Pyrolysis of Liulin Coal Simulated by GPU-Based ReaxFF MD withCheminformatics AnalysisMo Zheng,†,‡ Xiaoxia Li,*,† Jian Liu,†,‡ Ze Wang,† Xiaomin Gong,†,‡ Li Guo,*,† and Wenli Song†

†State Key Laboratory of Multiphase Complex Systems, Institute of Process Engineering, Chinese Academy of Sciences, No. 1Zhongguancun North Second Street, Beijing 100190, People’s Republic of China‡University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China

*S Supporting Information

ABSTRACT: In this study, the first GPU-enabled ReaxFF MD program with significantly improved performance, surpassingCPU implementations, was employed to explore the initial chemical mechanisms and product distributions in pyrolysis of Liulincoal, a bituminous coal from Shanxi, PRC. The largest coal model ever used in simulation via ReaxFF MD, the Liulin coalmolecular model consisting of 28 351 atoms was constructed based on a combination of experiments and classical coal models.The ReaxFF MD simulations at temperatures of 1000−2600 K were performed for 250 ps to investigate the temperature effectson the product profile and the initial chemical reactions of the Liulin coal model pyrolysis. The generation rates of C14−C40compounds and gas tend to equilibrate within 150−250 ps, indicating that the simulation should allow most of the thermaldecomposition reactions complete and the simulated product profiles are reasonable for understanding the chemical reactions ofthe Liulin coal pyrolysis. The product (gas, tar, and char) evolution tendencies with time and temperature observed in thesimulations are fairly in agreement with the experimental tendency reported in the literature. In particular, the evolution trends ofthree representative products (naphthalene, methyl-naphthalene and dimethyl-naphthalene) with temperature are very consistentwith Py-GC/MS experiments. The detailed chemical reactions of the pyrolysis simulation have been generated using VARMD(Visualization and Analysis of Reactive Molecular Dynamics), which was newly created to examine the complexity of thechemical reaction network in ReaxFF MD simulation. The generation and consumption of HO· and H3C· radicals with time andtemperature are reasonable and consistent both with the evolution of H2O and CH4, and with the detailed chemical reactionsobtained as well. The amount of six-membered ring structures was observed to decrease with time and temperature, because oftheir conversion into 5-membered rings or 7−9-membered rings or even-larger-membered ring structures that will further openand decompose into small fragments. This work demonstrates a new methodology for investigating coal pyrolysis mechanism bycombining GPU-enabled high-performance computing with cheminformatics analysis in ReaxFF MD.

■ INTRODUCTION

Coals have complex carbonaceous structures and represent amajority (70%) of China’s total energy consumption in 2009.1

Pyrolysis is the initial and fundamental step in most coalconversion processes, accounting for up to 70% of the weightloss suffered by the coal;2 this process controls the swelling,char reactivity, and physical structure of coal. The pyrolysisproducts are generally related with the ignition, temperatureand flame stability in coal combustion. Therefore, mechanisminvestigation of coal pyrolysis will assist in improving theefficiency and cleanliness of coal conversion and utilization.3−6

Coal pyrolysis refers to the thermal decomposition of coal inan inert atmosphere or in a vacuum at a specific temperature.3

Coal undergoes a rapid loss of moisture and volatiles, followedby a myriad of coupled complex reaction pathways whenpyrolysis occurs. However, the heterogeneous nature of coaland the complexity of the process have made it very difficult todetermine the mechanisms of coal pyrolysis, even with state-of-the-art experimental approaches.2 The progress of molecularmodeling based on the development of realistic atomisticrepresentations of coal in recent years7 provides a usefulapproach for better understandings of the coal molecularstructure and its initial mechanism for the effective utilization ofcoal in the pyrolysis system.

As a method for investigating chemical bonding with highaccuracy, density functional theory (DFT)8,9 is computationallyintensive and expensive, resulting in the fact that it has littleapplicability for large-scale coal models necessary to capture thecomplexity of coal pyrolysis. Classical molecular dynamics(classical MD or MD) has the ability for modeling large-scalesystem with millions of atoms10 but could not be used toexplore chemical reactions due to its physical elastic collisionbetween atoms with static bonds and fixed partial charges.11

The Reactive Force Field (ReaxFF), developed by van Duin etal.,11 is a general bond-order potential and can be used to fullyaddress the chemistry of dynamic bonds and polarizationeffects. It is able to describe the evolving of formation,transition, and dissociation of chemical bonds in a molecularsystem when combined with molecular dynamics (ReaxFFMD). ReaxFF MD is an atomistic-scale approach with accuracyclose to DFT but with very much reduced computationalcosts.12,13 Since the parameters of ReaxFF are derived fromDFT11 on transition-state energy, geometry data, and heat offormation of small molecules, it can be used to investigate

Received: October 28, 2013Revised: December 7, 2013Published: December 9, 2013

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© 2013 American Chemical Society 522 dx.doi.org/10.1021/ef402140n | Energy Fuels 2014, 28, 522−534

chemical reactions without any predefining of reactive sites orreaction pathways, providing a new and promising approach formolecular simulation of complex system with chemicalreactions.13

In addition, the accuracy and efficiency of ReaxFF moleculardynamics (ReaxFF MD) simulation has been demonstrated byapplications in a wide variety of materials.14−17 Especially,ReaxFF MD has been utilized in several studies to exploreinitial reactive mechanisms and kinetics associated withcombustion and pyrolysis processes of important compounds.Simulation of pyrolysis and combustion of importantcompounds including n-dodecane by Wang et al.,18 6-dicyclopropane-2,4-hexyne by Liu et al.,19 and n-heptane byDing et al.20 yielded reasonable results consistent with theexperimental data and their reaction mechanisms wereproposed accordingly. In 2009, Salmon et al. reported ReaxFFMD simulation of the thermal decomposition of two largemodels, namely, a macro-model containing 2692 atoms forMorwell brown coal21 and the algaenan Botryococcus brauniirace L biopolymer model that consists of 2966 atoms,22 both ofwhich reproduced some reactions observed in offline experi-ments, showing that such computation is useful in providingmolecular-based kinetic models for pyrolysis processes. Castro-Marcano et al.23 carried out ReaxFF MD simulation on a large-scale model of Illinois No. 6 coal char with 7458 atoms in orderto examine the complex char combustion chemistry and also todiscuss the role of sulfur within the model.24 Very recently,Zhang et al.25 combined ReaxFF method with DFT toinvestigate the reaction mechanism of coal pyrolysis andhydrogen production in supercritical water (SCW), whichrevealed the cooperative effects between SCW and coal.ReaxFF MD was also employed in our recent work26 toperform simulation of chemical reactions in pyrolysis of abituminous coal model with 4976 atoms to examine the nascentdecomposition mechanisms. These applications have shown thecapability and great potential of ReaxFF to handle complexchemistry of larger molecular systems with chemical reactionsin coal pyrolysis.Although ReaxFF MD allows direct and fast thermolysis

modeling for larger-scale molecular systems than DFTmethods, it is still a computationally intensive approach,∼10−50 times slower than classical MD.13 It would take 54days to perform a million-time-step simulation for PETNcrystal system with 16 240 atoms27 on a processor (DellPrecision T7500 desktop system) using LAMMPS FORTRANcodes. Obviously, larger coal models allow for more-detaileddescription in terms of coal structure which would be closer tothe real-world coal structure but have to suffer much longersimulation time when ReaxFF MD is applied in coal pyrolysis.23

