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Biofuels and Biomass
Pyrolysis characteristics and kinetics of coal-biomass blends during co-pyrolysis Xiye Chen, Li Liu, Linyao Zhang, Yan Zhao, and Penghua Qiu Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.8b03987 • Publication Date (Web): 16 Jan 2019 Downloaded from http://pubs.acs.org on January 17, 2019
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Pyrolysis characteristics and kinetics of coal-biomass blends during co-pyrolysis Xiye Chena,b, Li Liua, Linyao Zhanga,*, Yan Zhaoc, Penghua Qiua,* a School b
of Energy Science and Engineering, Harbin Institute of Technology, Harbin, China
Center for Biorefining, and Department of Bioproducts and Biosystems Engineering, University of
Minnesota, St. Paul, USA c
Liaoning Provincial Key Laboratory for Urban Ecology, Shenyang Academy of Environmental Science,
Shenyang, China *Corresponding Authors: Penghua Qiu, Tel: +86-451-8641-3231, ext.804. E-mail:
[email protected] Linyao Zhang, E-mail:
[email protected] School of Energy Science and Engineering, Harbin Institute of Technology, Harbin, China
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Abstract In this paper, the chemical structures of coal and three types of biomass were investigated by Fourier transform infrared spectroscopy (FTIR). In order to evaluate the effect of the biomass blending ratio, heating rate and biomass type on the co-pyrolysis behaviors, the pyrolysis behaviors of coal, three types of biomass and coal-biomass blends were studied through non-isothermal thermogravimetric analysis (TGA). The results expose that coal is rich in the aromatic C=C, however biomass is rich in O—H group and C—O group. For three types of biomass, the type of main functional groups is same, but the relative content of them is different. During the co-pyrolysis process of coal and biomass, the experimental RB is higher than the calculated values. Conversely, the experimental Rcoal are lower than the calculated values, whereas the experimental Tcoal shifts to lower temperature. Therefore, we can deduce that the interaction occurs during the co-pyrolysis process of coal and biomass. In addition, whether biomass has synergistic or inhibitory effect in the whole co-pyrolysis process is related to the mixing ratio, the heating rate and the type of biomass. Finally, the kinetic parameters of coal and sawdust pyrolysis process were obtained by simplified distributed activation energy model (DAEM) method. Keywords: co-pyrolysis; FTIR; TGA; kinetics; coal; biomass
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TB
Temperature corresponding to RB (oC)
Devolatilization rate (10-2∙%·s-1)
TSD
Temperature corresponding to RSD (oC)
Universal gas constant (8.314 J∙(mol∙K)-1)
ΔT1/2
Temperature interval corresponding to
Nomenclature R Rmax
Maximum devolatilization rate (10-2∙%·s-1)
R/Rmax=1/2 (oC)
Rmean Average devolatilization rate (10-2∙%·s-1)
Di
Devolatilization index (10-9∙%2∙ oC -3∙s-2)
Rcoal
Ycoal
Experimental values of coal
YB
Experimental values of biomass
Maximum devolatilization rates of peaks corresponding to the coal
RB
(10-2∙%·s-1)
Maximum devolatilization rates of peaks
Ycalculated Calculated values
corresponding to the biomass (10-2∙%·s-1)
xcoal
Mass ratios of coal in blends (%)
Maximum devolatilization rates of peaks
XB
Mass ratios of biomass in blends (%)
corresponding to SD (10-2∙%·s-1)
x
Conversion
Ts
Initial volatile release temperature (oC)
E
Activation energy (kJ∙mol-1)
Tmax
Maximum mass loss temperature (oC)
A
Frequency factor (s-1)
Tf
Temperature at the end of the main
β
Heating rate (K∙s-1)
pyrolysis stage (oC); Tf=2Tmax-Ts
f(E)
Pyrolysis activation energy distributions
RSD
Tcoal
Temperature corresponding to Rcoal (oC)
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1. Introduction Biomass energy belongs to renewable energy and it is the fourth largest energy source in the world after coal, petroleum and natural gas1. Biomass energy is being considered as an important sustainable energy all over the world due to its wide sources, low pollutions emission, renewable and near-zero CO2 emissions2. However, biomass has its drawbacks such as low bulk density, high moisture content, degradation during storage and low energy density and so on3, 4. The shortcomings of biomass are the critical challenges in large-scale industrial applications. The co-utilization of coal-biomass blends as feedstock can overcome the disadvantages of using biomass alone, and effectively use the advantages of biomass resources. In 2017, the Ministry of Energy and the Ministry of the Environment jointly issued a document "Notice on Pilot Work of Technical Transformation of Coal-Fired Coupled Biomass Power Generation", which will organize the construction of coal-fired coupled biomass power generation projects relying on the current efficient coal-fired power generation system and centralized pollutant treatment facilities. Therefore, biomass blending combustion technology has a broad application prospect in China. As the first step in all main thermochemical conversion routes, co-pyrolysis of coal and biomass can affect the subsequent reactions, therefore it plays an important role. Unfortunately, the specific characteristics and theories of co-pyrolysis of coal and biomass are still vague. Some research results showed that there were obvious positive synergistic interactions between coal and biomass during the co-pyrolysis for volatile release5-7. It has been suggested that biomass present in the coal/biomass blends supplies hydrogen to the subsequent reaction with coal8. However, some research results revealed that the pyrolysis processes of coal and biomass were independent during the co-pyrolysis, so the synergic effects between coal and biomass were very slight and negligible9-12. Moreover, some research results even proved that the interactions between the coal and biomass during the co-pyrolysis showed an inhibitive effect on thermal decomposition and volatile release, resulting in higher than expected char yields13-15. Thermogravimetric analysis (TGA) is a typical method for studying co-pyrolysis16-20. Some research results do not show non-linear effects, in part because biomass and coal volatiles evolve at different times due to relatively slow heating rates19. Haykiri-Acma et al.20 reports that synergies have only been noted when coal and biomass volatile evolution regimes overlap, for example very low rank lignite and peat. During the whole co-pyrolysis process，the synergistic effect between coal and biomass is controversial, and it is affected by many factors, such as biomass type, mixing ratio, the coal used, reaction equipment and so on. These inconsistent conclusions are interesting and need to be clarified. Accordingly, more detailed experiments and researches are still needed to help better understand the co-pyrolysis process of coal and biomass. During the whole co-pyrolysis process of coal and biomass, the existence of coal may affect the pyrolysis of biomass, while the presence of biomass may also affect the pyrolysis of coal. The synergistic effect between coal and biomass is influenced by the above two aspects. Therefore, in order to research the co-pyrolysis process of coal and biomass more comprehensively, the influence of coal and biomass on each other and the influence of both on the whole co-pyrolysis are researched by co-pyrolysis characteristic parameters in this research. The chemical structures of Shenmu coal and three types of biomass were investigated by Fourier transform infrared spectroscopy (FTIR). In addition, in order to evaluate the ACS Paragon Plus Environment
