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Article Cite This: Ind. Eng. Chem. Res. XXXX, XXX, XXX-XXX

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Reactive Molecular Dynamics Simulations of Biomass Pyrolysis and Combustion under Various Oxidative and Humidity Environments Chao Chen,† Lingling Zhao,*,† Jingfan Wang,† and Shangchao Lin*,‡ †

Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, School of Energy & Environment, Southeast University, Nanjing, Jiangsu 210096, China ‡ Department of Mechanical Engineering, Materials Science & Engineering Program, FAMU-FSU College of Engineering, Florida State University, Tallahassee, Florida 32310, United States S Supporting Information *

ABSTRACT: Biomass, as a renewable carbon neutral energy source with abundant reserves, is a good candidate for future energy supplies. In this paper, a simplified biomass model composed of cellulose, hemicellulose, and lignin, described by a carefully selected reactive force field (ReaxFF), is investigated using molecular dynamics (MD) simulations. The pyrolysis and combustion processes of the biomass under different temperatures and oxidative and humidity conditions, are studied. We find that the individual products from the pyrolysis of the three biomass components are similar, including H2O, H2, CO, CO2, and small organic molecules. The calculated activation energies for C−C bond dissociation are 34.53, 26.08, and 16.23 kJ mol−1, respectively, for cellulose, hemicellulose, and lignin, consistent with the trend in experiments. Interestingly, light tar (C5−13) production reaches a maximum under intermediate temperatures, which could be further explored to optimize the production of light tar as liquid fuels. Compared to biomass pyrolysis in vacuum, hydrothermal treatment makes the C−C bonds more difficult to dissociate, but C−O bonds more vulnerable due to stronger attacks from ·H radicals. Higher H2 concentration is produced under the H2O atmosphere, while more CO is formed under the mixed H2O/O2 atmosphere. During biomass combustion, CO2 mainly comes from the cracking and reforming of ·COOH and ·CHO radical groups or directly from CO oxidation. We also observe that during biomass combustion, the formation of CO is facilitated at higher temperatures, whereas CO2 production is favored at lower temperatures. More rapid decomposition and oxidation of biomass during combustion occur under fuel-lean conditions compared to fuel-rich conditions. Finally, more H2O and fewer H2 molecules are generated during the combustion process under the O2/CO2 atmosphere when increasing the concentration of CO2. On the basis of this theoretical study, a better understanding of the radicals, intermediates, products, and reaction kinetics involved in biomass pyrolysis and combustion could be achieved. generating process of biomass pyrolysis products.2−5 Using a thermogravimetric analyzer (TGA) and a pack bed, Yang et al.6,7 investigated the characteristics of pyrolysis of three major components of biomass and discussed the influence of temperature. On the basis of the pyrolysis characteristics of cellulose, xylan, lignin, and three biomass samples, Qu et al.8 investigated the temperature effect on pyrolysis products and comparison between biomass and its major components. Meanwhile, the effects of atmosphere on biomass pyrolysis have been extensively studied. Sasaki et al.9,10 reported higher yields of hydrolysis products in supercritical water, when investigating cellulose decomposition in subcritical and supercritical water. Furthermore, some research groups11,12 also

1. INTRODUCTION Biomass, as a renewable carbon neutral energy source with abundant reserves, is a good candidate in the near future energy supplies. Undoubtedly, it has attracted tremendous research interests with increasing concern over climate change and depletion of fossil fuels. Biomass can provide gaseous products (H2, CO2, and CO), liquid (tar and other organic molecules), and solid fuels (charcoal and pellet fuel) by thermochemical conversion.1 Among the thermochemical conversion, pyrolysis (or gasification at higher temperatures) and combustion are the primary methods for utilizing biomass energy.2 However, pyrolysis and combustion are such complex processes that can be affected by many factors and generally include a series of reactions. Significant experimental efforts have been devoted to exploring more cost-effective and environmentally friendly biomass utilization technology. Temperature and atmosphere are found to be two of the most vital factors to affect the © XXXX American Chemical Society

Received: Revised: Accepted: Published: A

April 25, 2017 September 30, 2017 October 6, 2017 October 6, 2017 DOI: 10.1021/acs.iecr.7b01714 Ind. Eng. Chem. Res. XXXX, XXX, XXX−XXX

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Industrial & Engineering Chemistry Research

production using ReaxFF, in-depth ReaxFF studies on biomass pyrolysis, and combustion, which could be influenced by temperature and reactive atmosphere, are yet to be reported. In this paper, four different versions of ReaxFF developed in the past decade are discussed, and based on this, C/H/O/N/ S2010 ReaxFF28 is recruited to investigate the reaction processes of pyrolysis and combustion of a biomass model that consists of three major components (lignin, cellulose, and hemicellulose). Furthermore, a range of MD simulations are conducted to study the effect of temperature and atmosphere on the biomass pyrolysis process. To determine the impact from oxidative and humidity atmosphere on biomass pyrolysis, simulations are conducted under different O2/H2O atmospheres. Additionally, simulations are conducted at a temperature range of 3000−4000 K under fuel-lean or fuel-rich condition to investigate the detailed mechanism of the oxidation/combustion of biomass. Finally, the influences of different CO2 concentrations on the biomass combustion under different O2/CO2 atmospheres are also discussed.

investigated biomass pyrolysis in oxidative atmospheres and found that oxygen has a significant influence on pyrolysis. Over the past decades, many studies have been reported on biomass combustion,13−17 suggesting that biomass combustion is a complex process that involves both homogeneous and heterogeneous chemical reactions. However, current research in biomass combustion might not be able to provide more details on the complex chemical events. Therefore, it is still challenging to describe and understand the initiation mechanism behind biomass pyrolysis and combustion considering their complexity, although numerous experimental studies have been carried out. Computer simulation is becoming an important approach for biomass research with its high accuracy and by providing fundamental insights at the molecular level to complement experimental studies. Beckham et al.18,19 performed molecular simulations to understand enzymatic deconstruction of cellulose and deconstruction of lignocellulosic biomass to produce fuels and chemicals. Broadbelt et al.20−22 reported a detailed mechanistic model built upon their experimental work for fast pyrolysis of glucose-based carbohydrates. The model was used to describe the decomposition of cellulosic polymer chains, reactions of intermediates, and formation of compounds. Using density functional theory (DFT) calculations, Mayes23,24 investigated the reaction mechanism for the primary reaction in cellulose thermal decomposition. Pfaendtner et al. presented a kinetic model for lignin pyrolysis involving hundreds of species and reactions, which allows one to predict the evolution of molecules and functional groups25 and also to investigate the mechanisms behind cellulose fragmentation.26 The reactive force field (ReaxFF),27 coupled with atomistic representation, provides a useful tool for studying the chemical events, which may be difficult to investigate by experiment methods alone in many situations, as well as to improve current kinetic models. Mattsson et al.28 studied two hydrocarbon polymers using classical molecular dynamics (MD) simulation with two versions of reactive force fields, ReaxFF and AIREBO (adaptive intermolecular reactive bond order) potential,29 respectively. They found that the results obtained using ReaxFF agree better with DFT calculated results and experimental data (in terms of density-pressure correlations) than those obtained using the AIREBO force field. Since developed in 2001, various versions of ReaxFF have been developed for different elements and used to analyze the mechanism behind the oxidation, pyrolysis, and combustion of different hydrocarbon fuels.30−36 Chenoweth et al.37 conducted a series of MD simulations using ReaxFF on different hydrocarbon (methane, o-xylene, propene, and benzene)/O2 systems to study the reaction mechanisms of hydrocarbons oxidation under high temperatures. CastroMarcano et al.38,39 constructed a large-scale molecular model of Illinois No. 6 coal based on experimental data and investigated the influence of sulfur content on coal pyrolysis and oxidation using ReaxFF simulations. Wang and coworkers40 used ReaxFF simulations to study the coal hydropyrolysis and desulfurization. They showed that hydropyrolysis could effectively improve the desulfurization rate. Some researchers41,42 also had investigated the effect of H2O on coal pyrolysis, hydrogen production, and chemical structure transformations. However, there is little work done to discuss which version of ReaxFF is more accurate and effective in describing the reaction details of carbon-containing organic compounds. Moreover, although Beste et al.43,44 studied the oxidation of lignin in softwood in view of carbon fiber

