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Kinetic study of biomass pellet pyrolysis by using distributed activation energy model and Coats Redfern methods and their comparison
Journal Pre-proofs Kinetic study of biomass pellet pyrolysis by using Distributed Activation Energy Model and Coats Redfern methods and their comparison Inamullah Mian, Xian Li, Yiming Jian, Omar D. Dacres, Mei Zhong, Jingmei Liu, Fengyun Ma, Noor Rahman PII: DOI: Reference:
S0960-8524(19)31329-X https://doi.org/10.1016/j.biortech.2019.122099 BITE 122099
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Bioresource Technology
Received Date: Revised Date: Accepted Date:
25 July 2019 29 August 2019 30 August 2019
Please cite this article as: Mian, I., Li, X., Jian, Y., Dacres, O.D., Zhong, M., Liu, J., Ma, F., Rahman, N., Kinetic study of biomass pellet pyrolysis by using Distributed Activation Energy Model and Coats Redfern methods and their comparison, Bioresource Technology (2019), doi: https://doi.org/10.1016/j.biortech.2019.122099
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Kinetic study of biomass pellet pyrolysis by using Distributed Activation Energy Model and Coats Redfern methods and their comparison Inamullah Miana, Xian Lia,b,*, Yiming Jiana, Omar D. Dacresb, Mei Zhonga, Jingmei Liua, Fengyun Maa, Noor Rahmanc aKey Laboratory of Coal Clean Conversion and Chemical Process Autonomous Region, College of Chemistry and Chemical Engineering, Xinjiang University, Urumqi830000, Xinjiang, China. bState Key Laboratory of Coal Combustion, School of Energy and Power Engineering, Huazhong University, Wuhan 430074,Hubei, China. cDepartment of Chemistry, Shaheed Banazir Bhutto University (18000) Sheringal, Dir (Upper) Khyber Pakhtunkhwa, Pakistan. Corresponding author: Xian Li Email: [email protected] Address: Key Laboratory of Coal Clean Conversion and Chemical Process Autonomous Region, College of Chemistry and Chemical Engineering, Xinjiang University, Urumqi830000, Xinjiang, China. Abstract In this study, the pyrolysis behavior and kinetics of raw biomasses and their pellets were studied by Coats Redfern and DAEM methods. The results demonstrated that the similar activation energies obtained by both methods confirmed accuracy of the kinetics calculation. The activation energy of the pellets was 132.49-232.44 kJ mol-1, slightly higher than those of raw biomasses, which was 120.58-210.55 kJ mol-1. The results from Coats Redfern method showed that the pyrolysis of all the samples were controlled by mass and heat diffusion. DAEM revealed that the activation energies of the pellets were higher than those of raw biomasses during hemicellulose and cellulose decomposition stages, and was opposite for the lignin decomposition stage. Physical structure characterization indicated that the pellets had smaller surface area and more compact surface than those of their raw biomasses. Hence, the mass and heat diffusion were suppressed and more cross-linking reactions occurred during pellets pyrolysis. Keywords: Pellet, Pyrolysis, Kinetics, Coast Redfern, DAEM.
1. Introduction Among the sustainable energy sources, biomass waste is believed to be a viable alternative to fossil fuels due to its capacity to support the transportation, power and chemical industries as a main source of inexhaustible, concentrated non-fossil carbon that is accessible on earth (McKendry, 2002). Biomass waste can be converted into various types of energy utilizing both biochemical (enzymatic) and thermo-chemical technologies (pyrolysis, liquefaction, gasification and combustion). The latter’s potential has come to the forefront because of higher efficiency (Munir et al., 2009). However, in the efforts to supplant coal, the utilization of biomass has clear limitation due to its inherent nature, such as low bulk and energy densities, high moisture and high oxygen content. Nonetheless, pelletization stands out as the most feasible and effective method for the conversion of biomass to a fuel that can be used as a substitute to coal (Patel et al., 2016). Ronsse et al. successfully produced pellets under pressure from eucalyptus grandis and eucalyptus spp at 120 °C (Ronsse et al., 2013). Rhén et al. also explored the preparation of Norway spruce sawdust pellets under the pressure from 46 to 114 MPa at the temperature from 26 to 144 °C with moisture content from 6.3% to 14.7% (Rhén et al., 2005). Resultantly, there is a consensus that pelletization can be used to overcome the volumetric density issues that biomass waste utilization faces and consequently can encourage their use in subsequent thermochemical processes, namely pyrolysis, combustion and gasification. Thermal behavior of the biomass pellet have been altered from the raw biomass. Pyrolysis is a vital thermochemical technique that facilitates the conversion of biomass feedstock to various types of high grade fuel (Van der Stelt et al., 2011). The resultant
pyrolytic biochar has upgraded fuel quality compared to raw biomass, such as grindability and expand energy density (Luo et al., 2015; Xiao et al., 2015). In addition, the pyrolysis is the devolatization stage in other thermo-chemical processes, for example, combustion and gasification. The main effects for biomass pyrolysis were not only the chemical reaction but also the mass and heat transfer. Pelletization was usually conducted under mechanical pressure at room temperature. So, it cannot alter the chemical composition and structure, ash composition and content of the biomass. As such, the volatile matter of the biomass pellet is similar to that of the raw biomass. But it was found that their thermal decomposition behavior were quite different from each other (Chin et al., 2013; Emadi et al., 2017; Marsh et al., 2007; Tong et al., 2018). Furthermore, thermal decomposition mechanism and kinetics of the biomass pellet were not clear and has not been studied extensively, which is essential for understanding the pyrolysis, gasification, and combustion behaviors of the biomass pellet. Hence, this study aims to investigate the thermal behavior and kinetics of biomass pellet. Thermogravimetric Analysis (TGA) is a commonly used method for the kinetic study of thermal-chemical conversion of solid fuels (Bach et al., 2015; Ru et al., 2015). Two wellestablished kinetic calculation methods are the Distributed Activation Energy Model (DAEM) method and Coats Redfern method. DAEM, a multiple reaction model, assumes that the decomposition mechanism follows a number of independent parallel first order reactions with altered activation energies, reflecting the variations in the bond strengths of species involved in the decomposition reaction. The DAEM method has been broadly used for examining the complex reactions of coal and biomass pyrolysis (de Caprariis et al., 2015; Hu et al., 2016).
Cai et al. examined the pyrolysis behavior of lignocellulosic biomass samples by considering the parallel reaction related to the primary pyrolysis of hemicellulose, cellulose and lignin (Cai et al., 2013). Kinetic study by DAEM revealed that the activation energy for lignin is widely contrasted to cellulose, which has the narrowest distribution (Bhavanam et al., 2015). It was determined that the activation energy of rice straw pyrolysis obtained by DAEM was around 160–270 kJ mol-1, while the activation energy of the torrefied sample, was 170–400 kJ mol-1 (Soria-Verdugo et al., 2013). Likewise, it was also detected that the activation energy distribution of bituminous coals and biomass follows the approximate Gaussian distribution (Li et al., 2009). The complex reactions of typical medical waste pyrolysis and the evolution of different volatile species were effectively clarified by DAEM model (Yan et al., 2009). The Coats Redfern method is adopted to assess validity of model-based method and provide the most appropriate reaction model for decomposition of biomass. This model-based method depends on Arrhenius equation. Zakrzewski et al. conducted the kinetics study of biomass under the non-isothermal condition at a heating rate of 5 °C min-1 using the CR method. The Ea and pre-exponential factors obtained were 93.1-174.9 kJ mol-1 and 4.9×104–7.1×1011 min-1 (Zakrzewski, 2003). Reina et al. also investigated the kinetics of forest wood and old furniture using TGA under isothermal condition via Coats Redfern method and recorded activation energy and pre-exponential factor values of 215.7-127.8 kJ mol-1 and 1.89×107-3.40×107 sec-1, respectively (Reina et al., 1998). As mentioned above, the thermal decomposition behavior of the biomass pellet is different from that of the raw biomass. This is possibly attributed to the differences in physical structures which have significant effect for the mass and heat transfer and crosslinking reactions during their pyrolysis. However, the behavior, kinetics and mechanism of
the biomass pellet thermal decomposition were not studied in detail. Thus, in this paper, the pyrolysis kinetics of the typical biomass pellets were studied by DAEM and Coats Redfern methods, and compared to that of the raw biomasses. 2. Materials and Methods 2.1.