The computational challenge to simulating a large-scale coalmodel comparable to real coal motivated us to have GMD-Reaxcreated.28 It is the first graphic processing unit (GPU)-enabledReaxFF molecular dynamics program with significantlyincreased computational capability of ReaxFF MD for largersystem size and longer time scale by taking advantage of asingle GPU attached to a desktop workstation. In terms of thesimulation time per time step, averaged over 100 steps, GMD-Reax achieved a high speed computation, i.e., 4 times fasterthan van Duin et al.’s FORTRAN codes in LAMMPS27 on 8CPU cores for simulated system with 27 283 atoms.28 WithGMD-Reax, it was proven to be practical to simulate pyrolysisof large coal models containing 10 000−30 000 atoms withReaxFF MD on a desktop workstation in an effort to investigate

the complex chemical reactions and understand the initialmechanisms.In this paper, ReaxFF MD simulation using GMD-Reax was

employed to perform pyrolysis reaction on a molecularrepresentation for Liulin bituminous coal to examine the initialdecomposition mechanisms and product distributions underdifferent temperature conditions. The Liulin coal model wasconstructed to contain 28 351 atoms and its pyrolysissimulation details using ReaxFF molecular simulation aredescribed, followed by the analysis of pyrolysis productdistributions and thermal decomposition mechanisms obtainedby using a C++ program VARMD created for revealing thedetailed chemical reactions. The strategy that combines GPU-enabled high performance computing with cheminformaticsanalysis of the simulation trajectory in ReaxFF MD suggests apractical approach for revealing the detailed chemical reactionsin pyrolysis simulation of more-realistic coal models, which isuseful for the optimization of coal conversion processes.

■ METHODSExperimental Analysis of Liulin Coal. Liulin coal is a bituminous

coal with a true density of 1.3 g/cm3 from Shanxi Province in China.The proximate and ultimate analysis results of Liulin coal are listed inTable 1.

Solid-state 13C NMR spectroscopy has been shown to be animportant tool in characterization of coal structure.29 13C NMR hasbeen used to quantify the average carbon skeletal structure of coal with12 parameters that describe the aromatic and aliphatic regions of thecoal matrix.30 The 13C NMR spectra of Liulin coal are presented inFigure 1. The structural parameters of Liulin coal obtained directlyfrom the 13C NMR spectrogram or by resolving overlapping peaks in

Table 1. Proximate and Ultimate Analysis of Liulin Coal

Ultimate Analysis (wt % daf)

C 88.4H 4.8O 5.2N 0.94S 0.46

Proximate Analysis (wt %)

moisture 0.66ash 11.32volatile 20.64

Figure 1. 13C NMR spectra of Liulin coal.

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the spectra are listed in Table 2, where fa′ is the percentage of carbonwith sp2 hybridization in aromatic carbon fa among the total carbon;

faH and fa

N are protonated and nonprotonated carbon with aromaticcharacteristic, respectively; fa

P, faB, and fa

S are phenolic or phenolicether, alkylated and bridgehead aromatic carbon type, showing howmuch hydrogen in aromatic rings taken place by oxygen, alkyl sidechain, or other functions. These structure properties of Liulin coal arearomatic constraints in constructing a molecular model for Liulin coal.fal is the total ratio of aliphatic carbon evaluated by summing fal*, fal

H,and fal

O. Note that since the value falH is greater than fal*, as shown in

Table 2, the methylene and methyne carbons play more significantroles than that of methyl in side chains and bridge chains. So it can beseen that the methylene in cycloalkane or in a side chain wouldaccount for a large proportion in the aliphatic carbon structure ofLiulin coal from the 13C NMR spectrogram and coal chemistry.Liulin Coal Model Construction. Coals are believed to have a

three-dimensional cross-linked macromolecular structure with organicand inorganic constituents heterogeneously. Because of various bondtypes and noncovalent interactions, any systematic description of coalstructure is seriously limited, because of the fact that there is a widevariety of coals.31 Over the last 70 years, more than 130 molecularlevel representations have been generated,7,32 which reflects thecomplexity of coal’s structure and numerous efforts in capturing coalstructure properties. Among those representations, the well-knownWiser bituminous model is considered as a comprehensive andreasonable high-volatile bituminous coal model that also containspotential reactive functional groups. The carbon skeletal representa-tions in the Wiser model are “structural alternatives”, which has anincreasing scale representing the rank transition from 76% to 90% Ccontent.32 Therefore, based on the Wiser model, a Liulin coalrepresentation was constructed to maintain consistency with theelemental analysis and the 13C NMR spectroscopy data from theexperiments mentioned above.The unimolecular structure representation of Liulin coal with the

molecular formula C312H259N3O16S was drawn by ChemSketch,33 asshown in Figure 2. Geometry optimization of the model was carriedout with Dreiding force field using the Forcite Module in MaterialsStudio (MS),34 and the molecular configuration of the model changedprofoundly.Subsequently, 37 optimized unimolecular structures were assembled

into a cubic box by using the construction function of the MSAmorphous Cell Module. According to extractability or other

experimental results in the literature,35−37 relatively small moleculeswith molecular weights of <1000 amu might be held in the coal.Therefore, small molecules with molecular weight within 200−500amu taking 21.6 wt % in the cubic were packed into the box, which issimilar with Argonne premium bituminous coals.36 The smallmolecules were taken from the fragments in the unimolecular Liulinmodel to help keep the proper composition of C, H, O, N, and S inthe Liulin bituminous coal model. The 3D molecular model wasconstructed at a low bulk density of 0.25 g/cm3 to avoid overlapping ofaromatic rings and other important functional groups. In order to havethe true density of Liulin coal, the initially constructed model wassubjected to compression and decompression using NPT ensemble atpressures of 0.1 GPa and 0.1 MPa, respectively. Energy minimizationby using the steepest descent method coupled with conjugate gradientmethod and equilibration at room temperature and one atmosphereusing NPT dynamics were the last steps to optimize the model asmuch as possible. The time step for the model construction process is1 fs. The finalized Liulin coal structure contains 28 351 atoms(C14782H12702N140O690S37), with an aromatic ratio of 0.76 and a heliumdensity of 1.28 g/cm3, as shown in Figure 3. This model was validatedbased on the broad agreement in physical structural propertiesbetween the model and the experimental data as listed in Table 3.

