; the reaction occurs under the first-order chemical reaction in this temperature range. Between 350 and 430 ˚C, the best correlation coefficients of three samples are obtained with the equation of Zhuravlev. It suggests that the thermal degradation occurs under kinetic-diffusion control again in the temperature range. It can be observed in Fig. 1 that there is a smaller peak in the DTG curves in the same temperature range. It indicates that the release rate of volatile matter increases. One may notice that the highest correlation coefficients are obtained for the one-way transport model in Tables 4–6 of the three samples around 430–750˚C. This may be ascribed to the fact that the thermal degradation of lignin dominates and it mainly yields charcoal and the release of volatile matter is quite small in this temperature range [8] . In a higher temperature range, heat transfer effects should be considered due to the development of strong temperature gradients within samples.

MIN Fan-fei et al

Table 4

109

Non-isothermal Kinetics of Pyrolysis of Three Kinds of Fresh Biomass

Correlation coefficients corresponding various kinetic mechanisms using the GR data set

Tm(K) Tn(K) D1 D2 D3 G-B Zh A2 A3 P-T1 P-T2 F1 F2 R1 R2 R3

503.15 513.15 0.974 0 0.978 0 0.981 7 0.979 3 0.987 7 0.817 9 0.762 2 0.829 0 0.635 2 0.915 9 0.936 6 0.890 1 0.903 7 0.907 9

513.15 523.15 0.947 9 0.956 6 0.964 5 0.959 4 0.977 0 0.747 2 0.680 0 0.764 8 0.547 8 0.872 6 0.908 5 0.826 5 0.850 9 0.858 5

523.15 533.15 0.923 7 0.938 7 0.952 1 0.943 4 0.972 4 0.693 3 0.613 5 0.719 7 0.477 7 0.843 2 0.897 1 0.769 6 0.809 2 0.821 1

533.15 543.15 0.932 9 0.951 0 0.966 3 0.956 7 0.986 2 0.719 9 0.628 6 0.755 8 0.490 7 0.874 8 0.934 6 0.776 8 0.831 5 0.847 1

543.15 553.15 0.961 9 0.977 8 0.988 9 0.982 3 0.996 3 0.820 8 0.739 4 0.857 6 0.637 6 0.938 8 0.980 9 0.835 7 0.897 8 0.913 4

553.15 563.15 0.985 1 0.994 7 0.995 3 0.995 9 0.979 7 0.926 7 0.877 9 0.951 1 0.839 9 0.984 1 0.989 0 0.898 9 0.958 4 0.969 8

563.15 573.15 0.977 1 0.997 7 0.991 5 0.998 4 0.947 7 0.973 0 0.947 5 0.986 4 0.949 2 0.994 8 0.959 8 0.875 7 0.976 2 0.988 2

573.15 583.15 0.727 6 0.909 1 0.996 1 0.962 4 0.929 9 0.968 0 0.939 3 0.992 8 0.977 9 0.997 4 0.929 3 0.497 2 0.890 2 0.953 2

583.15 593.15 0.795 2 0.935 0 0.997 9 0.977 1 0.878 0 0.988 6 0.973 2 0.997 5 0.991 9 0.997 4 0.870 1 0.615 9 0.934 0 0.977 6

593.15 603.15 0.911 9 0.992 6 0.980 5 0.998 8 0.861 0 0.985 6 0.984 2 0.975 5 0.976 2 0.972 3 0.846 6 0.721 2 0.986 1 0.993 9

Tm(K) Tn(K) D1 D2 D3 G-B Zh A2 A3 P-T1 P-T2 F1 F2 R1 R2 R3

623.15 663.15 0.869 1 0.885 4 0.900 6 0.890 7 0.926 7 0.167 8 0.073 4 0.184 0 0.059 4 0.491 5 0.566 4 0.415 9 0.453 7 0.466 3

663.15 703.15 0.899 1 0.916 8 0.932 7 0.922 4 0.957 2 0.487 2 0.401 5 0.513 3 0.282 3 0.714 5 0.791 4 0.623 5 0.670 6 0.685 6

703.15 743.15 0.978 0 0.985 5 0.990 9 0.987 6 0.995 8 0.848 4 0.807 9 0.865 5 0.758 9 0.934 6 0.967 6 0.881 9 0.911 0 0.919 4

