Pyrolysis Characteristics of Coal, Biomass, and Coal–Biomass Blends

Jun 29, 2015 - an inert atmosphere required by pyrolysis and also blow away produced volatiles from the particle to avoid secondary pyrolysis reaction...
0 downloads 0 Views 3MB Size
Article pubs.acs.org/EF

Pyrolysis Characteristics of Coal, Biomass, and Coal−Biomass Blends under High Heating Rate Conditions: Effects of Particle Diameter, Fuel Type, and Mixing Conditions Zhihua Wang,† Kaidi Wan,†,‡ Jun Xia,*,‡ Yong He,† Yingzu Liu,† and Jianzhong Liu† †

State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou 310027, China Department of Mechanical, Aerospace and Civil Engineering & Institute of Energy Futures, Brunel University London, Uxbridge UB8 3PH, United Kingdom



ABSTRACT: The pyrolysis characteristics of two Chinese coals, two biomass materials, and their blends were investigated by both experimental and numerical methods. Single particles of the coal and biomass were prepared for the pyrolysis experiment through grinding and pressing, while the blended particles were made by mixing the coal and biomass powder with different ratios before the pressing. Sample particles pyrolyzed in a single-particle reactor system, with the time history of the particle temperature and mass recorded. The analysis of the measured pyrolysis data of the coal, biomass, and coal−biomass blends indicate the absence of a synergistic effect between the coal and biomass pyrolysis. A numerical method coupling the chemical percolation devolatilization (CPD) model with a particle energy equation was employed to analyze the pyrolysis process. The model prediction agreed well with the experimental data for different particle diameters, fuel types, and blend mixing conditions. The fact that the co-pyrolysis of blended coal−biomass particles is well-predicted by the simple addition of the individual pyrolysis characteristics of its components also corroborates the lack of synergistic interactions. These findings will be useful for the co-combustion modeling of coal−biomass blends.

1. INTRODUCTION In recent years, the production and utilization of biomass energy have attracted much attention, because of the concerns of global warming and the shortage of fossil fuels.1−3 As a renewable and carbon neutral energy resource, biomass is estimated to provide ∼14% of the world’s current energy supply.4 However, burning biomass materials directly can be a method of low energy conversion efficiency, since biomass has a lower heating value, greater moisture content, and lower density, compared with fossil fuels.5 To overcome these obstacles, biomass fuels can be cothermochemically utilized with coal to reduce the greenhouse gas emissions in an economically attractive way, e.g., copyrolysis,4−7 co-combustion,8−10 and co-gasification.11−13 These promising co-thermochemical conversion technologies can produce a variety of chemical compounds and fuels through co-pyrolysis and co-gasification. They can also be used to produce energy through co-combustion. As the precursor and also the key stage of co-combustion and co-gasification, the copyrolysis process has significant influence on the product yields and kinetic characteristics during the co-thermochemical conversion.4,5 Therefore, it is important to understand the co-pyrolysis characteristics of coal−biomass blends for a better understanding of the co-thermochemical processes. Many researchers have studied the co-pyrolysis characteristics of different rank coals, such as peat,14 brown coal,15 bituminous coal,4 and anthracite,6 with a variety of biomass materials, including hazelnut shell,14,16 cotton residue,17 sawdust,15,18 wheat straw,19 rice straw,5 and so on. The interaction between coal and biomass during co-pyrolysis can significantly affect the physical and chemical properties of the products and the co-pyrolysis characteristics, which has thus © XXXX American Chemical Society