Materials One typical woody biomass wastes (pine) and a non-woody biomass wastes (corn
straw) were employed as the raw materials in this study. The samples were crushed and sieved to 70-170 mesh, and dried at 80 oC for more than 6 hrs. The proximate and ultimate analyses of the raw samples are shown in Table 1. 2.2.
Methods The pellets were prepared at 25 oC using a single pelletizer. This pelletizer consisted
of a piston that was 13.2 mm in diameter and 30 mm in length, and a cylinder of a 13.5 mm inside diameter and a length of 40 mm. 5 to 7 g biomass was added into the pelletizer, and then compressed to an extreme pressure of 280 Mpa for 3 min. The collected pellets were placed in a Ziploc bag and stored at room temperature. The nomenclature of the raw biomasses and biomass pellets is as follow: The corn straw, corn straw pellets, pine and pine pellets were abbreviated as RCS, CSP, RP and PP, respectively. 2.3.
Pyrolysis
The pyrolysis of the raw biomasses and pellets were conducted using a thermogravimetric analyzer (TGA, SDT Q600). 5 to 10 mg of sample was added into the TGA then and heated from 25 oC to 900 oC at a rate 3, 5, and 10 oC min-1. Highly pure nitrogen gas was introduced
as a carrier gas at a flow rate of 100 ml min-1. The TGA experiments were repeated three times and the mass loss curves were rather similar. 2.4.
Kinetic study
2.4.1. Coats Redfern method The kinetic studies of the raw biomass and their pellets were conducted using the Coats–Redfern equation (Coats et al., 1964), which was as follow:
( ) = 𝑙𝑛[ (1 ― )] ―
𝑙𝑛
𝑔(𝑥)
𝐴𝑅
2𝑅𝑇
𝐸1
𝑇2
𝛽𝐸
𝐸
𝑅𝑇
(1)
Where, R is the universal gas constant (8.314 J mol-1 K-1). T is the absolute temperature (K). t 𝑑𝑇
is the reaction time (min). β is a constant heating rate during gasification, 𝛽 𝑑𝑡 .𝑔(𝑥) is the reaction mechanism model in integral form as shown in table 3.
[ (1 ― )] in Eq. (1) is basically constant for
It is obvious that, the expression of 𝑙𝑛
𝐴𝑅
2𝑅𝑇
𝛽𝐸
𝐸
the activation energy values (E) in the temperature range of gasification or pyrolysis (Çepelioğullar et al., 2013; Cheng et al., 2012). So, a straight line can be achieved when the left side of Eq. (1) is plotted against 1/T. The value of E can then be obtained from the slope of the straight line (Yan et al., 2014). The pre-exponential factor (A) can be obtained through the intercept of Eq. (1). The reaction rate constant (k) was then achieved using Arrhenius’s equation (2).
( )
𝑘 = 𝐴𝑒𝑥𝑝
―𝐸
𝑅𝑇
(2)
The reaction mechanism model (g(X)) used for the calculation of homogeneous model (HM) or first order chemical reaction (O1), and shrinking core models (SCM) or phase boundary controlled reactions R3. Random nucleation and subsequent growth models A2, A3, dimensional diffusion models such as D1, D2, D3, and D4, were presented in Table 2. The HM assumes a homogeneous reaction throughout the biomass particle (Edreis et al., 2014). The reaction mechanism of the model O1 dictates that the process is controlled by chemical reactions, rather than the effect of intra-particle diffusion, and the reactions happen simultaneously in the whole particle. However, the models D1-D4 assume that the process is controlled by the diffusion phenomenon, rather than that of chemical reactions. On the other hand, the SCM postulates that the reaction initially occurs at the external surface of the particle and gradually moves into the interior as that core is shrunken in the unreacted solid. 2.4.2. DAEM method The distributed activation energy model (DAEM) was introduced for describing the pyrolysis of coal (Ferdous et al., 2002; Miura et al., 1998). The DAEM equation is indicated below: 𝑉
1 ― 𝑉∗ =
(
∞ ∫0 𝑒𝑥𝑝
𝑡 ―𝐴∫0𝑒
―𝐸𝑎 𝑅𝑇
)
𝑑𝑡 𝑓(𝐸)𝑑𝐸
(3)
Where V* is the effective volatile content, V is the volatile content at temperature T, f(E) is the distribution curve of activation energy that denotes the change in the activation energies of all the reactions and A is the frequency factor corresponding to the E values. Miura and Maki have proposed a method simplifying the integral component of Eq. (3) (Miura, 1995).
Through the integration of the Arrhenius equation, the simplified DAEM is derived accordingly:
( ) = 𝑙𝑛( ) +0.6075 ―
𝑙𝑛
𝛽
𝑇
2
𝐴𝑅
𝐸1
𝐸
𝑅𝑇
(4)
( ) against 1/T at particular values of V/V* at altered
From Eq. (4), the linear plot between 𝑙𝑛
𝛽
𝑇2
heating rates can be used to calculate the activation energy and frequency factor from the slope and intercept of Arrhenius plot, respectively. 3. Results and discussion 3.1.
Thermogravimetric analysis Figures 1 and 2 represent the TG and DTG profiles of the RCS, CSP, RP, and PP
pyrolysis at heating rates of 3, 5, and 10 oC min-1. The main weight loss area included three peaks: a shoulder peak at 215-320
oC
attributing to the thermal decomposition of
hemicellulose, a main peak at 320–400 °C correlating to the thermal decomposition of cellulose, and a widened peak at 160-700 oC linked to the thermal decomposition of lignin (Nhuchhen et al., 2014). Therefore, the thermal decompositions of biomasses are divided into three main stages as shown in Figure 1. Stage I, commonly known as the drying stage, occurs at relatively low temperatures less than 200 °C, which usually result in lower mass losses and is insignificant. In stage II, the greatest decompositions of the biomasses are noticeable and caused by the thermal decomposition of hemicellulose and cellulose. Finally, stage III occurs at temperatures above 500 °C and is characterized by the decomposition of carbonaceous materials where lignin macromolecules decompose at a very slow rate (Maiti et al., 2007). The overall pyrolysis behavior of the raw biomass and the pellet were similar to each other.
However, Figure 1 shows that the decomposition temperature of the biomass pellets were higher than those of the corresponding raw biomasses. The temperature corresponding to maximum weight loss rates of CSP and PP were 340 oC and 361 oC for the heating rate of 10 oC
min-1, respectively. They were comparatively higher than those of RCS and RP which
were 330 oC and 355 oC, respectively. Besides, Figures 1 and 2 show that the decomposition peaks shifted to higher temperature with an increasing heating rate for all of the raw biomasses and pellets. This is due to the poor thermal conductivity of the biomass as well as shorter residence times for biomass decomposition (Dacres et al., 2019; Edreis et al., 2018). 3.2.