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influence of the biomass blending ratio, heating rate and biomass type on the co-pyrolysis behaviors, the pyrolysis behaviors of coal, three types of biomass and coal-biomass blends were studied through non-isothermal thermogravimetric analysis. The synergistic effect of the co-pyrolysis process is reflected by the deviation between the experimental values and the calculated values, which were obtained by the pure biomass and coal pyrolysis processes. Finally, the kinetic parameters of coal and sawdust co-pyrolysis process were obtained by simplified distributed activation energy model (DAEM) method. 2. Materials and methods 2.1. Materials Table 1 Properties of samples Properties
Coal
SD
CS
RH
Proximate analysis (wt %, ad) M
1.51
3.02
4.06
3.66
A
9.20
1.40
2.09
18.72
V
32.37
81.44
76.63
62.64
FCc
56.92
14.13
17.22
14.98
C
72.36
45.43
43.21
36.08
H
4.52
6.18
5.95
5.20
Oc
11.06
43.46
43.39
35.71
N
0.95
0.33
0.62
0.52
St
0.40
0.18
0.68
0.11
28.87
18.30
17.49
14.90
Ultimate analysis (wt %, ad)
HHV (MJ∙kg-1)
Lignocellulosic composition (wt %) Cellulose
-
45.25
43.51
21.90
Hemicellulose
-
14.21
22.13
19.00
Lignin
-
28.34
11.08
17.80
-
12.20
23.28
41.30
Extractives and others
ad, air-dried; c, calculated by difference; t, total content. The materials used in this research were Shenmu bituminous coal, three types of biomass (manchurian walnut sawdust (SD), corn stalks (CS), rice husk (RH)) and coal-biomass blends. The samples were homogenized on a vortex mixer. The particle size of coal was in the size range of 45~100μm, while the particle size of biomass was less than 150μm. The samples were dried overnight at 40 oC before use. The properties of samples are listed in Table 1. According to the results of industrial analysis, the volatile matter content of the three biomasses is significantly higher than that of Shenmu coal, while the fixed carbon content is opposite, suggesting that the three biomasses may have a higher pyrolysis reactivity than Shenmu coal. The results of ultimate analysis shows that the content of C of the three biomasses is significantly lower than that of Shenmu coal, while the content of O and H is opposite, especially the content of O which researches 3-4 times that of Shenmu coal. This indicates that the macromolecular network of biomass has a ACS Paragon Plus Environment
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high abundance of non-carbon heteroatoms, especially O atoms, which reveals that biomass may have loose macromolecular structure core and richer side chain functional group structure. 2.2. FTIR Analysis The surface function groups of coal and biomass were analyzed using a Nicolet 5700 FTIR spectroscopy (Thermo Fisher scientific, Waltham, MA). The sample and KBr were dried for 24 h at 40 oC and 105 oC, respectively then ground together in a 1:200 ratio (0.5 mass% samples in mixture). KBr pellet was formed by 150 mg fine powder (10 MPa, 2 min) and then analyzed by FTIR spectroscopy. The samples were analyzed using 32 scans conducted from 4000 to 400 cm-1 with a spectral resolution of 4 cm-1. 2.3. Pyrolysis Experiment A Mettler-Toledo TGA/SDTA851e analyzer was used to record the samples mass change with temperature over the course of pyrolysis reaction. Thermogravimetric analysis was carried out at three different heating rates (20, 40 and 60 oC ∙min-1). In each pyrolysis experiment, approximately 20 mg of the previously prepared samples was placed in an Al2O3 ceramic crucible and heated from 40 oC to 1000 oC to ensure sufficient pyrolysis of samples. A flow rate of 20 ml∙min-1 of high purity nitrogen (99.999%) was supplied to the furnace as protective gas and a flow rate of 80 ml∙min-1 of high purity argon (99.999%) was used as reaction gas. Devolatilization index (Di) is a comprehensive evaluation of volatile release and can reflect pyrolysis reactivity of the whole pyrolysis process. The fuel with higher value of Di has better pyrolysis performance21. Di is determined by the equation as follow14, 21, 22: Di=[Rmax∙Rmean]/(Ts∙ Tmax∙ ΔT1/2) where Rmax is the maximum devolatilization rate, Rmean the average devolatilization rate, Ts the initial volatile release temperature, Tmax the corresponding temperature of Rmax, ΔT1/2 the temperature zone of R/Rmax=1/2. R is the devolatilization rate, R = d𝑤t dt, where wt is the mass percentage of the raw sample at time t. Rmax and R can be obtained from the DTG curves. In addition, in order to determine the synergistic effect during the co-pyrolysis process of coal and biomass, the measured values were compared with the calculated values. The calculated values were obtained by the additive model which supposed that no interactions occurred between two samples during co-processing23. So that the calculated values are the sum of the experimental values of each individual component with proportion to their blending weight ratio as follow: Ycalculated=xcoalYcoal+xBYB where Ycoal and YB are the measured experimental value of coal and biomass under the same condition , xcoal and xB the mass fraction of coal and biomass in blends, respectively. 2.4. Kinetic analysis In this study, a simple integral method (simplified DAEM) proposed by Miura24 was used to estimate the kinetic parameters of samples. The Arrhenius equation of the simplified DAEM is given as follows: ln(β/T2)= ln(A∙R/E)+0.6075-E/(R∙T) where β is the heating rate (K∙s-1), T the absolute temperature (K), E the activation energy (J∙mol-1), A the frequency factor (s-1) and R the universal gas constant (8.314 J∙(mol∙K)-1). ACS Paragon Plus Environment
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The values of the activation energy E and the frequency factor A can be obtained from experiments without assuming any functional forms for them. The procedure can be summarized as follows: (a) Measure the conversion rate x as a function of the absolute temperature T using at least three different heating rates. The pyrolysis conversion rate x can be calculated by x = (m0 - mt) (m0 - mf), where m0 is the original mass of the test sample, mt is the mass at time t and mf is the final mass at the end of pyrolysis. (b) Plot ln(β/T2) vs. 1/T for the different heating rates at the selected conversion rate (Arrhenius plot). (c) Linearize the data of the different heating rates and obtain the activation energy E from the slopes and the frequency factor A from the intercept. 3. Results and discussions 3.1. FTIR analysis of the coal and biomass
Fig.1 Infrared spectra of coal and three types of biomass (SD, CS and RH) Table 2 Band assignments for the FTIR spectra of coal and three types of biomass (SD, CS and RH) Coal NO.