been extensively investigated. For instance, Haykiri-Acma et al.14 reported the existence of synergistic interactions (nonadditive manner) between hazelnut shell and different rank coals during co-pyrolysis with a heating rate of 40 K/min in a nitrogen ambient flow, whereas Lu et al.6 indicated that the synergistic effect of co-pyrolysis between wood and coal at five different mass blending ratios was slight. Because of the different coal and biomass species, and also different pyrolysis temperatures, heating rates and mixing ratios, different conclusions on the co-pyrolysis characteristics and synergistic interactions were obtained. Some studies4,5,14,16 declare that the synergistic interactions between coal and biomass should be taken into account during the co-pyrolysis process, while the others6,15,17−20 report a lack of synergistic effect. However, the heating rate was always limited to a very low level (10−50 K/min) in most of those studies, because of the limitations of thermogravimetric analysis14−19 (TGA). For a realistic industrial furnace, the heating rate is much higher than the above-mentioned level. A higher heating rate could make a difference on the pyrolysis kinetics and products yield,21−23 e.g., the increase of volatile yields with the heating rate elevated.23−25 Therefore, the investigation of the co-pyrolysis characteristics of coal−biomass blends under higher heating rate conditions becomes necessary and important. There have been some studies considering high heating rate co-pyrolysis, using a drop-tube reactor18,26,27 and fluidized-bed reactor.28,29 Some of them26,27,29 confirmed a synergistic effect, while others did not.18,28 These conflicting claims can be due to Received: March 27, 2015 Revised: June 29, 2015

A

DOI: 10.1021/acs.energyfuels.5b00646 Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels the variation of coal, biomass, their blending ratio, etc. For example, Meesri et al.18 observed no synergistic effect of copyrolysis of sawdust and an Australia coal using a drop-tube reactor, where the blending ratios of the biomass were limited to 573 K) 418.6 for coal 210.0 for biomass 26.0 × 10−3 2.0 × 10−3 931.3 + 0.256T − 24.0T−2 2200 0.08

ref 10.0 × 10−3 703 × 10−6 50 (for T < 573 K) (for T > 573 K)

51 52 53 54 55

56 57 57

More details about this method can be found in our previous study with a full set of physical properties of the particles and the quartz reactor.30 Here, the key parameters used are summarized in Table 4. As a simplification, most physical properties of the biomass were set to be the same as that of coal, except for the pyrolysis heat.

4. RESULTS AND DISCUSSION 4.1. Pyrolysis Characteristics of Fuel Particles with Different Diameters. Pyrolysis data of ZD coal particles with different diameters were collected by the experimental setup and validated with the developed particle pyrolysis model. Figures 2 and 3 illustrate the time history of the temperature

Figure 3. Time history of the residual mass of ZD coal particles with different particle diameters.

heat conduction. After the particle core was heated, the temperature increased very quickly, because of the radiative heating. Finally, the temperature became stable since the particle achieved thermal equilibrium in the furnace. The equilibrium temperature of the 6 mm particle was a little lower than that of the 10 mm particle, because of the relatively larger convective heat loss of the 6 mm particle. From Figure 3, it can be found that the particle gradually yielded volatiles and its weight decreased during the pyrolysis process. Eventually, the rate of mass loss slowed and the particle residual mass remained stable. Generally, the model prediction of the pyrolysis of particles of different diameters agreed well with the measured data (Figures 2 and 3). At the time of 500 s, the measured volatile yields (100% − residual mass) were 30.9% (6 mm, dry basis), 30.9% (8 mm), and 31.1% (10 mm). Compared with the volatile mass fraction from the proximate analysis (30.86%; dry basis; measured at 1173 K; see Table 1), it can be concluded that the particles almost reached the maximum yields at 500 s. The model prediction of the volatile yields was 31.2% (6 mm, dry basis), 31.3% (8 mm), and 31.4% (10 mm). This little variance was due to the small difference in the particle equilibrium temperature with different diameters. The

Figure 2. Time history of the center temperature of ZD coal particles with different particle diameters.

and residual mass of the coal particles with different diameters (6, 8, and 10 mm). The initial mass of the particles was also different (∼152, ∼360, and ∼703 mg) to ensure the density was similar (∼1200 kg/m3 after the drying process). The furnace wall temperature was set to 1100 K. Each experiment was repeated three times and the average result is shown along with error bars which indicate the statistical uncertainty of the measurements. As plotted in Figure 2, the particle temperature at the beginning of pyrolysis maintained at 300 K (the ambient temperature) for a few seconds, which was due to the lag of D

DOI: 10.1021/acs.energyfuels.5b00646 Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels predicted volatile yields matched the experimental pyrolysis data within 0.4 wt % upon 500 s of the pyrolysis process. For the rate of mass loss, the present model also gave a reasonable prediction for different particle diameters (Figure 4). The measured maximum pyrolysis rate decreased from

Figure 5. Time history of the rate of temperature increase and massweighted average temperature of ZD coal particles with different particle diameters.

