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Thermal behavior of waste tea pyrolysis by TG-FTIR analysis
Energy 103 (2016) 533e542
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Thermal behavior of waste tea pyrolysis by TG-FTIR analysis Linghui Tian a, b, Boxiong Shen b, *, Huan Xu a, b, Fukuan Li a, Yinyin Wang a, Surjit Singh c a
College of Environmental Science & Engineering, Nankai University, Tianjin 300071, China School of Energy & Environmental Engineering, Hebei University of Technology, Tianjin 300401, China c Energy Research Institute, The University of Leeds, Leeds, LS2 9JT, UK b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 13 September 2015 Received in revised form 15 February 2016 Accepted 5 March 2016 Available online 28 March 2016
To investigate the kinetic behavior and the release of evolved gases in the pyrolysis process for waste tea, the technique of TG-FTIR at different heating rates was introduced in this study. The mass loss of the waste tea was divided into two steps: the volatilization of water at the temperature lower than 210 C, and the volatilization of CO2 and organic compounds at the temperature range of 210e550 C. FTIR (Fourier transform infrared) analysis indicated that CO2was the main gas released from the thermal degradation of waste tea. Moreover, the evolved gases from the waste tea also included H2O, CH3COOH, C6H5OH, C]C and so on. Three model-free models and three model-fitting models were applied to investigate the pyrolysis process of waste tea. According to the values of the activation energy by the three model-free models and the correlation coefficient from model-fitting models, the activation energy (207.96 kJ/mol ~ 222.6 kJ/mol) and pre-exponential factor were determined for the pyrolysis of waste tea. The results of the model-fitting models indicated that three-dimension diffusion (spherical symmetry) model conformed to the pyrolysis mechanism of waste tea well. © 2016 Elsevier Ltd. All rights reserved.
Keywords: Pyrolysis Waste tea Kinetics TG-FTIR Model simulation
1. Introduction A huge amount of energy has been consumed with the development of society. However the energy, especially fossil fuels like oil and coals that is limited and unrecoverable, is decreasing so rapidly that they would be exhausted in the future. As a renewable and environmental friendly resource, biomass would be one of the optimal substitutes for the fossil fuels [1]. Due to the lack of energy and environment pollution problems in recent years, the energy from biomass pyrolysis has been highly concerned [2]. As a method of thermochemical conversion under limited or no oxygen, the pyrolysis of biomass produces a mixture of gases, liquids (bio-oil) and solid (bio-char) at a certain temperature [3]. Tea is a kind of common greenery beverage in China. The output of tea in China was up to 1,450,000 tons each year before 2013 [4]. It is reported that the outputs of tea in China were 2.09 million tons and 2.27 million tons in 2014 and 2015, respectively. Moreover, tea beverage industry develops rapidly in recent decades. In China, tea beverage industry has already accounted for 20 percent of the domestic beverage market in just two decades. The prosperity of
* Corresponding author. Tel.: þ86 022 60435784. E-mail address: [email protected] (B. Shen). http://dx.doi.org/10.1016/j.energy.2016.03.022 0360-5442/© 2016 Elsevier Ltd. All rights reserved.
tea beverage industry promotes the consumption of the tea. The vast consumption of the tea raises a problem of the disposal of the waste tea, because more than 90% of the tea is left after the consumption. The technologies of pyrolysis and carbonization of waste tea to form bio-oil and bio-char is the focus of recycling in recent years [5]. During the pyrolysis process, the volatile of tea is condensed to produce oil and the char from the tea can be treated further by carbonization to produce activated carbon. Chao et al. [4] and Basak et al. [6] studied the characteristics of the activated carbon from waste tea. Yavuz et al. [7] conducted an experiment about the adsorption of methylene blue and phenol from waste tea. Anushka et al. [8] studied the sulfamethazine sorption by the biochar derived from waste tea. Nevertheless the pyrolysis of the tea is a very important process to recycle the waste of tea, there were few researches that reported the pyrolysis process of waste tea. The process of pyrolysis of tea will be studied in detail in this paper to reveal the principle of pyrolysis process and investigate the volatile composition from tea pyrolysis. Thermogravimetric analysis (TG), a thermal analysis tool, has been widely used to investigate the thermodynamic parameters of the decomposition, as well as the thermal stability of polymeric material under either inert gases or air atmosphere [9,10]. Nevertheless TG (thermogravimetry) can only reveal the change of mass loss with temperature. In order to observe the composition of
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evolved gas of the decomposition of materials in each stage, FTIR (Fourier transform infrared spectroscopy) is introduced to identify and quantify the functional groups and relevant small molecules of mixed gases like CO2, CO, H2O and CH4 [11,12]. TGeFTIR technology is to transmit the escaping gas composition during the weight loss of sample in thermogravimeter to the infrared gas detection cell via constant temperature pipeline, through infrared detection, analysis and judgment the structure of escaping gas composition, proceeds quantitative and qualitative analysis, respectively. This method, which can conduct simultaneous and continuous real time analysis, is a useful technology for obtaining mass loss with time and evaluating the composition of the volatiles produced by pyrolysis. This technique can give a deep insight into the evolution of pyrolysis and products information about the physicalechemical nature of the observed process. Therefore, it has been widely used in a variety of organic and inorganic materials in terms of thermal stability and thermal pyrolysis mechanism. Therefore, TG (thermogravimetry) combined with a FTIR (Fourier transform infrared), as a continuous real-time analysis, is widely used to detect the weight loss and evolved gases obtained during the TG process [13e16]. In this work, TG experiments on the waste tea at different heating rates were carried out to investigate the process of the pyrolysis, coupled with the FTIR (Fourier transform infrared spectroscopy) to characterize the evolved gases during this process. TGFTIR analyses can provide necessary dynamics information to reveal the pyrolysis mechanism and study thermal behavior of waste tea. Therefore, the kinetic analysis was also investigated by various kinetic models. 2. Experimental 2.1. Materials preparation The waste of tea was always produced by hot water soaking after several times. To simulate the production of waste tea, tea was soaked in hot water for 8 h and the water was changed every 2 h. Then the waste tea was taken out and dried naturally under the condition of good ventilation for 24 h. In order to remove the free moisture, the sample was dried in oven at 110 C for 2 h further. The sample was ground to 16e30 mesh before TG-FTIR experiment. The proximate analysis of waste tea was measured with the method of Chinese National Standards (GB/T 212-2008). The ultimate analysis of waste tea was conducted by Elemental analyzer EA3000 (Leeman). 2.2. Methods Thermogravimetric analysis (TG) was performed on a thermal analyzer (STA 449F3, NETZSCH Company, Germany) at heating rate of 10 C min1, 20 C min1 and 30 C min1 and N2 gas flow rate of 60 ml min1 and 200 ml min1, respectively. The TG-FTIR analysis was conducted on TG (thermogravimetry) (STA 449F3, NETZSCH Company, Germany) coupled with Fourier-transform infrared spectrometer (BRUKER TENSOR 27). Approximately 20 mg sample was heated from room temperature to 800 C at a heating rate of 10 C/min in an ultrahigh purity nitrogen flow (>99.999%, 60 cm3/ min). The transfer line used to connect TG and FTIR was a 1 m long stainless steel tube with an internal diameter of 2 mm, in order to reduce the possibility of gases condensing along the transfer line, the temperature in the gas cell and transfer line were set to 200 C. After the evolved gases of samples from TG passing through the FTIR cell, absorbance information was obtained at different wavenumber as a function of temperature. The FTIR spectrometer recorded spectra every 13.6 s. The spectra were collected over the range of 4000e600 cm1 at a resolution of 4 cm1. The experiment
started only when the whole system was stable. The experimental results of TG and FTIR were recorded automatically by a computer. Data was processed by the software OPUS 6.0 (Bruker Company, Germany). 2.3. Kinetic methods The kinetics of the pyrolysis process can be set up based on the TG data. There are considerable methods that can be applied to calculate kinetic parameters. There are a variety of kinetic methods with different approximations because of the diverse integral formula. The models used to calculate the kinetic parameters of pyrolysis are classified into two categories [17,18]. One is model-free methods such as Friedman method, FWO method (FlynneWalleOzawa method) and Kissinger-Akahira-Sunose method (KAS (KissingereAkahiraeSunose) method) without considering the mechanism functions [19e22]. The other kind is model-fitting methods depending on an assumption that the reaction model function is certain, including Coats-Redfern method, Horowitz-Metzger method and Van Krevelen method [18,23,24]. Model-free methods are thought as more reliable methods that can get more reliable activation energy [25]. Whereas, modelfitting methods as supplementary is used to obtain the activation energy, pre-exponential factor and the fittest kinetics model G(a) due to model-free methods' limitation that E can be calculated only. But in some cases, the variation of the activation energy (E) and pre-exponential factor (A) corresponds to the equation ln A ¼ a þ bE, where a and b are constants, which is called as “kinetic compensation effect” [31]. The value of activation energy obtained from model-free methods is generally used to verify the reliability of model-fitting method and select the pyrolysis mechanism. To compare the reliability of the models, both model-free method and model-fitting method were used to study the pyrolysis process. The previous results indicated that in the model-free method calculation, three heating rates are enough to obtain activation energy, so three heating rates (10 ml min1, 20 ml min1 and 30 ml min1) are also selected in the studies [18,25,31]. 3. Results and discussion 3.1. Raw material composition The results of proximate analysis and ultimate analysis of waste tea were shown in Table 1. It showed that the volatile matter and fixed carbon were the principal components of the dried waste tea, and there was low ash content in it. From Table 1, it was known that carbon and oxygen account for 48.4% and 39.6% respectively. The higher O/C ratio and lower H/O ratio indicted that volatile matter contains more oxygen-containing functional groups. 3.2. TG/FTIR analysis of pyrolysis The TG profiles and the corresponding DTG (derivative differential analysis of TG) for different heating rates as a function of temperature were shown in Fig. 1. During the thermal degradation
Table 1 The proximate analysis and ultimate analysis of waste tea. Proximate analysis (wt %, daa) Ash 3.3 a
VM 77.2
Dry ash basis.
water 2.6
Ultimate analysis (wt %, daa) FC 16.9
C 48.4
H 6.7
N 5.3
O 39.6
L. Tian et al. / Energy 103 (2016) 533e542
80
30 60 ml/min 200 ml/min
90 25
70
70
40 10
30 20
5
Mass (%)
15
50
DTG (%/min)
20
60
25
80
20
60 50
15
40 10
30 20
DTG (%/min)
10○C/min 20○C/min 30○C/min
90
Mass (%)
100
30
100
535
5
10
10 0 0
100
200
300
400
500
600
700
0 900
800
0 0
100
200
300
400
500
600
700
800
0 900
Temperature (○C)
Temperature (○C) Fig. 1. TG/DTG curves at heating rate of 10 C min1, 20 C min1, 30 C min1.