2. COMPUTATIONAL METHODS 2.1. Atomic Representation of Three Major Biomass Components and Simplified Biomass Model. Biomass is mainly composed of cellulose, hemicellulose, and lignin. In general, cellulose, hemicellulose, and lignin represent about 30−50 wt %, 20−30 wt %, and 20−25 wt % in biomass, respectively.45 Cellulose, consisting of linear chains of (1,4)-Dglucopyranose units, is a glucose polymer in which the units are in the β-1,4-linked configuration. Hemicellulose is a branched polymer that is almost entirely composed of sugars such as glucose, mannose, xylose, arabinose, and methyl glucuronic. Lignin, regarded as a group of amorphous, high molecular weight, and chemically complicated compounds, is a highly cross-linked polymer believed to be composed of three-carbon chains attached to rings of six carbon atoms. The chemical structure of the three major components and an example of an atomistic oxidative simulation system are shown in Figure 1. Specifically, cellulose is composed of chains of cellobiose repeat units,46 hemicellulose is composed of branched sugar

Figure 1. Chemical strutures of three major components of biomass: (a) cellulose; (b) hemicellulose; (c) lignin; (d) atomistic representation of simplified biomass model (C363H580O257) with oxygen. Color code: cyan, carbon; white, hydrogen; red, oxygen. B

DOI: 10.1021/acs.iecr.7b01714 Ind. Eng. Chem. Res. XXXX, XXX, XXX−XXX

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Figure 2. Distributions of major combustion products (CO2, CO) and O2 consumption obtained from ReaxFF MD simulations of the simplified biomass model at 3000 K and under fuel lean (Φ = 0.8) conditions using different versions of ReaxFF.

residues,47 lignin is composed of several coniferyl alcohol units connected through seven linkages based on the model used in a recent ReaxFF MD simulation study.44 Using the GlycoBioChem PRODRG2 Server,48 we constructed a simplified biomass model (C363H580O257) which is based on the mass ratio of cellulose, hemicellulose, and lignin (3:1:1). In consideration of the accepted computational expense, the number of atoms in the simplified biomass model here is 1200, consisting of six cellulose chains each composed of six glucose monomers, three hemicellulose polymers each composed of four sugar residues, and one lignin polymer as illustrated in Figure 1. 2.2. MD Simulation Details. The current MD simulations were performed using LAMMPS.49 The temperatures in this paper were set to be above normal experimental conditions to allow reactions to occur at the acceptable simulation time scale (hundreds of picoseconds), which is controlled by the NoseHoover thermostat50 with a 0.1 ps damping constant. In previous ReaxFF MD simulations for hydrocarbon pyrolysis and combustion, artificially increased temperatures have been employed to accelerate the reactions to match the limited time scale achievable in MD simulations.34,35,37−42,51 To analyze the products generated in the MD simulations, a value of 0.1 is used as the bond-order cutoff for the recognition of molecular species. In-house code based on MATLAB and C++ (see Appendices I and II in the Supporting Information) were applied to analyze the bond information files and product species files obtained from MD simulations, respectively. Although different versions of ReaxFF have been developed to describe complex reactions of hydrocarbons so far, some of them still have limited predictability for certain reactions. For example, in MD simulations using different ReaxFF versions C/ H/O200837 and C/H/O/N/S2010,38 the resulting yield ratios of CO2 and CO molecules and O2 consumptions differ a lot in oxidative environments. Therefore, in this work four versions of ReaxFF (C/H/O/N2003,52 C/H/O2008,37 C/H/O/N2009,53 and C/H/O/N/S201028) were studied to compare their simulation performances. Specifically, C/H/O/N200352 was used to investigate the initial shock-induced chemical events of nitramine RDX. Chenoweth et al. simulated the hydrogencarbon (propene, o-xylene, methane, and benzene) oxidation process using C/H/O2008.37 C/H/O/N200953 was adopted for simulating shockwave propagation through a single crystal of pentaerythritol tetranitrate. Finally, C/H/O/N/ S201028 was developed by Mattsson et al. to simulate two shocked hydrocarbon-based polymers, polyethylene (PE), and poly(4-methyl-1-pentene) (PMP). On the basis of our validation (see section 3.1) the optimal ReaxFF version (C/

H/O/N/S2010) was selected and employed in our all simulations to describe the chemical reactions. For the single component pyrolysis simulations, each of the three major components (cellulose, hemicellulose, and lignin) was first studied at 1000−2000 K for 400 ps. To get reliable statistics on the simulated products from pyrolysis of the three major components of biomass (cellulose, hemicellulose, and lignin), the number of simulated molecules (system size) should be sufficiently large.42 Therefore, a convergence test was performed by varying the system size and comparing the resulting mass ratios of the three components. Different numbers (1, 5, 10, and 15) of lignin, cellulose, and hemicellulose molecules were placed in a cubic box with increasing length of 20, 35, 40, and 50 Å, respectively. Then we obtained a correlation between the final mass ratios of the three components and the number of these molecules placed initially into the box. Our results indicated that when the number of cellulose molecules has achieved 15, the mass ratios of different components become almost stable (see Figure S1). Therefore, 15 lignin, cellulose, and hemicellulose molecules were placed into the cubic simulation box for the single component studies and data analyses. For the biomass pyrolysis simulations, the effects of temperature and atmospheres (H2O and H2O/O2) on biomass pyrolysis process and products distribution were investigated. The density effect was ignored in this paper, since, as indicated by Bhoi et al.,51 the density of the simulated system had a very little effect on the simulation results. Simulation systems composed of only the biomass (C363H580O257), the biomass with 1000 H2O molecules, and the biomass with 1000 H2O and 200 O2 molecules correspond to the vacuum, H2O, and H2O/ O2 pyrolysis conditions, respectively. They were placed in a periodic box of 100 Å × 100 Å × 100 Å with a density of ∼0.02 g/cm3 for biomass, ∼ 0.01 g/cm3 for oxygen, and ∼0.03 g/cm3 for water, respectively. The temperature of these systems was ramped up to specific values at a rate of 20 K/ps. Thereafter, a series of MD simulations (after energy minimization) under the NVT ensemble with a time step of 0.25 fs were conducted for 1 ns at a temperature of 1000, 1500, 1800, and 2000 K. To simulate the detailed mechanisms behind the oxidation/ combustion process, a periodic cubic box in length of 100 Å was built containing the simplified biomass model composed of 1200 atoms along with O2 or O2/CO2 atmospheres. The equivalence ratio, Φ, is defined as the fuel/oxygen ratio normalized by the actual stoichiometric fuel/oxygen ratio. The periodic systems with different oxygen concentrations (Φ = 0.8 and 1.2, which are assigned as the fuel-lean and fuel-rich conditions, respectively) were created to study the influence of oxidant concentrations on the final products distribution. C


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Figure 3. Time evolution (in number of molecules) of major pyrolysis products of cellulose, hemicellulose, and lignin at 2000 K.