The scale of the Liulin model (∼28 000 atoms) is among the largestcoal models in the literature and the largest used in the ReaxFF MDsimulation, to the best of our knowledge. In our earlier work,26 thescale of a bituminous coal model based on the combination of theclassical Wiser and Shinn models with small molecules added was∼5000 atoms, which is a practical scale for simulation using a desktopworkstation (8 CPU cores) with FORTRAN version of ReaxFF MD ina LAMMPS platform,27 in which a 250 ps simulation at hightemperatures was performed within a week. However, ReaxFF MD

Table 2. Structural Parameters of Liulin Coal SamplesObtained from 13C NMR

structural parameter value

fa 0.76fal 0.24faC 0.044

fa′ 0.73faH 0.40

faN 0.34

faP 0.004

faS 0.11

faB 0.19

fal* 0.065falH 0.122

falO 0.036

Note: fa = total aromatic carbon; fal = total aliphatic carbon; faC =

carbonyl; fa′ = aliphatic carbon in an aromatic ring; faH = aliphatic

carbon that has been protonated and aromatic; faN = nonprotonated

and aromatic aliphatic carbon; faP = aliphatic carbon in phenolic or

phenolic ether; faS = alkylated aromatic aliphatic carbon; fa

B = aliphaticcarbon in an aromatic bridgehead; fal* = aliphatic carbon in CH3; fal

H =aliphatic carbon in CH or CH2; fal

O = aliphatic carbon bonded tooxygen

Figure 2. Unimolecular model of Liulin coal constructed based on theWiser model.

Figure 3. Liulin coal structure with formula C14782H12702N140O690S37.

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simulation of the Liulin coal model with over 28 000 atoms would becloser to the coal structure in the real world, which would require amuch longer simulation time if the same LAMMPS FORTRAN codewas employed. Castro-Marcano et al.23 simulated the combustion ofan Illinois No. 6 coal char by ReaxFF MD, which was the largestsimulation system, with a total of 35 458 atoms (7458 atoms in coalchar + 14 000 O2 molecules). Using ADF software,11 ∼4−6 weeks fora typical equilibration and production run on four processors werereported. Fortunately, using GMD-Reax,28 the pyrolysis simulation ofthe largest coal model, the constructed Liulin coal model in this paperbecomes more practical. Approximately 5 days were required tosimulate one temperature condition using GMD-Reax running on aCentOS 5.4 server with an Intel Xeon E5620 2.4 GHz, 2 GB RAMwith a C2050 GPU card attached.ReaxFF Force Field and Simulation Details. ReaxFF is an

empirical reactive force field that can describe bond formation andcharge transfer for complex reactive molecular systems based on thebond-order concept developed by Tersoff38 and Brenner.39 Instead ofthe Lennard-Jones potential frequently used in classical force field,ReaxFF employed a distance-corrected Morse potential to describevan der Waals calculation aiming at the continuity of force filed. InCoulombic interactions, the atomic charges are dynamic and updatedat each time step, using the charge equilibration (QEq) method, whichwas developed by Rappe and Goddard40 and formulated by Nakano.41

Since the ReaxFF force field has demonstrated good agreement withDFT in reproducing the potentials of alkanes11 and aromatichydrocarbon42 with elemental oxygen,43 nitrogen, and sulfur,24 it isbelieved that it should be suitable for exploring the complex chemicalreactions in coal pyrolysis.In order to determine the thermal decomposition temperature of

Liulin coal model in Figure 3, heat-up simulation by ReaxFF MD wasperformed from 300 K to 2300 K for 250 ps with a rate of 8 K/ps. Itwas observed that the thermal decomposition of the model starts at∼1000−1300 K. Thus, the isothermal simulations were performed for250 ps at 15 different temperatures of 1000−2600 K using ReaxFFMD with NVT ensemble, where the bond-order and nonbondedcutoff were 0.3 and 10 Å, respectively, to study the productdistribution of Liulin coal pyrolysis. The parameters of reactive forcefield are from LAMMPS version released on September 17, 2011.During all NVT-ReaxFF MD simulations, periodic boundaryconditions were applied in all direction in the cubic box with anedge length of 71.4 Å, according to the minimum image convention.The velocity-Verlet algorithm was used to integrate Newton’s equationof motion using a time step of 0.25 fs, and the temperature wascontrolled using the Berendsen thermostat with a 0.1 ps dampingconstant.Because the simulation time scale of 250 ps is many magnitudes

lower than that employed in experiments (on the order of seconds),the simulation temperatures were increased to a range of 1000−2600K where chemical reactions occur within picoseconds, which might beconsidered as “time-scale compressing” for the temperature range of500−1100 K in experiments. Very recently, van Duin et al.44 observedthat the calculated rate, obtained for formaldehyde adsorption and

dissociation reactions on Fe(100) surface from DFT, was consistentwith ReaxFF MD, indicating that “time-scale compressing” isreasonable for simulations. Despite the time and temperaturedifferences between MD simulation and experiments, good agreementsin the initial reaction products were achieved in the previouswork.18−21

Cheminformatics Approach for Trajectory Analysis. TheLiulin coal model constructed in this paper is so large that the analysisof its simulation trajectories becomes quite challenging, because thereare a huge amount of molecules and fragments involved in numerouschemical reactions, which is beyond the capability of availableanalyzing tools and manual analysis. To tackle this challenge,cheminformatics approach was employed to create VARMD (Visual-ization and Analysis of Reactive Molecular Dynamics) for trajectoryanalysis obtained from ReaxFF MD simulation. VARMD is a C++program, newly developed by our group for investigating thecomplexity of the chemical reaction network in ReaxFF MDsimulation. Using VARMD, the detailed chemical reactions involvedin pyrolysis of Liulin coal model via ReaxFF MD were generated.