Bands/cm-1
Biomass (SD, CS and RH) Assignment
1
2990~2800 Aliphatic C—H stretching
2
1770~1640 C=O stretching
3
1640~1520 Aromatic C=C stretching
4
1520~1320
5 6
NO. Bands/cm-1
Assignment
1
3770~3000 O—H stretching
2
3000~2780
Aliphatic C—H deformation;
3
1800~1690 C=O stretching
Aromatic C=C stretching
4
1690~1400
1320~1130 C−O stretching
5
1400~1290 Aliphatic C—H deformation
900~720
6
1290~920
=C−H out-of-plane deformation
Aliphatic C—H stretching; aldehyde C—H stretching Aromatic C=C stretching C−O stretching
The FTIR spectra of coal and three types of biomass (SD, CS and RH) are presented in Fig.1. Table 2 shows the FTIR signals deriving from chemical functional groups of interest identified by several previous
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studies25-27. As illustrated in Fig.1, there are some noticeable differences between coal and biomass in the main functional groups. For coal, the strongest absorption peak is attributed to the stretching vibration of C=C in aromatic rings located in the 1640—1520 cm-1. In addition, the broad bands observed around 900—720 cm-1 are attributed to the out-of-plane deformation vibration of aromatic =C−H. These two absorption peaks indicate that the most abundant functional group in Shenmu coal macromolecular network is the condensed aromatic ring system as the basic structural unit. The peaks of aliphatic C—H stretching and deformation vibration is obviously observed around 2990—2800 cm-1 and 1400 cm-1, which indicates that the aliphatic side chains and bridge bonds are abundant in the macromolecular structure of Shenmu coal. Moreover, the stretching vibration peaks of C=O and C—O with weak strength appeared around 1770—1640 cm-1 and 1320—1130 cm-1 respectively, revealing the relatively low content of oxygen-containing functional groups in Shenmu coal. This also corresponds to the low O content in the ultimate analyses of Shenmu coal. However, for biomass, the most significant absorption peak is the O—H stretching vibration peak located in the 3770—3000 cm-1 band, which is not only strong, but also shows a state of broadening. The phenomenon indicates that there are abundant hydroxyl groups in the biomass macromolecular network, and a large number of hydrogen bonds are formed between the hydroxyl groups, which is corresponding to the high H and O content in the ultimate analyses of biomass. The C—O stretching vibration peak is obvious in the band of 1290—920 cm-1, indicating that the biomass macromolecular structure contains abundant alcoholic hydroxyl groups, phenolic hydroxyl groups and ether bonds. The relatively weak aliphatic C—H stretching, aliphatic C—H deformation vibration, C=O stretching and aromatic ring C=C stretching vibration peaks appear in the bands of 3000—2780 cm-1, 1400—1290 cm-1, 1800—1690 cm-1 and 1690—1400 cm-1 respectively, which reveal relatively low content of aliphatic chain, carbonyl and aromatic ring structures in the biomass macromolecular network. Cellulose, hemicellulose and lignin are the three main components of biomass. As the component with the highest content in biomass, the essence of cellulose is polyhydroxy aldehyde, which is a chain polymer composed of cellobiose monomers linked by glycosidic bonds, and its structure is relatively regular. There is a strong polar hydroxyl group on the cellulose glucose unit, and the hydrogen atom in the hydroxyl group is easy to attract the lone pair electrons on the oxygen atom with high electronegativity on other bonds to form the hydrogen bond. Thereby, the chains of the cellulose macromolecules can be entangled by hydrogen bonds to form macromolecules. Hemicellulose, as the adhesive between cellulose, is an amorphous substance composed of various glycosyl groups, uronic acid groups and so on. Compared with cellulose, hemicellulose has lower degree of polymerization, uneven structure and more abundant branched chains. Therefore, the functional group of cellulose and hemicellulose includes alcohol hydroxyl group, aldehyde group, carbonyl group and ether bond, which are also the main sources of corresponding absorption peaks in the infrared spectrum of the three biomasses. In addition, the two components also contain a small amount of aliphatic C—H and other groups. Lignin is an amorphous high molecular polymer with three-dimensional network structure formed by interconnecting the oxyphenylpropanol or its derivatives with ether bond and carbon-carbon bond. Compared with cellulose or hemicellulose, lignin contains a variety of side chain groups attached to the phenyl ring, buts lacks regularity and order between repeating units. The functional ACS Paragon Plus Environment
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groups contained in lignin include benzene ring, methoxy group, phenolic hydroxyl group, alcoholic hydroxyl group, carbonyl group and ether bond, etc.. Among them, benzene ring and methoxy group are the main sources of absorption peaks in the bands of 1690—1400 cm-1, 3000—2780 cm-1 and 1400—1290 cm-1 in the infrared spectra, and ether bond also has an important contribution to the absorption peaks in the bands of 1290—920 cm-1. Through the above analysis of infrared spectrum and chemical structure of Shenmu coal and three types of biomass, it can be found that there are significant differences between biomass and coal, both for the basic structural unit of the macromolecular network and for the form of the connection between the side chain structure on the unit and the unit. First, the basic structural unit of coal is the aromatic ring system with different degree of condensation, while that of biomass is cellobiose and oxyphenylpropanol or its derivatives. Compared with coal, the condensation degree of biomass basic structure units is significantly reduced, and the thermal stability is poor. Therefore, depolymerization and decomposition reactions may occur more easily during pyrolysis, resulting in lower pyrolysis characteristic temperatures or higher pyrolysis reaction rates. Secondly, compared with coal, the basic structural unit of cellulose, which is the most important component of biomass, is relatively single. This means the distribution of thermal decomposition activation energy is relatively concentrated, so the pyrolysis reaction of biomass may occur in a relatively narrow temperature range, that is, the release of pyrolysis volatiles is more concentrated. Finally, the connection between the basic structural units of coal is aliphatic chain, ether bond, hydrogen bond and so on, wherein the former two are dominant. While the basic structural units of biomass are connected by hydrogen bond, glycosidic bond, ether bond and carbon-carbon bond, wherein hydrogen bond plays an important role. Due to the lower bond energy of hydrogen bond compared with covalent bond, biomass may more easily complete the initial depolymerization and enter the active intermediate state during the pyrolysis process, thus reducing the corresponding pyrolysis characteristic temperature. Table 1 shows the lignocellulosic composition of SD, CS and RH are different. And Fig.1 shows that for SD, CS and RH, the type of main functional groups is same, but the relative content of them is different. As Lv et al.