743.15 783.15 0.993 8 0.996 5 0.996 2 0.996 7 0.989 6 0.929 0 0.899 6 0.943 8 0.872 3 0.980 2 0.994 4 0.936 3 0.963 0 0.969 7

783.15 823.15 0.998 0 0.995 9 0.989 8 0.994 1 0.973 4 0.972 4 0.853 8 0.982 3 0.942 5 0.995 9 0.990 9 0.965 5 0.987 5 0.991 6

823.15 863.15 0.998 5 0.992 8 0.981 7 0.989 3 0.957 0 0.989 3 0.977 0 0.995 1 0.974 2 0.997 0 0.976 9 0.973 6 0.995 5 0.997 6

863.15 903.15 0.998 1 0.988 5 0.971 3 0.982 9 0.939 2 0.997 1 0.991 6 0.997 3 0.992 3 0.990 9 0.956 5 0.976 7 0.998 6 0.997 9

903.15 943.15 0.997 4 0.982 0 0.956 4 0.973 1 0.918 3 0.995 1 0.997 5 0.987 1 0.995 0 0.975 5 0.929 6 0.979 7 0.997 3 0.991 7

943.15 983.15 0.996 5 0.970 9 0.933 1 0.956 4 0.895 5 0.971 2 0.980 5 0.955 5 0.967 4 0.944 6 0.898 9 0.981 6 0.985 3 0.971 4

983.15 1 023.15 0.956 6 0.984 4 0.916 8 0.956 9 0.882 4 0.938 9 0.950 0 0.920 2 0.927 3 0.914 0 0.882 0 0.796 0 0.982 3 0.954 9

Tm , Tn are two different degrees of conversion corresponding temperatures.

Table 5

Correlation coefficients corresponding various kinetic mechanisms using the WS data set

Tm(K) Tn(K) D1 D2 D3 G-B Zh A2 A3 P-T1 P-T2 F1 F2 R1 R2 R3

503.15 513.15 0.9923 0.994 0 0.995 5 0.994 6 0.997 6 0.781 5 0.672 9 0.794 3 0.381 6 0.932 1 0.948 8 0.915 1 0.922 4 0.925 7

513.15 523.15 0.987 3 0.990 6 0.993 5 0.991 7 0.997 3 0.793 0 0.708 4 0.807 8 0.495 9 0.924 5 0.947 6 0.894 4 0.910 4 0.915 3

523.15 533.15 0.971 3 0.978 7 0.985 1 0.981 0 0.993 8 0.747 4 0.660 1 0.768 9 0.474 8 0.895 8 0.933 2 0.844 9 0.872 2 0.880 4

533.15 543.15 0.957 7 0.970 1 0.980 4 0.973 9 0.993 2 0.719 7 0.623 2 0.750 5 0.448 2 0.884 0 0.936 0 0.805 7 0.848 6 0.861 2

543.15 553.15 0.960 5 0.975 5 0.986 5 0.979 8 0.995 8 0.760 1 0.663 0 0.798 4 0.513 1 0.912 3 0.964 8 0.811 4 0.869 3 0.885 2

553.15 563.15 0.975 4 0.987 8 0.993 0 0.990 5 0.987 0 0.857 8 0.779 9 0.893 5 0.686 9 0.960 5 0.987 9 0.857 5 0.922 8 0.937 9

563.15 573.15 0.976 9 0.993 2 0.993 4 0.995 1 0.967 8 0.926 3 0.874 0 0.954 7 0.847 7 0.985 2 0.981 6 0.862 6 0.951 8 0.967 5

573.15 583.15 0.886 0 0.967 8 0.997 6 0.996 1 0.959 0 0.934 2 0.887 3 0.970 6 0.915 0 0.990 3 0.969 5 0.684 2 0.913 7 0.952 3

583.15 593.15 0.541 5 0.796 5 0.983 9 0.893 2 0.948 5 0.912 3 0.859 7 0.970 3 0.941 7 0.984 8 0.946 0 0.262 5 0.767 7 0.877 7

593.15 603.15 0.145 1 0.880 2 0.960 1 0.947 2 0.923 1 0.974 2 0.950 6 0.995 5 0.985 8 0.999 0 0.890 1 0.112 1 0.884 3 0.983 7