■ RESULTS AND DISCUSSIONPyrolysis Product Profile of Liulin Coal Model. Product

Distribution in General. A series of ReaxFF pyrolysissimulations of Liulin coal structure at different temperatures(1000−2600 K) were performed to evaluate the temperatureeffects on the product distributions and initial mechanisms. Thecompound analysis in pyrolysis products generated within 250ps at 1000−2600 K shows that the decomposition occurs veryquickly and the decomposing rate of macromolecules increaseswith temperature. Among the total 37 C312H259N3O16Sunimolecules in the model, 29 are cracked into small fragmentswithin 250 ps at 1200 K and decomposition will be completedwithin 25 ps at a temperature of 2000 K. The evolution ofproduct classes with time and temperature are displayed inFigures 4 and 5. Because it is hard to select calibration

compounds in coal tar, measuring tar molecular distributionsexperimentally29 is moderately challenging and controversial. Inorder to agree with molecular weights of tars calculated fromthe CPD model and other experimental data,29 C14−C40 andC5−C13 fragments with molecular weights of 80−600 amu areconsidered as tar in Figure 5. In addition, the fragments with <4carbon atoms are considered as gas in this case.C40+ compounds are observed to dominate in the coal

pyrolysis system, and they represent 76.28% of the mass at

Table 3. Structure Properties between the Experiments andthe Molecular Model for Liulin Coal

Liulin coal (daf,experiment)

3D model of Liulincoal

composition (%)C 88.4 87.1H 4.8 6.3N 0.94 0.96O 5.2 5.1S 0.46 0.47

aromatic ratio 0.76 0.76protonated andaromatic

0.39 0.37

aromatic bridgehead 0.19 0.17

Figure 4. Weight percentage of product compounds obtained from250 ps ReaxFF MD simulation of Liulin coal pyrolysis using the NVTensemble at 1000−2600 K.

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1000 K after 250 ps (Figure 4). Because heavy compoundsdecompose rapidly into smaller fragments at high temperatures,the weight percentage of C40+ compounds decreases as thetemperature grows. The char production approaches aminimum value of 51.38 wt % at 2200 K and increases rapidlywhen the temperature is higher than 2200 K, which is displayedat Figure 4. The phenomena indicate that a large number ofsecondary reactions such as dehydrogenation of aromaticstructures, condensation of the aromatic nuclei into coal char,and cross-linking reactions are carried out at temperatures of∼2000 K or higher, leading to the amount of coal char growingat much higher temperature. The fact that the evolution ofheavy tar C14−C40 increases at 1000−2000 K and thendecreases at 2200−2600 K also manifests secondary reactionsat temperatures higher than ∼2000 K. The amount of light tarC5−C13 increases with elevated temperature within the range of1000−2400 K but subtly decreases at 2600 K, while the amountof both organic gas with <4 carbons and inorganic gas rises atall simulated temperatures.Figure 5 shows the details of coal tar and gas behavior. As the

temperature increases, the thermodegradation of Liuliu coalbecomes fast, accompanied by tar and gas that has beengenerated. The amount of C14−C40 compounds increasesrapidly with temperature at early stages, but then reaches a

plateau and gently fluctuates with time at temperatures lowerthan 2300 K. When the temperature is at or higher than 2300K, the number of C14−C40 fragments keeps decreasing at thelate stages (Figure 5a). As shown in Figure 5b, the C5−C13

compounds continuously rise when the temperature is higherthan 1600 K. This is because tar generation and decompositionoccur competitively. Since lower temperatures favor targeneration and higher temperatures favor decomposition ratherthan addition and recombination, Liulin coal macromoleculesdecompose into much smaller fragments at higher temper-atures. When the temperature is even higher (2500 and 2600K), the C5−C13 compounds start to decrease with time at thelate stage, which might be due to their decomposing intosmaller molecules or cross-linking reactions into coal char. Thetotal amount of C1−C4 molecules increases as the pyrolysistemperature rises, shown in Figure 5c, which is in agreementwith the common coal chemistry interpretation that smallfragments would much more easily be released from char athigher temperatures.31 It is worthwhile noting that all theconcentration profiles of tar and gas in Figure 5 increasenoticeably at 1800 K or higher, which suggests 1900 K is mostlikely the starting temperature for different mechanisms, i.e., thecross-linking or other secondary reactions take place to producea much larger number of compounds.

Figure 5. Distribution of products obtained from ReaxFF MD simulations of coal pyrolysis at 1400−2600 K for 250 ps with the time step of 0.25 fs:(a) heavy tar of C14−C40, (b) light tar of C5−C13, and (c) gas of C1−C4.

Table 4. Molecular Structures of Products during Pyrolysis Simulations via ReaxFF MD after 250 ps at 1900 K

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Table 4 lists molecular structures of some typical compoundsobserved during the 250 ps simulation period at 1900 K byusing VARMD. These compounds may be considered as therepresentative pyrolysis product cursors of Liulin coal’sstructure based on the constructed model in this paper.Gas Behaviors. The gas evolutions of H2O, CO2, CO, CH4,

and H2 with time and temperature are shown in Figure 6. InFigure 6a, the initial products are H2O and H2 observed at 1000K, followed by CO2 at 1200 K and CO at 1400 K. The finalproduct is CH4, which appears at 1500 K. The similar resultswere obtained at isothermal simulation; the result at 1700 K isshown in Figure 6b as an example. The first product is H2Oemerging at 12.5 ps, followed by CO2 at 25 ps, CO at 37.5 ps,methane at 75 ps, and H2 at 100 ps. Except for H2, thesequence for the gasesH2O, CO2, CO, and CH4is veryconsistent with the small gas evolution experiments.45 Thedifference in H2 production might be due to the fact that the H2

molecule is the smallest molecule and much more sensitive to

increased temperature than other gas molecules. These resultsare consistent with our earlier work.26

When the temperature is lower than 1700 K, very smallamounts of CH4 and H2 are generated. However, the productprofiles of both CH4 and H2 are sharply increasing when thetemperature is at or higher than 1700 K, particularly within thetemperature range of 1900−2000 K (Figure 6a). The very highnumber of H2 and CH4 at 1900 and 2000 K, combined with theobservation of the jump in tar generation at 1900 and 2000 K,as shown in Figure 5, indicate that the starting temperature ofsecondary reactions should be 1900 K; once again, this suggeststhe occurrence of cross-linking or other secondary reactions,which produce large amounts of CH4 and H2, because of ringcondensation and methyl groups.46

As shown in Figure 6a, the amounts of both CO2 and COincrease with temperature within the temperature range of1000−1700 K, where it is observed that these species mostlycome from carboxyl and carbonyl functional groups. Theamount of CO2 reaches its maximum at 1700 K, then plateaus

Figure 6. Evolution of gas obtained from coal pyrolysis simulations using ReaxFF MD over 250 ps with the time step of 0.25 fs (a) at temperaturesof 1000−2200 K, and (b) with time at 1700 K.