28
reported, the infrared spectra of three components (cellulose, hemicellulose and lignin) in corn
stalks have similar characteristics, but the absorption intensity is different. For example, the O—H group in CS is more abundant than that in SD and RH. The absorbance peak near 3400 cm-1 (the stretching vibrations of O—H group) of lignocellulosic shows a total tendency as follows: hemicellulose > cellulose > lignin28. Table 1 shows the hemicellulose in CS is higher than that in SD and RH. In addition, the ash content of RH is very high (18.72 wt %), and is 13 times and 9 times more than that of SD and CS, respectively. Therefore, the absorption peak intensity of RH is lower than that of CS and SD. 3.2. Pyrolysis characteristics 3.2.1 Effect of sawdust blending ratio Taking the heating rate of 60 oC∙min-1 for example, Fig.2 illustrates the thermogravimetric (TG) and the derivative thermogravimetric (DTG) curves of coal, SD and their blends, and Table 3 presents their corresponding pyrolysis characteristic parameters. As shown in Fig.2, the pyrolysis process of coal is obvious different from SD. The initial decomposition of coal begins at 247 oC while SD starts at about 200 ACS Paragon Plus Environment
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What is more, the half-peak width (ΔT1/2) of coal is 81.80 oC and the maximum weight loss rate is
0.2168%∙s-1 at temperature near to 495 oC. For the SD samples, the half-peak width is 59.7 oC with the maximum weight loss rate of 0.9714%∙s-1 at temperature close to 408.5 oC. The devolatilization index (Di) of coal and SD are 1.24×10-9∙%2∙oC-3∙s-2 and 38.46×10-9∙%2∙oC-3∙s-2, respectively. In conclusion, compared with coal, the characteristic temperature of SD volatiles release is lower, and the release is more intense and concentrated, that is, the pyrolysis reactivity of SD is stronger. As mentioned above, this mainly due to the differences in their chemical structures (see Fig.1). From Fig.2, for SD, a minor shoulder is detected in the DTG curves at nearly 350 oC, ahead of the maximum weight loss rate. The slight shoulder (at 350 oC) is consistent with the hemicellulose decomposition, while the main DTG peak (at 408 oC) corresponds to cellulose (a major wood component) decomposition. In addition, the lignin decomposition is completed over an extensive temperature range of 240-900 oC28, 29, which causes a tailing peak in the DTG curves. The coal-SD blends reveal three thermal evolution profiles with the first peak (slight shoulder) at around 350 oC, the second peak at approximately 410 oC, while the third peak at nearly 485 oC. By comparing with single-fuel pyrolysis process, we find that the first two peaks presented in coal-SD blends correspond to SD decomposition, while the third peak is the result of the presence of coal in the blends. The maximum devolatilization rate of peak corresponding to SD (RSD) increases with increasing percentage of SD in the blends as depicted in Fig.2(b) and Table 3, whereas the maximum devolatilization rate of peak corresponding to coal (Rcoal) decreases. It appears that TSD and Tcoal (temperature corresponding to RSD and Rcoal, respectively) decreases with increasing percentage of SD in the blends. In addition, the increase of peak height or maximum reaction rate seems to be related to the mass ratio of SD in the blended fuel.
Fig.2 TG and DTG curves of coal, SD and their blends at β=60 oC∙min-1 (a): TG curves; (b): DTG curves Table 3 represents the experimental and calculated values of pyrolysis characteristic parameters of various blends at heating rate of 60 oC∙min-1. We can find that during the coal/SD co-pyrolysis process, the experimental RSD are higher than the calculated values, while the experimental TSD shifts to higher temperature. Conversely, the experimental Rcoal are lower than the calculated values, whereas the experimental Tcoal shifts to lower temperature. Therefore, we can deduce that the interaction occurs during the co-pyrolysis process of coal and SD. For blends, the experimental values of Di and the char yield are
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respectively greater than and less than the calculated values, indicating that there are synergistic and inhibitory effects on volatile release and char yield respectively. Table 3 Pyrolysis characteristic parameters for coal, SD and their blends at β=60 oC∙min-1 Parameters
Blends
Coal
25% SD
Exp
50% SD
75% SD
Exp
Cal
Exp
Cal
Exp
Cal
SD Exp
Ts (oC)
246.95
201.72
199.36
200.47
199.20
200.49
199.04
198.89
Tf (oC)
744.39
627.66
620.45
623.91
620.33
619.49
620.22
618.08
TSD (oC)
-
414.69
409.91
412.19
409.77
409.99
409.63
408.49
RSD (10-2∙%·s-1)
-
-28.32
-26.63
-51.60
-50.20
-75.00
-73.71
-97.14
Tcoal (oC)
495.67
492.15
494.93
488.72
493.16
464.25
484.24
-
Rcoal (10-2∙%·s-1)
-21.68
-16.87
-17.35
-12.43
-13.04
-8.70
-8.90
-
Tmax (oC)
495.67
414.69
409.91
412.19
409.77
409.99
409.63
408.49
(10-2∙%·s-1)
-21.68
-28.32
-26.63
-51.60
-50.20
-75.00
-73.71
-97.14
Rmean (10-2∙%·s-1)
-5.73
-9.09
-9.04
-12.44
-12.44
-16.00
-15.83
-19.29
ΔT1/2 (oC)
81.80
61.51
61.83
59.79
60.50
59.56
60.14
59.97
1.24
5.00
4.77
12.99
12.65
24.45
23.80
38.46
65.53
51.85
52.41
39.03
39.34
25.92
26.30
13.29
Rmax
Di (10-9∙%2∙oC-3∙s-2) Char yield (%)
In the temperature range near TSD, SD is in the main pyrolysis stage, and its matrix undergoes strong pyrolysis reactions and releases a large amount of volatiles. While coal is in the initial pyrolysis stage, and its matrix only undergoes preliminary depolymerization and decomposition reactions with releasing a small amount of volatiles. Therefore, near TSD, the interaction between the volatile matters of SD and coal char is the main mechanism of synergistic effect. As mentioned above, compared with coal, the macromolecular structure of biomass contains more hydroxyl groups. So the volatiles released by SD near TSD contain a large number of small free radicals such as OH free radical and H free radical, these free radicals not only have strong ability to combine with other free radicals, but also have strong penetrating ability (due to the small space steric resistance), which can enter into the char matrix while in contact with the coal char30. At the same time, preliminary depolymerization and decomposition reactions are taking place inside the coal char matrix, and a rich fragment structure is formed. These fragment structures are combined with the above-mentioned small volume free radicals from biomass volatiles, and thus are rapidly stabilized. It inhibits the occurrence of secondary cracking reactions, and finally makes the whole fragment structure escape in the form of volatiles, which improves the devolatilization rate of coal near TSD. And then, the increased devolatilization rate of coal is superimposed with the devolitilization of SD, making the experimental RSD and TSD higher than the corresponding calculated values. In the temperature range around Tcoal, coal is in the main pyrolysis stage, and its matrix undergoes strong pyrolysis reactions and releases a large amount of volatiles. While SD is in the secondary degassing stage, and its matrix mainly undergoes aromatization of saturated hydrocarbons and dehydrocondensation of aromatic rings with releasing a small amount of small molecular gas. Therefore, near Tcoal, the interaction ACS Paragon Plus Environment