Kinetic analysis
3.2.1. Coats Redfern Method The Coats Redfern method was contracted for the kinetics calculation by using the TGA data represented in Figure 1. The temperature range studied was from 230-390 oC, which is the corresponding main thermal decomposition range of the samples. Table 3 displays the values of E, A, and R2 from the heating rate of 5 oC min-1 for all of the models highlighted in Table 2. It is well known that the pre-exponential factor and activation energy are related to the material structure and reactivity, respectively. Reactions of high activation energies require higher temperature or longer reaction times (Edreis et al., 2014). The activation energies for the pyrolysis of RCS, CSP, RP and PP invariably between the ranges of 20.36-92.05, 27.31115.66, 26.04-112.87 and 28.89-125.21 kJ mol-1, respectively. These values were obtained by using O1, R3, A2 and A3 models for all of the heating rates. Contrastingly, the values derived using the D1-D4 models were relatively higher, which were 120.58-173.15, 163.83-215.10,
145.70-210.55 and 158.18-232.44 kJ mol-1, respectively. It is therefore essential to ascertain which model is the most suitable in describing the pyrolysis of raw biomass and their pellets. The highest value of correlation coefficient (R2) of RCS, CSP, RP and PP, were 0.990, 0.999, 0.996 and 0.997, respectively, which were achieved by model O1, R3, A2, and A3. Whereas, for model D1-D4, the highest values of R2 were 0.998, 0.997, 0.996 and 0.997 respectively. Although the values of R2 obtained by different model for different samples were different, all the values were rather high. As such, it is virtually impossible to judge which model is most suitable according to only the value of R2. Nonetheless, it is important to note that the activation energy of all the samples increased as the heating rates were increased. This is due to the effect of heat and mass diffusion as discussed above. In other words, the reaction rate was dominated by the mass or heat diffusion rather than the chemical reaction. Among the models employed in this work, only the D models are diffusion controlled model. Therefore, it implies that the value of the activation energy obtained by D models should be most suitable for the raw biomasses and pellets. From this stand point, if the values of R2 for all of the D models were compared, it clearly demonstrates that the values of D2, D3 and D4 were relatively higher than D1. This observation consolidates the claim that the twodimensional and three-dimensional diffusion models are more suitable than one-dimensional diffusion to describe pyrolysis of the raw biomass and pellets. From Table 3, it is quite apparent that the activation energies of the pellets were higher than those of the raw biomasses. The physical structure which are driving forces for mass and heat diffusion, underwent changes during the pelletization process and are thus possible contributing factors to this observed phenomenon. This is analyzed in detail in the further sections.
3.2.2. DAEM method In order to further validate the activation energy of the raw biomass and pellet pyrolysis, the DAEM method was employed for a comparative kinetic analysis. Three TGA curves with different heating rates were used for the calculation. The conversion mass ratios from 0.1 to 0.9 with a step-size of 0.1 that occurred during the mass degradation of pyrolysis was analyzed. The linear plots for the derivation of DAEM methods are shown in Figure 3, which is used for the calculation of kinetic parameters, namely the activation energy, linear correlation coefficient, R2 and A values. The R2 values were higher than 0.97 for all of the samples. Furthermore, the activation energy distribution against the degree of conversion are shown in Figure 4. The activation energy increased as the degree of conversion (mass ratio) increased for the four samples, as illustrated in Figure 4. The increase was relatively gradual at mass conversion ratios up to 0.7. However, the activation energy increase was much more significant at the conversions higher than 0.7. The Figure 1 and 2 clearly showed that hemicellulose firstly thermally decomposed at low temperatures, followed by the cellulose decomposition which was the main mass loss peak. The total mass loss of the hemicellulose and cellulose decomposition accounted for about 70% of the samples mass. According to the proportion of hemicellulose and cellulose degraded during pyrolysis as well as the activation energy distribution versus the conversion rate, it can be concluded that the activation energies of the hemicellulose and cellulose decomposition were approximately 140-200 kJ mol-1. The activation energy of the lignin decomposition ranged from 200-240 kJ mol-1. This general
activation energy range is rather helpful in simplifying the complexity of modeling decomposition behavior of biomasses. The difference of activation energy for raw biomass and pellet are also shown in Figure 4. At the hemicellulose and cellulose decomposition stage, the activation energy of the pellets were slightly higher than their corresponding raw biomasses. This should be due to the denser internal structures of the pellets which cause more difficult mass and heat diffusion and more cross-linking reaction between the molecules during the thermal decomposition. Throughout the lignin decomposition stage, the activation energy of the raw biomasses increased sharply, and was higher than those of their corresponding pellets. It is well-known that the thermal decomposition of raw biomass contains two main stages which are termed as the volatile release and char decomposition stages. Consequently, it can be concluded that the activation energy of volatile release stage of raw biomasses were lower than those of their pellets. Contrastly, the activation energy of char decomposition of the raw biomasses were higher than those of their pellets. 3.3.
Comparison of Coats Redfern Method and DAEM By contracting the Coats Redfern Method, the activation energy was considered as a
constant in the overall pyrolysis process. Various reaction models with varying physical meanings can be employed. The reaction mechanism can then be investigated and understood according to the physical meaning of the aforementioned models. However, it is well established that the nature of chemical reaction as well as the mechanisms are different for biomass pyrolysis at different conversion ratios and temperatures. On the other hand, the DAEM investigates the activation energy by building correlation between activation energy
and conversion ratios, but the reaction model is not considered. Hence, it is necessary to compare and combine the results obtained by the two different methods in order to confirm the accuracy of the calculation and understand deeply the reaction mechanism. The activation energy obtained by the Coats Redfern method using D1-D4 models of the RCS, CSP, RP and PP pyrolysis were 120.58-173.15, 163.83-215.10, 145.70-210.55 and 158.18-232.44 kJ mol-1, respectively. Whereas, the activation energy of the four samples obtained by DAEM method were 141.11-213.58, 157.37-228.52, 132.58-194.47 and 127.49201.77 kJ mol-1, for the same temperature range. The activation energies obtained by the two methods were similar to each other for all the four samples. Furthermore, the activation energies determined by similar or different methods from literatures, as shown in Table 4. The Table showed the activation energies of raw biomasses. However, the activation energy obtained by other models, such as O, R, and A reaction models, as shown in Table 3, were significantly different. This implies the diffusion model was most suitable to explicate the raw biomass and pellet pyrolysis. Furthermore, the activation energies of the pellet pyrolysis were higher than those of their corresponding raw biomasses. The reason should be confirmed through the comparisons of their physical structures as mentioned above. 3.4.
Mechanism discussion The pyrolysis mechanism of biomasses have been studied widely by the other
researchers (Bhavanam et al., 2015; Cai et al., 2013; de Caprariis et al., 2015). But it is necessary to investigate the reason for the difference of activation energy of the pellet and raw biomass. The kinetic calculation performed by the two methods proved that the activation energies of the raw biomasses were lower than those of the corresponding pellets during