4.2. Pyrolysis Characteristics of Fuel Particles with Different Fuel Types. Pyrolysis characteristics of four different fuel particles (ZD coal, DT coal, WH biomass, CS biomass) were investigated by both experimental and numerical methods, and the results are compared in Figures 6−9. All

Figure 4. Time history of the rate of mass loss of ZD coal particles with different particle diameters.

0.43% s−1 (6 mm) to 0.28% s−1 (10 mm), and the corresponding time increased from 52 s to 74 s. Similar trends could be observed in the model results. Besides, in the model prediction, there were two peaks in the time history of the mass loss rate while the particle diameter was small (6 mm), which was due to the different chemical reaction routes at low and high temperatures.49 When the particle diameter became large (10 mm), this two-peak phenomenon was alleviated. The reason lies in that the temperature difference inside the 10 mm particle was bigger, which resulted in the larger difference of pyrolysis rates inside. The trough of the rate of mass loss in one part of the particle could be balanced by a peak in another part. Thus, the variation of the rate of mass loss of the entire particle was smooth. For the experimental data, similar trends can be observed in the zoomed-in subfigure. There exists one difference for the small and big particles. For the 6 mm particle, multiple peaks can be found on the rate of mass loss curve due to the release of different volatile species at different temperatures. Whereas, for the 10 mm particle, this multiplepeak phenomenon became hardly noticeable. It should be stressed that the model predicts the rate of mass loss of both the big and small particles acceptably well. The mass-weighted average temperature and its rate of increase calculated by the present model are shown in Figure 5. The mass-weighted average temperature is the average of the particle temperature at each node using its mass as the weighting factor, which is a characteristic temperature for the entire particle. The time history of the average temperature is similar to that of the center temperature (Figure 2), except for the short lag at the beginning. The smaller particle showed a higher rate of temperature increase, since its total heat capacity was lower. There is a small peak in the rate of temperature increase of the 6 mm particle at ∼50 s, because the corresponding pyrolysis rate (Figure 4) decreased and the endothermic effect of pyrolysis is reduced. A similar phenomenon can be found on the 8 mm and 10 mm particles, although it is not very obvious.

Figure 6. Time history of the center temperature of fuel particles with different fuel types.

particles had similar initial volumes (diameter of 8 mm) and initial masses (~360 mg). The furnace wall temperature was set to 1100 K. It can be found that the model prediction of the particle temperature (Figure 6), residual mass (Figure 7), and rate of mass loss (Figure 8) agreed well with the experimental data. At the time of 500 s, the measured volatile yields were 24.0% (DT coal, dry basis), 30.9% (ZD coal), 75.0% (CS biomass), and 81.6% (WH biomass). These values are very close to the volatile mass fraction from the proximate analysis (dry basis; measured at 1173 K; see Table 1). The predicted volatile yields were 24.6% (DT coal, dry basis), 31.3% (ZD coal), 74.5% (CS biomass), and 80.9% (WH biomass), which matched the experimental data within 0.7 wt %. There is an obvious difference in the pyrolysis characteristics between the coal and the biomass. The biomass contained more volatiles (more than 75%) and had a tendency to be pyrolyzed faster (being completed within ∼60 s) with a much E

DOI: 10.1021/acs.energyfuels.5b00646 Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels

that of coal particles at the beginning, but it took a sharp rise at ∼60 s (the corresponding peak of the rate of temperature increase can be found in Figure 9). This is because the pyrolysis rate of the biomass was larger (Figure 8), and the cooling effect of pyrolysis was stronger than that of the coal particles. (The cooling effect of pyrolysis is caused by two reasons.30 First, the pyrolysis process is an endothermic reaction. Second, when the generated volatile flowed through the particle from inside to outside, it would cause convective cooling effects on the particle.) Hence, the temperature of the biomass particles was lower in the first stage. However, when the pyrolysis of the biomass was complete after ∼60 s, this pyrolysis cooling effect disappeared and the total heat capacity of the biomass particles decreased significantly to a very low level (the particle lost most of its original mass). The particle temperature then increased very quickly. The coal particles also experienced a similar but very smooth process; therefore, only a tiny peak could be observed in the rate of temperature increase (see Figure 9). 4.3. Pyrolysis Characteristics of Blended Fuel Particles under Different Mixing Conditions. Pyrolysis characteristics of four different blended coal−biomass particles (ZD/ WH, ZD/CS, DT/WH, DT/CS) with three different mixing ratios (coal/biomass = 80:20, 50:50, 20:80) were investigated, and the experimental data and model prediction are compared in Figures 10−13. All particles had similar initial volumes (diameter of 8 mm) and initial masses (∼360 mg). The furnace wall temperature was set to 1100 K. From Figures 10 and 11, the experimental data (open symbols), weighted average of the individual coal and biomass experimental data in Section 4.2 (solid symbols), and model prediction (lines) agreed well with each other under all of the mixing conditions. The blended particles showed the average pyrolysis characteristics of the individual coal and biomass particles, with a linear relationship weighted by the coal/biomass ratio. This co-pyrolysis characteristic was not limited to a certain type of coal or biomass, but could be found in all cases with different combinations of the coals and biomass (Figures 10−13a−d). As the biomass percentage increased, the temperature of the blended particles exhibited a sharper increase after ∼60 s (Figures 10 and 13) and the corresponding peak of the rate of temperature increase (Figure 13) was higher. The pyrolysis rate also increased, along with the mass fraction of the biomass (Figure 12). On the other hand, the time of the maximum pyrolysis rate (∼50 s in Figure 12) and the temperature rise peak (∼60 s in Figure 13) were almost not varied with the mixing ratio, since these specific times were similar for the individual coals and biomass (Figures 8 and 9). At the time of 500 s, the total volatile yields from the experimental data, the averaged data of the individual coal and biomass experiments, the model prediction and the average of the volatile mass fraction from the proximate analysis (dry basis; measured at 1173 K; see Table 1) are summarized in Figure 14. These four datasets matched each other within a difference of 2.0 wt %. This perfect linear relationship with different coal/biomass ratio on the total volatile yields suggests the absence of synergistic effects between the coal and the biomass, which is consistent with previous studies of copyrolysis characteristics of the coal−biomass blends.15,17−20,30 Because of the similar carbon-molecular frameworks of macromolecular structures in both the coal and the biomass, the active radicals produced during the pyrolysis process are also similar.18 Hence, there is no catalytic agent that could induce synergistic chemical reactions.

Figure 7. Time history of the residual mass of fuel particles with different fuel types.

Figure 8. Time history of the rate of mass loss of fuel particles with different fuel types.

Figure 9. Time history of the rate of temperature increase and massweighted average temperature of fuel particles with different fuel types.

larger pyrolysis rate (Figure 8). In comparison, the coal particles still experienced mass loss after 300 s. This difference also affected the temperature increase in the particles. In Figure 6, the temperature of biomass particles was slightly lower than F

DOI: 10.1021/acs.energyfuels.5b00646 Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels

Figure 10. Time history of the center temperature of blended particles under different mixing conditions.

Figure 11. Time history of the residual mass of blended particles under different mixing conditions.

G

DOI: 10.1021/acs.energyfuels.5b00646 Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels

Figure 12. Time history of the rate of mass loss of blended particles under different mixing conditions.

Figure 13. Time history of the rate of temperature increase and mass-weighted average temperature of blended particles under different mixing conditions.

H

DOI: 10.1021/acs.energyfuels.5b00646 Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels

Figure 14. Total volatile yields of blended particles under different mixing conditions. [Legend: exp., experimental data; avg., averaged data of individual coal and biomass experiments; mod., model prediction; and prox., average of the volatile mass fraction from proximate analysis.]