Fig. 2. TG/DTG curves at N2 gas flow rate of 60 ml/min1 and 200 ml/min1.
process, nearly 75% of mass loss was observed for all the heating rates at 800 C and the thermal degradation process of the samples at different heating rates involved a multistep mass loss behavior. The DTG curves for the samples at different heating rates displayed a similar mass loss behavior, which exhibited two main degradation steps and peaks. The first step occurred at the temperature less than 200 C, with a small DTG peak at 78 C for the heating rate of 10 C min1, 93 C for the heating rate of 20 C min1 and 108 C for the heating rate of 30 C min1, respectively. The first peak was attributed to the evaporation of moisture mainly. The second step, which was the major thermal loss of waste tea, took place at the temperature range of 210e550 C and resulted in a sharp DTG peak at 347 C for the heating rate of 10 C min1, 353 C for the heating rate of 20 C min1, and 361 C for the heating rate of 30 C min1 respectively. The variation observed in the temperature range of 210e515 C could be due to higher volatile matter released from the degradation of the organic compounds and lower fixed carbon content in the sample. The reaction in step 2 was considered to be the combination of the thermal degradation of cellulose, hemicellulose and lignin [26]. And thus two small fluctuations took place in the thermal degradation process at 247 C and 400 C for the heating rate of 10 C min1, 257 C and 422 C for the heating rate of 20 C min1, 264 C and 426 C for the heating rate of 30 C.min 1. The pyrolysis temperature of hemicellulose was lower at about 200 Ce300 C, while the pyrolysis of cellulose accelerated after 300 C. Lignin was generally pyrolyzed in a wide range of 200e500 C. Thus the first shoulder peak in Fig. 1 may be contributed to the exhaustion of hemicellulose before the decomposition of cellulose. That the pyrolysis rate of lignin speeded up with temperature increasing was the main cause of the other fluctuation. As shown in Fig. 1 and Table 2, the reaction rate was growing with the increase of heating rate, which indicated higher heating rate was helpful for the pyrolysis reaction. The peaks of high
heating rate occurred at higher temperature than that of low heating rate due to the thermal lag. The TG profiles and the corresponding DTG (derivative differential analysis of TG) for N2 gas flow rate as a function of temperature were shown in Fig. 2. The effect of N2 gas flow rate on TG (DTG) was much less than that of heating rate on TG (DTG). In the TG analysis system, the heating rate is always low enough (from 5 C min1 to 80 C min1) to ensure the emission of volatile be finished easily, so the external diffusion is considered to be not an important factor during the pyrolysis reaction. To verify the assumption, the N2 gas flow rate was changed from 60 ml min1 to 200 ml min1 to consider the effect of external diffusion on the pyrolysis reaction (Fig. 2.). From Fig. 2, it is known that the effect of N2 gas flow rate on TG/DTG is weak, which was also found by the literature that the pyrolysis of waste tea was proceed in a pyrolysis reactor with different N2 gas flow rate [7,8]. Considered the fact that N2 gas flow rate is not an important factor on TG/DTG, the TG data under different heating rates are used to the kinetic analysis in the following studies. TG-FTIR analysis can not only keep a record of the mass change in the sample as the temperature during the pyrolysis, it can also reflect the relative content of evolved gases based on the FTIR data. Because absorbance of various evolved gases measured from FTIR should have a linear relationship with their concentrations according to BeereLambert's law. Therefore, the evolved yields of specific gas species can be mirrored by comparing the FTIR absorbance areas or heights [30,32e35]. However, just a type of substance instead of a certain material can be determined because of the limitation of the FTIR technology and the complexity of the evolved gases produced in the pyrolysis process. Moreover, there were still some materials in absence of dipole moment that cannot be detected by FTIR, such as H2 and Cl2 [33]. The major gases detected by FTIR during the pyrolysis of waste tea were shown in Fig. 3 and Table 8 listed their features. According
Table 2 Temperature interval and mass loss in different regions for all samples. Heating rate
10 C/min 20 C/min 30 C/min
Stage 1
Stage 2
Temperature interval ( C)
T
30e210 30e210 30e210
79 93 108
max
( C)
Residue (%)
Mass loss (%)
Temperature interval ( C)
T
8.491 8.624 8.742
210e515 210e530 210e550
347 353 361
max
( C)
Mass loss (%) 68.306 66.459 65.589
23.203 24.917 25.671
536
L. Tian et al. / Energy 103 (2016) 533e542
Fig. 3. TG-FTIR analysis of evolved gases from the pyrolysis of waste tea at heating rate of 10 C min1.
to the characteristic wave numbers of gas species [2,30,32], CH4 (3045e2875 cm1), CO2 (2240-2335 cm1), H2O 1 (1750e1250 cm ), CH3COOH (1900e1603 cm1), HCOOH (1200e1100 cm1), C6H5OH (1400e1200 cm1), CH3CH2OH(11001000 cm1) and C]C (1600-1450 cm1) were apparently observed in the FTIR spectrum experiment. Furthermore, the transmittance of evolved gases as a function of temperature was curved (Fig. 4). A relative amount of the main gases during pyrolysis can be calculated by integration from the FTIR profiles. Therefore, CH4, CO2, H2O, CH3COOH, HCOOH, C6H5OH CH3CH2OH and C]C were identified as the main evolved gases from the pyrolysis of waste tea. Generally, the organic gases were classified as non-condensable and condensable products. The non-condensable gas products from waste tea might be mostly CO2 and CH4, which were hard to be collected. CO2 was the dominating gas that evolved from the waste tea in the thermal decomposition process. CO2 accounts for approximately 60 percent of all the evolved gases. Such a large quality of CO2 might be due to the high oxygen content in waste tea (as shown in Table 1). The release of CO2 gas began at about 250 C and reached a peak at 490 C. The previous results by FTIR analysis for lignins pyrolysis and asphalt binder combustion showed that
Table 3 Different pyrolysis mechanisms. Mechanism
Mechanism description
f(a)
g(a)
R1 R2 R3 D1 D2 D3 D4
Shrinking core model (n ¼ 1) Shrinking core model (n ¼ 2) Shrinking core model (n ¼ 3) One-dimension diffusion Two-dimension diffusion Three-dimension diffusion (spherical symmetry) Three-dimension diffusion (cylindrical symmetry)
1 2 (1-a)1/2 3 (1-a)2/3 1/2a1 [-ln (1-a)]1 3/2 (1-a)2/3 (1-(1-a)1/3)1 3/2 [(1-a)1/3e1]1
a
A1.5 A2 A3 A4 A4/5 A5/6 F1 F1.5 F2 P1.5 P2 P3
Nucleation growth model (n ¼ 3/2) Nucleation growth model (n ¼ 2) Nucleation growth model (n ¼ 3) Nucleation growth model (n ¼ 4) Nucleation growth model (n ¼ 4/5) Nucleation growth model (n ¼ 6/5) Reaction order model (n ¼ 1) Reaction order model (n ¼ 3/2) Reaction order model (n ¼ 2) Power law (2/3) Power law (1/2) Power law (1/3)
3/2 (1-a)[-ln (1-a)]1/3 2 (1-a)[-ln (1-a)]1/2 3 (1-a)[-ln (1-a)]2/3 4 (1-a)[-ln (1-a)]3/4 4/5 (1-a)[-ln (1-a)]1/4 5/6 (1-a)[-ln (1-a)]1/5 1-a (1-a)3/2 (1-a)2 3/2a1/3 2a1/2 3a2/3
1-(1-a)1/2 1 -(1 -a)1/3
a2
(1-a)ln (1-a)þa [1-(1 -a)1/3]2 1-23a-(1-a)2/3 [-ln (1-a)]2/3 [-ln (1-a)]1/2 [-ln (1-a)]1/3 [-ln (1-a)]1/4 [-ln (1-a)]5/4 [-ln (1-a)]6/5 -ln (1-a) 2 [(1-a)1/2e1] (1-a)1-1
a2/3 a1/2 a1/3
Table 4 The parameters of different pyrolysis mechanisms by Coats-Redfern method. Mechanism
R1 R2 R3 D1 D2 D3 D4 A1.5 A2 A3 A4 A4/5 A5/6 F1 F1.5 F2 P1.5 P2 P3
10 C/min
20 C/min 1
R
0.9508 0.9836 0.9894 0.9569 0.9759 0.9905 0.9824 0.9914 0.9906 0.9886 0.9860 0.9924 0.9923 0.9921 0.9753 0.9444 0.9434 0.9344 0.9093
E (kJ/mol)
A (min
)
75.10 94.05 101.61 160.20 182.49 213.22 192.47 75.90 54.43 32.96 22.22 151.06 144.62 118.85 150.10 187.20 46.74 32.56 18.37
3.64 1.23 4.34 8.35 5.27 8.64 1.00 7.61 7.95 70.05 5.70 4.07 1.1 5.63 4.86 1.39 1.03 48.08 1.83
105 107 107 1012 1014 1016 1015 105 103
1012 1012 109 1012 1016 103
2
30 C/min 1
E (kJ/mol)
A (min
70.47 90.44 98.91 151.10 174.96 207.96 184.74 74.49 53.33 32.17 21.59 148.54 142.19 116.80 150.85 191.77 43.60 30.16 16.72
2.21 8.995 3.78 1.7 1.42 3.48 2.54 9.21 1.07 1.06 9.06 3.32 9.23 5.43 7.83 4.49 8.92 49.85 2.23
2
)
R
105 106 107 1012 1014 1016 1014 105 104 102
1012 1011 109 1012 1016 102
0.9402 0.9822 0.9891 0.9480 0.9718 0.9903 0.9802 0.9924 0.9916 0.9898 0.9873 0.9933 0.9932 0.9930 0.9705 0.9306 0.9306 0.91861 0.88443
E (kJ/mol)
A (min1)
72.88 92.35 100.56 156.02 179.30 211.37 188.88 75.06 53.73 32.41 21.75 149.70 143.30 117.71 150.31 189.35 45.17 31.32 17.46
4.47 1.56 6.07 4.58 3.20 6.01 5.37 1.26 1.50 1.51 13.03 4.24 1.19 7.15 7.07 2.52 1.65 87.89 3.77
105 107 107 1012 1014 1016 1014 106 104 102 1012 1012 109 1012 1016 103
R2 0.9469 0.9843 0.9897 0.9538 0.9752 0.9908 0.9827 0.9909 0.99 0.9878 0.9849 0.9919 0.9919 0.9916 0.9666 0.9246 0.9385 0.9280 0.8983
L. Tian et al. / Energy 103 (2016) 533e542
537
Table 5 The parameters of different pyrolysis mechanisms by Horowitz-Metzger method. 10 C/min Mechanism
E (kJ/mol)
R1 R2 R3 D1 D2 D3 D4 A1.5 A2 A3 A4 A4/5 A5/6 F1 F1.5 F2 P1.5 P2 P3
85.21 106.92 115.88 170.43 195.50 231.75 207.17 91.19 68.39 45.60 34.21 171.00 164.16 136.79 175.99 223.28 56.82 42.62 28.39
20 C/min A (min1) 2.85 1.67 7.75 6.08 6.66 3.31 1.78 1.78 1.50 1.13 89.90 2.21 5.52 2.11 9.03 1.998 8.635 4.37 19.51
R2 6
10 108 108 1013 1015 1018 1016 107 105 103
1014 1013 1011 1014 1019 103 102
E (kJ/mol)
0.9531 0.9847 0.9908 0.9531 0.9735 0.9908 0.9811 0.9930 0.9930 0.9930 0.9930 0.9930 0.9930 0.9930 0.9705 0.9316 0.9531 0.9531 0.9531
76.96 98.23 106.99 153.90 178.42 214.01 189.87 85.02 63.76 42.49 31.88 159.40 153.00 127.52 165.92 212.35 51.29 38.47 25.64
30 C/min A (min1) 8.4 4.36 1.92 2.95 2.76 1.11 6.88 7.99 9.60 1.03 97.86 2.84 7.86 4.63 1.54 2.56 4.65 3.18 19.31
R2 5
10 107 108 1012 1014 1017 1014 106 104 103 1013 1012 1010 1014 1018 103 102
E (kJ/mol)
0.9536 0.9859 0.9923 0.9536 0.9742 0.9923 0.9821 0.9957 0.9957 0.9957 0.9957 0.9957 0.9957 0.9957 0.9761 0.9410 0.9536 0.9536 0.9536
80.35 100.27 108.40 160.67 183.79 216.79 194.44 84.86 63.63 42.43 31.82 159.08 152.71 127.26 162.31 204.45 53.56 40.16 26.80
A (min1) 2.10 7.79 2.95 1.16 7.74 1.71 1.6 9.44 1.20 1.37 1.34 2.72 7.69 4.84 7.59 4.87 9.93 6.27 35.46
R2 6
10 107 108 1013 1014 1017 1015 106 105 103 102 1013 1012 1010 1013 1017 103 102
0.9543 0.9849 0.991 0.9543 0.974 0.991 0.9814 0.9941 0.9941 0.9941 0.9941 0.9941 0.9941 0.9941 0.9746 0.9387 0.9543 0.9543 0.9543
Table 6 The parameters of different pyrolysis mechanisms by VanKrevelen method. 10 C/min
Mechanism
E (kJ/mol) R1 R2 R3 D1 D2 D3 D4 A1.5 A2 A3 A4 A4/5 A5/6 F1 F1.5 F2 P1.5 P2 P3