Tm(K) Tn(K) D1 D2 D3 G-B Zh A2 A3 P-T1 P-T2 F1 F2 R1 R2 R3

623.15 663.15 0.994 1 0.996 2 0.997 8 0.996 8 0.999 5 0.444 6 0.317 0 0.458 9 0.119 5 0.792 8 0.836 7 0.744 4 0.769 1 0.777 1

663.15 703.15 0.979 4 0.983 9 0.987 0 0.985 1 0.989 3 0.099 1 0.070 0 0.134 6 0.240 6 0.719 7 0.822 9 0.581 1 0.654 4 0.677 1

703.15 743.15 0.974 9 0.977 6 0.977 8 0.977 9 0.973 1 0.561 0 0.391 3 0.614 5 0.162 8 0.876 7 0.933 4 0.761 3 0.828 0 0.846 1

743.15 783.15 0.998 0 0.996 0 0.990 8 0.994 6 0.976 0 0.927 7 0.886 1 0.946 9 0.843 3 0.988 5 0.994 9 0.939 9 0.971 6 0.978 7

783.15 823.15 0.998 2 0.993 1 0.983 1 0.990 0 0.960 2 0.961 5 0.930 2 0.977 0 0.901 0 0.995 8 0.984 4 0.950 7 0.985 3 0.990 9

823.15 863.15 0.998 8 0.990 1 0.974 5 0.985 0 0.944 0 0.985 5 0.966 4 0.993 8 0.962 2 0.995 4 0.966 6 0.959 1 0.994 6 0.997 4

863.15 903.15 0.998 0 0.983 4 0.960 5 0.975 6 0.924 2 0.995 6 0.989 8 0.993 6 0.990 1 0.983 8 0.940 6 0.967 7 0.997 9 0.995 6

903.15 943.15 0.997 4 0.975 1 0.943 1 0.963 3 0.904 7 0.987 7 0.992 9 0.975 6 0.986 7 0.962 1 0.912 9 0.972 3 0.993 6 0.984 3

943.15 983.15 0.999 2 0.970 9 0.924 7 0.952 2 0.888 8 0.962 5 0.973 4 0.944 3 0.956 3 0.933 7 0.890 6 0.949 9 0.983 8 0.965 8

983.15 1 023.15 0.768 7 0.995 6 0.925 9 0.976 8 0.888 3 0.953 3 0.966 3 0.929 7 0.938 5 0.921 9 0.882 3 0.435 8 0.997 7 0.973 5

110

Journal of China University of Mining & Technology

Table 6

Vol.17

No.1

Correlation coefficients corresponding various kinetic mechanisms using the CS data set

Tm(K)

503.15

513.15

523.15

533.15

543.15

553.15

563.15

573.15

583.15

593.15

Tn(K)