5. CONCLUSION Pyrolysis characteristics of two Chinese coals, two biomass materials, and their blends were studied by both experimental and numerical methods. The influence of the particle diameter, fuel type, and blend mixing condition to the particle pyrolysis characteristics was investigated. A smaller particle experiences a higher heating rate and larger pyrolysis rate, and its pyrolysis characteristics are more sensitive to the interaction between heating of the furnace and cooling effect of pyrolysis. The coal and biomass have very different pyrolysis characteristics. The biomass contains more volatiles and get pyrolyzed faster, which results in the difference in the time history of the mass loss and temperature rise. For the blended coal−biomass particles, a perfect agreement can be found in all mixing conditions between experimental data of the blends and linear average of the individual components, indicating a lack of synergistic effects. A method of coupling the CPD model with a particle energy equation was adopted to simulate the particle pyrolysis process. Model predictions of the particle pyrolysis with different particle diameters, fuel types, and blend mixing conditions agreed well with the experimental data. The co-pyrolysis of blended coal−biomass particle was modeled by a simple addition of the individual pyrolysis characteristics of its components, which also implies that there is no interaction between the coal and the biomass. The lack of synergistic effects between the coal and biomass in the co-pyrolysis means that we can use the individual pyrolysis data of coal and biomass to predict the pyrolysis characteristics of coal−biomass blends. Besides, the co-pyrolysis model of the blends can also be derived directly from the

existing pyrolysis models of the individual coal and biomass, which would be beneficial to the co-combustion modeling of the coal−biomass blends.



AUTHOR INFORMATION

Corresponding Author

*Tel.: +44 (0)1895 265 433. E-mail: [email protected]. Notes

The authors declare no competing financial interest.



ACKNOWLEDGMENTS This work was supported by the National Basic Research Program of China (No. 2012CB214906), National Natural Science Foundation of China (Nos. 51390491, 51422605, 51406178) and Project funded by China Postdoctoral Science Foundation (No. 2014M551732). K.D.W. would like to acknowledge the support from the China Scholarship Council.



I

NOMENCLATURE Cp = specific heat, J/(kg K) c1 = first radiation constant, W m2 c2 = second radiation constant, m K d = diameter, m Ebλ = blackbody emissive power, W/m3 h = convective heat-transfer coefficient, W/(m2 K) Nu = Nusselt number Pr = Prandtl number r = radius coordinate, m Rp = particle outer radius, m Re = Reynolds number t = time, s DOI: 10.1021/acs.energyfuels.5b00646 Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels

(10) Basu, P.; Butler, J.; Leon, M. A. Biomass co-firing options on the emission reduction and electricity generation costs in coal-fired power plants. Renewable Energy 2011, 36 (1), 282−288. (11) Zhang, Q.; Li, Q.; Zhang, L.; Wang, Z.; Jing, X.; Yu, Z.; Song, S.; Fang, Y. Preliminary study on co-gasification behavior of deoiled asphalt with coal and biomass. Appl. Energy 2014, 132, 426−434. (12) Masnadi, M. S.; Grace, J. R.; Bi, X. T.; Lim, C. J.; Ellis, N. From fossil fuels towards renewables: Inhibitory and catalytic effects on carbon thermochemical conversion during co-gasification of biomass with fossil fuels. Appl. Energy 2015, 140, 196−209. (13) Emami Taba, L.; Irfan, M. F.; Wan Daud, W. A. M.; Chakrabarti, M. H. The effect of temperature on various parameters in coal, biomass and CO-gasification: A review. Renewable Sustainable Energy Rev. 2012, 16 (8), 5584−5596. (14) Haykiri-Acma, H.; Yaman, S. Interaction between biomass and different rank coals during co-pyrolysis. Renewable Energy 2010, 35 (1), 288−292. (15) Sadhukhan, A. K.; Gupta, P.; Goyal, T.; Saha, R. K. Modelling of pyrolysis of coal-biomass blends using thermogravimetric analysis. Bioresour. Technol. 2008, 99 (17), 8022−8026. (16) Haykiri-Acma, H.; Yaman, S. Synergy in devolatilization characteristics of lignite and hazelnut shell during co-pyrolysis. Fuel 2007, 86 (3), 373−380. (17) Kastanaki, E.; Vamvuka, D.; Grammelis, P.; Kakaras, E. Thermogravimetric studies of the behavior of lignite−biomass blends during devolatilization. Fuel Process. Technol. 2002, 77−78, 159−166. (18) Meesri, C.; Moghtaderi, B. Lack of synergetic effects in the pyrolytic characteristics of woody biomass/coal blends under low and high heating rate regimes. Biomass Bioenergy 2002, 23 (1), 55−66. (19) Vuthaluru, H. B. Thermal behaviour of coal/biomass blends during co-pyrolysis. Fuel Process. Technol. 2004, 85 (2−3), 141−155. (20) Pan, Y. G.; Velo, E.; Puigjaner, L. Pyrolysis of blends of biomass with poor coals. Fuel 1996, 75 (4), 412−418. (21) Griffin, T. P.; Howard, J. B.; Peters, W. A. An experimental and modeling study of heating rate and particle size effects in bituminous coal pyrolysis. Energy Fuels 1993, 7 (2), 297−305. (22) Wiktorsson, L. P.; Wanzl, W. Kinetic parameters for coal pyrolysis at low and high heating ratesA comparison of data from different laboratory equipment. Fuel 2000, 79 (6), 701−716. (23) Haykiri-Acma, H.; Yaman, S.; Kucukbayrak, S. Effect of heating rate on the pyrolysis yields of rapeseed. Renewable Energy 2006, 31 (6), 803−810. (24) Niksa, S.; Heyd, L. E.; Russel, W. B.; Saville, D. A. On the role of heating rate in rapid coal devolatilization. Symp. (Int.) Combust., [Proc.] 1985, 20 (1), 1445−1453. (25) Gibbins-Matham, J.; Kandiyoti, R. Coal pyrolysis yields from fast and slow heating in a wire-mesh apparatus with a gas sweep. Energy Fuels 1988, 2 (4), 505−511. (26) Zhang, L.; Xu, S.; Zhao, W.; Liu, S. Co-pyrolysis of biomass and coal in a free fall reactor. Fuel 2007, 86 (3), 353−359. (27) Krerkkaiwan, S.; Fushimi, C.; Tsutsumi, A.; Kuchonthara, P. Synergetic effect during co-pyrolysis/gasification of biomass and subbituminous coal. Fuel Process. Technol. 2013, 115, 11−18. (28) Collot, A. G.; Zhuo, Y.; Dugwell, D.; Kandiyoti, R. Co-pyrolysis and co-gasification of coal and biomass in bench-scale fixed-bed and fluidised bed reactors. Fuel 1999, 78 (6), 667−679. (29) Mao, Y.; Dong, L.; Dong, Y.; Liu, W.; Chang, J.; Yang, S.; Lv, Z.; Fan, P. Fast co-pyrolysis of biomass and lignite in a micro fluidized bed reactor analyzer. Bioresour. Technol. 2015, 181, 155−162. (30) Wan, K. D.; Wang, Z. H.; He, Y.; Xia, J.; Zhou, Z. J.; Zhou, J. H.; Cen, K. F. Experimental and modeling study of pyrolysis of coal, biomass and blended coal−biomass particles. Fuel 2015, 139, 356− 364. (31) Ross, D. P.; Heidenreich, C. A.; Zhang, D. K. Devolatilisation times of coal particles in a fluidised-bed. Fuel 2000, 79 (8), 873−883. (32) Gaston, K. R.; Jarvis, M. W.; Pepiot, P.; Smith, K. M.; Frederick, W. J., Jr; Nimlos, M. R. Biomass pyrolysis and gasification of varying particle sizes in a fluidized-bed reactor. Energy Fuels 2011, 25 (8), 3747−3757.