79.41 100.19 108.72 163.97 188.02 222.60 199.15 84.03 61.74 39.44 28.29 162.08 155.39 128.63 165.88 210.68 51.22 37.12 23.03
20 C/min A (min 2.23 6.02 2.01 4.2 2.77 5.85 5.6 4.36 4.06 3.35 0.28 3.85 9.91 4.25 2.53 5.33 70.23 3.62 0.17
1
2
)
R 4
10 105 106 1011 1013 1015 1013 104 102
1011 1010 108 1012 1016
E (kJ/mol)
0.9567 0.9861 0.9915 0.9567 0.9759 0.9915 0.9829 0.9923 0.9923 0.9923 0.9923 0.9923 0.9923 0.9923 0.9678 0.9271 0.9567 0.9567 0.9567
76.49 96.70 104.97 158.17 181.61 215.14 192.42 81.06 59.49 37.93 27.15 156.53 150.06 124.19 159.94 202.96 49.26 35.64 22.03
30 C/min 1
A (min 2.03 4.80 1.47 1.77 9.98 1.56 1.85 3.10 3.52 3.55 0.33 1.37 3.73 2.03 8.32 1.13 82.83 4.86 0.25
)
R 4
10 105 107 1011 1012 1015 1013 104 102
2
E (kJ/mol)
0.9512 0.9834 0.9901 0.9512 0.9718 0.9901 0.9797 0.9944 0.9944 0.9944 0.9944 0.9944 0.9944 0.9944 0.9763 0.9412 0.9512 0.9512 0.9512
1011 1010 108 1011 1016
79.25 99.00 107.02 163.76 186.77 219.31 197.27 81.97 60.16 38.35 27.45 158.30 151.76 125.59 159.93 201.12 51.08 36.99 22.90
A (min1) 4.05 8.19 2.37 5 2.41 2.88 4.1 4.82 5.50 5.58 0.52 2.08 5.68 3.12 8.64 7.2 1.47 8.15 0.40
R2 4
10 105 106 1011 1013 1015 1013 104 102
1011 1010 108 1011 1015 102
0.9562 0.9853 0.991 0.9562 0.9751 0.991 0.9821 0.9934 0.9934 0.9934 0.9934 0.9934 0.9934 0.9934 0.9730 0.9359 0.9562 0.9562 0.9562
Table 7 Comparison of different kinetic models. Friedman FWO method method E (kJ/mol)
216 ± 15
KAS method
221 ± 13 220 ± 12
Coats-Redfern method
Horowitz-Metzger method
VanKrevelen method
10 C/min
20 C/min
30 C/min
10 C/min
20 C/min
30 C/min
10 C/min
20 C/min
30 C/min
213.22
207.96
211.37
231.75
214.01
216.79
222.60
215.14
219.31
A (min1)
8.64 1016
3.48 1016
6.01 1016
3.31 1018
1.11 1017
1.71 1017
5.85 1015
1.56 1015
2.88 1015
F(a)
3/2 (1-a)2/3 [1-(1-a)1/3]1
3/2 (1-a)2/3 [1-(1-a)1/3]1
3/2 (1-a)2/3 [1-(1-a)1/3]1
Table 8 The main gases evolved from waste tea. Absorbance gas
Wavenumber (cm1)
Temperature range ( C)
Maximum temperature ( C)
Percentage (%)
CH4 CO2 H2O CH3COOH C¼C C6H5OH HCOOH CH3CH2OH
2875e3045 2240e2335 1250e1750 1603e1900 1450e1600 1200e1400 1100e1200 1000e1100
250e550 250e650 275e675 275e550 100e625 275e625 275e500 250e550
471.75 492.11 430.17 391.36 411.80 391.36 382.16 391.36
2.23 61.18 14.00 9.68 5.93 3.98 1.60 1.38
538
L. Tian et al. / Energy 103 (2016) 533e542
increasing in the pyrolysis of cinnamyl diesters by TG-FTIR analysis [39].
3.3. Kinetics of pyrolysis process In the thermal reaction of this study, the mass loss can be expressed by Eq. (1).
. m0 mf
a ¼ ðm0 mt Þ
(1)
Whereais conversion rate of the sample, m0 is the initial mass of the sample, mt is the mass a time t, mf is the final mass of the sample after pyrolysis. The mass loss rate can be described as follow:
da=dt ¼ kf ðaÞ
Fig. 4. The FTIR spectrum of the evolved gases from waste tea.
the break and reforming of the some thermolabile functional groups such as carboxyl (COOH), carbonyl (C]O) and ether groups (R-O-R) contributed to the release of CO2 at lower temperature [33,37]. And another increase of CO2 evolution was detected above 650 C, which may be the decomposition of the evolved gases. CH4 was only a small fraction of the evolved gases. From Fig. 4, it can be concluded that the temperature at which the peak of CH4 occurred was higher than that of other organic matters. And the release of CH4 gas was also later than other gases and reached a maximum at 470 C. Therefore it could be inferred that CH4 would be evolved from other organics, which was confirmed in previous papers. Many references using FTIR analysis (Fu et al. in agricultural residues pyrolysis [2], Xu et al. in biomass co-gasification [26] and Wang et al. in lignins pyrolysis [33]) indicated that the formation of methane and held the opinion that the fragmentation of the side chains was the major source of the formation of methane (CH4) below 500 C. CH4 can also be formed from the demethylation of the methoxyl groups (eOeCH3), which was significant especially at above 400 C [14,37,38]. The cracking of the aromatic rings at higher temperature in biomass made another contribution to the formation of methane [2,26]. Except CH4 and CO2, the other gas products were made up of H2O, CH3COOH, HCOOH, C6H5OH, CH3CH2OH and C]C. Alcohols were detected at a temperature of 250e550 C with a peak at 390 C and the temperature range that alcohols produced was small. It indicated that the cracking of methoxy groups was the main contribution to the formation of methanol in lignins pyrolysis by FTIR analysis. And eCH2OH group in the alkyl side chains was also another source of alcohol [33]. Phenols started to be produced at about 100 C and had a higher release rate at 300e600 C accompanied with a top at 391 C. Wang et al. [33] suggested that phenols were initially due to the dehydration of hydroxyl groups in the alkyl side chain, and then the cracking of ether bonds. It showed that H2O took up about 14% of the evolved gases from tea waste, including 3% free water based on proximate analysis (Table 1) and generated water from other organic matter. Organic acids initiated to release at 250 C and finished at 550 C. HCOOH reached the maximum at 380 C earlier than the peak temperature of the acetic acid (CH3COOH), which implied that the formation of formic acid was easier than that of acetic acid. Alkene (C]C) grew slowly after 100 C and accelerated to produce at 280 C with a peak at 410 C in tea waste pyrolysis, which was considered as the growing cleavage of the bonds in esters with b-hydrogen with the temperature
(2)
Where t is the time (min), k can be calculated by Arrhenius equation:
E k ¼ A exp RT
(3)
Where A is pre-exponential factor (min1), E is the activation energy of the reaction (kJ mol1), R is the gas constant (8.314 J mol1 K1), T is the absolute temperature (K), f(a) is a mathematical model that simulates the reaction. So Eq. (2) and Eq. (3) can be combined as follows.
da=dt ¼ AexpðE=RTÞf ðaÞ
(4)
The heating rate is assumed as b ¼ dT/dt, so a new equation combined with the equations above is obtained as follows:
bda=dT ¼ AexpðE=RTÞf ðaÞ It is assumed thata ¼ follows.
gðaÞ ¼
Za 0
da A ¼ f ðaÞ b
Z
0
a
(5) da , the equation is expressed as f ðaÞ
ZT expðE=RTÞdT
(6)
T0
3.3.1. Model-free methods Model-free methods have caused wide attention to calculate activation energy because the higher reliable of activated energy can be obtained without assumption of the mechanism function. Consequently, model-free methods are currently applied to verify the accuracy of model-fitting methods and select pyrolysis mechanism from model-fitting methods. In general, Friedman method, FlynneWalleOzawa method and Kissinger-Akahira-Sunose method are the popular model-free methods. (1) Friedman method Friedman method (Eq. (7)) [21] was obtained by taking the logarithms of both sides of Eq. (5).
lnb
da ¼ ln½A$f ðaÞ E=RT dT
(7)
Because the value of ln[A$f(a)] was a constant whena was given. The value of ln(b da/dT) was a function of 1/T at different heating rates with a slope of (eE/R), from which E at different a values can be acquired.
L. Tian et al. / Energy 103 (2016) 533e542
(2) FlynneWalleOzawa method Eq. (8) was obtained by the FWO (FlynneWalleOzawa) method [27] from Eq. (6):
A$E 1:0516E 5:335 lnb ¼ ln R$gðaÞ RT
(8)
(3) KissingereAkahiraeSunose method Eq. (6) can be conversed to KAS (KissingereAkahiraeSunose) Eq. (9) [22], as the following:
b T2
¼ ln
AR E E$gðaÞ RT
certain mechanism model. Hence the pyrolysis process in 0.3 < a < 0.7 was studied in detail by the models. Three model-fitting methods were described as follows. (1) Coats-Redfern method
Due to ln [A$E/R$g(a) ] has no relation to b, E could be calculated by the slope of (1.0516E/R) of the straight line plotted by Eq. (8) with ln(b) as y-axis and 1/T as x-axis.
ln
539
(9)
The value of ln (A$R/E$g(a)) was independent of T and b. According to the Eq. (9), ln Tb2 should have a linear relation with 1/T with (E/R) as the slope. The plots of model-free methods according to Friedman method, FWO method and KAS method were shown in Fig. 5, and the activated energy values obtained at different conversion rates were shown in Fig. 6.It can be seen from Fig. 6 that activated energy (E) calculated by different methods had the similar varying trend with little distinction. The values of activation energy remained nearly unchanged when a was in the range of 0.3 < a < 0.7, which indicated that the reaction during 0.3 < a < 0.7 might follow a certain reaction mechanism. However, the changes in activated energy were obvious at lower conversion rates (a < 0.3) and at higher (a > 0.7) conversion rates. The activated energy values (when 0.3 < a < 0.7) were almost constant, approximately 220 ± 12 kJ/mol. During this range, the most intense pyrolysis process took place. Therefore, 0.3 < a < 0.7 was the dominating range needed to be studied in detail. The activation energy at lower (a < 0.3) a values was higher and gradually decreased, indicating the initiation of the pyrolysis reaction required more energy due to the co-pyrolysis of cellulose and hemicellulose. Otherwise, the activation energy values at higher a values (a > 0.7) became higher, indicating the pyrolysis at high temperatures became difficult again, which was consistent with the result in Fig. 1 that the pyrolysis of cellulose and lignin existed at a > 0.7 simultaneously. 3.3.2. Model-fitting methods Model-fitting methods with an assumption of reaction model function f(a), can calculate both the values of E and A. Although model-free methods were generally regarded as reliable ones, E was the only parameter obtained from model-free methods. Consequently, it was recommended that model-fitting methods and model-free methods should support with each other to investigate the pyrolysis mechanism. Coats-Redfern method, Horowitz-Metzger method and VanKrevelen method are common model-fitting methods reported. In this paper, the process of the thermal degradation was studied by these three methods [18]. Several solid-state pyrolysis mechanisms were listed in Table 3 in order to determine the degradation mechanism of waste tea [23]. The kinetic parameters (E and A) can be obtained based on the assumption that a single reaction took place in a certain reaction. As mentioned above, the values of activated energy (E) when a < 0.3 or a > 0.7 changed greatly, which indicated complicated multiple reactions may generate in the stages. The pyrolysis occurred at 0.3 < a < 0.7 was the main reaction and it could be suitable for a
An approximate equation was obtained with integration by Coats-Redfern method [28]:
gðaÞ ¼
ART 2 2RT E=RT e 1 E bE
(10)
A new equation correlated ln [g(a)/T2] with 1/T was obtained by taking logs.