Figure 7. Evolution of naphthalene, methyl naphthalene, and dimethyl naphthalene with temperature obtained from Liulin coal pyrolysis by (a) Py-GC/MS experiments, as well as ReaxFF MD simulations at (b) 87.5 ps, (c) 187.5 ps, and (d) 250 ps.

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and gently fluctuates with temperature while CO maintains itsmonotonous increase with temperature. These results suggestthat the temperature for CO generation is higher than CO2generation, which is attributed to that probably the carbonylfunction groups will be detached at higher temperature, leadingto CO generation.Representative Product Profiles. In order to compare the

simulation results with experiments, Liulin coal pyrolysisexperiments were carried out using pyrolysis gas chromatog-raphy/mass spectrometry (Py-GC/MS), which is a techniquethat allows direct chromatographic separation of vapors evolvedfrom flash pyrolysis process,47 to evaluate the thermaldecomposition behavior of Liulin coal at 673−1073 K. Py-GC/MS was performed using a CDS Pyroprobe Model 5200pyrolyzer coupled with a gas chromatography (Thermo TraceGC) that is linked to a quadrupole mass spectrometer (ThermoISQ MS). The GC column was an FFAP of 30 m × 0.25 mm(inner diameter) with a 0.25 μm coated film. The mass spectrawere recorded at electron impact ionization energy of 70 eVwhile the flow rate was kept constant. The identification of thespecies from the MS detector was achieved based on a NISTMS library.By analyzing the pyrolysates of Liulin coal carried out by Py-

GC/MS with heating rate of 20 000 K/s at 673−1073 K, theevolution of three typical compoundsnamely naphthalene,methyl naphthalene, and dimethyl naphthaleneis shown inFigure 7a. The absolute areas of all three compounds increasewith increasing temperature within the temperature range of673−973 K. The steepness of increasing tendency withtemperature for all naphthalenes then becomes gentle at highertemperatures, while clearly the amount of methyl naphthaleneand dimethyl naphthalene tends toward the maximum whenthe temperature is at or higher than 973 K. The simulatedprofiles of the three compounds from ReaxFF MD at 87.5 psand 1900−2500 K are plotted in Figure 7b, which are inreasonable agreement with the tendency obtained in theexperiments, although in different temperature ranges. As thesimulation proceeds, the profiles of both naphthalene andmethyl naphthalene are consistent with experimental resultswithin 187.5 ps at 2200 K, as shown in Figure 7c. The numberof dimethyl naphthalene reaches its maximum then decreases at2300−2600 K, which is slightly different from Py-GC/MSexperiments. The profile tendency of both methyl naphthaleneand dimethyl naphthalene obtained experimentally tends toreach their maximum at 1073 K, which is the highesttemperature in the experiments. When the simulation temper-ature is further increased to 2600 K, it seems that methyl

naphthalene also tends to decrease. The reason for the profilediscrepancy between the ReaxFF MD simulation and Py-GC/MS experiments might be the fact that the experimentaltemperature was not high enough to have the decreasingtendency observed for methyl naphthalene and dimethylnaphthalene, and temperature effects on the three compoundsare slightly different. Dimethyl naphthalene is likely moresensitive when the temperature increases in ReaxFF MDsimulation. At very high temperature, dimethyl naphthalene willtend to decompose to methyl naphthalene and further intonaphthalene or other smaller fragments, which is confirmed byFigure 7d (this figure shows that both the amounts of methylnaphthalene and dimethyl naphthalene decrease to almost zeroafter 250 ps at 2600 K). The profile evolution of naphthalenesobtained in ReaxFF MD suggests that the approach ofincreased temperature is useful in exploring the behavior ofcoal pyrolysis, which is hardly accessible in experiments.

Validation of the Results. The time scale of the simulation(250 ps) is extremely short, many magnitudes lower than theseconds that was used in experiments. In order to clarifywhether this time scale is suitable for a simulated coal pyrolysissystem, the generation rates of C14−C40 compounds and gas areshown in Figure 8. At the early stage (0−50 ps) of simulation atdifferent temperatures, a large number of C14−C40 compoundsand gas are generated and the generation rates are relativelyhigh for both. As the simulation proceeds, generation rates aredecreasing rapidly and swing around zero at the late stage,indicating that the numbers of C14−C40 compounds and gas donot change very much and the pyrolysis systems tend toequilibrate at 150−250 ps between 1000 and 2000 K. Hence,the 250 ps simulation time should allow most of the thermaldecomposition reactions complete and the product profilesobserved in the 250 ps simulation are reasonable forunderstanding the chemical reactions of the Liulin coalpyrolysis.

Initial Mechanisms of Liulin Coal Pyrolysis. ThermalDecomposition of Liulin Coal. The number of reactionsdisplayed in Figure 9 was calculated by VARMD, giving anoverview of the degradation kinetics of the Liulin coal structurewithin the 250 ps NVT simulations. It is observed that, as thesimulation proceeds, the number of reactions increases withtemperature, which indicates that pyrolysis reactions are moreand more intense at high temperature. It is found that over2500 reactions (including reversible reactions) might havetaken place at 2000 K within 250 ps with a trajectory outputinterval of 12.5 ps. As expected, it is indicated that many morereactions can be exhibited for smaller output interval.

Figure 8. Generation rates of C14−C40 compounds and gas with time and temperature obtained from Liulin coal pyrolysis simulation at 1000−2000K: (a) C14−C40 compounds and (b) gas.

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Approximately 10 300 reactions are found at 2000 K within thesame simulation period when the output interval of thetrajectory was reduced to 2.5 ps. The number of reactionsapparently increases with temperature when the simulationtime elapses, then tends to be steady with time at constanttemperature, which agrees well with the observations in Figure9.Analysis of the simulation trajectories from ReaxFF MD

shows the complex initiation chemistry for the Liulin coalpyrolysis process. Figure 10 shows the initial pyrolysis stages ofunimolecular Liulin coal model obtained from ReaxFF MD byVARMD. It is found that the coal thermolysis process isprimarily initialized by bond dissociation of alkyl-aryl etherbridges in coal structure (labeled “①” in Figure 10) to twofragments with radicals. This could be due to the fact of a lower

bond dissociation energy of C−O−C bonds than that of C−Cbonds, which was proved by previous experiments.48,49 Somefragments will be released as tar if they are small enough tovaporize under typical pyrolysis conditions, with examplesshown in the labeled tars in Figure 10. Results in Figure 10 isfairly consistent with the hypothetical picture of coal’s organicstructure at stages of pyrolysis reported by Serio et al.50 in 1987.