T = temperature, K u = velocity of volatile, m/s Greek Symbols

ρ = density, kg/m3 λ = thermal conductivity, W/(m K) ω = volatile generation rate, s−1 ΔH = heat of pyrolysis reaction, kJ/kg σ = Stefan−Boltzmann constant, W/(m2 K4) ε = emissivity δ = thickness, m α = absorptivity τ = transmissivity γ = reflectivity

Subscripts

0 = initial condition p = particle qz = quartz s = solid v = volatile w = furnace wall ∞ = nitrogen in the reactor λ = wavelength Definition of the Kinetic Parameters for the CPD Model

Eb = bridge breaking activation energy, kcal/mol Ab = bridge breaking frequency factor, s−1 σb = standard deviation of Eb, kcal/mol Eg = gas formation activation energy, kcal/mol Ag = gas release frequency factor, s−1 σg = standard deviation of Eg, kcal/mol ρ′ = kinetic ratio of bridge breaking to char formation Ec = difference in activation energy between bridge breaking and char formation, kcal/mol Ecross = cross-linking activation energy, kcal/mol Across = cross-linking frequency factor, s−1



REFERENCES

(1) Prvulovic, S.; Gluvakov, Z.; Tolmac, J.; Tolmac, D.; Matic, M.; Brkic, M. Methods for Determination of Biomass Energy Pellet Quality. Energy Fuels 2014, 28 (3), 2013−2018. (2) Hamilton, J. E.; Adams, J. M.; Northrop, W. F. Particulate and Aromatic Hydrocarbon Emissions from a Small-Scale Biomass Gasifier−Generator System. Energy Fuels 2014, 28 (5), 3255−3261. (3) Gunarathne, D. S.; Mueller, A.; Fleck, S.; Kolb, T.; Chmielewski, J. K.; Yang, W.; Blasiak, W. Gasification Characteristics of Hydrothermal Carbonized Biomass in an Updraft Pilot-Scale Gasifier. Energy Fuels 2014, 28 (3), 1992−2002. (4) 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. Bioresour. Technol. 2014, 169, 220−228. (5) Li, S.; Chen, X.; Liu, A.; Wang, L.; Yu, G. Co-pyrolysis characteristic of biomass and bituminous coal. Bioresour. Technol. 2015, 179, 414−420. (6) Lu, K. M.; Lee, W. J.; Chen, W. H.; Lin, T. C. Thermogravimetric analysis and kinetics of co-pyrolysis of raw/torrefied wood and coal blends. Appl. Energy 2013, 105, 57−65. (7) Aboyade, A. O.; Carrier, M.; Meyer, E. L.; Knoetze, H.; Görgens, J. F. Slow and pressurized co-pyrolysis of coal and agricultural residues. Energy Convers. Manage. 2013, 65, 198−207. (8) Schakel, W.; Meerman, H.; Talaei, A.; Ramírez, A.; Faaij, A. Comparative life cycle assessment of biomass co-firing plants with carbon capture and storage. Appl. Energy 2014, 131, 441−467. (9) Shafie, S.; Mahlia, T.; Masjuki, H. Life cycle assessment of rice straw co-firing with coal power generation in Malaysia. Energy 2013, 57, 284−294. J

DOI: 10.1021/acs.energyfuels.5b00646 Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels

Experimental results and model predictions. Energy Fuels 2004, 18 (4), 1021−1031. (56) Sergeev, O.; Shashkov, A.; Umanskii, A. Thermophysical properties of quartz glass. J. Eng. Phys. 1982, 43 (6), 1375−1383. (57) Bansal, N. P.; Doremus, R. H. Handbook of Glass Properties; Academic Press: Orlando, FL, 1986.