volatile release stage. Whereas, in the char decomposition stage, the activation energies of the raw biomasses increased and higher than those of pellets. The main effects for the biomass pyrolysis are its chemical composition and structure, physical structure, ash composition and catalytic effect, and so on. The chemical composition and structure, such as the cellulose content, hemicellulose content and lignin content, and ash composition of the raw biomass and pellet can be regarded to be similar, considering that the pelleting process was conducted under mechanical pressure at room temperature and mere physical process. Besides, some characterizations related to the chemical structure and composition of the raw biomass and pellet were conducted, for example elemental analysis, FTIR and so on. It was found that the characteristics of the pellet and its corresponding raw biomass were rather similar. Also the potassium content and existing form cannot be changed for the same reason. In this case, the catalytic performance of the potassium for the raw biomass and the pellet should be similar. Furthermore, it was found that the diffusion model was more suitable to describe the pyrolysis of raw biomass and pellet. As such, the physical characterization of biomasses and their pellets are considered to the predominant factors contributing to the activation energy distribution. The physical structure and surface morphology of the raw biomasses and pellets were characterized by a scanning electron microscopy and a specific surface area and pore size analyzer (BET). There are some reasons for the high activation energy of pellets as compared to raw biomasses. One such possible reason is the compact structure of pellets as indicated in SEM images as compared to raw biomasses. Another contributing factor is short of pores of the pellets as compared to those of corresponding raw biomass, elucidated by means of total pore volume, average pore size and specific surface area as shown in Table 5. These features can
suppress the mass and heat diffusion and cause more cross-linking reaction during the thermal decomposition of pellet (Gao et al., 2017; Mallick et al., 2018). The volatile release during pellet pyrolysis can be then suppressed and delayed. 4. Conclusion Pyrolysis kinetics of biomass pellets were examined using Coats Redfern and DAEM methods. The pyrolysis process of the raw biomass and pellet was controlled by mass and heat diffusion and can be described well by diffusion models. The activation energies of the pellets were higher than those of the raw biomasses during the hemicellulose and cellulose decomposition stages, and opposite trend was observed during lignin decomposition stage. The main reasons were that the pellet had smaller surface area and more compact surface compared to the raw biomasses. This suppressed the mass and heat diffusion and caused more cross-linking reactions during pyrolysis of the pellet. Supplementary data E-supplementary data for this work can be found in online version of the paper. Acknowledgment This research was financially supported by the National Natural Science Foundation of China (NO. 21776109, 21606187, 51661145010), the Double first-class research funding of China-EU Institute for Clean and Renewable Energy (ICARE-RP-2018-BIOMASS-003), and the foundation of State Key Laboratory of Coal Combustion (FSKLCCB1805). The authors also want to express their gratitude to the Analytical and Testing Center of Huazhong
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21. Mallick, D., Poddar, M.K., Mahanta, P., Moholkar, V.S., 2018. Discernment of synergism in pyrolysis of biomass blends using thermogravimetric analysis. Bioresour. Technol. 261, 294-305. 22. Marsh, R., Griffiths, A., Williams, K., Wilcox, S., 2007. Physical and thermal properties of extruded refuse derived fuel. Fuel Process. Technol. 88(7), 701-706. 23. Maiti, S., Purakayastha, S., Ghosh, B., 2007. Thermal characterization of mustard straw and stalk in nitrogen at different heating rates. Fuel 86(10-11), 1513-1518. 24. McKendry, P., 2002. Energy production from biomass (part 1): overview of biomass. Bioresour. Technol. 83(1), 37-46. 25. Miura, K., 1995. A new and simple method to estimate f (E) and k0 (E) in the distributed activation energy model from three sets of experimental data. Energy Fuels 9(2), 302-307. 26. Miura, K., Maki, T., 1998. A simple method for estimating f (E) and k 0 (E) in the distributed activation energy model. Energy Fuels 12(5), 864-869. 27. Munir, S., Daood, S., Nimmo, W., Cunliffe, A., Gibbs, B., 2009. Thermal analysis and devolatilization kinetics of cotton stalk, sugar cane bagasse and shea meal under nitrogen and air atmospheres. Bioresour. Technol. 100(3), 1413-1418. 28. Nhuchhen, D.R., Basu, P., 2014. Experimental investigation of mildly pressurized torrefaction in air and nitrogen. Energy Fuels 28(5), 3110-3121. 29. Patel, M., Zhang, X., Kumar, A., 2016. Techno-economic and life cycle assessment on lignocellulosic biomass thermochemical conversion technologies: A review. Renew. Sust. Energ. Rev. 53, 1486-1499. 30. Reina, J., Velo, E., Puigjaner, L., 1998. Thermogravimetric study of the pyrolysis of waste wood. Thermochim. Acta 320(1-2), 161-167. 31. Rhén, C., Gref, R., Sjöström, M., Wästerlund, I., 2005. Effects of raw material moisture content, densification pressure and temperature on some properties of Norway spruce pellets. Fuel Process. Technol. 87(1), 11-16. 32. Ronsse, F., Van Hecke, S., Dickinson, D., Prins, W., 2013. Production and characterization of slow pyrolysis biochar: influence of feedstock type and pyrolysis conditions. Gcb Bioenergy 5(2), 104-115.
33. Ru, B., Wang, S., Dai, G., Zhang, L., 2015. Effect of torrefaction on biomass physicochemical characteristics and the resulting pyrolysis behavior. Energy Fuels 29(9), 5865-5874. 34. Shen, D., Gu, S., Jin, B., Fang, M., 2011. Thermal degradation mechanisms of wood under inert and oxidative environments using DAEM methods. Bioresour. Technol. 102(2), 2047-2052. 35. Sonoyama, N., Hayashi, J.-i., 2013. Characterisation of coal and biomass based on kinetic parameter distributions for pyrolysis. Fuel 114, 206-215. 36. Soria-Verdugo, A., Garcia-Hernando, N., Garcia-Gutierrez, L., Ruiz-Rivas, U., 2013. Analysis of biomass and sewage sludge devolatilization using the distributed activation energy model. Energy Convers. Manage. 65, 239-244. 37. Tong, S., Xiao, L., Li, X., Zhu, X., Liu, H., Luo, G., Worasuwannarak, N., Kerdsuwan, S., Fungtammasan, B., Yao, H., 2018. A gas-pressurized torrefaction method for biomass wastes. Energy Convers. Manage. 173, 29-36. 38. Van der Stelt, M.J.C., Gerhauser, H., Kiel, J.H.A., Ptasinski, K.J., 2011. Biomass upgrading by torrefaction for the production of biofuels: A review. Biomass Bioenergy 35(9), 3748-3762. 39. Xiao, L., Zhu, X., Li, X., Zhang, Z., Ashida, R., Miura, K., Luo, G., Liu, W., Yao, H., 2015. Effect of pressurized torrefaction pretreatments on biomass CO2 gasification. Energy Fuels 29(11), 7309-7316. 40. Yan, J., Zhu, H., Jiang, X., Chi, Y., Cen, K., 2009. Analysis of volatile species kinetics during typical medical waste materials pyrolysis using a distributed activation energy model. J. Hazard. Mater. 162(2-3), 646-651. 41. Yan, L., He, B., Hao, T., Pei, X., Li, X., Wang, C., Duan, Z., 2014. Thermogravimetric study on the pressurized hydropyrolysis kinetics of a lignite coal. Int J. Hydrog. Energy 39(15), 7826-7833. 42. Zakrzewski, R., 2003. Pyrolysis kinetics of wood comparison of iso and polythermal thermogravimetric methods. Electronic Journal of Polish Agricultural Universities. Series Wood Technol. 6(2) 116-121.
Figure Captions Figure 1: TGA curves of RCS, CSP, RP and PP at heating rates of 3-10 oC min-1. Figure 2: DTG curves of RCS, CSP, RP and PP at heating rates of 3-10 oC min-1. Figure 3: Arrhenius plot of DAEM method at the selected (𝜶) for raw biomass and pellet. Figure 4: Activation energy distribution against degree of conversion using DAEM method. Table List Table 1: Proximate and ultimate analyses of pine and corn straw. Table 2: Expressions of f(x) and g(x) for the kinetic model functions. Table 3: Kinetic calculation results for raw biomasses and pellets by Coats Redfern method. Table 4: Comparison of the activation energies obtained by this study with those from literature. Table 5: The specific surface area, pore volume, and pore size of raw biomasses and pellets.
Figures
Figure 1: TGA curves of RCS, CSP, RP and PP at heating rates of 3-10 oC min-1.
Figure 2: DTG curves of RCS, CSP, RP and PP at heating rates of 3-10 oC min-1.
Figure 3: Arrhenius plot of DAEM method at the selected (𝜶) for raw biomass and pellet.
Figure 4: Activation energy distribution against degree of conversion using DAEM method.
Tables Table 1: Proximate and ultimate analyses of pine and corn straw. Biomass
Proximate analysis
sample
(wt. %)
Ultimate analysis (wt.%, daf)
HHV (mJ kg-1)
Aad
Vdaf
FCdaf*
C
H
O*
N
S
Pine
0.90
86.78
13.22
49.59
5.37
42.71
2.28
0.05
16.81
Corn Straw
6.02
80.39
19.61
43.52
5.11
48.43
2.80
0.14
13.35
* By difference. (A=ash content, V=volatile matter, FC=fixed carbon).