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between the volatiles of coal and the char of SD is the main mechanism of synergistic effect. Compared with coal, biomass contains more alkali metal K, while monovalent alkali metal ions (Na and K) can significantly catalyze the secondary cracking of volatiles and their precursors, thus reducing the devolatilization rate31. Near Tcoal, a large amount of volatiles (and their precursors) from coal are in connect with K distributed on the surface of biomass char, and are catalytically cracked to form small molecular gases and macromolecular components. The small molecular gases escape directly due to the small steric hindrance, while the macromolecular components remain in the biomass char matrix and become part of the solid product. At the same time, the devolatilization rate of coal shows a trend of increasing first and then decreasing, while the catalytic cracking of K causes the process of increasing to end prematurely. Finally, the maximum devolatilization rate of coal and the corresponding temperature both become lower. And then, the changed devolatilization rate of coal is superimposed with the devolatilization rate of SD, making the experimental Rcoal and Tcoal lower than the corresponding calculated values. 3.2.2 Effect of heating rate The experimental and calculated TG and DTG curves and the pyrolysis characteristic parameters at different heating rates (20, 40 and 60 oC∙min-1) for coal-SD blends at 50 wt.% are shown in Fig.3 and Table 4, respectively. As shown in Table 4, with increasing heating rate, all the characteristic temperatures of pyrolysis move toward the higher temperature, while the half peak width (ΔT1/2), the maximum decomposition rate (Rmax) and the devolatilization index (Di) increase. Also the fuel with higher value of Di has better pyrolysis performance. Therefore, we can deduce that increasing the heating rate can effectively improve the pyrolysis reactivity of sample. From Fig.3, we can observe that increasing the heating rate also has similar effect on the maximum water loss rate and its corresponding temperature in the dry degassing stage. At the same pyrolysis temperature, the sample residues with higher heating rate are slightly higher than that with lower heating rate. The above phenomena can be mainly explained by two reasons. On the one hand, with increasing heating rate, the residence time of the ambient temperature becomes shorter, and the temperature gradient on the surface and inner core of the particles becomes larger15, 32. That makes all the pyrolysis characteristic temperatures move to higher temperature. Another effect of heating rate increase is that the ambient temperature will go through a wider interval per unit time. At the same initial temperature, the average temperature of the sample per unit time increases, resulting in an increase of the maximum decomposition rate with increasing heating rate. Fig.3 also compares the difference between experimental and calculated TG and DTG curves for coal-SD blends with 50 wt.% SD at different heating rates, and their corresponding pyrolysis characteristic parameters are listed in Table 4. We can find that the experimental RSD and TSD are larger than the calculated values. In contrast, the experimental Rcoal and Tcoal are less than the calculated values. These phenomena exist at heating rates of 20, 40 and 60 oC∙min-1, indicating that the interaction occurs during the co-pyrolysis process of coal and SD. Di can comprehensively evaluate the volatile release and pyrolysis reactivity of the whole pyrolysis process. However, the difference between the experimental and calculated value of Di is inconsistent at different heating rates. At heating rate of 20, 40 and 60 oC∙min-1, the experimental values of Di are respectively less than, equal to and greater than the calculated values, which shows that the influence ACS Paragon Plus Environment
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of heating rate on the volatile release of the whole co-pyrolysis process is different. However, the experimental values of char yield are less than the calculated values, which indicates that there is inhibitory effect on the char yield during the whole co-pyrolysis process.
Fig.3 TG and DTG curves for coal/SD (50:50) blends at different heating rates (a): TG curves; (b): DTG curves Table 4 Pyrolysis characteristic parameters for coal-SD (50:50) blends at different heating rates Parameters
β= 20 oC∙min-1
β= 40 oC∙min-1
β= 60 oC∙min-1
Exp
Exp
Exp
Cal
Cal
Cal
Ts (oC)
164.48 163.47
187.36 188.15
200.47 199.20
Tf (oC)
598.56 597.65
613.75 611.00
623.91 620.33
TSD (oC)
381.52 380.56
400.56 399.57
412.19 409.77
RSD (10-2∙%·s-1)
-19.18
-35.92
-51.60
Tcoal (oC)
468.27 469.15
Rcoal (10-2∙%·s-1)
-3.69
-18.81 -3.89
-35.08
476.79 481.81 -7.88
-8.34
-50.20
488.72 493.16 -12.43
-13.04
Tmax (oC)
381.52 380.56
400.56 399.57
412.19 409.77
Rmax (10-2∙%·s-1)
-19.18
-18.81
-35.92
-35.08
-51.60
-50.20
Rmean (10-2∙%·s-1)
-4.01
-3.99
-8.17
-8.17
-12.44
-12.44
ΔT1/2 (oC)
45.70
44.80
53.27
51.95
59.79
60.50
2.68
2.69
7.34
7.34
12.99
12.65
37.64
38.10
38.14
38.78
39.03
39.34
Di (10-9∙%2∙oC-3∙s-2) Char yield (%) 3.2.3 Effect of biomass type
TG and DTG curves and pyrolysis characteristic parameters for three types of biomass (SD, CS and RH) and coal-biomass blends with 50% biomass at β=60 oC∙min-1 are shown in Fig.4 and Table 5, respectively. The tendency of the final weight loss of different biomass is shown by the TG curves as follows: SD > CS > RH, which is consistent with the volatile matter content in proximate analyses. The DTG curves show that the pyrolysis process of these three biomasses is obviously different, especially CS. The initial pyrolysis temperature of CS is about 50 oC lower than that of SD and RH. In addition, there are three clearly pronounced peaks for CS pyrolysis, but an obvious peak and a very slight shoulder are found for SD and RH
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pyrolysis. AS mentioned in above, the hemicellulose and extractives in CS is higher than that in SD and RH. The maximum decomposition rate and its corresponding temperature both show the following tendency: SD > CS > RH. The comprehensive devolatilization index Di shows a tendency as follows: CS > SD > RH, indicating that the pyrolysis reactivity also follows this trend. The difference of pyrolysis behavior is determined by the difference in the macromolecular chemical structure of the three biomasses. Although the three types of biomass have the same type of main functional groups, the content of the main functional groups and minerals is different.