Detailed Analysis of the Radical Behaviors. The primarypyrolysis of coal model is initialized by thermal decompositionat bridged bonds of coal structure to produce unstable radicalssuch as HO· and H3C· that will attack polyaromatic sheets ofmacromoelcules and other molecular fragments, which inducethe radical propagation to have gas released and tar generated.Figures 11 and 12 plot the evolution of two main radicals, HO·and H3C·, as a function of time under different conditions.Released from some methyl and ethyl side chains, most of theH3C· is generated at very early stages of the pyrolysis process,suggesting that these aliphatic side chains are probablydetached easily to generate active radicals that promotereactions in pyrolysis systems. When the temperature is lowerthan 1700 K, the number of H3C· reaches a specified value andthen fluctuates in all the simulation time (Figure 11a).However, as the temperature is at or higher than 1700 K,H3C· reaches its maximum at the early stage and then decreases(Figure 11b) due to hydrogen abstraction reaction to form CH4or other reactions. This observation is confirmed by Figure 11c,which shows that few CH4 are produced at 1000−1600 K andthe starting temperature of increasing CH4 is exactly that ofdecreasing H3C·. Particularly, when compared with theincreasing tendency of its maximum at 1800 and 1900 K, adrop for the maximum amount of H3C· and, meanwhile, asignificant rise for CH4 can be found at ∼25 ps at 2000 K,which suggests the amount of CH4 at the early stage mightmainly come from H3C· radicals.As shown in Figure 12a, the HO· profile undergoes clearly a

decreasing trend with time in the simulation of Liulin coal

Figure 9. Evolution of reactions with time and temperature obtainedfrom ReaxFF MD simulations via VARMD.

Figure 10. Initial pyrolysis stages of unimolecular Liulin coal model obtained from ReaxFF MD by VARMD. (The circled number in red indicatesthe thermodecomposition sequence.)

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pyrolysis at high temperatures. It is observed that most of HO·comes from either the bridged bonds breaking or hydroxyl

groups dropping off from nonaromatic rings at very early stagesof the Liulin coal pyrolysis. Subsequently, the HO· radicals

Figure 11. Distribution of H3C· and CH4 obtained from Liulin coal pyrolysis simulations using ReaxFF MD for 250 ps with the time step of 0.25 fs:(a) H3C· at 1000−1600 K, (b) H3C· at 1700−2000 K, and (c) CH4 at 1000−2000 K.

Figure 12. Distribution of HO· and H2O obtained from Liulin coal pyrolysis simulations using ReaxFF MD at 1000−2000 K for 250 ps with thetime step of 0.25 fs: (a) HO· and (b) H2O.

Table 5. Reactions Involving H3C· and HO· Obtained from Liulin Coal Pyrolysis via ReaxFF MD at 1900 K

reactions involving H3C· reactions involving HO·

Radical GenerationC312H259O16N3S → C281H228O16N2S + C30H28N + CH3 C312H259O16N3S → C307H253O15N3S + HO + C5H5

C312H259O16N3S → C275H220O12N2S + 2HO + CHO2 + C30H28N + C5H5 + CH3 C312H259O16N3S → C281H229O14N2S + CHO + C30H28N + HOC312H259O16N3S → C291H236O12N3S + CHO + HO + CHO2 + C18H17 + CH3 C275H219O14N2 + HS → C275H215O11N2S + 2H2O + HOC312H259O16N3S → C310H254O13N3S + HO + CHO2 + CH3 C65H51O3N → C65H50O2N + HO

Radical ConsumptionCH3 + C22H22O → CH4 + C22H21O C28H28O + HO → C28H27O + H2OC281H226O14N2S + 2HO + CH3 → C282H230O16N2 + HS C272H215O11N2S + HO + C24H22ON + C13H8 → C13H7 + C47H37O2N +

C225H179O10NS + H2 + C24H21ONCH3 + C24H23ON → CH4 + C24H21N + HO HO + C16H16 → H2O + C16H15

CH3 + C276H225O14N2S + C30H28N → CH4 + C306H251O13N3S + HO HO + C193H154O12NS + CH3 → H2O + C35H30O2N + C159H125O10 + HS

Figure 13. Distribution of 5- and 6-membered rings obtained from ReaxFF MD simulations of coal pyrolysis at 1000−2000 K for 250 ps with thetime step of 0.25 fs: (a) 5-membered rings and (b) 6-membered rings.

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would bind to pyrolysis intermediates and induce furtherbreaking into much smaller products or to generate H2O.Correspondingly, as expected, the amount of water rises withtemperature and time in Figure 12b, which shows the oppositetendency of the HO·, indicating that hydroxyl radicals areclosely relevant to H2O generation. HO· radicals tends to bestable by increased hydrogen abstractions with time to produceH2O. However, it is interesting to note that the number of H2Oand HO· at different temperatures represents differentcorresponding relations. When the temperature is lower than1600 K, the number of H2O generated is less than that of HO·radicals consumed, indicating that HO· may help other reactiveprocesses in addition to H2O generation. The consumption ofHO· radicals and generation of H2O reach an equilibrium at1600−1800 K. At 1900 K or even higher, H2O generationwould be greater than HO· reduction. At high temperatures,H2O comes from not only HO· radicals but also othergenerating paths, which requires further investigation.In particular, the number of HO· radicals is larger than that

of H3C·, which indicates that HO· plays a dominant role inpromoting primary reactions in the bituminous coal pyrolysissystem. Table 5 lists typical examples in some H3C·- and HO·-related reactions, including their generation and consumptionduring Liulin coal pyrolysis simulation at 1900 K, which revealsfurther details for results in Figures 11 and 12. The mainradicals generated at the early stages confirm that the pyrolysisof coal is a radical-driven process, which is in good agreementwith the fundamental steps of coal pyrolysis.3