Table 2: Expressions of f(x) and g(x) for the kinetic model functions. Model
Symbol
ʄ(x)
g(x)
O1
(1 - x)
-ln(1 - x)
R3
3(1 - x)2/3
1 - (1 - x)1/3
One-dimensional diffusion
D1
α-1/2
α2
Two-dimensional diffusion,
D2
[-ln(1-α)]-1
α+(1-α) ln(1-α)
D3
3(1-α)2/3[1-(1-α)1/3]-1/2
[1-(1-α)1/3]2
D4
(1 − 2α/3) − (1 − α) 2/3
(1 − 2α/3) − (1 − α)
Chemical reaction (HM) Frist- order Phase boundary controlled reactions (SCM) Three dimensions (Contracting Sphere)
cylindrical symmetry Three-dimensional diffusion, spherical symmetry Three-dimensional diffusion,
2/3
cylindrical symmetry Random nucleation and
A2
2(1-α) [-ln(1-α)]1/2
[-ln(1-α)]1/2
A3
3(1-α) [-ln(1-α)]1/3
[-ln(1-α)]1/3
subsequent growth Random nucleation and subsequent growth
Table 3: Kinetic calculation results for raw biomasses and pellets by Coats Redfern method. Samples RCS
CSP
RP
PP
Symbols R3 O1 A2 A3 D1 D2 D3 D4 R3 O1 A2 A3 D1 D2 D3 D4 R3 O1 A2 A3 D1 D2 D3 D4 R3 O1 A2 A3 D1 D2 D3 D4
E ( kJ mol-1) 81.45 91.71 41.11 24.25 138.29 153.36 172.39 159.62 100.94 113.96 52.04 31.39 168.72 187.66 211.76 195.58 91.13 102.82 46.45 27.66 153.43 170.5 192.17 177.63 104.09 117.29 53.71 32.52 174.31 193.59 218.05 201.63
R2 0.995 0.988 0.985 0.982 0.988 0.994 0.995 0.996 0.991 0.995 0.995 0.993 0.964 0.980 0.992 0.985 0.995 0.995 0.994 0.993 0.975 0.988 0.995 0.991 0.993 0.994 0.993 0.991 0.973 0.985 0.993 0.989
A (S-1) 2.24*103 2.01*104 2.01*103 3.66*104 1.39*108 2.25*109 3.88*1010 2.10*109 1.99*104 1.06*106 4.05*102 1.43*104 2.70*1010 8.57*1011 3.58*1013 1.06*1012 2.22*103 9.01*104 1.18*103 2.79*104 9.44*108 2.01*1010 5.02*1011 2.11*1010 3.83*104 2.12*107 301*102 1.19104 8.48*1010 2.86*1012 1.28*1014 3.64*1012
Table 4: Comparison of the activation energies obtained by this study with those from literature. Samples
E ( kJ mol-1)
References
sewage sludge
170.32-400.81
(Adak et al., 2005)
cotton husk
187.42–269.09
(Bhavanam et al., 2015)
Sugarcane trash
135.07–320.00
(Sonoyama et al., 2013)
Pine wood
168.30-264.87
(Deka et al., 2002)
Birch and pine
161.74-225.65
(Shen et al., 2011)
peanut-shell
164.75-300.04
(Cai et al., 2008)
Raw biomass
120.58-210.55
This study
Biomass pellet
132.49-232.44
This study
Table 5: The specific surface area, pore volume, and pore size of raw biomasses and pellets. Sample
Specific surface area
Total pore volume
Average pore size
(m2 g-1)
(cm3 g-1)
(nm)
RCS
7.095
0.012
7.475
CSP
6.819
0.011
7.122
RP
9.665
0.014
6.937
PP
8.495
0.012
5.337
Highlights Kinetics of biomass pellets were studied by Coats Redfern and DAEM methods. Pellet pyrolysis was controlled by mass and heat diffusion. Pellet had higher Ea than biomass during hemicellulose and cellulose decomposition. It was opposite for the lignin decomposition stage.
Graphical abstract
S0960-8524(19)31329-X https://doi.org/10.1016/j.biortech.2019.122099 BITE 122099
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Bioresource Technology
Received Date: Revised Date: Accepted Date:
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Please cite this article as: Mian, I., Li, X., Jian, Y., Dacres, O.D., Zhong, M., Liu, J., Ma, F., Rahman, N., Kinetic study of biomass pellet pyrolysis by using Distributed Activation Energy Model and Coats Redfern methods and their comparison, Bioresource Technology (2019), doi: https://doi.org/10.1016/j.biortech.2019.122099
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Kinetic study of biomass pellet pyrolysis by using Distributed Activation Energy Model and Coats Redfern methods and their comparison Inamullah Miana, Xian Lia,b,*, Yiming Jiana, Omar D. Dacresb, Mei Zhonga, Jingmei Liua, Fengyun Maa, Noor Rahmanc aKey Laboratory of Coal Clean Conversion and Chemical Process Autonomous Region, College of Chemistry and Chemical Engineering, Xinjiang University, Urumqi830000, Xinjiang, China. bState Key Laboratory of Coal Combustion, School of Energy and Power Engineering, Huazhong University, Wuhan 430074,Hubei, China. cDepartment of Chemistry, Shaheed Banazir Bhutto University (18000) Sheringal, Dir (Upper) Khyber Pakhtunkhwa, Pakistan. Corresponding author: Xian Li Email: [email protected] Address: Key Laboratory of Coal Clean Conversion and Chemical Process Autonomous Region, College of Chemistry and Chemical Engineering, Xinjiang University, Urumqi830000, Xinjiang, China. Abstract In this study, the pyrolysis behavior and kinetics of raw biomasses and their pellets were studied by Coats Redfern and DAEM methods. The results demonstrated that the similar activation energies obtained by both methods confirmed accuracy of the kinetics calculation. The activation energy of the pellets was 132.49-232.44 kJ mol-1, slightly higher than those of raw biomasses, which was 120.58-210.55 kJ mol-1. The results from Coats Redfern method showed that the pyrolysis of all the samples were controlled by mass and heat diffusion. DAEM revealed that the activation energies of the pellets were higher than those of raw biomasses during hemicellulose and cellulose decomposition stages, and was opposite for the lignin decomposition stage. Physical structure characterization indicated that the pellets had smaller surface area and more compact surface than those of their raw biomasses. Hence, the mass and heat diffusion were suppressed and more cross-linking reactions occurred during pellets pyrolysis. Keywords: Pellet, Pyrolysis, Kinetics, Coast Redfern, DAEM.
1. Introduction Among the sustainable energy sources, biomass waste is believed to be a viable alternative to fossil fuels due to its capacity to support the transportation, power and chemical industries as a main source of inexhaustible, concentrated non-fossil carbon that is accessible on earth (McKendry, 2002). Biomass waste can be converted into various types of energy utilizing both biochemical (enzymatic) and thermo-chemical technologies (pyrolysis, liquefaction, gasification and combustion). The latter’s potential has come to the forefront because of higher efficiency (Munir et al., 2009). However, in the efforts to supplant coal, the utilization of biomass has clear limitation due to its inherent nature, such as low bulk and energy densities, high moisture and high oxygen content. Nonetheless, pelletization stands out as the most feasible and effective method for the conversion of biomass to a fuel that can be used as a substitute to coal (Patel et al., 2016). Ronsse et al. successfully produced pellets under pressure from eucalyptus grandis and eucalyptus spp at 120 °C (Ronsse et al., 2013). Rhén et al. also explored the preparation of Norway spruce sawdust pellets under the pressure from 46 to 114 MPa at the temperature from 26 to 144 °C with moisture content from 6.3% to 14.7% (Rhén et al., 2005). Resultantly, there is a consensus that pelletization can be used to overcome the volumetric density issues that biomass waste utilization faces and consequently can encourage their use in subsequent thermochemical processes, namely pyrolysis, combustion and gasification. Thermal behavior of the biomass pellet have been altered from the raw biomass. Pyrolysis is a vital thermochemical technique that facilitates the conversion of biomass feedstock to various types of high grade fuel (Van der Stelt et al., 2011). The resultant
pyrolytic biochar has upgraded fuel quality compared to raw biomass, such as grindability and expand energy density (Luo et al., 2015; Xiao et al., 2015). In addition, the pyrolysis is the devolatization stage in other thermo-chemical processes, for example, combustion and gasification. The main effects for biomass pyrolysis were not only the chemical reaction but also the mass and heat transfer. Pelletization was usually conducted under mechanical pressure at room temperature. So, it cannot alter the chemical composition and structure, ash composition and content of the biomass. As such, the volatile matter of the biomass pellet is similar to that of the raw biomass. But it was found that their thermal decomposition behavior were quite different from each other (Chin et al., 2013; Emadi et al., 2017; Marsh et al., 2007; Tong et al., 2018). Furthermore, thermal decomposition mechanism and kinetics of the biomass pellet were not clear and has not been studied extensively, which is essential for understanding the pyrolysis, gasification, and combustion behaviors of the biomass pellet. Hence, this study aims to investigate the thermal behavior and kinetics of biomass pellet. Thermogravimetric Analysis (TGA) is a commonly used method for the kinetic study of thermal-chemical conversion of solid fuels (Bach et al., 2015; Ru et al., 2015). Two wellestablished kinetic calculation methods are the Distributed Activation Energy Model (DAEM) method and Coats Redfern method. DAEM, a multiple reaction model, assumes that the decomposition mechanism follows a number of independent parallel first order reactions with altered activation energies, reflecting the variations in the bond strengths of species involved in the decomposition reaction. The DAEM method has been broadly used for examining the complex reactions of coal and biomass pyrolysis (de Caprariis et al., 2015; Hu et al., 2016).