Fig.4 TG and DTG curves for biomass and coal-biomass blends with 50% biomass at β=60 oC∙min-1 (a): TG curves; (b): DTG curves Table 5 Pyrolysis characteristic parameters for biomass and coal-biomass blends at β=60 oC∙min-1 SD
CS
RH
Exp
Exp
Exp
Ts (oC)
198.89
151.61
Tf (oC)
618.08
TSD (oC) RSD (10-2∙%·s-1)
Parameters
Blends 50% SD
50% CS
50% RH
Exp
Cal
Exp
Cal
Exp
Cal
199.63
200.47
199.20
161.91
154.91
200.86
199.57
624.38
575.22
623.91
620.33
613.78
621.06
573.54
575.83
408.49
388.00
387.43
412.19
409.77
387.85
387.98
387.20
387.70
-97.14
-77.20
-71.87
-51.60
-50.20
-40.05
-39.58
-36.93
-36.78
Tcoal (oC)
-
-
-
488.72
493.16
489.14
494.11
491.47
493.89
Rcoal (10-2∙%·s-1)
-
-
-
-12.43
-13.04
-12.70
-13.05
-13.18
-13.55
Tmax (oC)
408.49
388.00
387.43
412.19
409.77
387.85
387.98
387.20
387.70
Rmax (10-2∙%·s-1)
-97.14
-77.20
-71.87
-51.60
-50.20
-40.05
-39.58
-36.93
-36.78
Rmean (10-2∙%·s-1)
-19.29
-16.57
-16.03
-12.44
-12.44
-11.40
-10.96
-10.80
-10.81
ΔT1/2 (oC)
59.97
55.30
68.71
59.79
60.50
63.40
56.20
68.26
68.91
Di (10-9∙%2∙oC-3∙s-2)
38.46
39.32
21.67
12.99
12.65
11.47
12.85
7.51
7.45
Char yield (%)
13.29
16.56
32.76
39.03
39.34
40.56
41.06
49.76
49.21
For coal-biomass blends with 50% biomass, compared to the curves of single fuels, the shape and position of the blends peaks in the DTG curves change little. The experimental values of Ts and RB are greater than the calculated value, while the experimental values of Rcoal and Tcoal are lower than the calculated value. The ACS Paragon Plus Environment
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experimental values of Tcoal are lower than the calculated value, indicating that the main interval of coal pyrolysis moved toward the lower temperature. For three types of biomass, the influence of different biomass on coal is consistent during the co-pyrolysis process of coal and biomass. However, for three types of biomass, the variation trend of the experimental values of TB and the calculated value is inconsistent, indicating that the effect of coal on different type biomass is different during the co-pyrolysis process of coal and biomass. The above phenomena indicate that interactions have occurred during the co-pyrolysis of coal and biomass. Especially for coal /CS blends, there are obvious interactions in the range of 200-350 oC. For coal blends with SD, CS and RH, the experimental values of char yield are respectively less than, less than and greater than the calculated values indicating that whether biomass has synergistic or inhibitory effect in the whole co-pyrolysis process is related to the type of biomass. This is because the content of the main functional groups and minerals is different, although the three types of biomass have the same type of main functional groups. In addition, the three components (cellulose, hemicellulose and lignin) in biomass are different. Wu et al.
29
reports that the cellulose shows positive synergistic effects on the thermal
decomposition of the coal bituminous coal. However, for hemicellulose and lignin, whether positive or negative synergistic is related to the mixed ratio and temperature range. 3.3. Kinetics analysis 3.3.1. Activation energy The Arrhenius plots of coal, SD and their blends are performed to obtain activation energy E at different conversion x values and the results are described in Fig.5. The linear and parallel development for different conversion x at various heating rates indicates that the process of thermal decomposition of present samples can be represented by a set of similar single reactions29. In this study, we believe that when the linear correlation coefficient |r| between ln(β/T2) and 1/T is maintained above 0.99 (r is negative), the simplified DAEM method is accurate in describing the experimental data and the obtained kinetic parameters are more reliable33. Table 6 shows the linear correlation coefficients between ln(β/T2) and 1/T at different x values. Therefore, in this study, the conversion degrees from 0.10 to 0.55, 0.10 to 0.70, 0.05 to 0.80, 0.05 to 0.85 and 0.05 to 0.85 are considered for coal, blends with 25%, 50% and 75% SD and SD, respectively.
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Fig.5 Arrhenius plots of samples at different x values (a) coal; (b) blends with 25% SD; (c) blends with 50% SD; (d) blends with 75% SD; (e) SD Table 6 Linear correlations coefficients of samples at different x values x
|r| coal
0.05 -
75% CS 50% CS 25% CS CS -
x
|r| coal
75% CS 50% CS 25% CS CS
0.9914
0.9992 0.9977
0.50 0.9993
0.9973
1.0000
0.9998 0.9996
0.10 0.9935
0.9962
0.9996
0.9999 1.0000
0.55 0.9975
0.9983
1.0000
0.9999 0.9997
0.15 0.9984
0.9985
0.9999
0.9998 1.0000
0.60 -
0.9986
1.0000
0.9999 0.9997
0.20 0.9994
0.9995
0.9999
0.9999 0.9999
0.65 -
0.9982
0.9999
0.9999 0.9997
0.25 0.9997
0.9999
1.0000
0.9999 0.9999
0.70 -
0.9959
0.9997
0.9999 0.9998
0.30 0.9998
1.0000
1.0000
0.9998 0.9998
0.75 -
-
0.9988
1.0000 0.9998
0.35 0.9998
1.0000
1.0000
0.9998 0.9998
0.80 -
-
0.9897
0.9995 0.9998
0.40 0.9998
0.9999
1.0000
0.9998 0.9997
0.85 -
-
0.45 0.9997
0.9998
1.0000
0.9998 0.9996
-
0.9914 0.9994
Fig.6 shows the activation energies of coal, blends with 25%, 50% and 75% SD and SD at different conversion x values. As shown in Fig.6, the activation energy of the five samples increases monotonously with the conversion x increasing. This phenomenon is a crucial characteristic of multi-step reactions, which indicates the complexity of the pyrolysis process of coal and SD. The pyrolysis activation energy of SD is
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much less than that of coal in the main reaction stage. Especially in the later stage of pyrolysis reaction, the activation energy of coal increases significantly, indicating that the coal undergoes severe pyrolysis reactions in the high temperature zone.
Fig.6 Activation energy E vs conversion x estimated from the Arrhenius plot From Table 1, we can find that the moisture content in Shenmu coal is very low. And the conversion degree from 0.10 to 0.55 is considered for coal. Therefore, for coal when the conversion x is in the range of 0.10-0.15, it has already entered the second stage of coal pyrolysis (initial pyrolysis stage). At this stage, the weak aliphatic bonds of coal begin to break down to produce small molecular gas, while the carboxyl group begins to decompose to release CO2. The activation energy is comparatively low because the energy of the weak aliphatic bonds is very low. The main pyrolysis stage is located at the conversion of 0.20-0.75. Considering the accuracy and reliability of activation energy, we only studied the conversion x below 0.55. So when the conversion x is in the range of 0.20-0.55, it belongs to the main pyrolysis stage. At this stage, the coal macromolecular structure undergoes extensive depolymerization and decomposition reactions, and a large amount of tar-based volatiles and char are generated. At the meantime, a large amount of heat is absorbed. Therefore, the activation energy of coal increases gradually. With the further increase of conversion, it will enter the carbonization stage of coal and the activation energy of coal pyrolysis will be greatly improved. Unfortunately, we do not research the activation energy of the secondary degassing stage considering of the accuracy and reliability of activation energy. For SD, when the conversion x is less than 0.40, the activation energy increases with the conversion x increasing. At this stage, the pyrolysis temperature is not high corresponding to the drying of SD and partial pyrolysis of hemicellulose. The reaction activity is relatively high and the activation energy is relatively low.