(33) Di Blasi, C.; Branca, C. Temperatures of wood particles in a hot sand bed fluidized by nitrogen. Energy Fuels 2003, 17 (1), 247−254. (34) Nygård, H. S.; Danielsen, F.; Olsen, E. Thermal history of wood particles in molten salt pyrolysis. Energy Fuels 2012, 26 (10), 6419− 6425. (35) Wan, K. D.; Xia, J.; Wang, Z. H.; Wrobel, L. C.; Cen, K. F. Large eddy simulation of coal jet pyrolysis using an online CPD model. Presented at the 10th Asia−Pacific Conference on Combustion, Beijing, China, July 19−22, 2015. (36) Yang, S. Analysis and Measuring Technology of Feed Quality (in Chin.); Beijing Agricultural University Press: Beijing, China, 1999. (37) Fletcher, T. H.; Kerstein, A. R.; Pugmire, R. J.; Solum, M. S.; Grant, D. M. Chemical Percolation Model for Devolatilization. 3. Direct Use of C-13 NMR Data to Predict Effects of Coal Type. Energy Fuels 1992, 6 (4), 414−431. (38) Grant, D. M.; Pugmire, R. J.; Fletcher, T. H.; Kerstein, A. R. Chemical-Model of Coal Devolatilization Using Percolation Lattice Statistics. Energy Fuels 1989, 3 (2), 175−186. (39) Badzioch, S.; Hawksley, P. G. Kinetics of thermal decomposition of pulverized coal particles. Ind. Eng. Chem. Process Des. Dev. 1970, 9 (4), 521−530. (40) Kobayashi, H.; Howard, J.; Sarofim, A. F. Coal devolatilization at high temperatures. Symp. (Int.) Combust., [Proc.] 1977, 16 (1), 411− 425. (41) Fletcher, T. H.; Kerstein, A. R.; Pugmire, R. J.; Grant, D. M. Chemical percolation model for devolatilization. 2. Temperature and heating rate effects on product yields. Energy Fuels 1990, 4 (1), 54−60. (42) Sheng, C. D.; Azevedo, J. L. T. Modeling biomass devolatilization using the chemical percolation devolatilization model for the main components. Proc. Combust. Inst. 2002, 29, 407−414. (43) Vizzini, G.; Bardi, A.; Biagini, E.; Falcitelli, M.; Tognotti, L. Prediction of Rapid Biomass Devolatilization Yields with an Upgraded Version of the Bio-CPD Model. Presented at the 31st Meeting of the Italian Section of the Combustion Institute, Torino, Italy, 2008. (44) Fletcher, T. H.; Pond, H. R.; Webster, J.; Wooters, J.; Baxter, L. L. Prediction of Tar and Light Gas during Pyrolysis of Black Liquor and Biomass. Energy Fuels 2012, 26 (6), 3381−3387. (45) Lewis, A. D.; Fletcher, T. H. Prediction of Sawdust Pyrolysis Yields from a Flat-Flame Burner Using the CPD Model. Energy Fuels 2013, 27 (2), 942−953. (46) Genetti, D.; Fletcher, T. H.; Pugmire, R. J. Development and application of a correlation of 13C NMR chemical structural analyses of coal based on elemental composition and volatile matter content. Energy Fuels 1999, 13 (1), 60−68. (47) Heidenreich, C. A.; Yan, H. M.; Zhang, D. K. Mathematical modelling of pyrolysis of large coal particles - estimation of kinetic parameters for methane evolution. Fuel 1999, 78 (5), 557−566. (48) Adesanya, B. A.; Pham, H. N. Mathematical modelling of devolatilization of large coal particles in a convective environment. Fuel 1995, 74 (6), 896−902. (49) Solomon, P. R.; Serio, M. A.; Suuberg, E. M. Coal pyrolysis: Experiments, kinetic rates and mechanisms. Prog. Energy Combust. Sci. 1992, 18 (2), 133−220. (50) Lu, H.; Ip, E.; Scott, J.; Foster, P.; Vickers, M.; Baxter, L. L. Effects of particle shape and size on devolatilization of biomass particle. Fuel 2010, 89 (5), 1156−1168. (51) Agroskin, A. A.; Gonczarow, E. I.; Makeev, L. A.; Jakunin, W. P. Thermal capacity and heat of pyrolysis of Donbass coal. Koki Chimija 1970, 5, 8−13. (52) Porteiro, J.; Miguez, J. L.; Granada, E.; Moran, J. C. Granada, E.;Moran, J. C. Mathematical modelling of the combustion of a single wood particle. Fuel Process. Technol. 2006, 87 (2), 169−175. (53) Agroskin, A. A. The change of heat and temperature transfer coefficient of coal during heating. Bergakad. Freiberg 1957, 9, 177−186. (54) Genetti, D. B. An advanced model of coal devolatilization based on chemical structure. M.S. Thesis, Brigham Young University, Provo, UT, 1999. (55) Bharadwaj, A.; Baxter, L. L.; Robinson, A. L. Effects of intraparticle heat and mass transfer on biomass devolatilization: K

DOI: 10.1021/acs.energyfuels.5b00646 Energy Fuels XXXX, XXX, XXX−XXX