gðaÞ AR 2RT E 1 ¼ ln ln E RT bE T2
(11)
Due to 2RT/E << 1, so the equation can be converted as follows.
gðaÞ AR E ¼ ln ln bE RT T2
(12)
The values of activation energy (E), pre-exponential factor (A) and correlation coefficient (R2) were obtained By Coats-Redfern method and listed in Table 4. It was known that the activated energy values obtained by model-free methods were generally proposed as an assessment criterion to determine the most suitable kinetic model [29]. The activated energy of 220 ± 12 kJ/mol was obtained by model-free methods when 0.3 < a < 0.7. Except activated energy, the higher correlation coefficient was another crucial reference to evaluate the optimization mechanism as well [30]. According to both of the evaluation criteria mentioned above, Table 4 showed that the mechanism of D3 with activated energy of 213.22 kJ/mol and R2 of 0.9905 was the fittest mechanism that can be used to describe the kinetic of pyrolysis process best. (2) Horowitz-Metzger method The characteristic temperature Tmax and the temperature difference h were introduced for Horowitz-Metzger method. Tmax was short for the temperature of the maximum of the reaction rate; h was defined as the temperature difference between T and Tmax, that is, h ¼ TTmax. With the two parameters, the linearized equation was given as follows [31]:
lnðgðaÞÞ ¼
2 Eh ARTmax E þ ln 2 RTmax bE RTmax
(13)
According to Eq. (13), a straight line can be plotted by ln [g (a)] ART 2
versus h with RTE2 as the slope and ln bEmax RTEmax as the intercept. max Activated energy (E) and pre-exponential factor (A) can be calcuART 2
lated by the slope (RTE2 ) and the intercept (ln bEmax RTEmax ) respecmax tively. The parameters of different pyrolysis mechanisms by Horowitz-Metzger method were shown in Table 5. According to the value of E and the correlation coefficient (R2), D3 was also chosen as the most probable model to describe the pyrolysis mechanism of waste tea. (3) Van Krevelen method As mentioned above, Tmax was referred as the temperature of the peak value of the reaction rate. Eq. (14) can be obtained by Van Krevelen method [24]:
540
L. Tian et al. / Energy 103 (2016) 533e542
-5.0
-6.0
ln(β dα/dT)
-6.5 -7.0 -7.5 -8.0 -8.5
3.25 3.00 2.75 2.50 2.25
-9.0 0.0012 0.0013 0.0014 0.0015 0.0016 0.0017 0.0018 0.0019 0.0020
(b)
conversion rate 3.50
ln(β)
-5.5
3.75
(a)
conversion rate 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
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
2.00 0.0012 0.0013 0.0014 0.0015 0.0016 0.0017 0.0018 0.0019 0.0020
1/T
1/T -8.5
-9.0
ln (β/T2)
-9.5
-10.0
-10.5
-11.0
(c)
conversion rate 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
-11.5 0.0012 0.0013 0.0014 0.0015 0.0016 0.0017 0.0018 0.0019 0.0020
1/T Fig. 5. The plots of model-free methods according to (a) Friedman method, (b) FWO and (c) KAS.
E ! 1 E 0:368 RTmax lnðgðaÞÞ ¼ln þ1 Tmax b RTmax E þ 1 lnT þ RTmax A
(14)
A plot of ln [g(a)] has a liner relation with lnT. The activation energy can be calculated from the slope (E/RTmax þ 1). The intercept and the activation energy decided the value of pre-exponential factor (A). The parameters of different pyrolysis mechanisms by Van Krevelen method were listed in Table 6. According to the activated energy and the correlation coefficient (R2), D3 was decided to be the optimal model applied to describe the pyrolysis mechanism of waste tea. 3.3.3. Comparison of different models and different biomass The model-free methods, Friedman method, FWO method and KAS method, came to a conclusion that the values of the activation energy (E) were almost invariable during 0.3 < a < 0.7. Three model-fitting methods (Coats-Redfern method, Horowitz-Metzger method and Van Krevelen method) were applied to study the
kinetic mechanism in this region (0.3 < a < 0.7). The fitting plot of Coats-Redfern method, Horowitz-Metzger method and Van Krevelen method with the experimental data were shown in Fig. 7. All the results of three model-fitting models concluded that D3 model (three-dimension diffusion (spherical symmetry) model) conformed to the pyrolysis mechanism best among the common mechanisms listed in Table 3. From Table 7, the activated energy (E) at different heating rates obtained by D3 based on an assumption of spherical solid particles, were almost identical, which indicated that the heating rate was also not a dominating factor influence the kinetics mechanism in pyrolysis process. As was shown in Table 7, the E values obtained by model-free methods were similar to the ones by model-fitting methods, which indicated that three modelfitting methods can all be suitable to describe the pyrolysis of waste tea. In diffusion-controlled reactions (D3 mechanism), the rate of product formation was inversely proportional to the gas diffusion, that is internal diffusion (the movement and transport of gas molecules inside the solid particle) or external diffusion (the movement and transport of gas molecules outside the solid particle) [23,29]. It showed in Fig. 2 that the N2 gas flow rate the effect of N2 gas flow rate on pyrolysis process is weak, indicated that the
L. Tian et al. / Energy 103 (2016) 533e542
external diffusion is considered to be not an important factor during the pyrolysis reaction. In fact the D3 model indicated that the pyrolysis of waste tea may confirm a kind of internal diffusion mechanism but not external diffusion. When compared with the activation energy for other biomass, waste tea (207.96 kJ/mol ~ 222.6 kJ/mol) was demonstrated to be a material not easy to be pyrolyzed. For example, the activation energy were 294.8 kJ/mol for MSW, 80.0 kJ/mol for waste tire, 97.8 kJ/ mol for RDF (refused derived fuel) and 42.2 kJ/mol for pine wood waste [34], respectively. The activation energy for the pyrolysis of lignin and milled wood lignin were demonstrated to be 135.84e168.44 kJ/mol and 148.77e184.16 kJ/mol [35,36], respectively. However, some marine biomass were reported to have high activation energy for pyrolysis, such as Corallina pilulifera (247.7 kJ/ mol) [40] and Enteromorpha prolifera (228.09 kJ/mol) [41]. The activation energy for rice husk pyrolysis was 229.91 kJ/mol [16]. The variety of activation energies for different biomass pyrolysis may be due to the different compositions in the biomass. The loss of the soluble component when the tea is used as beverage might result in the less volatile, which increases the difficulty of tea
600
Friedman method FWO method KAS method
500
300
200
100
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
α Fig. 6. Activated energy of waste tea by Model-free methods at different a.
-12
1
(a)
(b)
10○C/min 20○C/min 30○C/min
-13 -14
10○C/min 20○C/min 30○C/min
0 -1
-15
-2
ln (g(α))
-16 -17 -18
-3 -4 -5
-19
-6
-20 -21 0.0014
0.0015
0.0016
0.0017
0.0018
-7 -80 -70 -60 -50 -40 -30 -20 -10 0
0.0019
10 20 30 40 50 60 70 80
T-Tmax
1/T
0
(c)
10○C/min 20○C/min
-1
30○C/min
-2 -3
ln (g(α))
ln (g(α)/T2)
E (kJ/mol)
400
0 0.0
541
-4 -5 -6 -7 -8 6.30
6.35
6.40
6.45
6.50
6.55
6.60
lnT Fig. 7. The fitting plot of Coats-Redfern method (a), Horowitz-Metzger method (b) and Van-Krevelen method (c) with the experimental data.
542
L. Tian et al. / Energy 103 (2016) 533e542
pyrolysis. Nevertheless, the pyrolysis of waste tea demonstrated to be easier than MSW, rice husk and some marine biomass. 4. Conclusions The kinetic behavior and the release of evolved gases in the pyrolysis process for waste tea were investigated by TG-FTIR at different heating rates in this study. The degradation process of waste tea was divided into two steps. The mass loss in first step was due to the volatilization of water from the sample, which occurred at the temperature below 210 C. The second step at the temperature range of 210e550 C showed a sharp DTG peak due to the volatilization of the organic compounds in the sample and the mass loss was on account of the interaction of cellulose, hemicellulose and lignin. FTIR analysis indicated that CO2 was the main gas released from the thermal degradation of waste tea. Moreover, the evolved gases from the waste tea also included H2O, CH3COOH, C6H5OH, C]C and so on. The evolved temperatures of released gases differentiated from each other due to the different compositions characteristic. Three model-free models (Friedman method, FWO method and KAS method) and three model-fitting models (Coats-Redfern method, Horowitz-Metzger method and Van Krevelen method) were applied to study the pyrolysis process of waste tea. According to the values of the activation energy by the three model-free models and the correlation coefficient from model-fitting models, the activation energy (207.96 kJ/mol ~ 222.6 kJ/mol) and preexponential factor were determined for the pyrolysis of waste tea when 0.3 < a < 0.7, in which most of waste tea pyrolyzed. The results of the model-fitting models indicated that D3 model (Threedimension diffusion (spherical symmetry) model) conformed to the pyrolysis mechanism of waste tea well. Acknowledgments The project was supported by the National Natural Science Foundation of China (51541602). References [1] Liu Q, Zhong Z, Wang S, Luo Z. Interactions of biomass components during pyrolysis: a TG-FTIR study. J Anal Appl Pyrol 2011;90(2):213e8. [2] Fu P, Yi W, Bai X, Li Z, Hu S, Xiang J. Effect of temperature on gas composition and char structural features of pyrolyzed agricultural residues. Bioresour Technol 2011;102(17):8211e9. [3] Gu X, Ma X, Li L, Liu C, Cheng K, Li Z. Pyrolysis of poplar wood sawdust by TGFTIR and PyeGC/MS. J Anal Appl Pyrol 2013;102:16e23. [4] Peng C, Yan X-b, Wang R-t, Lang J-w, Ou Y-j, Xue Q-j. Promising activated carbons derived from waste tea-leaves and their application in high performance supercapacitors electrodes. Electrochim Acta 2013;87:401e8. _ Kul S¸Ç. Pyrolysis of apricot kernel shell in a fixed-bed reactor: [5] Demiral I, characterization of bio-oil and char. J Anal Appl Pyrolysis 2014;107:17e24. € [6] Uzun BB, Apaydin-Varol E, Ates¸ F, Ozbay N, Pütün AE. Synthetic fuel production from tea waste: characterisation of bio-oil and bio-char. Fuel 2010;89(1):176e84. [7] Gokce Y, Aktas Z. Nitric acid modification of activated carbon produced from waste tea and adsorption of methylene blue and phenol. Appl Surf Sci 2014;313:352e9. [8] Rajapaksha AU, Vithanage M, Zhang M, Ahmad M, Mohan D, Chang SX, et al. Pyrolysis condition affected sulfamethazine sorption by tea waste biochars. Bioresour Technol 2014;166:303e8. [9] Alshehri SM, Al-Fawaz A, Ahamad T. Thermal kinetic parameters and evolved gas analysis (TGeFTIReMS) for thioureaeformaldehyde based polymer metal complexes. J Anal Appl Pyrol 2013;101:215e21. [10] Ren Q, Zhao C, Wu X, Liang C, Chen X, Shen J, et al. TG-FTIR study on copyrolysis of municipal solid waste with biomass. Bioresour Technol 2009;100(17):4054e7. [11] Fasina O, Littlefield B. TG-FTIR analysis of pecan shells thermal decomposition. Fuel Process Technol 2012;102:61e6.