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When the conversion x is in the range of 0.45-0.75, the activation energy is slightly higher than the previous stage, but it does not change much. This is because the pyrolysis temperature range of cellulose and lignin covers this range of conversion x. During this period, part of cellulose and a small amount of lignin continue to stably decompose, so the activation energy does not change much over the entire interval. When the conversion x is greater than 0.80, the volatile content decreases, the basic pyrolysis of cellulose is completed, and the activation energy increases significantly. At this time, the conversion of semi-coke rapidly increases, and the originally disordered carbon structure gradually becomes more orderly. The reduction of the active site leads to a rapid decrease in the reactivity, which is numerically manifested as a significant increase in the activation energy value34. As shown in Fig.6, in the early stage of the reaction for blends with 25%, 50% and 75% SD, when the conversion x is less than 0.40, 0.6 and 0.7, respectively, the corresponding pyrolysis temperature is below 400 oC. The pyrolysis of coal just begins. The conversion of co-pyrolysis is mainly caused by the decomposition of biomass especially the pyrolysis of hemicellulose in biomass. Therefore, the growth trend of the activation energy curve is closer to that of biomass. When the conversion x is in the range of 0.45-0.70, 0.65-0.80 and 0.70-0.85, respectively, the corresponding pyrolysis temperature is in the range of 400-500 oC. During this period, the cellulose and lignin in biomass begin to decompose. In addition, coal also undergoes pyrolysis reactions. The activation energy of blends is affected by both coal and SD. 3.3.2. Pyrolysis activation energy distributions f(E) and Kinetic compensation effect The pyrolysis activation energy distributions f(E) of samples are shown in Fig.7. As shown in Fig.7, the pyrolysis activation energy distributions f(E) of coal and SD follow a Gaussian distribution, however, the pyrolysis activation energy distributions f(E) of blends are not the Gaussian function. For coal and SD, the purple lines are the single Gaussian fit with the fitting peak center at 196.5 kJ∙mol-1 and 130.97 kJ∙mol-1, respectively. This indicates that under the experimental conditions, the pyrolysis activation energy is mainly concentrated in 196.50 kJ∙mol-1 and 130.97 kJ∙mol-1 for coal and SD, respectively. The full width at half maximum (FWHM) of the Gaussian fitting function for coal and SD is 59.21 kJ∙mol-1 and 0.19 kJ∙mol-1, respectively. FWHM of SD is less than that of coal, however, the peak height of SD is more prominent than that of coal. This means that the activation energy distribution of SD is more concentrated than that of coal. The pyrolysis activation energy distributions f(E) of blends all present a bimodal distribution. The most prominent peaks for blends with 25%, 50% and 75% SD locate at 122.69 kJ∙mol-1, 124.40 kJ∙mol-1 and 125.05 kJ∙mol-1, respectively, which are not much different and are close to the peak of SD. This shows that the location of the most important interval of pyrolysis activation energy distribution of blends is similar to that of SD. The relationships of lnA vs E of samples are shown in Fig.7. It can be found that during the entire pyrolysis process, the frequency factor is not a constant, but increases with increasing the activation energy. The kinetic compensatory effect of the pyrolysis reaction is exhibited. The decrease in the rate constant caused by the increase in activation energy is compensated to some extent by the exponential increase of the frequency factor, which is beneficial to the reaction.
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Fig.7 Pyrolysis activation energy distributions f(E) and relationships of lnA vs E of samples (a) coal; (b) blends with 25% SD; (c) blends with 50% SD; (d) blends with 75% SD; (e) SD 4. Conclusions In this work, the chemical structures of coal, three types of biomass were studied. Thermogravimetric analysis was used to explore pyrolysis behaviors and reaction kinetic during pyrolysis of coal, three types of biomass and coal-biomass blends. The simplified DAEM method was used to analyze the general kinetic characteristics of coal and SD pyrolysis. The results indicate that: 1) Coal is rich in the aromatic C=C, however biomass is rich in O—H group and C—O group. For three types of biomass, the type of main functional groups is same, but the relative content of them is different.
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2) During the co-pyrolysis process of coal and biomass, the experimental RB is higher than the calculated values. Conversely, the experimental Rcoal are lower than the calculated values, whereas the experimental Tcoal shifts to lower temperature. Therefore, there are interactions between coal and biomass. 3) In the early stage of the reaction for blends, the growth trend of the activation energy curve is closer to that of biomass. With the pyrolysis temperature increasing, the activation energy of blends is affected by both coal and SD. Acknowledgements This work was supported by China Scholarship Council (No. 201806120161) and the National Key R&D Program of China (Grant numbers 2017YFB0602000). References 1. Saxena, R. C.; Adhikari, D. K.; Goyal, H. B., Biomass-based energy fuel through biochemical routes: A review. Renewable and Sustainable Energy Reviews 2009, 13, (1), 167-178. 2. Liu, J.; Jia, Y., Application and Development Expectation of Biomass Pellet Fuel in Shenyang. Environmental Protection Science 2007,, (06), 12-14. 3. Wei, J.; Gong, Y.; Guo, Q.; Chen, X.; Ding, L.; Yu, G., A mechanism investigation of synergy behaviour variations during blended char co-gasification of biomass and different rank coals. Renewable Energy 2019, 131, 597-605. 4. Wei, J.; Guo, Q.; Ding, L.; Yoshikawa, K.; Yu, G., Synergy mechanism analysis of petroleum coke and municipal solid waste (MSW)-derived hydrochar co-gasification. Applied Energy 2017, 206, 1354-1363. 5. Aboyade, A. O.; Görgens, J. F.; Carrier, M.; Meyer, E. L.; Knoetze, J. H., Thermogravimetric study of the pyrolysis characteristics and kinetics of coal blends with corn and sugarcane residues. Fuel Processing Technology 2013, 106, 310-320. 6. Sonobe, T.; Worasuwannarak, N.; Pipatmanomai, S., Synergies in co-pyrolysis of Thai lignite and corncob. Fuel Processing Technology 2008, 89, (12), 1371-1378. 7. Soncini, R. M.; Means, N. C.; Weiland, N. T., Co-pyrolysis of low rank coals and biomass: Product distributions. Fuel 2013, 112, 74-82. 8. Stiller, A. H.; Dadyburjor, D. B.; Wann, J.; Tian, D.; Zondlo, J. W., Co-processing of agricultural and biomass waste with coal. Fuel Processing Technology 1996, 49, (1), 167-175. 9. Ferrara, F.; Orsini, A.; Plaisant, A.; Pettinau, A., Pyrolysis of coal, biomass and their blends: Performance assessment by thermogravimetric analysis. Bioresource Technology 2014, 171, 433-441. 10. Lu, K.; Lee, W.; Chen, W.; Lin, T., Thermogravimetric analysis and kinetics of co-pyrolysis of raw/torrefied wood and coal blends. Applied Energy 2013, 105, 57-65. 11. Masnadi, M. S.; Habibi, R.; Kopyscinski, J.; Hill, J. M.; Bi, X.; Lim, C. J.; Ellis, N.; Grace, J. R., Fuel characterization and co-pyrolysis kinetics of biomass and fossil fuels. Fuel 2014, 117, 1204-1214. 12. Montiano, M. G.; Díaz-Faes, E.; Barriocanal, C., Kinetics of co-pyrolysis of sawdust, coal and tar. Bioresource Technology 2016, 205, 222-229. 13. Quan, C.; Xu, S.; An, Y.; Liu, X., Co-pyrolysis of biomass and coal blend by TG and in a free fall reactor. Journal of Thermal Analysis and Calorimetry 2014, 117, (2), 817-823.