3.2.1. Pyrolysis Product Analysis. The major species of pyrolysis products obtained from the three major components (shown in Figure 3) are similar, including H2O, H2, CO, CO2, and some organic molecules. H2O is the product with the largest amount at 400 ps pyrolysis process for all simulations. We observed that lignin has a higher CH4 yield and H2O are generated more rapidly than other molecules at the initial time. The results also indicate that lignin does more contribution to the release of CH4 from biomass pyrolysis possibly on account of its highest O−CH3 content (see Figure 1). Cellulose and hemicellulose produce much more H2O molecules than other produced species which may be due to the high alcohol content. Lignin, cellulose, and hemicelluloses all own higher CO and H2 yield at 2000 K, which shows consistency with the tendencies of experimental results.6 Generally, the phenomena observed show that CO2 is primarily from the cracking and reforming of ·COOH and ·CO-R groups, while CO is mainly from the cracking of these groups. In addition, H2 is mainly from the cracking and deformation of ·CH3 and ·H groups and H2O is mainly from the formation of ·H and ·OH groups, while CH4 is generally from the cracking of O−CH3 groups. Figure 4

Moreover, different CO2 concentrations on the combustion under O2/CO2 atmospheres (Φ = 1.0) (30% O2/70% CO2, 50% O2/50% CO2, and 70% O2/30% CO2) were further studied. The temperature of these systems was ramped up to 3000, 3500, and 4000 K at a rate of 20 K/ps.

3. RESULTS AND DISCUSSION 3.1. Rationale for Force Field Selection. Four different versions of ReaxFF are studied here using high temperature oxidation MD simulations. Figure 2 shows the concentration profiles of major products (CO and CO2) and O2 consumption obtained from oxidation simulations of the simplified biomass model at 3000 K. The initial model systems are the same for these four cases, while we applied different ReaxFF parameters. In general, along with the time, CO and CO2 are generated with increasing consumption of O2 molecules. In addition, among all the ReaxFF versions, when C/H/O2008 is adopted in the oxidation simulations, only a small percentage of O2 react with the organic matter. Comparing with the large amount of CO generation, the concentration of generated CO2 is incredibly low. Some researchers explained this observation by referring to the fact that the subsequent oxidation process is relatively slow and occurs later in the combustion reactions.27 However, when using C/H/O/N/S2010, we found that a lot more CO2 is produced when compared with other three ReaxFF versions, in addition to the observations that fewer CO are generated and more O2 are consumed in the simulations using C/H/O/N/S2010. This agrees well with the experimental observation that the concentration of generated CO2 is several times of that of CO during combustion.54 In summary, we found that C/H/O/N/S2010 ReaxFF is a better choice to describe the oxidative reactions of hydrocarbons, considering the more reasonable ratio of CO/CO2 and O2 consumption tendency. So the C/H/O/N/S2010 force field is employed here to analyze the biomass pyrolysis and combustion. 3.2. Simulation Results on Biomass Pyrolysis. When biomass is pyrolyzed at a temperature range from 1000 to 2000 K, a large amount of important intermediates and valuable gases are generated. The product molecules that decomposed during the pyrolysis process can be divided into four categories:42 (1) gas (C1−4) referring to organic gas molecules with 1−4 C atoms; (2) light tar (C5−13) referring to organic molecules with 5−13 C atoms; (3) heavy tar (C14−39) referring to organic molecules with 14−39 C atoms; (4) char (C40+) referring to organic molecules with more than 40 C atoms. In this work, this classification method is adopted to analyze pyrolysis products.

Figure 4. Mass ratios of organic products obtained from 400 ps ReaxFF MD simulations of the pyrolysis process of cellulose, hemicellulose, and lignin at 1500 K.

shows the final mass ratios of organic products obtained from pyrolysis simulations of cellulose, hemicellulose, and lignin at 1500 K. Light tar (C5−13) exhibits the largest proportion among other pyrolysis products over all three major components. Lower temperatures (ranging from 1000 to 1800 K) were chosen to investigate the pyrolysis kinetics of cellulose, hemicellulose, and lignin degradation under the vacuum D


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Figure 5. Temperature-dependent dissociation rates (k) of C−C bonds during the pyrolysis processes of cellulose, hemicellulose, and lignin.

forming small molecules. It can be seen that a significant amount of hydrogen radicals are generated during pyrolysis which may be due to a large number of hydrogen abstraction reactions occurring on the biomass, and therefore, some may react with hydroxyls (−OH) to form H2O molecules. Figure 6

condition. As the skeleton of organic matters, the dissociation kinetics of C−C (including aromatic CC) bonds plays a vital role in the organic reactions. Therefore, we utilized the timedependent number of C−C bonds in the system to measure the degree of reactions here. The reaction process is assumed to follow the first order for our subsequent reaction kinetics analysis (see Figure S2 for verification, showing that the number of C−C bonds decay almost exponentially with time). Specifically, the reaction rate constant, k, is determined by the following equation:55 ln N0 − ln Nteq = kteq

(1)

where N0 and Nteq are the number of C−C bonds at the initial stage (t = 0) and the equilibrium stage (t = teq), respectively (see Table S1 for details). From the Arrhenius equation: ⎛ E ⎞ k = A exp⎜ − a ⎟ ⎝ kBT ⎠

(2)

the activation energy (Ea) and the pre-exponential factor (A) are calculated by linear fitting. The plots in Figure 5 display the relationship between ln k and 1000 , from which we calculated T the C−C bond activation energy, Ea, being 34.53, 26.08, and 16.23 kJ mol−1 for cellulose, hemicellulose, and lignin, respectively. Although the calculated Ea values are all smaller than the experimental values of 195−286, 80−116, and 18−65 kJ mol−1 for cellulose, hemicellulose, and lignin,56 they exhibit the same trend as experiments, where the values of Ea follow the trend: cellulose > hemicellulose > lignin. Such trend might be due to the large amount of the weaker aromatic CC bonds present in lignin, in addition to the higher degree of crystallinity in cellulose as compared to the two highly branched and amorphous counterparts. The smaller Ea values calculated here might be due to the fact that (i) the first-order reaction model and the Arrhenius law is less accurate for analyzing high temperature MD simulations; (ii) reported experimental activation energies are higher due to diffusion-limited reactions, which is neglected in the nanoscale MD simulations conducted here; and (iii) the simple molecular biomass models constructed here are only approximate and might lead to underestimated activation energies. In this paper, the simplified biomass model (C363H580O257) is based on the mass ratio of cellulose, hemicellulose, and lignin (3:1:1). According to the biomass pyrolysis simulation results, at the initial stage, except for some ·H radicals, we observed very few small molecules in the system, which may be due to the cracking of the whole structure that happens first instead of