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Energy journal homepage: www.elsevier.com/locate/energy
Thermal behavior of waste tea pyrolysis by TG-FTIR analysis Linghui Tian a, b, Boxiong Shen b, *, Huan Xu a, b, Fukuan Li a, Yinyin Wang a, Surjit Singh c a
College of Environmental Science & Engineering, Nankai University, Tianjin 300071, China School of Energy & Environmental Engineering, Hebei University of Technology, Tianjin 300401, China c Energy Research Institute, The University of Leeds, Leeds, LS2 9JT, UK b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 13 September 2015 Received in revised form 15 February 2016 Accepted 5 March 2016 Available online 28 March 2016
To investigate the kinetic behavior and the release of evolved gases in the pyrolysis process for waste tea, the technique of TG-FTIR at different heating rates was introduced in this study. The mass loss of the waste tea was divided into two steps: the volatilization of water at the temperature lower than 210 C, and the volatilization of CO2 and organic compounds at the temperature range of 210e550 C. FTIR (Fourier transform infrared) analysis indicated that CO2was the main gas released from the thermal degradation of waste tea. Moreover, the evolved gases from the waste tea also included H2O, CH3COOH, C6H5OH, C]C and so on. Three model-free models and three model-fitting models were applied to investigate the pyrolysis process of waste tea. According to the values of the activation energy by the three model-free models and the correlation coefficient from model-fitting models, the activation energy (207.96 kJ/mol ~ 222.6 kJ/mol) and pre-exponential factor were determined for the pyrolysis of waste tea. The results of the model-fitting models indicated that three-dimension diffusion (spherical symmetry) model conformed to the pyrolysis mechanism of waste tea well. © 2016 Elsevier Ltd. All rights reserved.
Keywords: Pyrolysis Waste tea Kinetics TG-FTIR Model simulation
1. Introduction A huge amount of energy has been consumed with the development of society. However the energy, especially fossil fuels like oil and coals that is limited and unrecoverable, is decreasing so rapidly that they would be exhausted in the future. As a renewable and environmental friendly resource, biomass would be one of the optimal substitutes for the fossil fuels [1]. Due to the lack of energy and environment pollution problems in recent years, the energy from biomass pyrolysis has been highly concerned [2]. As a method of thermochemical conversion under limited or no oxygen, the pyrolysis of biomass produces a mixture of gases, liquids (bio-oil) and solid (bio-char) at a certain temperature [3]. Tea is a kind of common greenery beverage in China. The output of tea in China was up to 1,450,000 tons each year before 2013 [4]. It is reported that the outputs of tea in China were 2.09 million tons and 2.27 million tons in 2014 and 2015, respectively. Moreover, tea beverage industry develops rapidly in recent decades. In China, tea beverage industry has already accounted for 20 percent of the domestic beverage market in just two decades. The prosperity of
* Corresponding author. Tel.: þ86 022 60435784. E-mail address: [email protected] (B. Shen). http://dx.doi.org/10.1016/j.energy.2016.03.022 0360-5442/© 2016 Elsevier Ltd. All rights reserved.
tea beverage industry promotes the consumption of the tea. The vast consumption of the tea raises a problem of the disposal of the waste tea, because more than 90% of the tea is left after the consumption. The technologies of pyrolysis and carbonization of waste tea to form bio-oil and bio-char is the focus of recycling in recent years [5]. During the pyrolysis process, the volatile of tea is condensed to produce oil and the char from the tea can be treated further by carbonization to produce activated carbon. Chao et al. [4] and Basak et al. [6] studied the characteristics of the activated carbon from waste tea. Yavuz et al. [7] conducted an experiment about the adsorption of methylene blue and phenol from waste tea. Anushka et al. [8] studied the sulfamethazine sorption by the biochar derived from waste tea. Nevertheless the pyrolysis of the tea is a very important process to recycle the waste of tea, there were few researches that reported the pyrolysis process of waste tea. The process of pyrolysis of tea will be studied in detail in this paper to reveal the principle of pyrolysis process and investigate the volatile composition from tea pyrolysis. Thermogravimetric analysis (TG), a thermal analysis tool, has been widely used to investigate the thermodynamic parameters of the decomposition, as well as the thermal stability of polymeric material under either inert gases or air atmosphere [9,10]. Nevertheless TG (thermogravimetry) can only reveal the change of mass loss with temperature. In order to observe the composition of
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evolved gas of the decomposition of materials in each stage, FTIR (Fourier transform infrared spectroscopy) is introduced to identify and quantify the functional groups and relevant small molecules of mixed gases like CO2, CO, H2O and CH4 [11,12]. TGeFTIR technology is to transmit the escaping gas composition during the weight loss of sample in thermogravimeter to the infrared gas detection cell via constant temperature pipeline, through infrared detection, analysis and judgment the structure of escaping gas composition, proceeds quantitative and qualitative analysis, respectively. This method, which can conduct simultaneous and continuous real time analysis, is a useful technology for obtaining mass loss with time and evaluating the composition of the volatiles produced by pyrolysis. This technique can give a deep insight into the evolution of pyrolysis and products information about the physicalechemical nature of the observed process. Therefore, it has been widely used in a variety of organic and inorganic materials in terms of thermal stability and thermal pyrolysis mechanism. Therefore, TG (thermogravimetry) combined with a FTIR (Fourier transform infrared), as a continuous real-time analysis, is widely used to detect the weight loss and evolved gases obtained during the TG process [13e16]. In this work, TG experiments on the waste tea at different heating rates were carried out to investigate the process of the pyrolysis, coupled with the FTIR (Fourier transform infrared spectroscopy) to characterize the evolved gases during this process. TGFTIR analyses can provide necessary dynamics information to reveal the pyrolysis mechanism and study thermal behavior of waste tea. Therefore, the kinetic analysis was also investigated by various kinetic models. 2. Experimental 2.1. Materials preparation The waste of tea was always produced by hot water soaking after several times. To simulate the production of waste tea, tea was soaked in hot water for 8 h and the water was changed every 2 h. Then the waste tea was taken out and dried naturally under the condition of good ventilation for 24 h. In order to remove the free moisture, the sample was dried in oven at 110 C for 2 h further. The sample was ground to 16e30 mesh before TG-FTIR experiment. The proximate analysis of waste tea was measured with the method of Chinese National Standards (GB/T 212-2008). The ultimate analysis of waste tea was conducted by Elemental analyzer EA3000 (Leeman). 2.2. Methods Thermogravimetric analysis (TG) was performed on a thermal analyzer (STA 449F3, NETZSCH Company, Germany) at heating rate of 10 C min1, 20 C min1 and 30 C min1 and N2 gas flow rate of 60 ml min1 and 200 ml min1, respectively. The TG-FTIR analysis was conducted on TG (thermogravimetry) (STA 449F3, NETZSCH Company, Germany) coupled with Fourier-transform infrared spectrometer (BRUKER TENSOR 27). Approximately 20 mg sample was heated from room temperature to 800 C at a heating rate of 10 C/min in an ultrahigh purity nitrogen flow (>99.999%, 60 cm3/ min). The transfer line used to connect TG and FTIR was a 1 m long stainless steel tube with an internal diameter of 2 mm, in order to reduce the possibility of gases condensing along the transfer line, the temperature in the gas cell and transfer line were set to 200 C. After the evolved gases of samples from TG passing through the FTIR cell, absorbance information was obtained at different wavenumber as a function of temperature. The FTIR spectrometer recorded spectra every 13.6 s. The spectra were collected over the range of 4000e600 cm1 at a resolution of 4 cm1. The experiment
started only when the whole system was stable. The experimental results of TG and FTIR were recorded automatically by a computer. Data was processed by the software OPUS 6.0 (Bruker Company, Germany). 2.3. Kinetic methods The kinetics of the pyrolysis process can be set up based on the TG data. There are considerable methods that can be applied to calculate kinetic parameters. There are a variety of kinetic methods with different approximations because of the diverse integral formula. The models used to calculate the kinetic parameters of pyrolysis are classified into two categories [17,18]. One is model-free methods such as Friedman method, FWO method (FlynneWalleOzawa method) and Kissinger-Akahira-Sunose method (KAS (KissingereAkahiraeSunose) method) without considering the mechanism functions [19e22]. The other kind is model-fitting methods depending on an assumption that the reaction model function is certain, including Coats-Redfern method, Horowitz-Metzger method and Van Krevelen method [18,23,24]. Model-free methods are thought as more reliable methods that can get more reliable activation energy [25]. Whereas, modelfitting methods as supplementary is used to obtain the activation energy, pre-exponential factor and the fittest kinetics model G(a) due to model-free methods' limitation that E can be calculated only. But in some cases, the variation of the activation energy (E) and pre-exponential factor (A) corresponds to the equation ln A ¼ a þ bE, where a and b are constants, which is called as “kinetic compensation effect” [31]. The value of activation energy obtained from model-free methods is generally used to verify the reliability of model-fitting method and select the pyrolysis mechanism. To compare the reliability of the models, both model-free method and model-fitting method were used to study the pyrolysis process. The previous results indicated that in the model-free method calculation, three heating rates are enough to obtain activation energy, so three heating rates (10 ml min1, 20 ml min1 and 30 ml min1) are also selected in the studies [18,25,31]. 3. Results and discussion 3.1. Raw material composition The results of proximate analysis and ultimate analysis of waste tea were shown in Table 1. It showed that the volatile matter and fixed carbon were the principal components of the dried waste tea, and there was low ash content in it. From Table 1, it was known that carbon and oxygen account for 48.4% and 39.6% respectively. The higher O/C ratio and lower H/O ratio indicted that volatile matter contains more oxygen-containing functional groups. 3.2. TG/FTIR analysis of pyrolysis The TG profiles and the corresponding DTG (derivative differential analysis of TG) for different heating rates as a function of temperature were shown in Fig. 1. During the thermal degradation
Table 1 The proximate analysis and ultimate analysis of waste tea. Proximate analysis (wt %, daa) Ash 3.3 a
VM 77.2
Dry ash basis.
water 2.6
Ultimate analysis (wt %, daa) FC 16.9
C 48.4
H 6.7
N 5.3
O 39.6
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80
30 60 ml/min 200 ml/min
90 25
70
70
40 10
30 20
5
Mass (%)
15
50
DTG (%/min)
20
60
25
80
20
60 50
15
40 10
30 20
DTG (%/min)
10○C/min 20○C/min 30○C/min
90
Mass (%)
100
30
100
535
5
10
10 0 0
100
200
300
400
500
600
700
0 900
800
0 0
100
200
300
400
500
600
700
800
0 900
Temperature (○C)
Temperature (○C) Fig. 1. TG/DTG curves at heating rate of 10 C min1, 20 C min1, 30 C min1.