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14. Chen, X.; Liu, L.; Zhang, L.; Zhao, Y.; Zhang, Z.; Xie, X.; Qiu, P.; Chen, G.; Pei, J., Thermogravimetric analysis and kinetics of the co-pyrolysis of coal blends with corn stalks. Thermochimica Acta 2018, 659, (Supplement C), 59-65. 15. Li, S.; Chen, X.; Liu, A.; Wang, L.; Yu, G., Co-pyrolysis characteristic of biomass and bituminous coal. Bioresource Technology 2015, 179, 414-420. 16. Chen, W.; Wu, J., An evaluation on rice husks and pulverized coal blends using a drop tube furnace and a thermogravimetric analyzer for application to a blast furnace. Energy 2009, 34, (10), 1458-1466. 17. Pan, Y. G.; Velo, E.; Puigjaner, L., Pyrolysis of blends of biomass with poor coals. Fuel 1996, 75, (4), 412-418. 18. Ulloa, C. A.; Gordon, A. L.; García, X. A., Thermogravimetric study of interactions in the pyrolysis of blends of coal with radiata pine sawdust. Fuel Processing Technology 2009, 90, (4), 583-590. 19. Jones, J. M.; Kubacki, M.; Kubica, K.; Ross, A. B.; Williams, A., Devolatilisation characteristics of coal and biomass blends. Journal of Analytical and Applied Pyrolysis 2005, 74, (1-2), 502-511. 20. Haykiri-Acma, H.; Yaman, S., Interaction between biomass and different rank coals during co-pyrolysis. Renewable Energy 2010, 35, (1), 288-292. 21. Qian, W.; Xie, Q.; Huang, Y.; Dang, J.; Sun, K.; Yang, Q.; Wang, J., Combustion characteristics of semicokes derived from pyrolysis of low rank bituminous coal. International Journal of Mining Science and Technology 2012, 22, (5), 645-650. 22. Wu, Z.; Wang, S.; Zhao, J.; Chen, L.; Meng, H., Thermal Behavior and Char Structure Evolution of Bituminous Coal Blends with Edible Fungi Residue during Co-Pyrolysis. Energy & Fuels 2014, 28, (3), 1792-1801. 23. Park, D. K.; Kim, S. D.; Lee, S. H.; Lee, J. G., Co-pyrolysis characteristics of sawdust and coal blend in TGA and a fixed bed reactor. Bioresource Technology 2010, 101, (15), 6151-6156. 24. Miura, K.; Maki, T., A simple method for estimating f (E) and k 0 (E) in the distributed activation energy model. Energy & Fuels 1998, 12, (5), 864-869. 25. Chen, Y.; Liu, B.; Yang, H.; Yang, Q.; Chen, H., Evolution of functional groups and pore structure during cotton and corn stalks torrefaction and its correlation with hydrophobicity. Fuel 2014, 137, 41-49. 26. Chen, X.; Zhao, Y.; Liu, L.; Zhang, L.; Zhang, Z.; Qiu, P., Evaluation of chemical structure, pyrolysis reactivity and gaseous products of Shenmu coal of different particle sizes. Journal of Analytical and Applied Pyrolysis 2018, 130, 294-304. 27. Chen, Y.; Mastalerz, M.; Schimmelmann, A., Characterization of chemical functional groups in macerals across different coal ranks via micro-FTIR spectroscopy. International Journal of Coal Geology 2012, 104, 22-33. 28. Lv, G.; Wu, S., Analytical pyrolysis studies of corn stalk and its three main components by TG-MS and Py-GC/MS. Journal of Analytical and Applied Pyrolysis 2012, 97, 11-18. 29. Wu, Z.; Wang, S.; Zhao, J.; Chen, L.; Meng, H., Synergistic effect on thermal behavior during co-pyrolysis of lignocellulosic biomass model components blend with bituminous coal. Bioresource Technology 2014, 169, 220-228. ACS Paragon Plus Environment
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Page 22 of 22
30. Wu, H.; Quyn, D. M.; Li, C., Volatilisation and catalytic effects of alkali and alkaline earth metallic species during the pyrolysis and gasification of Victorian brown coal. Part III. The importance of the interactions between volatiles and char at high temperature. Fuel 2002, 81, (8), 1033-1039. 31. Sathe, C.; Hayashi, J.; Li, C.; Chiba, T., Combined effects of pressure and ion-exchangeable metallic species on pyrolysis of Victorian lignite. Fuel 2003, 82, (3), 343-350. 32. Yıldız, Z.; Uzun, H.; Ceylan, S.; Topcu, Y., Application of artificial neural networks to co-combustion of hazelnut husk–lignite coal blends. Bioresource Technology 2016, 200, 42-47. 33. Penghua, Q.; Yan, Z.; Xiye, C.; JianJian, X.; Yawen, D.; Laixi, F.; Shaozeng, S., Effects of alkali and alkaline earth metallic species on pyrolysis characteristics and kinetics of Zhundong coal. Journal of Fuel Chemistry and Technology 2014, 42, (10), 1178-1189. 34. Dengyu, C.; Xifeng, Z., Thermal reaction mechanism of biomass and determination of activation energy. Journal of Fuel Chemistry and Technology 2011, 39, (09), 670-674.
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