Figure 6. Time evolution (in number of molecules) of major species generated in ReaxFF MD simulations of biomass pyrolysis at 2000 K.

gives an overview of major products formation during biomass pyrolysis process at 2000 K. The number of product molecules increases along with time and will gradually reach equilibrium due to the completion of the initial and subsequent reaction processes. H2O molecules are generated significantly as a result of high alcohol content in the structure of simplified biomass model. During the process, a quite number of flammable gases (H2, CO, and CH4) are produced which could not be ignored as compared to other species. The results show that the CO molecules generated are 2−4 times higher than CO2 which is consistent with the experimental observations.6 3.2.2. Effect of Temperature on Pyrolysis. Figure 7 shows the time evolution of C−C, C−H, and C−O bonds in the biomass pyrolysis system. Constant temperature MD simulations are conducted for 1 ns at temperatures of 1000, 1500, 1800, and 2000 K. The results show that the number of bonds (C−C, C−H, and C−O) decreases with time, as expected from spontaneous bond dissociation. While C−C and C−H bonds decrease slowly over time, most of the C−O bonds in the biomass structure break easily at the initial stage (within ∼100 ps) and the number of C−O bonds remains stable toward equilibrium, reflecting the lower C−O bond energy. The influence of temperature on the products distribution of biomass pyrolysis is also studied, as shown in Figure 8. The rich E


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Figure 7. Time evolution of the numbers of C−C, C−H, and C−O bonds in the system during biomass pyrolysis at different temperatures. Color code for the snapshots of various bonds: cyan, carbon; white, hydrogen; and red, oxygen.

Figure 8. Time evolution of H2, H2O, and CO molecules generated during biomass pyrolysis at different temperatures.

pyrolytic species (H2, H2O, and CO) are analyzed while the amounts of other products released are generally small. It can be found from Figure 8 that the molecular numbers of H2, H2O, and CO increase with the temperatures. At low temperature, the production yields of species are generally small and even become zero due to the lower reactivity. In contrast, those systems reach chemical equilibrium faster at low temperature. Especially, the temperature affects the production of hydrogen obviously and hydrogen formation is favored at high temperature. As also shown in Figure 8, H2O molecules are released rapidly and first (∼60 ps) during the pyrolysis process which is consistent with the numerous reported observations. Although there are many ·H radicals in the systems during simulation processes, there are not many H2 molecule and they are released later than other observed molecules. Figure 9 shows the weight percentages of organic products obtained from MD simulations of biomass pyrolysis at 1000, 1500, 1800, and 2000 K, respectively. Higher temperatures favor char and tar decomposition rather than combination and regeneration. At lower temperatures, the gas yields (C1−4) and light tar (C5−13) increase with the rising temperature. Heavy tar (C14−39) and char (C40+) are dominant initially and observed to decrease with temperature. Interestingly, light tar (C5−13) achieves a maximum weight of 49.62% under around 1500 K, which could be further explored to maximize the production of light tar as energy fuels. This may be because light tar (C5−13) generation, gas yields (C1−4) generation, heavy tar (C14−39), and char (C40+) recombinations occur competitively with each other. The decompositions of heavy tar (C14−39) and char (C40+) at around 1500 K contribute to the increment of light tar (C5−13) compounds. These observations indicate that the biomass pyrolysis temperature is a vital factor to influence product distribution. Lower temperatures favor generation of high-carbon-content species,

Figure 9. Weight percentage of organic products obtained from ReaxFF MD simulations of biomass pyrolysis.

while more small organic compounds are generated at higher temperatures. The high temperature MD simulations can help us get a clear understanding of the chemical mechanism behind the biomass pyrolysis process and other secondary reactions, which is hard to obtain from experiments. 3.2.3. Effect of Atmosphere on Pyrolysis. A better understanding on the effect of atmosphere on pyrolysis products can help enhance biomass utilization. As indicated in the previous studies,5 hydrothermal treatment provides lots of potential advantages with comparison of other biofuel production methods. Under oxidative atmospheres,11,12 the pyrolysis products owned more CO and a higher calorific value. The reaction mechanisms of biomass pyrolysis and product distributions at different atmospheres are investigated at 2000 K. The time evolutions of C−C and C−O bonds during the biomass pyrolysis process under vacuum and H2O (hydroF


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indicates that some secondary reactions such as decomposition of aromatic structures and condensation of the aromatic nuclei into char occur during the simulation process. From Figure 11c, it can be seen that char (C40+) yield showed a similar trend with that in Figure 11b and then decomposed rapidly at a very short time on account of the oxidative atmosphere. It reached a considerable value after 280 ps, maybe due to the effect of the water clusters. Furthermore, gas, light tar, and heavy tar reached a maximum value at around 150, 100, and 250 ps, respectively. Figure 12 shows the time evolution of typical combustible gas (CO and H2) generation during pyrolysis process at different atmosphere conditions at 2000 K. As shown in Figure 12(a), hydrogen production is highest in supercritical water which achieves a good agreement with experimental results.57−59 During the simulation, we observed that H2 molecules are produced mainly through the following two paths: (1) two ·H radicals released from the biomass fragments and react with each other to form H2; (2) ·H radicals from water clusters react with water molecules to produce H2. It is also been found that very little H2 molecules are generated at H2O/O2 atmosphere because of hydrogen abstraction by O2 or ·O radicals. From Figure 12(b), it can be seen that more CO molecules are produced on account of oxidative environment at H2O/O2 atmosphere compensating the reduction of CO yield due to hydrothermal treatment. But the number of CO molecules achieves a maximum value at ∼125 ps and then decreases which may due to subsequent reactions between CO and H2O or others. As shown in Figure 12(c), CO molecules are generated a lot and H2 is very few at the first 400 ps because of oxidative environment. Along with supercritical water added, a large amount of H2 are generated at a very short time (from ∼400 to 420 ps), while some CO molecules are converted into other substances. The simulation results suggest that biomass pyrolysis first happened in oxidative atmosphere and then had supercritical water added, which may lead to higher CO and H2 production. 3.3. Simulation Results on Biomass Combustion. To investigate the detailed mechanism behind the oxidation/ combustion process, a series of ReaxFF combustion simulations were performed under different O2 concentrations and at temperatures of 3000, 3500, and 4000 K. Further, different numbers of CO2 molecules were added into the simulated oxidative systems in order to get a clear picture about biomass combustion in O2/CO2 atmosphere. 3.3.1. Combustion Product AnalysisCO2 Formation. As the main product of organic molecule oxidation, CO2 is constantly formed during biomass combustion simulations.

thermal) atmospheres are shown in Figure 10. Interestingly, the number of C−C bonds in the hydrothermal environment does