Fig. 2. TG/DTG curves at N2 gas flow rate of 60 ml/min1 and 200 ml/min1.
process, nearly 75% of mass loss was observed for all the heating rates at 800 C and the thermal degradation process of the samples at different heating rates involved a multistep mass loss behavior. The DTG curves for the samples at different heating rates displayed a similar mass loss behavior, which exhibited two main degradation steps and peaks. The first step occurred at the temperature less than 200 C, with a small DTG peak at 78 C for the heating rate of 10 C min1, 93 C for the heating rate of 20 C min1 and 108 C for the heating rate of 30 C min1, respectively. The first peak was attributed to the evaporation of moisture mainly. The second step, which was the major thermal loss of waste tea, took place at the temperature range of 210e550 C and resulted in a sharp DTG peak at 347 C for the heating rate of 10 C min1, 353 C for the heating rate of 20 C min1, and 361 C for the heating rate of 30 C min1 respectively. The variation observed in the temperature range of 210e515 C could be due to higher volatile matter released from the degradation of the organic compounds and lower fixed carbon content in the sample. The reaction in step 2 was considered to be the combination of the thermal degradation of cellulose, hemicellulose and lignin [26]. And thus two small fluctuations took place in the thermal degradation process at 247 C and 400 C for the heating rate of 10 C min1, 257 C and 422 C for the heating rate of 20 C min1, 264 C and 426 C for the heating rate of 30 C.min 1. The pyrolysis temperature of hemicellulose was lower at about 200 Ce300 C, while the pyrolysis of cellulose accelerated after 300 C. Lignin was generally pyrolyzed in a wide range of 200e500 C. Thus the first shoulder peak in Fig. 1 may be contributed to the exhaustion of hemicellulose before the decomposition of cellulose. That the pyrolysis rate of lignin speeded up with temperature increasing was the main cause of the other fluctuation. As shown in Fig. 1 and Table 2, the reaction rate was growing with the increase of heating rate, which indicated higher heating rate was helpful for the pyrolysis reaction. The peaks of high
heating rate occurred at higher temperature than that of low heating rate due to the thermal lag. The TG profiles and the corresponding DTG (derivative differential analysis of TG) for N2 gas flow rate as a function of temperature were shown in Fig. 2. The effect of N2 gas flow rate on TG (DTG) was much less than that of heating rate on TG (DTG). In the TG analysis system, the heating rate is always low enough (from 5 C min1 to 80 C min1) to ensure the emission of volatile be finished easily, so the external diffusion is considered to be not an important factor during the pyrolysis reaction. To verify the assumption, the N2 gas flow rate was changed from 60 ml min1 to 200 ml min1 to consider the effect of external diffusion on the pyrolysis reaction (Fig. 2.). From Fig. 2, it is known that the effect of N2 gas flow rate on TG/DTG is weak, which was also found by the literature that the pyrolysis of waste tea was proceed in a pyrolysis reactor with different N2 gas flow rate [7,8]. Considered the fact that N2 gas flow rate is not an important factor on TG/DTG, the TG data under different heating rates are used to the kinetic analysis in the following studies. TG-FTIR analysis can not only keep a record of the mass change in the sample as the temperature during the pyrolysis, it can also reflect the relative content of evolved gases based on the FTIR data. Because absorbance of various evolved gases measured from FTIR should have a linear relationship with their concentrations according to BeereLambert's law. Therefore, the evolved yields of specific gas species can be mirrored by comparing the FTIR absorbance areas or heights [30,32e35]. However, just a type of substance instead of a certain material can be determined because of the limitation of the FTIR technology and the complexity of the evolved gases produced in the pyrolysis process. Moreover, there were still some materials in absence of dipole moment that cannot be detected by FTIR, such as H2 and Cl2 [33]. The major gases detected by FTIR during the pyrolysis of waste tea were shown in Fig. 3 and Table 8 listed their features. According
Table 2 Temperature interval and mass loss in different regions for all samples. Heating rate
10 C/min 20 C/min 30 C/min
Stage 1
Stage 2
Temperature interval ( C)
T
30e210 30e210 30e210
79 93 108
max
( C)
Residue (%)
Mass loss (%)
Temperature interval ( C)
T
8.491 8.624 8.742
210e515 210e530 210e550
347 353 361
max
( C)
Mass loss (%) 68.306 66.459 65.589
23.203 24.917 25.671
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Fig. 3. TG-FTIR analysis of evolved gases from the pyrolysis of waste tea at heating rate of 10 C min1.
to the characteristic wave numbers of gas species [2,30,32], CH4 (3045e2875 cm1), CO2 (2240-2335 cm1), H2O 1 (1750e1250 cm ), CH3COOH (1900e1603 cm1), HCOOH (1200e1100 cm1), C6H5OH (1400e1200 cm1), CH3CH2OH(11001000 cm1) and C]C (1600-1450 cm1) were apparently observed in the FTIR spectrum experiment. Furthermore, the transmittance of evolved gases as a function of temperature was curved (Fig. 4). A relative amount of the main gases during pyrolysis can be calculated by integration from the FTIR profiles. Therefore, CH4, CO2, H2O, CH3COOH, HCOOH, C6H5OH CH3CH2OH and C]C were identified as the main evolved gases from the pyrolysis of waste tea. Generally, the organic gases were classified as non-condensable and condensable products. The non-condensable gas products from waste tea might be mostly CO2 and CH4, which were hard to be collected. CO2 was the dominating gas that evolved from the waste tea in the thermal decomposition process. CO2 accounts for approximately 60 percent of all the evolved gases. Such a large quality of CO2 might be due to the high oxygen content in waste tea (as shown in Table 1). The release of CO2 gas began at about 250 C and reached a peak at 490 C. The previous results by FTIR analysis for lignins pyrolysis and asphalt binder combustion showed that
Table 3 Different pyrolysis mechanisms. Mechanism
Mechanism description
f(a)
g(a)
R1 R2 R3 D1 D2 D3 D4
Shrinking core model (n ¼ 1) Shrinking core model (n ¼ 2) Shrinking core model (n ¼ 3) One-dimension diffusion Two-dimension diffusion Three-dimension diffusion (spherical symmetry) Three-dimension diffusion (cylindrical symmetry)
1 2 (1-a)1/2 3 (1-a)2/3 1/2a1 [-ln (1-a)]1 3/2 (1-a)2/3 (1-(1-a)1/3)1 3/2 [(1-a)1/3e1]1
a
A1.5 A2 A3 A4 A4/5 A5/6 F1 F1.5 F2 P1.5 P2 P3
Nucleation growth model (n ¼ 3/2) Nucleation growth model (n ¼ 2) Nucleation growth model (n ¼ 3) Nucleation growth model (n ¼ 4) Nucleation growth model (n ¼ 4/5) Nucleation growth model (n ¼ 6/5) Reaction order model (n ¼ 1) Reaction order model (n ¼ 3/2) Reaction order model (n ¼ 2) Power law (2/3) Power law (1/2) Power law (1/3)
3/2 (1-a)[-ln (1-a)]1/3 2 (1-a)[-ln (1-a)]1/2 3 (1-a)[-ln (1-a)]2/3 4 (1-a)[-ln (1-a)]3/4 4/5 (1-a)[-ln (1-a)]1/4 5/6 (1-a)[-ln (1-a)]1/5 1-a (1-a)3/2 (1-a)2 3/2a1/3 2a1/2 3a2/3
1-(1-a)1/2 1 -(1 -a)1/3
a2
(1-a)ln (1-a)þa [1-(1 -a)1/3]2 1-23a-(1-a)2/3 [-ln (1-a)]2/3 [-ln (1-a)]1/2 [-ln (1-a)]1/3 [-ln (1-a)]1/4 [-ln (1-a)]5/4 [-ln (1-a)]6/5 -ln (1-a) 2 [(1-a)1/2e1] (1-a)1-1
a2/3 a1/2 a1/3
Table 4 The parameters of different pyrolysis mechanisms by Coats-Redfern method. Mechanism
R1 R2 R3 D1 D2 D3 D4 A1.5 A2 A3 A4 A4/5 A5/6 F1 F1.5 F2 P1.5 P2 P3
10 C/min
20 C/min 1
R
0.9508 0.9836 0.9894 0.9569 0.9759 0.9905 0.9824 0.9914 0.9906 0.9886 0.9860 0.9924 0.9923 0.9921 0.9753 0.9444 0.9434 0.9344 0.9093
E (kJ/mol)
A (min
)
75.10 94.05 101.61 160.20 182.49 213.22 192.47 75.90 54.43 32.96 22.22 151.06 144.62 118.85 150.10 187.20 46.74 32.56 18.37
3.64 1.23 4.34 8.35 5.27 8.64 1.00 7.61 7.95 70.05 5.70 4.07 1.1 5.63 4.86 1.39 1.03 48.08 1.83
105 107 107 1012 1014 1016 1015 105 103
1012 1012 109 1012 1016 103
2
30 C/min 1
E (kJ/mol)
A (min
70.47 90.44 98.91 151.10 174.96 207.96 184.74 74.49 53.33 32.17 21.59 148.54 142.19 116.80 150.85 191.77 43.60 30.16 16.72
2.21 8.995 3.78 1.7 1.42 3.48 2.54 9.21 1.07 1.06 9.06 3.32 9.23 5.43 7.83 4.49 8.92 49.85 2.23
2
)
R
105 106 107 1012 1014 1016 1014 105 104 102
1012 1011 109 1012 1016 102
0.9402 0.9822 0.9891 0.9480 0.9718 0.9903 0.9802 0.9924 0.9916 0.9898 0.9873 0.9933 0.9932 0.9930 0.9705 0.9306 0.9306 0.91861 0.88443
E (kJ/mol)
A (min1)
72.88 92.35 100.56 156.02 179.30 211.37 188.88 75.06 53.73 32.41 21.75 149.70 143.30 117.71 150.31 189.35 45.17 31.32 17.46
4.47 1.56 6.07 4.58 3.20 6.01 5.37 1.26 1.50 1.51 13.03 4.24 1.19 7.15 7.07 2.52 1.65 87.89 3.77
105 107 107 1012 1014 1016 1014 106 104 102 1012 1012 109 1012 1016 103
R2 0.9469 0.9843 0.9897 0.9538 0.9752 0.9908 0.9827 0.9909 0.99 0.9878 0.9849 0.9919 0.9919 0.9916 0.9666 0.9246 0.9385 0.9280 0.8983
L. Tian et al. / Energy 103 (2016) 533e542
537
Table 5 The parameters of different pyrolysis mechanisms by Horowitz-Metzger method. 10 C/min Mechanism
E (kJ/mol)
R1 R2 R3 D1 D2 D3 D4 A1.5 A2 A3 A4 A4/5 A5/6 F1 F1.5 F2 P1.5 P2 P3
85.21 106.92 115.88 170.43 195.50 231.75 207.17 91.19 68.39 45.60 34.21 171.00 164.16 136.79 175.99 223.28 56.82 42.62 28.39
20 C/min A (min1) 2.85 1.67 7.75 6.08 6.66 3.31 1.78 1.78 1.50 1.13 89.90 2.21 5.52 2.11 9.03 1.998 8.635 4.37 19.51
R2 6
10 108 108 1013 1015 1018 1016 107 105 103
1014 1013 1011 1014 1019 103 102
E (kJ/mol)
0.9531 0.9847 0.9908 0.9531 0.9735 0.9908 0.9811 0.9930 0.9930 0.9930 0.9930 0.9930 0.9930 0.9930 0.9705 0.9316 0.9531 0.9531 0.9531
76.96 98.23 106.99 153.90 178.42 214.01 189.87 85.02 63.76 42.49 31.88 159.40 153.00 127.52 165.92 212.35 51.29 38.47 25.64
30 C/min A (min1) 8.4 4.36 1.92 2.95 2.76 1.11 6.88 7.99 9.60 1.03 97.86 2.84 7.86 4.63 1.54 2.56 4.65 3.18 19.31
R2 5
10 107 108 1012 1014 1017 1014 106 104 103 1013 1012 1010 1014 1018 103 102
E (kJ/mol)
0.9536 0.9859 0.9923 0.9536 0.9742 0.9923 0.9821 0.9957 0.9957 0.9957 0.9957 0.9957 0.9957 0.9957 0.9761 0.9410 0.9536 0.9536 0.9536
80.35 100.27 108.40 160.67 183.79 216.79 194.44 84.86 63.63 42.43 31.82 159.08 152.71 127.26 162.31 204.45 53.56 40.16 26.80
A (min1) 2.10 7.79 2.95 1.16 7.74 1.71 1.6 9.44 1.20 1.37 1.34 2.72 7.69 4.84 7.59 4.87 9.93 6.27 35.46
R2 6
10 107 108 1013 1014 1017 1015 106 105 103 102 1013 1012 1010 1013 1017 103 102
0.9543 0.9849 0.991 0.9543 0.974 0.991 0.9814 0.9941 0.9941 0.9941 0.9941 0.9941 0.9941 0.9941 0.9746 0.9387 0.9543 0.9543 0.9543
Table 6 The parameters of different pyrolysis mechanisms by VanKrevelen method. 10 C/min
Mechanism
E (kJ/mol) R1 R2 R3 D1 D2 D3 D4 A1.5 A2 A3 A4 A4/5 A5/6 F1 F1.5 F2 P1.5 P2 P3