Typical Reaction Schemes in Liuliu Coal PyrolysisSimulation. Figure 13 shows the transformations in ringstructures in ReaxFF pyrolysis simulation at constant temper-atures. The Liulin coal model structure initially contains 225 5-membered rings and 2757 6-membered hydroaromatic ringswith C and H atoms only. As shown in Figure 13b, 6-membered rings keep their amount stable when temperature islower than 1500 K, then decompose or transform to othercompounds with the increasing temperature. The decreasingratio of 6-membered rings is closely related with temperature,i.e., the higher the temperature, the greater the decreasing ratio.With the increase of temperature and time, the number of 5-membered rings steadily increases over the course of thesimulations, which may suggest that decomposition of 6-membered ring structures might be related to the formation of5-membered rings.With the assistance of VARMD, the detailed chemical

reaction schemes between two time intervals with samplinginterval of 12.5 ps were generated, among which the conversionof six-membered ring structures into five-membered rings wereindeed observed. As a typical example shown in Figure 14,initially, the aliphatic side chain (ethyl) of benzene ringsreleases a methyl radical because of high temperature, leadingto a methylene with a lone pair electron, which might destroythe conjugated structure of benzene rings. Next, the attachedmethylene with a lone pair electron to benzene ring is likely toabstract hydrogen at its neighboring carbon atom of thebenzene ring to form high rigid 3-membered ring structures.Then, these three-membered ring structures lead to theconnection of the bridged carbon atom on the 3-memberedring with its meta atom on benzene ring to form a five-membered ring and another three-membered ring. Two three-membered ring structures open their rings to generate arelatively stable 5-membered ring structure, which decomposesto form other smaller fragments afterward. To reduce the

computational cost, relatively large time intervals were used forreaction analysis and the reaction scheme in Figure 14 wasgenerated between many 12.5 ps intervals and not necessarilyelementary reactions. Thus, fragments with many unsaturatedpositions do exist, because of its kinetics process at hightemperature, which were observed in VARMD analysis.Six-membered ring structures that have a functional group

with lone pair electrons would also undergo the similar processto form 7−9-membered rings or even larger-membered ringstructures that will further open and decompose into smallfragments as displayed in Figure 15. Initially, hydroxyl radical inhexamethylene is released because of carboxyl radical attackingto form CO2 and H2O, resulting in fragments with lone pairelectrons. Coupled with lone pair electrons and high temper-atures, the conjugated structure of benzene rings is destroyed,leading to two 6-membered rings transforming into 5- and 7-membered rings. These structures have very short life spans anddecompose into other fragments rapidly.

■ CONCLUSIONThe product profile and initial chemical reactions of Liulin coalpyrolysis have been investigated using ReaxFF moleculardynamics. GMD-Reax, which is the first GPU-enabled ReaxFFMD program, was employed to explore the pyrolysis of acomplex bituminous model with 28 351 atoms, which is thelargest coal model ever used in simulation. The model with acalculated helium density of 1.28 g/cm3 was constructed basedon a combination of experiments and classical coal models. Thechemical and physical parameters of the coal model are broadlyconsistent with the experimental data including industrialanalysis, elemental analysis, and 13C NMR data of Liulin coal.The GMD-Reax simulations were performed at 1000−2600 Kfor 250 ps to explore the temperature effects on products andreactions of the Liulin coal pyrolysis.The simulation results show that the generation rates of

C14−C40 compounds and gas tend to equilibrate at 150−250ps, indicating that the simulation can allow completion of mostof the thermal decomposition reactions and the simulatedproduct profiles are reasonable for understanding the chemicalreactions of the Liulin coal pyrolysis.The concentration profile of major pyrolysis products were

also obtained from the simulations. The amount of C40+compounds is observed to reach its minimum at 2200 Kwhile C14−C40 compounds reach its maximum at ∼2000 K.The number of C5−C13 compounds rises continuously with

Figure 14. Example of conversion of 6-membered ring into 5-membered ring observed during the ReaxFF MD simulation of Liulincoal pyrolysis at 1900 K, the trajectory analyzed by VARMD.

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temperature (for temperatures of >1700 K) and the profile ofgas molecules increases rapidly with temperature. Except forH2, the generation sequence of gas, H2O, CO2, CO, and CH4 isvery consistent with the small gas evolution experiments. Inparticular, the evolution tendencies of three representativeproducts (naphthalene, methyl naphthalene, and dimethylnaphthalene) at 1900−2600 K agree well with the Py-GC/MS experiments performed at 673−1073 K. The reasonableprofile evolution of naphthalenes obtained in ReaxFF MDsimulations in temperature ranges that are hardly accessible inexperiments suggest that the increased temperature approachemployed in the simulations is very useful in exploring thebehavior of coal pyrolysis that are hardly accessibleexperimentally.VARMD, which is a C++ program that has been newly

created for cheminformatics analysis in ReaxFF MD simulation,was used to generate the detailed chemical reactions of thepyrolysis simulation. It is readily observable that the coalthermolysis process is primarily initialized by bond dissociationof alkyl-aryl ether bridges, followed by a large amount ofradicals such as H3C· and HO· generated to promote thepyrolysis chemistry. The generation and consumption of HO·and H3C· radicals with time and temperature calculated fromthe simulation results are reasonable and consistent with theevolution of H2O and CH4, as well as with the detailedchemical reactions obtained. In addition, the number of 6-membered ring structures was observed to decrease as afunction of time and temperature, because of their conversionto 5-membered rings or 7- to 9-membered rings or even largernumber-membered ring structures that will further open anddecompose into small fragments.Through this work, a new methodology has been

demonstrated for investigating coal pyrolysis mechanisms, acombination of GPU-enabled high performance computing ofReaxFF MD simulation with cheminformatics based analysis forrevealing the detailed chemical reactions using VARMD. Themethodology will assist for a profound understanding of thecomplex chemical reactions that occur in the thermolysis ofcomplicated molecular systems.

■ ASSOCIATED CONTENT*S Supporting Informationffield.reax, the parameters of ReaxFF force field used in thesimulations. This material is available free of charge via theInternet at http://pubs.acs.org.

■ AUTHOR INFORMATIONCorresponding Authors*Phone: 86-10-82544944. Fax: 86-10-82544945. E-mail: xxia@home.ipe.ac.cn.*Phone: 86-10-82544945. Fax: 86-10-82544945. E-mail: lguo@home.ipe.ac.cn.

Author ContributionsThe manuscript was written through contributions of allauthors. All authors have given approval to the final version ofthe manuscript.

NotesThe authors declare no competing financial interest.

■ ACKNOWLEDGMENTSThis work was supported by the grants from the NationalNatural Science Foundation of China (Nos. 21073195 and21373227) and from China’s State Key Laboratory ofMultiphase Complex Systems (No. MPCS-2012-A-05).