513.15

523.15

533.15

543.15

553.15

563.15

573.15

583.15

593.15

603.15

D1

0.986 5

0.973 2

0.945 2

0.913 8

0.925 5

0.973 7

0.991 5

0.961 5

0.625 2

0.723 4

D2

0.989 1

0.978 7

0.956 3

0.932 3

0.946 5

0.984 1

0.991 8

0.995 2

0.870 8

0.894 4

D3

0.991 5

0.983 6

0.966 2

0.948 8

0.963 8

0.988 9

0.982 5

0.987 3

0.996 6

0.996 9

G-B

0.990 0

0.980 5

0.959 8

0.938 2

0.952 9

0.986 4

0.989 4

0.997 0

0.954 5

0.954 0

Zh

0.995 0

0.991 1

0.981 3

0.973 0

0.985 0

0.985 4

0.953 9

0.931 0

0.901 4

0.883 0

A2

0.791 8

0.761 6

0.697 0

0.630 9

0.670 1

0.835 2

0.943 2

0.978 0

0.985 8

0.978 1

A3

0.704 6

0.676 9

0.601 9

0.517 5

0.546 9

0.741 7

0.901 1

0.955 5

0.965 1

0.957 4

P-T1

0.803 9

0.778 2

0.721 6

0.667 8

0.718 0

0.874 5

0.960 9

0.989 0

0.997 4

0.996 0

P-T2

0.468 2

0.473 1

0.406 3

0.321 8

0.367 1

0.624 8

0.868 4

0.962 5

0.992 6

0.988 8

F1

0.922 5

0.899 3

0.859 4

0.826 1

0.865 7

0.954 1

0.983 4

0.993 3

0.996 4

0.997 8

F2

0.941 3

0.928 2

0.905 2

0.892 7

0.934 7

0.981 9

0.967 6

0.941 6

0.896 5

0.873 0

R1

0.899 4

0.862 4

0.798 6

0.730 5

0.742 3

0.853 9

0.921 5

0.836 2

0.389 5

0.528 7

R2

0.911 5

0.881 9

0.831 1

0.782 4

0.812 5

0.917 8

0.971 2

0.974 3

0.881 4

0.897 0

R3

0.915 3

0.887 9

0.840 9

0.797 8

0.831 9

0.932 4

0.978 1

0.989 0

0.962 0

0.957 5

Tm(K)

623.15

663.15

703.15

743.15

783.15

823.15

863.15

903.15

943.15

983.15

Tn(K)

663.15

703.15

743.15

783.15

823.15

863.15

903.15

943.15

983.15

1 023.15

D1

0.986 8

0.989 3

0.996 0

0.997 3

0.997 1

0.997 3

0.997 3

0.996 9

0.997 8

0.995 6

D2

0.990 3

0.992 1

0.995 3

0.994 8

0.991 1

0.987 7

0.983 0

0.976 3

0.969 8

0.968 7

D3

0.993 2

0.993 7

0.992 8

0.989 0

0.980 6

0.971 9

0.960 9

0.946 5

0.930 2

0.911 1

G-B

0.991 3

0.992 8

0.994 6

0.993 1

0.987 8

0.982 5

0.975 4

0.965 4

0.954 3

0.943 1

Zh

0.997 0

0.993 8

0.984 6

0.973 6

0.957 6

0.942 0

0.925 5

0.908 5

0.893 6

0.882 8

A2

0.377 2

0.769 9

0.896 9

0.928 2

0.963 6

0.984 4

0.994 5

0.990 8

0.972 4

0.936 6

A3

0.248 1

0.699 1

0.849 2

0.885 3

0.932 9

0.964 8

0.984 7

0.993 5

0.982 0

0.947 3

P-T1

0.394 3

0.789 3

0.914 6

0.947 7

0.978 4

0.992 5

0.993 8

0.980 9

0.955 8

0.919 4

P-T2

0.056 9

0.586 1

0.782 3

0.840 5

0.912 2

0.959 3

0.986 9

0.989 7

0.968 8

0.927 4

F1

0.749 6

0.917 7

0.976 1

0.988 9

0.995 4

0.993 8

0.984 8

0.967 4

0.943 5

0.912 5

F2

0.803 7

0.952 2

0.991 8

0.993 8

0.982 3

0.964 7

0.942 8

0.918 7

0.897 4

0.882 8

R1

0.699 5

0.867 7

0.935 4

0.940 7

0.954 0

0.960 3

0.965 4

0.969 2

0.965 1

0.899 0

R2

0.720 2

0.894 8

0.959 6

0.972 6

0.986 5

0.994 0

0.997 3

0.994 9

0.986 7

0.971 8

R3

0.730 2

0.902 9

0.965 9

0.979 5

0.991 5

0.996 3

0.995 6

0.987 4

0.972 0

0.947 2

4 Conclusions 1) The pyrolysis process of fresh biomass can be divided into three stages. The first stage is loss of moisture from the samples. The second thermal degradation occurs around 180–370 ˚C and reflects largely the thermal decomposition of cellulose and hemicelluloses. The third one starts around >370 ˚C which largely reflects the thermal decomposition of lignin. 2) The values of activation energy of three types of fresh biomass vary in different thermal degradation

temperature ranges; the values of activation energy are in the ranges of 35–207 kJ/mol for the entire temperature range. 3) The Popescu method was successfully applied to determine the reaction mechanism of samples. It indicates that the pyrolysis process of fresh biomass cannot be well described using only one reaction mechanism function. 4) The pyrolysis process is well described by the mechanism of a first-order chemical reaction and kinetic-diffusion control. Kinetic-diffusion control is the dominating reaction mechanism.

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