Figure 10. Time evolution of the numbers of C−C and C−O bonds in the system during biomass pyrolysis under vacuum and H2O atmospheres at 2000 K.

not decrease much with time compared to the vacuum case. In contrast, more C−O bonds break rapidly in the hydrothermal environment compared to the vacuum case, which is the primary reaction pathway for biomass decomposition. This finding indicates that hydrothermal treatment makes the C−C bonds more difficult to dissociate, but the C−O bonds more vulnerable. This may occur because (i) the high C−O−C content in biomass would be attacked more from ·H radicals generated by H2O or hydrogen abstractions from the biomass, where the −O− group is expected to have high electronic affinity with ·H, and (ii) although an excess amount of H2O is present, dehydration reactions at the C−O−H group commonly occur (see Figure S3 for the time evolutions of O−H bonds) in hydrothermal environments at elevated temperatures and pressures.5 Figure 11 shows distributions of main organic compounds at different atmospheres over time. Case a is performed at vacuum environment, case b is conducted in supercritical water, and case c is simulated at H2O/O2 atmosphere. As shown in Figure 11a, the gas (C1−4) category is the biggest fraction, while char (C40+) and heavy tar (C14−39) are very small at the end, although char and heavy tar initially represent about 20% and 80%, respectively, by weight. Comparing the product distributions of Figure 11a and Figure 11b, it can be seen that the light tar (C5−13) yield by hydrothermal treatment is increased significantly. Surprisingly, the char (C40+) yield is increased sharply and then reduced to zero from ∼85 ps. This

Figure 11. Time evolution of the weight percentage of major organic compounds released during biomass pyrolysis at different atmospheres at 2000 K: (a) pyrolysis in vacuum, (b) pyrolysis in H2O, and (c) pyrolysis in H2O/O2. G


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Figure 12. Time evolution of combustible gas (a) H2 and (b) CO molecules generated during 2000 K biomass pyrolysis at different atmospheres. (c) Time evolution of H2 and CO molecules generated during biomass pyrolysis at oxidative environment for 400 ps and then O2/H2O atmosphere for 200 ps.

Figure 13. Intermediate products and reactions observed at the last few steps of CO2 formation at 3000 K in ReaxFF simulations of biomass combustion.

CO oxidation. More details about the information on bond breaking and forming during CO2 generation processes can be seen from Figure 14. Further, we counted the numbers of CO2 molecules generated from different sources during biomass combustion simulation at 3000 K. The total number of CO2 molecules is 30. It can be seen from Table 1, external oxidation, which means organic matter reacting with oxygen in the system,

Detailed analysis of CO2 formation can help us understand the mechanism of combustion processes. The intermediate products and reactions observed at the last few steps of CO2 formation at 3000 K during biomass combustion are shown in Figure 13. It can be found that small fragments and ·H radicals are released from large molecules, and then oxidized by ·O, and finally generate CO2. Seen from this figure, CO2 is mainly from the cracking and reforming of ·COOH and ·CHO groups or H


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Figure 14. Atomic representations of the last few reaction steps of CO2 generation observed during biomass combustion simulation at 3000 K: (I) CO oxidation; (II) ·CHO oxidation (III); ·COOH crack (IV); ·CHO reform. Color code: cyan, carbon; white, hydrogen; red, oxygen.

Table 1. Formation of CO2 Observed during a 3000 K Biomass Combustion Simulation formation of CO2 (total number = 30) external oxidation (O2) lignin cellulose hemicellulose total

direct oxidation (CO, ·CHO)

indirect oxidation (·COOH)

total

internal oxidation (·O)

total

1 9 4 14

0 6 0 6

1 (3.3%) 15 (50%) 4 (13.3%) 20 (66.7%)

1 (3.3%) 6 (20%) 3 (10%) 10 (33.3%)

2 (6.7%) 21 (70%) 7 (23.3%)

Figure 15. Time evolution of major species (O2, H2, H2O, CO, and CO2) obtained from ReaxFF biomass combustion simulations at 3000, 3500, and 4000 K under fuel-lean (Φ = 0.8) or fuel-rich (Φ = 1.2) conditions.

3.3.2. Effect of O2 Concentration. After analyzing a series of MD simulations, we observed that, in general, the main initiation reaction involved for oxidation is either hydrogen abstraction or decomposition of the whole structure to generate smaller fragments that are subsequently oxidized. Figure 15 shows time evolution of molecular numbers of the major species (O2, H2, H2O, CO, and CO2) obtained from ReaxFF biomass combustion simulations at 3000, 3500, and 4000 K, as well as under fuel-lean and fuel-rich conditions. It is found and expected that O2 was consumed more rapidly at higher temperatures and the initiation reaction time is less. Under fuellean combustion, we observed that the production generation

instead of internal O atoms, is the primary reaction source of CO2 formation which accounts for 66.7%. It may be due to the existence of a large amount of oxygen in the system and high reaction temperature. Moreover, compared to the cracking of · COOH generating CO2, direct oxidation accounts for 70% in the external oxidation forming CO2. Certainly, the internal oxidation and cracking of ·COOH groups cannot be ignored in the investigation of CO2 formation mechanism. Further, among the three major components in the biomass, we can find that CO2 is mostly from cellulose while lignin generates fewest CO2 due to their smaller mass ratio in the biomass and chemical structure. I


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Figure 16. Reaction rates of major species (O2, H2, H2O, CO, and CO2) obtained from ReaxFF biomass combustion simulations at 3000, 3500, and 4000 K under (a) fuel-lean and (b) fuel-rich conditions.

rate is greater than that under fuel-rich conditions which may be on account of a higher concentration of oxygen. The more O2 that is present in the systems, the easier the biomass structure decomposes. Because of the superfluous oxygen in the fuel-lean conditions, the amount of CO2 is found higher and O2 decreased more rapidly compared with that in the fuel-rich condition. In the fuel-rich condition, more H2 and CO molecules are observed due to incomplete conversion for the lack of oxygen. The concentration of CO2 is 2−3 times the amount of CO which is consistent with the simulation results that Castro-Marcano et al. had done.38 3.3.3. Effect of Temperature. O2 consumption rates and H2, H2O, CO, and CO2 generation rates at different temperatures (3000, 3500, and 4000 K), shown in Figure 16, are about 108− 109 mol L−1 s−1. The species’ rates of generation or consumption are determined by the following equation r=

(N90% − N10%) NA

/(Vt )

Figure 17. Molar ratios of generated CO/CO2 under fuel-lean and fuel-rich conditions obtained from 1 ns of ReaxFF MD simulations at different temperatures.