Cai et al. examined the pyrolysis behavior of lignocellulosic biomass samples by considering the parallel reaction related to the primary pyrolysis of hemicellulose, cellulose and lignin (Cai et al., 2013). Kinetic study by DAEM revealed that the activation energy for lignin is widely contrasted to cellulose, which has the narrowest distribution (Bhavanam et al., 2015). It was determined that the activation energy of rice straw pyrolysis obtained by DAEM was around 160–270 kJ mol-1, while the activation energy of the torrefied sample, was 170–400 kJ mol-1 (Soria-Verdugo et al., 2013). Likewise, it was also detected that the activation energy distribution of bituminous coals and biomass follows the approximate Gaussian distribution (Li et al., 2009). The complex reactions of typical medical waste pyrolysis and the evolution of different volatile species were effectively clarified by DAEM model (Yan et al., 2009). The Coats Redfern method is adopted to assess validity of model-based method and provide the most appropriate reaction model for decomposition of biomass. This model-based method depends on Arrhenius equation. Zakrzewski et al. conducted the kinetics study of biomass under the non-isothermal condition at a heating rate of 5 °C min-1 using the CR method. The Ea and pre-exponential factors obtained were 93.1-174.9 kJ mol-1 and 4.9×104–7.1×1011 min-1 (Zakrzewski, 2003). Reina et al. also investigated the kinetics of forest wood and old furniture using TGA under isothermal condition via Coats Redfern method and recorded activation energy and pre-exponential factor values of 215.7-127.8 kJ mol-1 and 1.89×107-3.40×107 sec-1, respectively (Reina et al., 1998). As mentioned above, the thermal decomposition behavior of the biomass pellet is different from that of the raw biomass. This is possibly attributed to the differences in physical structures which have significant effect for the mass and heat transfer and crosslinking reactions during their pyrolysis. However, the behavior, kinetics and mechanism of
the biomass pellet thermal decomposition were not studied in detail. Thus, in this paper, the pyrolysis kinetics of the typical biomass pellets were studied by DAEM and Coats Redfern methods, and compared to that of the raw biomasses. 2. Materials and Methods 2.1.
Materials One typical woody biomass wastes (pine) and a non-woody biomass wastes (corn
straw) were employed as the raw materials in this study. The samples were crushed and sieved to 70-170 mesh, and dried at 80 oC for more than 6 hrs. The proximate and ultimate analyses of the raw samples are shown in Table 1. 2.2.
Methods The pellets were prepared at 25 oC using a single pelletizer. This pelletizer consisted
of a piston that was 13.2 mm in diameter and 30 mm in length, and a cylinder of a 13.5 mm inside diameter and a length of 40 mm. 5 to 7 g biomass was added into the pelletizer, and then compressed to an extreme pressure of 280 Mpa for 3 min. The collected pellets were placed in a Ziploc bag and stored at room temperature. The nomenclature of the raw biomasses and biomass pellets is as follow: The corn straw, corn straw pellets, pine and pine pellets were abbreviated as RCS, CSP, RP and PP, respectively. 2.3.
Pyrolysis
The pyrolysis of the raw biomasses and pellets were conducted using a thermogravimetric analyzer (TGA, SDT Q600). 5 to 10 mg of sample was added into the TGA then and heated from 25 oC to 900 oC at a rate 3, 5, and 10 oC min-1. Highly pure nitrogen gas was introduced
as a carrier gas at a flow rate of 100 ml min-1. The TGA experiments were repeated three times and the mass loss curves were rather similar. 2.4.
Kinetic study
2.4.1. Coats Redfern method The kinetic studies of the raw biomass and their pellets were conducted using the Coats–Redfern equation (Coats et al., 1964), which was as follow:
( ) = 𝑙𝑛[ (1 ― )] ―
𝑙𝑛
𝑔(𝑥)
𝐴𝑅
2𝑅𝑇
𝐸1
𝑇2
𝛽𝐸
𝐸
𝑅𝑇
(1)
Where, R is the universal gas constant (8.314 J mol-1 K-1). T is the absolute temperature (K). t 𝑑𝑇
is the reaction time (min). β is a constant heating rate during gasification, 𝛽 𝑑𝑡 .𝑔(𝑥) is the reaction mechanism model in integral form as shown in table 3.
[ (1 ― )] in Eq. (1) is basically constant for
It is obvious that, the expression of 𝑙𝑛
𝐴𝑅
2𝑅𝑇
𝛽𝐸
𝐸
the activation energy values (E) in the temperature range of gasification or pyrolysis (Çepelioğullar et al., 2013; Cheng et al., 2012). So, a straight line can be achieved when the left side of Eq. (1) is plotted against 1/T. The value of E can then be obtained from the slope of the straight line (Yan et al., 2014). The pre-exponential factor (A) can be obtained through the intercept of Eq. (1). The reaction rate constant (k) was then achieved using Arrhenius’s equation (2).
( )
𝑘 = 𝐴𝑒𝑥𝑝
―𝐸
𝑅𝑇
(2)
The reaction mechanism model (g(X)) used for the calculation of homogeneous model (HM) or first order chemical reaction (O1), and shrinking core models (SCM) or phase boundary controlled reactions R3. Random nucleation and subsequent growth models A2, A3, dimensional diffusion models such as D1, D2, D3, and D4, were presented in Table 2. The HM assumes a homogeneous reaction throughout the biomass particle (Edreis et al., 2014). The reaction mechanism of the model O1 dictates that the process is controlled by chemical reactions, rather than the effect of intra-particle diffusion, and the reactions happen simultaneously in the whole particle. However, the models D1-D4 assume that the process is controlled by the diffusion phenomenon, rather than that of chemical reactions. On the other hand, the SCM postulates that the reaction initially occurs at the external surface of the particle and gradually moves into the interior as that core is shrunken in the unreacted solid. 2.4.2. DAEM method The distributed activation energy model (DAEM) was introduced for describing the pyrolysis of coal (Ferdous et al., 2002; Miura et al., 1998). The DAEM equation is indicated below: 𝑉
1 ― 𝑉∗ =
(
∞ ∫0 𝑒𝑥𝑝
𝑡 ―𝐴∫0𝑒
―𝐸𝑎 𝑅𝑇
)
𝑑𝑡 𝑓(𝐸)𝑑𝐸
(3)
Where V* is the effective volatile content, V is the volatile content at temperature T, f(E) is the distribution curve of activation energy that denotes the change in the activation energies of all the reactions and A is the frequency factor corresponding to the E values. Miura and Maki have proposed a method simplifying the integral component of Eq. (3) (Miura, 1995).
Through the integration of the Arrhenius equation, the simplified DAEM is derived accordingly:
( ) = 𝑙𝑛( ) +0.6075 ―
𝑙𝑛
𝛽
𝑇
2
𝐴𝑅
𝐸1
𝐸
𝑅𝑇
(4)
( ) against 1/T at particular values of V/V* at altered
From Eq. (4), the linear plot between 𝑙𝑛
𝛽
𝑇2
heating rates can be used to calculate the activation energy and frequency factor from the slope and intercept of Arrhenius plot, respectively. 3. Results and discussion 3.1.
Thermogravimetric analysis Figures 1 and 2 represent the TG and DTG profiles of the RCS, CSP, RP, and PP
pyrolysis at heating rates of 3, 5, and 10 oC min-1. The main weight loss area included three peaks: a shoulder peak at 215-320
oC
attributing to the thermal decomposition of
hemicellulose, a main peak at 320–400 °C correlating to the thermal decomposition of cellulose, and a widened peak at 160-700 oC linked to the thermal decomposition of lignin (Nhuchhen et al., 2014). Therefore, the thermal decompositions of biomasses are divided into three main stages as shown in Figure 1. Stage I, commonly known as the drying stage, occurs at relatively low temperatures less than 200 °C, which usually result in lower mass losses and is insignificant. In stage II, the greatest decompositions of the biomasses are noticeable and caused by the thermal decomposition of hemicellulose and cellulose. Finally, stage III occurs at temperatures above 500 °C and is characterized by the decomposition of carbonaceous materials where lignin macromolecules decompose at a very slow rate (Maiti et al., 2007). The overall pyrolysis behavior of the raw biomass and the pellet were similar to each other.