79.41 100.19 108.72 163.97 188.02 222.60 199.15 84.03 61.74 39.44 28.29 162.08 155.39 128.63 165.88 210.68 51.22 37.12 23.03
20 C/min A (min 2.23 6.02 2.01 4.2 2.77 5.85 5.6 4.36 4.06 3.35 0.28 3.85 9.91 4.25 2.53 5.33 70.23 3.62 0.17
1
2
)
R 4
10 105 106 1011 1013 1015 1013 104 102
1011 1010 108 1012 1016
E (kJ/mol)
0.9567 0.9861 0.9915 0.9567 0.9759 0.9915 0.9829 0.9923 0.9923 0.9923 0.9923 0.9923 0.9923 0.9923 0.9678 0.9271 0.9567 0.9567 0.9567
76.49 96.70 104.97 158.17 181.61 215.14 192.42 81.06 59.49 37.93 27.15 156.53 150.06 124.19 159.94 202.96 49.26 35.64 22.03
30 C/min 1
A (min 2.03 4.80 1.47 1.77 9.98 1.56 1.85 3.10 3.52 3.55 0.33 1.37 3.73 2.03 8.32 1.13 82.83 4.86 0.25
)
R 4
10 105 107 1011 1012 1015 1013 104 102
2
E (kJ/mol)
0.9512 0.9834 0.9901 0.9512 0.9718 0.9901 0.9797 0.9944 0.9944 0.9944 0.9944 0.9944 0.9944 0.9944 0.9763 0.9412 0.9512 0.9512 0.9512
1011 1010 108 1011 1016
79.25 99.00 107.02 163.76 186.77 219.31 197.27 81.97 60.16 38.35 27.45 158.30 151.76 125.59 159.93 201.12 51.08 36.99 22.90
A (min1) 4.05 8.19 2.37 5 2.41 2.88 4.1 4.82 5.50 5.58 0.52 2.08 5.68 3.12 8.64 7.2 1.47 8.15 0.40
R2 4
10 105 106 1011 1013 1015 1013 104 102
1011 1010 108 1011 1015 102
0.9562 0.9853 0.991 0.9562 0.9751 0.991 0.9821 0.9934 0.9934 0.9934 0.9934 0.9934 0.9934 0.9934 0.9730 0.9359 0.9562 0.9562 0.9562
Table 7 Comparison of different kinetic models. Friedman FWO method method E (kJ/mol)
216 ± 15
KAS method
221 ± 13 220 ± 12
Coats-Redfern method
Horowitz-Metzger method
VanKrevelen method
10 C/min
20 C/min
30 C/min
10 C/min
20 C/min
30 C/min
10 C/min
20 C/min
30 C/min
213.22
207.96
211.37
231.75
214.01
216.79
222.60
215.14
219.31
A (min1)
8.64 1016
3.48 1016
6.01 1016
3.31 1018
1.11 1017
1.71 1017
5.85 1015
1.56 1015
2.88 1015
F(a)
3/2 (1-a)2/3 [1-(1-a)1/3]1
3/2 (1-a)2/3 [1-(1-a)1/3]1
3/2 (1-a)2/3 [1-(1-a)1/3]1
Table 8 The main gases evolved from waste tea. Absorbance gas
Wavenumber (cm1)
Temperature range ( C)
Maximum temperature ( C)
Percentage (%)
CH4 CO2 H2O CH3COOH C¼C C6H5OH HCOOH CH3CH2OH
2875e3045 2240e2335 1250e1750 1603e1900 1450e1600 1200e1400 1100e1200 1000e1100
250e550 250e650 275e675 275e550 100e625 275e625 275e500 250e550
471.75 492.11 430.17 391.36 411.80 391.36 382.16 391.36
2.23 61.18 14.00 9.68 5.93 3.98 1.60 1.38
538
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increasing in the pyrolysis of cinnamyl diesters by TG-FTIR analysis [39].
3.3. Kinetics of pyrolysis process In the thermal reaction of this study, the mass loss can be expressed by Eq. (1).
. m0 mf
a ¼ ðm0 mt Þ
(1)
Whereais conversion rate of the sample, m0 is the initial mass of the sample, mt is the mass a time t, mf is the final mass of the sample after pyrolysis. The mass loss rate can be described as follow:
da=dt ¼ kf ðaÞ
Fig. 4. The FTIR spectrum of the evolved gases from waste tea.
the break and reforming of the some thermolabile functional groups such as carboxyl (COOH), carbonyl (C]O) and ether groups (R-O-R) contributed to the release of CO2 at lower temperature [33,37]. And another increase of CO2 evolution was detected above 650 C, which may be the decomposition of the evolved gases. CH4 was only a small fraction of the evolved gases. From Fig. 4, it can be concluded that the temperature at which the peak of CH4 occurred was higher than that of other organic matters. And the release of CH4 gas was also later than other gases and reached a maximum at 470 C. Therefore it could be inferred that CH4 would be evolved from other organics, which was confirmed in previous papers. Many references using FTIR analysis (Fu et al. in agricultural residues pyrolysis [2], Xu et al. in biomass co-gasification [26] and Wang et al. in lignins pyrolysis [33]) indicated that the formation of methane and held the opinion that the fragmentation of the side chains was the major source of the formation of methane (CH4) below 500 C. CH4 can also be formed from the demethylation of the methoxyl groups (eOeCH3), which was significant especially at above 400 C [14,37,38]. The cracking of the aromatic rings at higher temperature in biomass made another contribution to the formation of methane [2,26]. Except CH4 and CO2, the other gas products were made up of H2O, CH3COOH, HCOOH, C6H5OH, CH3CH2OH and C]C. Alcohols were detected at a temperature of 250e550 C with a peak at 390 C and the temperature range that alcohols produced was small. It indicated that the cracking of methoxy groups was the main contribution to the formation of methanol in lignins pyrolysis by FTIR analysis. And eCH2OH group in the alkyl side chains was also another source of alcohol [33]. Phenols started to be produced at about 100 C and had a higher release rate at 300e600 C accompanied with a top at 391 C. Wang et al. [33] suggested that phenols were initially due to the dehydration of hydroxyl groups in the alkyl side chain, and then the cracking of ether bonds. It showed that H2O took up about 14% of the evolved gases from tea waste, including 3% free water based on proximate analysis (Table 1) and generated water from other organic matter. Organic acids initiated to release at 250 C and finished at 550 C. HCOOH reached the maximum at 380 C earlier than the peak temperature of the acetic acid (CH3COOH), which implied that the formation of formic acid was easier than that of acetic acid. Alkene (C]C) grew slowly after 100 C and accelerated to produce at 280 C with a peak at 410 C in tea waste pyrolysis, which was considered as the growing cleavage of the bonds in esters with b-hydrogen with the temperature
(2)
Where t is the time (min), k can be calculated by Arrhenius equation:
E k ¼ A exp RT
(3)
Where A is pre-exponential factor (min1), E is the activation energy of the reaction (kJ mol1), R is the gas constant (8.314 J mol1 K1), T is the absolute temperature (K), f(a) is a mathematical model that simulates the reaction. So Eq. (2) and Eq. (3) can be combined as follows.
da=dt ¼ AexpðE=RTÞf ðaÞ
(4)
The heating rate is assumed as b ¼ dT/dt, so a new equation combined with the equations above is obtained as follows:
bda=dT ¼ AexpðE=RTÞf ðaÞ It is assumed thata ¼ follows.
gðaÞ ¼
Za 0
da A ¼ f ðaÞ b
Z
0
a
(5) da , the equation is expressed as f ðaÞ
ZT expðE=RTÞdT
(6)
T0
3.3.1. Model-free methods Model-free methods have caused wide attention to calculate activation energy because the higher reliable of activated energy can be obtained without assumption of the mechanism function. Consequently, model-free methods are currently applied to verify the accuracy of model-fitting methods and select pyrolysis mechanism from model-fitting methods. In general, Friedman method, FlynneWalleOzawa method and Kissinger-Akahira-Sunose method are the popular model-free methods. (1) Friedman method Friedman method (Eq. (7)) [21] was obtained by taking the logarithms of both sides of Eq. (5).
lnb
da ¼ ln½A$f ðaÞ E=RT dT
(7)
Because the value of ln[A$f(a)] was a constant whena was given. The value of ln(b da/dT) was a function of 1/T at different heating rates with a slope of (eE/R), from which E at different a values can be acquired.
L. Tian et al. / Energy 103 (2016) 533e542
(2) FlynneWalleOzawa method Eq. (8) was obtained by the FWO (FlynneWalleOzawa) method [27] from Eq. (6):
A$E 1:0516E 5:335 lnb ¼ ln R$gðaÞ RT
(8)
(3) KissingereAkahiraeSunose method Eq. (6) can be conversed to KAS (KissingereAkahiraeSunose) Eq. (9) [22], as the following:
b T2
¼ ln
AR E E$gðaÞ RT
certain mechanism model. Hence the pyrolysis process in 0.3 < a < 0.7 was studied in detail by the models. Three model-fitting methods were described as follows. (1) Coats-Redfern method
Due to ln [A$E/R$g(a) ] has no relation to b, E could be calculated by the slope of (1.0516E/R) of the straight line plotted by Eq. (8) with ln(b) as y-axis and 1/T as x-axis.
ln
539
(9)
The value of ln (A$R/E$g(a)) was independent of T and b. According to the Eq. (9), ln Tb2 should have a linear relation with 1/T with (E/R) as the slope. The plots of model-free methods according to Friedman method, FWO method and KAS method were shown in Fig. 5, and the activated energy values obtained at different conversion rates were shown in Fig. 6.It can be seen from Fig. 6 that activated energy (E) calculated by different methods had the similar varying trend with little distinction. The values of activation energy remained nearly unchanged when a was in the range of 0.3 < a < 0.7, which indicated that the reaction during 0.3 < a < 0.7 might follow a certain reaction mechanism. However, the changes in activated energy were obvious at lower conversion rates (a < 0.3) and at higher (a > 0.7) conversion rates. The activated energy values (when 0.3 < a < 0.7) were almost constant, approximately 220 ± 12 kJ/mol. During this range, the most intense pyrolysis process took place. Therefore, 0.3 < a < 0.7 was the dominating range needed to be studied in detail. The activation energy at lower (a < 0.3) a values was higher and gradually decreased, indicating the initiation of the pyrolysis reaction required more energy due to the co-pyrolysis of cellulose and hemicellulose. Otherwise, the activation energy values at higher a values (a > 0.7) became higher, indicating the pyrolysis at high temperatures became difficult again, which was consistent with the result in Fig. 1 that the pyrolysis of cellulose and lignin existed at a > 0.7 simultaneously. 3.3.2. Model-fitting methods Model-fitting methods with an assumption of reaction model function f(a), can calculate both the values of E and A. Although model-free methods were generally regarded as reliable ones, E was the only parameter obtained from model-free methods. Consequently, it was recommended that model-fitting methods and model-free methods should support with each other to investigate the pyrolysis mechanism. Coats-Redfern method, Horowitz-Metzger method and VanKrevelen method are common model-fitting methods reported. In this paper, the process of the thermal degradation was studied by these three methods [18]. Several solid-state pyrolysis mechanisms were listed in Table 3 in order to determine the degradation mechanism of waste tea [23]. The kinetic parameters (E and A) can be obtained based on the assumption that a single reaction took place in a certain reaction. As mentioned above, the values of activated energy (E) when a < 0.3 or a > 0.7 changed greatly, which indicated complicated multiple reactions may generate in the stages. The pyrolysis occurred at 0.3 < a < 0.7 was the main reaction and it could be suitable for a
An approximate equation was obtained with integration by Coats-Redfern method [28]:
gðaÞ ¼
ART 2 2RT E=RT e 1 E bE
(10)
A new equation correlated ln [g(a)/T2] with 1/T was obtained by taking logs.