(3)

where N90% and N10% are 90% and 10% of the final numbers of the consumed (for O2) or generated (for H2, H2O, CO, and CO2) molecules, respectively. NA is the Avogadro’s constant; V is the volume of the simulation box; and t is the time span between N10% and N90%. It is found and expected that the higher temperature leads to the faster reaction rate due to more frequent molecular collisions. As shown in the figure, the reaction rates of O2 consumption are the fastest among the major species at 3000 and 3500 K. It suggests that O2 consumption has a low activation energy which makes the reaction fast. At 4000 K, the CO generation rate is faster than the O2 and reaction rates of others, which indicates that a higher temperature favors CO generation. When the simulation temperature increases, the reaction rate differences among different species become larger. Additionally, as shown in Figure 16, H2O, H2, and CO2 generation rates are little affected by temperature, especially for H2O. In contrast, the CO generation rate and O2 consumption rate are affected significantly by temperature. Figure 17 shows the molar ratio of CO/CO2 obtained from 1 ns of ReaxFF MD simulations under different temperatures. As can be seen, the molar ratio of CO/CO2 generally rises with increasing temperature. CO formation is facilitated by high temperature whereas CO2 production is favored at the lower temperature studied, because that CO molecule is more stable than CO2 at higher temperature, which shows a good

agreement with the relevant experimental results.60,61 The ratio of CO/CO2 in fuel lean conditions is higher compared to that in fuel rich conditions on account of higher concentration of O2 in the initial system. The numbers of residual O2 molecules and ·O and ·OH radicals obtained from the MD combustion simulation results are shown in Table 2. It can be seen that more ·O and ·OH radicals and decomposed fragments are observed at higher temperature. Compared to that under fuel-rich conditions, more oxygen and fragments are found on account of the excess oxygen in the initial fuel-lean systems. 3.3.4. Combustion under O2/CO2 Atmosphere. O2/CO2 combustion technology is considered to be one of the most promising technologies to reduce the CO2 emissions.62 A series of ReaxFF simulations were performed to investigate the effect of CO2 concentrations on the combustion under different O2/ CO2 atmospheres and the simulation results are shown in Figure 18 and 19. From Figure 18, we can observe the similar tendencies at different atmospheres, specifically, the amount of CO molecules increases while the amount of CO2 decreases with increasing temperature. Moreover, it can be found that CO concentration is much higher in O2/CO2 conditions in comparison to that in pure O2 conditions due to decomposition of CO2 at high temperature. Figure 19 analyzes the time J


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Industrial & Engineering Chemistry Research Table 2. Number of O2 Molecules, ·O, and ·OH Radicals Observed after 1 ns of ReaxFF Combustion Simulation 3000 K

3500 K

4000 K

combustion environment

O2

·O

·OH

decomposed

O2

·O

·OH

decomposed

O2

·O

·OH

decomposed

fuel-lean (Φ = 0.8) fuel-rich (Φ = 1.2)

73 20

152 121

93 59

944 870

69 16

161 130

102 61

996 886

72 14

158 125

126 92

992 893

molecular numbers of H2, H2O, and CO increase with the temperature, as expected from the transition-state theory. H2O molecules are found to be released rapidly and before any other products during the pyrolysis process. Furthermore, larger organic molecules may prefer to be generated in the H2O and H2O/O2 atmospheres. For example, light tar (C5−13) yield by hydrothermal treatment is increased significantly due to attacks on the C−O−C groups from the ·H radicals generated by H2O or hydrogen abstractions from the biomass, as well as the dehydration reactions at the C−O−H groups. According to our ReaxFF MD simulation studies, gas (C1−4), light tar (C5−13), and heavy tar (C14−39) reach maximum yield at around 150, 100, and 250 ps, respectively, under H2O/O2 atmospheres. In the combustion simulations, the results indicate that the chemical system tends to be more reactive under higher temperatures. CO2 generation is probably mainly from the cracking and reforming of ·COOH and ·CHO groups or CO oxidation. At fuel-lean conditions, more rapid oxidation and combustion of the structures are observed and compared with fuel-rich combustion. We observed that the possible initiation reactions involved in oxidation is either hydrogen abstraction or thermal decomposition of the whole structure to produce smaller fragments that are subsequently oxidized. Additionally, biomass combustion in the O2/CO2 atmosphere is simulated to study the influence of CO2 on the product distribution. We found that more H2O and fewer H2 molecules are generated during the combustion process with an increase in the concentration of CO2. Although the findings enabled by the reactive MD simulations under the designed conditions presented in this paper are qualitative due to the limited accuracy of ReaxFF, this work can offer new atomistic insights on relevant chemical reactions and molecular phenomenon to build a better understanding for potential utilizations of biomass.

Figure 18. Distribution of major products obtained from 1 ns of ReaxFF MD simulations of the combustion process.

evolution of H2O and H2 molecules at 3000 K under different atmospheres. We observed that more H2O molecules and fewer H2 molecules are generated during the combustion process with increasing concentration of CO2. This may occur because higher CO2 concentration hinders CO reacting with H2O to form CO2 and H2.

4. CONCLUSIONS In this work, the carefully selected ReaxFF (C/H/O/N/S2010) was used to conduct pyrolysis and combustion MD simulations of the simplified biomass model to offer atomistic insights on the chemical reaction events. The calculated activation energies during the pyrolysis of each of the three major biomass components in vacuum are consistent with the trend in experiments, in which the energy values follow the trend cellulose > hemicellulose > lignin. In pyrolysis simulations of the biomass model, while C−C and C−H bonds decrease slowly over time, most of the C−O bonds in the biomass structure break easily at the initial stage (within ∼100 ps), reflecting the lower C−O bond energy. We found that the

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.iecr.7b01714.

Figure 19. Time evolution of H2O and H2 molecules at 3000 K under different O2/CO2 atmospheres. K