However, Figure 1 shows that the decomposition temperature of the biomass pellets were higher than those of the corresponding raw biomasses. The temperature corresponding to maximum weight loss rates of CSP and PP were 340 oC and 361 oC for the heating rate of 10 oC
min-1, respectively. They were comparatively higher than those of RCS and RP which
were 330 oC and 355 oC, respectively. Besides, Figures 1 and 2 show that the decomposition peaks shifted to higher temperature with an increasing heating rate for all of the raw biomasses and pellets. This is due to the poor thermal conductivity of the biomass as well as shorter residence times for biomass decomposition (Dacres et al., 2019; Edreis et al., 2018). 3.2.
Kinetic analysis
3.2.1. Coats Redfern Method The Coats Redfern method was contracted for the kinetics calculation by using the TGA data represented in Figure 1. The temperature range studied was from 230-390 oC, which is the corresponding main thermal decomposition range of the samples. Table 3 displays the values of E, A, and R2 from the heating rate of 5 oC min-1 for all of the models highlighted in Table 2. It is well known that the pre-exponential factor and activation energy are related to the material structure and reactivity, respectively. Reactions of high activation energies require higher temperature or longer reaction times (Edreis et al., 2014). The activation energies for the pyrolysis of RCS, CSP, RP and PP invariably between the ranges of 20.36-92.05, 27.31115.66, 26.04-112.87 and 28.89-125.21 kJ mol-1, respectively. These values were obtained by using O1, R3, A2 and A3 models for all of the heating rates. Contrastingly, the values derived using the D1-D4 models were relatively higher, which were 120.58-173.15, 163.83-215.10,
145.70-210.55 and 158.18-232.44 kJ mol-1, respectively. It is therefore essential to ascertain which model is the most suitable in describing the pyrolysis of raw biomass and their pellets. The highest value of correlation coefficient (R2) of RCS, CSP, RP and PP, were 0.990, 0.999, 0.996 and 0.997, respectively, which were achieved by model O1, R3, A2, and A3. Whereas, for model D1-D4, the highest values of R2 were 0.998, 0.997, 0.996 and 0.997 respectively. Although the values of R2 obtained by different model for different samples were different, all the values were rather high. As such, it is virtually impossible to judge which model is most suitable according to only the value of R2. Nonetheless, it is important to note that the activation energy of all the samples increased as the heating rates were increased. This is due to the effect of heat and mass diffusion as discussed above. In other words, the reaction rate was dominated by the mass or heat diffusion rather than the chemical reaction. Among the models employed in this work, only the D models are diffusion controlled model. Therefore, it implies that the value of the activation energy obtained by D models should be most suitable for the raw biomasses and pellets. From this stand point, if the values of R2 for all of the D models were compared, it clearly demonstrates that the values of D2, D3 and D4 were relatively higher than D1. This observation consolidates the claim that the twodimensional and three-dimensional diffusion models are more suitable than one-dimensional diffusion to describe pyrolysis of the raw biomass and pellets. From Table 3, it is quite apparent that the activation energies of the pellets were higher than those of the raw biomasses. The physical structure which are driving forces for mass and heat diffusion, underwent changes during the pelletization process and are thus possible contributing factors to this observed phenomenon. This is analyzed in detail in the further sections.
3.2.2. DAEM method In order to further validate the activation energy of the raw biomass and pellet pyrolysis, the DAEM method was employed for a comparative kinetic analysis. Three TGA curves with different heating rates were used for the calculation. The conversion mass ratios from 0.1 to 0.9 with a step-size of 0.1 that occurred during the mass degradation of pyrolysis was analyzed. The linear plots for the derivation of DAEM methods are shown in Figure 3, which is used for the calculation of kinetic parameters, namely the activation energy, linear correlation coefficient, R2 and A values. The R2 values were higher than 0.97 for all of the samples. Furthermore, the activation energy distribution against the degree of conversion are shown in Figure 4. The activation energy increased as the degree of conversion (mass ratio) increased for the four samples, as illustrated in Figure 4. The increase was relatively gradual at mass conversion ratios up to 0.7. However, the activation energy increase was much more significant at the conversions higher than 0.7. The Figure 1 and 2 clearly showed that hemicellulose firstly thermally decomposed at low temperatures, followed by the cellulose decomposition which was the main mass loss peak. The total mass loss of the hemicellulose and cellulose decomposition accounted for about 70% of the samples mass. According to the proportion of hemicellulose and cellulose degraded during pyrolysis as well as the activation energy distribution versus the conversion rate, it can be concluded that the activation energies of the hemicellulose and cellulose decomposition were approximately 140-200 kJ mol-1. The activation energy of the lignin decomposition ranged from 200-240 kJ mol-1. This general
activation energy range is rather helpful in simplifying the complexity of modeling decomposition behavior of biomasses. The difference of activation energy for raw biomass and pellet are also shown in Figure 4. At the hemicellulose and cellulose decomposition stage, the activation energy of the pellets were slightly higher than their corresponding raw biomasses. This should be due to the denser internal structures of the pellets which cause more difficult mass and heat diffusion and more cross-linking reaction between the molecules during the thermal decomposition. Throughout the lignin decomposition stage, the activation energy of the raw biomasses increased sharply, and was higher than those of their corresponding pellets. It is well-known that the thermal decomposition of raw biomass contains two main stages which are termed as the volatile release and char decomposition stages. Consequently, it can be concluded that the activation energy of volatile release stage of raw biomasses were lower than those of their pellets. Contrastly, the activation energy of char decomposition of the raw biomasses were higher than those of their pellets. 3.3.
Comparison of Coats Redfern Method and DAEM By contracting the Coats Redfern Method, the activation energy was considered as a
constant in the overall pyrolysis process. Various reaction models with varying physical meanings can be employed. The reaction mechanism can then be investigated and understood according to the physical meaning of the aforementioned models. However, it is well established that the nature of chemical reaction as well as the mechanisms are different for biomass pyrolysis at different conversion ratios and temperatures. On the other hand, the DAEM investigates the activation energy by building correlation between activation energy
and conversion ratios, but the reaction model is not considered. Hence, it is necessary to compare and combine the results obtained by the two different methods in order to confirm the accuracy of the calculation and understand deeply the reaction mechanism. The activation energy obtained by the Coats Redfern method using D1-D4 models of the RCS, CSP, RP and PP pyrolysis were 120.58-173.15, 163.83-215.10, 145.70-210.55 and 158.18-232.44 kJ mol-1, respectively. Whereas, the activation energy of the four samples obtained by DAEM method were 141.11-213.58, 157.37-228.52, 132.58-194.47 and 127.49201.77 kJ mol-1, for the same temperature range. The activation energies obtained by the two methods were similar to each other for all the four samples. Furthermore, the activation energies determined by similar or different methods from literatures, as shown in Table 4. The Table showed the activation energies of raw biomasses. However, the activation energy obtained by other models, such as O, R, and A reaction models, as shown in Table 3, were significantly different. This implies the diffusion model was most suitable to explicate the raw biomass and pellet pyrolysis. Furthermore, the activation energies of the pellet pyrolysis were higher than those of their corresponding raw biomasses. The reason should be confirmed through the comparisons of their physical structures as mentioned above. 3.4.