gðaÞ AR 2RT E 1 ¼ ln ln E RT bE T2
(11)
Due to 2RT/E << 1, so the equation can be converted as follows.
gðaÞ AR E ¼ ln ln bE RT T2
(12)
The values of activation energy (E), pre-exponential factor (A) and correlation coefficient (R2) were obtained By Coats-Redfern method and listed in Table 4. It was known that the activated energy values obtained by model-free methods were generally proposed as an assessment criterion to determine the most suitable kinetic model [29]. The activated energy of 220 ± 12 kJ/mol was obtained by model-free methods when 0.3 < a < 0.7. Except activated energy, the higher correlation coefficient was another crucial reference to evaluate the optimization mechanism as well [30]. According to both of the evaluation criteria mentioned above, Table 4 showed that the mechanism of D3 with activated energy of 213.22 kJ/mol and R2 of 0.9905 was the fittest mechanism that can be used to describe the kinetic of pyrolysis process best. (2) Horowitz-Metzger method The characteristic temperature Tmax and the temperature difference h were introduced for Horowitz-Metzger method. Tmax was short for the temperature of the maximum of the reaction rate; h was defined as the temperature difference between T and Tmax, that is, h ¼ TTmax. With the two parameters, the linearized equation was given as follows [31]:
lnðgðaÞÞ ¼
2 Eh ARTmax E þ ln 2 RTmax bE RTmax
(13)
According to Eq. (13), a straight line can be plotted by ln [g (a)] ART 2
versus h with RTE2 as the slope and ln bEmax RTEmax as the intercept. max Activated energy (E) and pre-exponential factor (A) can be calcuART 2
lated by the slope (RTE2 ) and the intercept (ln bEmax RTEmax ) respecmax tively. The parameters of different pyrolysis mechanisms by Horowitz-Metzger method were shown in Table 5. According to the value of E and the correlation coefficient (R2), D3 was also chosen as the most probable model to describe the pyrolysis mechanism of waste tea. (3) Van Krevelen method As mentioned above, Tmax was referred as the temperature of the peak value of the reaction rate. Eq. (14) can be obtained by Van Krevelen method [24]:
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L. Tian et al. / Energy 103 (2016) 533e542
-5.0
-6.0
ln(β dα/dT)
-6.5 -7.0 -7.5 -8.0 -8.5
3.25 3.00 2.75 2.50 2.25
-9.0 0.0012 0.0013 0.0014 0.0015 0.0016 0.0017 0.0018 0.0019 0.0020
(b)
conversion rate 3.50
ln(β)
-5.5
3.75
(a)
conversion rate 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
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
2.00 0.0012 0.0013 0.0014 0.0015 0.0016 0.0017 0.0018 0.0019 0.0020
1/T
1/T -8.5
-9.0
ln (β/T2)
-9.5
-10.0
-10.5
-11.0
(c)
conversion rate 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
-11.5 0.0012 0.0013 0.0014 0.0015 0.0016 0.0017 0.0018 0.0019 0.0020
1/T Fig. 5. The plots of model-free methods according to (a) Friedman method, (b) FWO and (c) KAS.
E ! 1 E 0:368 RTmax lnðgðaÞÞ ¼ln þ1 Tmax b RTmax E þ 1 lnT þ RTmax A
(14)
A plot of ln [g(a)] has a liner relation with lnT. The activation energy can be calculated from the slope (E/RTmax þ 1). The intercept and the activation energy decided the value of pre-exponential factor (A). The parameters of different pyrolysis mechanisms by Van Krevelen method were listed in Table 6. According to the activated energy and the correlation coefficient (R2), D3 was decided to be the optimal model applied to describe the pyrolysis mechanism of waste tea. 3.3.3. Comparison of different models and different biomass The model-free methods, Friedman method, FWO method and KAS method, came to a conclusion that the values of the activation energy (E) were almost invariable during 0.3 < a < 0.7. Three model-fitting methods (Coats-Redfern method, Horowitz-Metzger method and Van Krevelen method) were applied to study the
kinetic mechanism in this region (0.3 < a < 0.7). The fitting plot of Coats-Redfern method, Horowitz-Metzger method and Van Krevelen method with the experimental data were shown in Fig. 7. All the results of three model-fitting models concluded that D3 model (three-dimension diffusion (spherical symmetry) model) conformed to the pyrolysis mechanism best among the common mechanisms listed in Table 3. From Table 7, the activated energy (E) at different heating rates obtained by D3 based on an assumption of spherical solid particles, were almost identical, which indicated that the heating rate was also not a dominating factor influence the kinetics mechanism in pyrolysis process. As was shown in Table 7, the E values obtained by model-free methods were similar to the ones by model-fitting methods, which indicated that three modelfitting methods can all be suitable to describe the pyrolysis of waste tea. In diffusion-controlled reactions (D3 mechanism), the rate of product formation was inversely proportional to the gas diffusion, that is internal diffusion (the movement and transport of gas molecules inside the solid particle) or external diffusion (the movement and transport of gas molecules outside the solid particle) [23,29]. It showed in Fig. 2 that the N2 gas flow rate the effect of N2 gas flow rate on pyrolysis process is weak, indicated that the
L. Tian et al. / Energy 103 (2016) 533e542
external diffusion is considered to be not an important factor during the pyrolysis reaction. In fact the D3 model indicated that the pyrolysis of waste tea may confirm a kind of internal diffusion mechanism but not external diffusion. When compared with the activation energy for other biomass, waste tea (207.96 kJ/mol ~ 222.6 kJ/mol) was demonstrated to be a material not easy to be pyrolyzed. For example, the activation energy were 294.8 kJ/mol for MSW, 80.0 kJ/mol for waste tire, 97.8 kJ/ mol for RDF (refused derived fuel) and 42.2 kJ/mol for pine wood waste [34], respectively. The activation energy for the pyrolysis of lignin and milled wood lignin were demonstrated to be 135.84e168.44 kJ/mol and 148.77e184.16 kJ/mol [35,36], respectively. However, some marine biomass were reported to have high activation energy for pyrolysis, such as Corallina pilulifera (247.7 kJ/ mol) [40] and Enteromorpha prolifera (228.09 kJ/mol) [41]. The activation energy for rice husk pyrolysis was 229.91 kJ/mol [16]. The variety of activation energies for different biomass pyrolysis may be due to the different compositions in the biomass. The loss of the soluble component when the tea is used as beverage might result in the less volatile, which increases the difficulty of tea
600
Friedman method FWO method KAS method
500
300
200
100
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
α Fig. 6. Activated energy of waste tea by Model-free methods at different a.
-12
1
(a)
(b)
10○C/min 20○C/min 30○C/min
-13 -14
10○C/min 20○C/min 30○C/min
0 -1
-15
-2
ln (g(α))
-16 -17 -18
-3 -4 -5
-19
-6
-20 -21 0.0014
0.0015
0.0016
0.0017
0.0018
-7 -80 -70 -60 -50 -40 -30 -20 -10 0
0.0019
10 20 30 40 50 60 70 80
T-Tmax
1/T
0
(c)
10○C/min 20○C/min
-1
30○C/min
-2 -3
ln (g(α))
ln (g(α)/T2)
E (kJ/mol)
400
0 0.0
541
-4 -5 -6 -7 -8 6.30
6.35
6.40
6.45
6.50
6.55
6.60
lnT Fig. 7. The fitting plot of Coats-Redfern method (a), Horowitz-Metzger method (b) and Van-Krevelen method (c) with the experimental data.
542
L. Tian et al. / Energy 103 (2016) 533e542
pyrolysis. Nevertheless, the pyrolysis of waste tea demonstrated to be easier than MSW, rice husk and some marine biomass. 4. Conclusions The kinetic behavior and the release of evolved gases in the pyrolysis process for waste tea were investigated by TG-FTIR at different heating rates in this study. The degradation process of waste tea was divided into two steps. The mass loss in first step was due to the volatilization of water from the sample, which occurred at the temperature below 210 C. The second step at the temperature range of 210e550 C showed a sharp DTG peak due to the volatilization of the organic compounds in the sample and the mass loss was on account of the interaction of cellulose, hemicellulose and lignin. FTIR analysis indicated that CO2 was the main gas released from the thermal degradation of waste tea. Moreover, the evolved gases from the waste tea also included H2O, CH3COOH, C6H5OH, C]C and so on. The evolved temperatures of released gases differentiated from each other due to the different compositions characteristic. Three model-free models (Friedman method, FWO method and KAS method) and three model-fitting models (Coats-Redfern method, Horowitz-Metzger method and Van Krevelen method) were applied to study the pyrolysis process of waste tea. According to the values of the activation energy by the three model-free models and the correlation coefficient from model-fitting models, the activation energy (207.96 kJ/mol ~ 222.6 kJ/mol) and preexponential factor were determined for the pyrolysis of waste tea when 0.3 < a < 0.7, in which most of waste tea pyrolyzed. The results of the model-fitting models indicated that D3 model (Threedimension diffusion (spherical symmetry) model) conformed to the pyrolysis mechanism of waste tea well. Acknowledgments The project was supported by the National Natural Science Foundation of China (51541602). References [1] Liu Q, Zhong Z, Wang S, Luo Z. Interactions of biomass components during pyrolysis: a TG-FTIR study. J Anal Appl Pyrol 2011;90(2):213e8. [2] Fu P, Yi W, Bai X, Li Z, Hu S, Xiang J. Effect of temperature on gas composition and char structural features of pyrolyzed agricultural residues. Bioresour Technol 2011;102(17):8211e9. [3] Gu X, Ma X, Li L, Liu C, Cheng K, Li Z. Pyrolysis of poplar wood sawdust by TGFTIR and PyeGC/MS. J Anal Appl Pyrol 2013;102:16e23. [4] Peng C, Yan X-b, Wang R-t, Lang J-w, Ou Y-j, Xue Q-j. Promising activated carbons derived from waste tea-leaves and their application in high performance supercapacitors electrodes. Electrochim Acta 2013;87:401e8. _ Kul S¸Ç. Pyrolysis of apricot kernel shell in a fixed-bed reactor: [5] Demiral I, characterization of bio-oil and char. J Anal Appl Pyrolysis 2014;107:17e24. € [6] Uzun BB, Apaydin-Varol E, Ates¸ F, Ozbay N, Pütün AE. Synthetic fuel production from tea waste: characterisation of bio-oil and bio-char. Fuel 2010;89(1):176e84. [7] Gokce Y, Aktas Z. Nitric acid modification of activated carbon produced from waste tea and adsorption of methylene blue and phenol. Appl Surf Sci 2014;313:352e9. [8] Rajapaksha AU, Vithanage M, Zhang M, Ahmad M, Mohan D, Chang SX, et al. Pyrolysis condition affected sulfamethazine sorption by tea waste biochars. Bioresour Technol 2014;166:303e8. [9] Alshehri SM, Al-Fawaz A, Ahamad T. Thermal kinetic parameters and evolved gas analysis (TGeFTIReMS) for thioureaeformaldehyde based polymer metal complexes. J Anal Appl Pyrol 2013;101:215e21. [10] Ren Q, Zhao C, Wu X, Liang C, Chen X, Shen J, et al. TG-FTIR study on copyrolysis of municipal solid waste with biomass. Bioresour Technol 2009;100(17):4054e7. [11] Fasina O, Littlefield B. TG-FTIR analysis of pecan shells thermal decomposition. Fuel Process Technol 2012;102:61e6.
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