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(13) Nussbaumer, T. Combustion and co-combustion of biomass: fundamentals, technologies, and primary measures for emission reduction. Energy Fuels 2003, 17 (6), 1510−1521. (14) Demirbas, A. Potential applications of renewable energy sources, biomass combustion problems in boiler power systems and combustion related environmental issues. Prog. Energy Combust. Sci. 2005, 31 (2), 171−192. (15) Khan, A. A.; de Jong, W.; Jansens, P. J.; Spliethoff, H. Biomass combustion in fluidized bed boilers: Potential problems and remedies. Fuel Process. Technol. 2009, 90 (1), 21−50. (16) Simoneit, B. R. T.; Rogge, W. F.; Mazurek, M. A.; Standley, L. J.; Hildemann, L. M.; Cass, G. R. Lignin Pyrolysis Products, Lignans, and Resin Acids as Specific Tracers of Plant Classes in Emissions from Biomass Combustion. Environ. Sci. Technol. 1993, 27 (12), 2533− 2541. (17) vandenBroek, R.; Faaij, A.; vanWijk, A. Biomass combustion for power generation. Biomass Bioenergy 1996, 11 (4), 271−281. (18) Beckham, G. T.; Bomble, Y. J.; Bayer, E. A.; Himmel, M. E.; Crowley, M. F. Applications of computational science for understanding enzymatic deconstruction of cellulose. Curr. Opin. Biotechnol. 2011, 22 (2), 231−8. (19) Chundawat, S. P.; Beckham, G. T.; Himmel, M. E.; Dale, B. E. Deconstruction of lignocellulosic biomass to fuels and chemicals. Annu. Rev. Chem. Biomol. Eng. 2011, 2, 121−45. (20) Vinu, R.; Broadbelt, L. J. A mechanistic model of fast pyrolysis of glucose-based carbohydrates to predict bio-oil composition. Energy Environ. Sci. 2012, 5 (12), 9808. (21) Zhou, X.; Nolte, M. W.; Mayes, H. B.; Shanks, B. H.; Broadbelt, L. J. Experimental and Mechanistic Modeling of Fast Pyrolysis of Neat Glucose-Based Carbohydrates. 1. Experiments and Development of a Detailed Mechanistic Model. Ind. Eng. Chem. Res. 2014, 53 (34), 13274−13289. (22) Zhou, X.; Nolte, M. W.; Shanks, B. H.; Broadbelt, L. J. Experimental and Mechanistic Modeling of Fast Pyrolysis of Neat Glucose-Based Carbohydrates. 2. Validation and Evaluation of the Mechanistic Model. Ind. Eng. Chem. Res. 2014, 53 (34), 13290−13301. (23) Mayes, H. B.; Broadbelt, L. J. Unraveling the reactions that unravel cellulose. J. Phys. Chem. A 2012, 116 (26), 7098−106. (24) Mayes, H. B.; Nolte, M. W.; Beckham, G. T.; Shanks, B. H.; Broadbelt, L. J. The Alpha−Bet(a) of Glucose Pyrolysis: Computational and Experimental Investigations of 5-Hydroxymethylfurfural and Levoglucosan Formation Reveal Implications for Cellulose Pyrolysis. ACS Sustainable Chem. Eng. 2014, 2 (6), 1461−1473. (25) Hough, B. R.; Schwartz, D. T.; Pfaendtner, J. Detailed Kinetic Modeling of Lignin Pyrolysis for Process Optimization. Ind. Eng. Chem. Res. 2016, 55 (34), 9147−9153. (26) Krumm, C.; Pfaendtner, J.; Dauenhauer, P. J. Millisecond Pulsed Films Unify the Mechanisms of Cellulose Fragmentation. Chem. Mater. 2016, 28 (9), 3108−3114. (27) Van Duin, A. C.; Dasgupta, S.; Lorant, F.; Goddard, W. A. ReaxFF: a reactive force field for hydrocarbons. J. Phys. Chem. A 2001, 105 (41), 9396−9409. (28) Mattsson, T. R.; Lane, J. M. D.; Cochrane, K. R.; Desjarlais, M. P.; Thompson, A. P.; Pierce, F.; Grest, G. S. First-principles and classical molecular dynamics simulation of shocked polymers. Phys. Rev. B: Condens. Matter Mater. Phys. 2010, 81 (5), 054103. (29) Stuart, S. J.; Tutein, A. B.; Harrison, J. A. A reactive potential for hydrocarbons with intermolecular interactions. J. Chem. Phys. 2000, 112 (14), 6472−6486. (30) Yan, G.; Zhang, Z.; Yan, K. Reactive molecular dynamics simulations of the initial stage of brown coal oxidation at high temperatures. Mol. Phys. 2013, 111 (1), 147−156. (31) Lümmen, N. Aggregation of carbon in an atmosphere of molecular hydrogen investigated by ReaxFF-molecular dynamics simulations. Comput. Mater. Sci. 2010, 49 (2), 243−252. (32) Lümmen, N. ReaxFF-molecular dynamics simulations of nonoxidative and non-catalyzed thermal decomposition of methane at high temperatures. Phys. Chem. Chem. Phys. 2010, 12 (28), 7883−7893.

MATLAB codes for analyzing the bond dissociation kinetics using the bond information computed by the ReaxFF potential; C++ source codes for analyzing reaction products using the chemical species information computed by the ReaxFF potential; additional figures and tables (PDF)

AUTHOR INFORMATION

Corresponding Authors

*Tel.: (86) 138 5168 0995. E-mail: [email protected]. *Tel.: (850) 645 0138. E-mail: [email protected]. ORCID

Lingling Zhao: 0000-0002-1532-7247 Shangchao Lin: 0000-0002-6810-1380 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS L.Z. would like to acknowledge the financial support from the National Natural Science Foundation of China (Grant No. 51376045). S.L. would like to acknowledge the startup funding from the Energy Materials Initiative at the Florida State University.

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REFERENCES

(1) Babu, B. V. Biomass pyrolysis: a state-of-the-art review. Biofuels, Bioprod. Biorefin. 2008, 2 (5), 393−414. (2) Kirubakaran, V.; Sivaramakrishnan, V.; Nalini, R.; Sekar, T.; Premalatha, M.; Subramanian, P. A review on gasification of biomass. Renewable Sustainable Energy Rev. 2009, 13 (1), 179−186. (3) Liu, R.; Deng, C.; Wang, J. Fast Pyrolysis of Corn Straw for Biooil Production in a Bench-scale Fluidized Bed Reactor. Energy Sources, Part A 2010, 32 (1), 10−19. (4) Ronghou, L.; Weisheng, N.; Xiaofang, Y.; Tianshu, L.; Chunmei, Z.; Jinyang, L. Effects of biomass fast pyrolysis key parameters on yields and distributions of products. Trans. Chin. Soc. Agric. Eng. 2003, 19 (5), 204−208. (5) Peterson, A. A.; Vogel, F.; Lachance, R. P.; Fröling, M.; Antal, M. J., Jr; Tester, J. W. Thermochemical biofuel production in hydrothermal media: a review of sub-and supercritical water technologies. Energy Environ. Sci. 2008, 1 (1), 32−65. (6) Yang, H. P.; Yan, R.; Chen, H. P.; Lee, D. H.; Zheng, C. G. Characteristics of hemicellulose, cellulose and lignin pyrolysis. Fuel 2007, 86 (12−13), 1781−1788. (7) Yang, H. P.; Yan, R.; Chen, H. P.; Zheng, C. G.; Lee, D. H.; Liang, D. T. In-depth investigation of biomass pyrolysis based on three major components: Hemicellulose, cellulose and lignin. Energy Fuels 2006, 20 (1), 388−393. (8) Qu, T. T.; Guo, W. J.; Shen, L. H.; Xiao, J.; Zhao, K. Experimental Study of Biomass Pyrolysis Based on Three Major Components: Hemicellulose, Cellulose, and Lignin. Ind. Eng. Chem. Res. 2011, 50 (18), 10424−10433. (9) Sasaki, M.; Kabyemela, B.; Malaluan, R.; Hirose, S.; Takeda, N.; Adschiri, T.; Arai, K. Cellulose hydrolysis in subcritical and supercritical water. J. Supercrit. Fluids 1998, 13 (1), 261−268. (10) Sasaki, M.; Fang, Z.; Fukushima, Y.; Adschiri, T.; Arai, K. Dissolution and hydrolysis of cellulose in subcritical and supercritical water. Ind. Eng. Chem. Res. 2000, 39 (8), 2883−2890. (11) Daouk, E.; Van de Steene, L.; Paviet, F.; Salvador, S. Thick wood particle pyrolysis in an oxidative atmosphere. Chem. Eng. Sci. 2015, 126, 608−615. (12) Shen, D.; Zhang, L.; Xue, J.; Guan, S.; Liu, Q.; Xiao, R. Thermal degradation of xylan-based hemicellulose under oxidative atmosphere. Carbohydr. Polym. 2015, 127, 363−371. L