Mechanism discussion The pyrolysis mechanism of biomasses have been studied widely by the other
researchers (Bhavanam et al., 2015; Cai et al., 2013; de Caprariis et al., 2015). But it is necessary to investigate the reason for the difference of activation energy of the pellet and raw biomass. The kinetic calculation performed by the two methods proved that the activation energies of the raw biomasses were lower than those of the corresponding pellets during
volatile release stage. Whereas, in the char decomposition stage, the activation energies of the raw biomasses increased and higher than those of pellets. The main effects for the biomass pyrolysis are its chemical composition and structure, physical structure, ash composition and catalytic effect, and so on. The chemical composition and structure, such as the cellulose content, hemicellulose content and lignin content, and ash composition of the raw biomass and pellet can be regarded to be similar, considering that the pelleting process was conducted under mechanical pressure at room temperature and mere physical process. Besides, some characterizations related to the chemical structure and composition of the raw biomass and pellet were conducted, for example elemental analysis, FTIR and so on. It was found that the characteristics of the pellet and its corresponding raw biomass were rather similar. Also the potassium content and existing form cannot be changed for the same reason. In this case, the catalytic performance of the potassium for the raw biomass and the pellet should be similar. Furthermore, it was found that the diffusion model was more suitable to describe the pyrolysis of raw biomass and pellet. As such, the physical characterization of biomasses and their pellets are considered to the predominant factors contributing to the activation energy distribution. The physical structure and surface morphology of the raw biomasses and pellets were characterized by a scanning electron microscopy and a specific surface area and pore size analyzer (BET). There are some reasons for the high activation energy of pellets as compared to raw biomasses. One such possible reason is the compact structure of pellets as indicated in SEM images as compared to raw biomasses. Another contributing factor is short of pores of the pellets as compared to those of corresponding raw biomass, elucidated by means of total pore volume, average pore size and specific surface area as shown in Table 5. These features can
suppress the mass and heat diffusion and cause more cross-linking reaction during the thermal decomposition of pellet (Gao et al., 2017; Mallick et al., 2018). The volatile release during pellet pyrolysis can be then suppressed and delayed. 4. Conclusion Pyrolysis kinetics of biomass pellets were examined using Coats Redfern and DAEM methods. The pyrolysis process of the raw biomass and pellet was controlled by mass and heat diffusion and can be described well by diffusion models. The activation energies of the pellets were higher than those of the raw biomasses during the hemicellulose and cellulose decomposition stages, and opposite trend was observed during lignin decomposition stage. The main reasons were that the pellet had smaller surface area and more compact surface compared to the raw biomasses. This suppressed the mass and heat diffusion and caused more cross-linking reactions during pyrolysis of the pellet. Supplementary data E-supplementary data for this work can be found in online version of the paper. Acknowledgment This research was financially supported by the National Natural Science Foundation of China (NO. 21776109, 21606187, 51661145010), the Double first-class research funding of China-EU Institute for Clean and Renewable Energy (ICARE-RP-2018-BIOMASS-003), and the foundation of State Key Laboratory of Coal Combustion (FSKLCCB1805). The authors also want to express their gratitude to the Analytical and Testing Center of Huazhong
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Figure Captions Figure 1: TGA curves of RCS, CSP, RP and PP at heating rates of 3-10 oC min-1. Figure 2: DTG curves of RCS, CSP, RP and PP at heating rates of 3-10 oC min-1. Figure 3: Arrhenius plot of DAEM method at the selected (𝜶) for raw biomass and pellet. Figure 4: Activation energy distribution against degree of conversion using DAEM method. Table List Table 1: Proximate and ultimate analyses of pine and corn straw. Table 2: Expressions of f(x) and g(x) for the kinetic model functions. Table 3: Kinetic calculation results for raw biomasses and pellets by Coats Redfern method. Table 4: Comparison of the activation energies obtained by this study with those from literature. Table 5: The specific surface area, pore volume, and pore size of raw biomasses and pellets.
Figures
Figure 1: TGA curves of RCS, CSP, RP and PP at heating rates of 3-10 oC min-1.
Figure 2: DTG curves of RCS, CSP, RP and PP at heating rates of 3-10 oC min-1.
Figure 3: Arrhenius plot of DAEM method at the selected (𝜶) for raw biomass and pellet.
Figure 4: Activation energy distribution against degree of conversion using DAEM method.
Tables Table 1: Proximate and ultimate analyses of pine and corn straw. Biomass
Proximate analysis
sample
(wt. %)
Ultimate analysis (wt.%, daf)
HHV (mJ kg-1)
Aad
Vdaf
FCdaf*
C
H
O*
N
S
Pine
0.90
86.78
13.22
49.59
5.37
42.71
2.28
0.05
16.81
Corn Straw
6.02
80.39
19.61
43.52
5.11
48.43
2.80
0.14
13.35
* By difference. (A=ash content, V=volatile matter, FC=fixed carbon).
Table 2: Expressions of f(x) and g(x) for the kinetic model functions. Model
Symbol
ʄ(x)
g(x)
O1
(1 - x)
-ln(1 - x)
R3
3(1 - x)2/3
1 - (1 - x)1/3
One-dimensional diffusion
D1
α-1/2
α2
Two-dimensional diffusion,
D2
[-ln(1-α)]-1
α+(1-α) ln(1-α)
D3
3(1-α)2/3[1-(1-α)1/3]-1/2
[1-(1-α)1/3]2
D4
(1 − 2α/3) − (1 − α) 2/3
(1 − 2α/3) − (1 − α)
Chemical reaction (HM) Frist- order Phase boundary controlled reactions (SCM) Three dimensions (Contracting Sphere)
cylindrical symmetry Three-dimensional diffusion, spherical symmetry Three-dimensional diffusion,
2/3
cylindrical symmetry Random nucleation and
A2
2(1-α) [-ln(1-α)]1/2
[-ln(1-α)]1/2
A3
3(1-α) [-ln(1-α)]1/3
[-ln(1-α)]1/3
subsequent growth Random nucleation and subsequent growth
Table 3: Kinetic calculation results for raw biomasses and pellets by Coats Redfern method. Samples RCS
CSP
RP
PP
Symbols R3 O1 A2 A3 D1 D2 D3 D4 R3 O1 A2 A3 D1 D2 D3 D4 R3 O1 A2 A3 D1 D2 D3 D4 R3 O1 A2 A3 D1 D2 D3 D4
E ( kJ mol-1) 81.45 91.71 41.11 24.25 138.29 153.36 172.39 159.62 100.94 113.96 52.04 31.39 168.72 187.66 211.76 195.58 91.13 102.82 46.45 27.66 153.43 170.5 192.17 177.63 104.09 117.29 53.71 32.52 174.31 193.59 218.05 201.63
R2 0.995 0.988 0.985 0.982 0.988 0.994 0.995 0.996 0.991 0.995 0.995 0.993 0.964 0.980 0.992 0.985 0.995 0.995 0.994 0.993 0.975 0.988 0.995 0.991 0.993 0.994 0.993 0.991 0.973 0.985 0.993 0.989
A (S-1) 2.24*103 2.01*104 2.01*103 3.66*104 1.39*108 2.25*109 3.88*1010 2.10*109 1.99*104 1.06*106 4.05*102 1.43*104 2.70*1010 8.57*1011 3.58*1013 1.06*1012 2.22*103 9.01*104 1.18*103 2.79*104 9.44*108 2.01*1010 5.02*1011 2.11*1010 3.83*104 2.12*107 301*102 1.19104 8.48*1010 2.86*1012 1.28*1014 3.64*1012
Table 4: Comparison of the activation energies obtained by this study with those from literature. Samples
E ( kJ mol-1)
References
sewage sludge
170.32-400.81
(Adak et al., 2005)
cotton husk
187.42–269.09
(Bhavanam et al., 2015)
Sugarcane trash
135.07–320.00
(Sonoyama et al., 2013)
Pine wood
168.30-264.87
(Deka et al., 2002)
Birch and pine
161.74-225.65
(Shen et al., 2011)
peanut-shell
164.75-300.04
(Cai et al., 2008)
Raw biomass
120.58-210.55
This study
Biomass pellet
132.49-232.44
This study
Table 5: The specific surface area, pore volume, and pore size of raw biomasses and pellets. Sample
Specific surface area
Total pore volume
Average pore size
(m2 g-1)
(cm3 g-1)
(nm)
RCS
7.095
0.012
7.475
CSP
6.819
0.011
7.122
RP
9.665
0.014
6.937
PP
8.495
0.012
5.337
Highlights Kinetics of biomass pellets were studied by Coats Redfern and DAEM methods. Pellet pyrolysis was controlled by mass and heat diffusion. Pellet had higher Ea than biomass during hemicellulose and cellulose decomposition. It was opposite for the lignin decomposition stage.
Graphical abstract