Article


pentaerythritol tetranitrate. J. Phys. Chem. B 2009, 113 (40), 13142− 13151. (54) Zeng-hua, L.; Ya-li, W.; Na, S.; Yong-liang, Y.; Yu-jing, Y. Experiment study of model compound oxidation on spontaneous combustion of coal. Procedia Earth Planet. Sci. 2009, 1 (1), 123−129. (55) Zhang, J.; Gu, J.; Han, Y.; Li, W.; Gan, Z.; Gu, J. Supercritical Water Oxidation vs Supercritical Water Gasification: Which Process Is Better for Explosive Wastewater Treatment? Ind. Eng. Chem. Res. 2015, 54 (4), 1251−1260. (56) Branca, C.; Albano, A.; Di Blasi, C. Critical evaluation of global mechanisms of wood devolatilization. Thermochim. Acta 2005, 429 (2), 133−141. (57) Yoshida, T.; Oshima, Y.; Matsumura, Y. Gasification of biomass model compounds and real biomass in supercritical water. Biomass Bioenergy 2004, 26 (1), 71−78. (58) Calzavara, Y.; Joussot-Dubien, C.; Boissonnet, G.; Sarrade, S. Evaluation of biomass gasification in supercritical water process for hydrogen production. Energy Convers. Manage. 2005, 46 (4), 615−631. (59) Matsumura, Y.; Minowa, T.; Potic, B.; Kersten, S. R.; Prins, W.; van Swaaij, W. P.; van de Beld, B.; Elliott, D. C.; Neuenschwander, G. G.; Kruse, A. Biomass gasification in near-and super-critical water: status and prospects. Biomass Bioenergy 2005, 29 (4), 269−292. (60) Hobbs, M.; Radulovic, P.; Smoot, L. Combustion and gasification of coals in fixed-beds. Prog. Energy Combust. Sci. 1993, 19 (6), 505−586. (61) Mae, K.; Shindo, H.; Miura, K. A new two-step oxidative degradation method for producing valuable chemicals from low rank coals under mild conditions. Energy Fuels 2001, 15 (3), 611−617. (62) Damen, K.; van Troost, M.; Faaij, A.; Turkenburg, W. A comparison of electricity and hydrogen production systems with CO 2 capture and storage. Part A: Review and selection of promising conversion and capture technologies. Prog. Energy Combust. Sci. 2006, 32 (2), 215−246.

(33) Chen, B.; Diao, Z.-j.; Lu, H.-y. Using the ReaxFF reactive force field for molecular dynamics simulations of the spontaneous combustion of lignite with the Hatcher lignite model. Fuel 2014, 116, 7−13. (34) Liu, L.; Bai, C.; Sun, H.; Goddard, W. A., III Mechanism and kinetics for the initial steps of pyrolysis and combustion of 1, 6dicyclopropane-2, 4-hexyne from ReaxFF reactive dynamics. J. Phys. Chem. A 2011, 115 (19), 4941−4950. (35) Guo, F.; Cheng, X.; Zhang, H. ReaxFF Molecular Dynamics Study of Initial Mechanism of JP-10 Combustion. Combust. Sci. Technol. 2012, 184 (9), 1233−1243. (36) Döntgen, M.; Przybylski-Freund, M.-D.; Kröger, L. C.; Kopp, W. A.; Ismail, A. E.; Leonhard, K. Automated discovery of reaction pathways, rate constants and transition states using reactive molecular dynamics simulations. J. Chem. Theory Comput. 2015, 11 (6), 2517− 2524. (37) Chenoweth, K.; van Duin, A. C.; Goddard, W. A. ReaxFF reactive force field for molecular dynamics simulations of hydrocarbon oxidation. J. Phys. Chem. A 2008, 112 (5), 1040−1053. (38) Castro-Marcano, F.; Kamat, A. M.; Russo, M. F.; van Duin, A. C.; Mathews, J. P. Combustion of an Illinois No. 6 coal char simulated using an atomistic char representation and the ReaxFF reactive force field. Combust. Flame 2012, 159 (3), 1272−1285. (39) Castro-Marcano, F.; van Duin, A.; Mathews, J. In ReaxFF molecular dynamics pyrolysis simulations of a large-scale model of Illinois No. 6 coal including the role of organic sulfur. International Pittsburgh Coal Conference, 2012; Pittsburgh, PA USA, 2012. (40) Wang, H.; Feng, Y.; Zhang, X.; Lin, W.; Zhao, Y. Study of coal hydropyrolysis and desulfurization by ReaxFF molecular dynamics simulation. Fuel 2015, 145, 241−248. (41) Zhang, J.; Weng, X.; Han, Y.; Li, W.; Cheng, J.; Gan, Z.; Gu, J. The effect of supercritical water on coal pyrolysis and hydrogen production: a combined ReaxFF and DFT study. Fuel 2013, 108, 682− 690. (42) Li, G. Y.; Ding, J. X.; Zhang, H.; Hou, C. X.; Wang, F.; Li, Y. Y.; Liang, Y. H. ReaxFF simulations of hydrothermal treatment of lignite and its impact on chemical structures. Fuel 2015, 154, 243−251. (43) Beste, A. ReaxFF study of the oxidation of lignin model compounds for the most common linkages in softwood in view of carbon fiber production. J. Phys. Chem. A 2014, 118 (5), 803−814. (44) Beste, A. ReaxFF Study of the Oxidation of Softwood Lignin in View of Carbon Fiber Production. Energy Fuels 2014, 28 (11), 7007− 7013. (45) McKendry, P. Energy production from biomass (part 1): overview of biomass. Bioresour. Technol. 2002, 83 (1), 37−46. (46) Wohlert, J.; Berglund, L. A. A Coarse-Grained Model for Molecular Dynamics Simulations of Native Cellulose. J. Chem. Theory Comput. 2011, 7 (3), 753−760. (47) Puls, J. In Chemistry and Biochemistry of Hemicelluloses: Relationship between Hemicellulose Structure and Enzymes Required for Hydrolysis; Macromolecular Symposia; Wiley Online Library, 1997; pp 183−196. (48) Schuttelkopf, A. W.; van Aalten, D. M. F. PRODRG: a tool for high-throughput crystallography of protein-ligand complexes. Acta Crystallogr., Sect. D: Biol. Crystallogr. 2004, 60, 1355−1363. (49) Aktulga, H. M.; Fogarty, J. C.; Pandit, S. A.; Grama, A. Y. Parallel reactive molecular dynamics: Numerical methods and algorithmic techniques. Parallel Comput 2012, 38 (4−5), 245−259. (50) Shuichi, N. Constant temperature molecular dynamics methods. Prog. Theor. Phys. Suppl. 1991, 103, 1−46. (51) Bhoi, S.; Banerjee, T.; Mohanty, K. Molecular dynamic simulation of spontaneous combustion and pyrolysis of brown coal using ReaxFF. Fuel 2014, 136, 326−333. (52) Strachan, A.; van Duin, A. C. T.; Chakraborty, D.; Dasgupta, S.; Goddard, W. A. Shock waves in high-energy materials: The initial chemical events in nitramine RDX. Phys. Rev. Lett. 2003, 91 (9), 098301. (53) Budzien, J.; Thompson, A. P.; Zybin, S. V. Reactive molecular dynamics simulations of shock through a single crystal of M


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