■ REFERENCES(1) U.S. Energy Information Administration (EIA). China AnaylsisReport. Available via the Internet at http://www.eia.gov/countries/cab.cfm?fips=CH, accessed October 11, 2013.(2) Solomon, P. R.; Fletcher, T. H.; Pugmire, R. J. Progress in CoalPyrolysis. Fuel 1993, 72 (5), 587−597.(3) Gavalas, G. R.; Cheong, P. H. K.; Jain, R. Model of CoalPyrolysis. 1. Qualitative Development. Ind. Eng. Chem. Res. 1981, 20(2), 113−122.(4) Gavalas, G. R.; Jain, R.; Cheong, P. H. K. Model of CoalPyrolysis. 2. Quantitative Formulation and Results. Ind. Eng. Chem. Res.1981, 20 (2), 122−132.(5) Stock, L. M. Coal Pyrolysis. Acc. Chem. Res. 1989, 22 (12), 427−433.(6) Gibbinsmatham, J.; Kandiyoti, R. Coal Pyrolysis Yields from Fastand Slow Heating in a Wire-Mesh Apparatus with a Gas Sweep. EnergyFuels 1988, 2 (4), 505−511.(7) Mathews, J. P.; van Duin, A. C. T.; Chaffee, A. L. The utility ofcoal molecular models. Fuel Process. Technol. 2011, 92 (4), 718−728.

Figure 15. Example of conversion of 6-membered ring into 7-membered ring observed during the ReaxFF MD simulation of Liulin coal pyrolysis at1900 K, the trajectory analyzed by VARMD.

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dx.doi.org/10.1021/ef402140n | Energy Fuels 2014, 28, 522−534532

(8) Parr, R. G. Density-Functional Theory. Chem. Eng. News 1990, 68(29), 45−45.(9) Friesner, R. A. Ab initio quantum chemistry: Methodology andapplications. Proc. Natl. Acad. Sci. U.S.A. 2005, 102 (19), 6648−6653.(10) Klein, M. L.; Shinoda, W. Large-scale molecular dynamicssimulations of self-assembling systems. Science 2008, 321 (5890),798−800.(11) van Duin, A. C. T.; Dasgupta, S.; Lorant, F.; Goddard, W. A.ReaxFF: A reactive force field for hydrocarbons. J. Phys. Chem. A 2001,105 (41), 9396−9409.(12) Nomura, K. I.; Kalia, R. K.; Nakano, A.; Vashishta, P. A scalableparallel algorithm for large-scale reactive force-field moleculardynamics simulations. Comput. Phys. Commun. 2008, 178 (2), 73−87.(13) van Duin, A. C. T. Reactive Molecular Dynamics Modeling andAdvanced Power Generation Applications. Presented at the 2010University Turbine Systems Research (UTSR) Workshop, October 19−21, 2010. Available via the Internet at http://www.netl.doe.gov/publications/proceedings/10/utsr/presentations/thursday/vanDuin.pdf, accessed December 5, 2013.(14) Chenoweth, K.; van Duin, A. C. T.; Persson, P.; Cheng, M. J.;Oxgaard, J.; Goddard, W. A. Development and application of a ReaxFFreactive force field for oxidative dehydrogenation on vanadium oxidecatalysts. J. Phys. Chem. C 2008, 112 (37), 14645−14654.(15) Goddard, W. A.; Mueller, J. E.; Chenoweth, K.; van Duin, A. C.T. ReaxFF Monte Carlo reactive dynamics: Application to resolvingthe partial occupations of the M1 phase of the MoVNbTeO catalyst.Catal. Today 2010, 157 (1−4), 71−76.(16) Bedrov, D.; Smith, G. D.; van Duin, A. C. T. Reactions ofSingly-Reduced Ethylene Carbonate in Lithium Battery Electrolytes: AMolecular Dynamics Simulation Study Using the ReaxFF. J. Phys.Chem. A 2012, 116 (11), 2978−2985.(17) Huang, L. L.; Bandosz, T.; Joshi, K. L.; van Duin, A. C. T.;Gubbins, K. E. Reactive adsorption of ammonia and ammonia/wateron CuBTC metal-organic framework: A ReaxFF molecular dynamicssimulation. J. Chem. Phys. 2013, 138 (3), 034103. (DOI: http://dx.doi.org/10.1063/1.4774332).(18) Wang, Q. D.; Wang, J. B.; Li, J. Q.; Tan, N. X.; Li, X. Y. Reactivemolecular dynamics simulation and chemical kinetic modeling ofpyrolysis and combustion of n-dodecane. Combust. Flame 2011, 158(2), 217−226.(19) Liu, L. C.; Bai, C.; Sun, H.; Goddard, W. A. Mechanism andKinetics for the Initial Steps of Pyrolysis and Combustion of 1,6-Dicyclopropane-2,4-hexyne from ReaxFF Reactive Dynamics. J. Phys.Chem. A 2011, 115 (19), 4941−4950.(20) Ding, J. X.; Zhang, L.; Zhang, Y.; Han, K. L. A ReactiveMolecular Dynamics Study of n-Heptane Pyrolysis at High Temper-ature. J. Phys. Chem. A 2013, 117 (16), 3266−3278.(21) Salmon, E.; van Duin, A. C. T.; Lorant, F.; Marquaire, P. M.;Goddard, W. A. Early maturation processes in coal. Part 2: Reactivedynamics simulations using the ReaxFF reactive force field on MorwellBrown coal structures. Org. Geochem. 2009, 40 (12), 1195−1209.(22) Salmon, E.; van Duin, A. C. T.; Lorant, F.; Marquaire, P. M.;Goddard, W. A. Thermal decomposition process in algaenan ofBotryococcus braunii race L. Part 2: Molecular dynamics simulationsusing the ReaxFF reactive force field. Org. Geochem. 2009, 40 (3),416−427.(23) Castro-Marcano, F.; Kamat, A. M.; Russo, M. F.; van Duin, A. C.T.; Mathews, J. P. Combustion of an Illinois No. 6 coal char simulatedusing an atomistic char representation and the ReaxFF reactive forcefield. Combust. Flame 2012, 159 (3), 1272−1285.(24) Castro-Marcano, F.; van Duin, A. C. T.; Mathews, J. P. ReaxFFMolecular Dynamics Pyrolysis Simulations of a Large-Scale Model ofIllinois No. 6 Coal Including the Role of Organic Sulfur. Presented atthe International Pittsburgh Coal Conference; Pittsburgh, PA, USA,2012.(25) Zhang, J. L.; Weng, X. X.; Han, Y.; Li, W.; Cheng, J. Y.; Gan, Z.X.; Gu, J. J. The effect of supercritical water on coal pyrolysis andhydrogen production: A combined ReaxFF and DFT study. Fuel 2013,108, 682−690.

(26) Zheng, M.; Li, X.; Liu, J.; Guo, L. Initial Chemical ReactionSimulation of Coal Pyrolysis via ReaxFF Molecular Dynamics. EnergyFuels 2013, 27 (6), 2942−2951.(27) Sandia National Laboratories. LAMMPS. Available via theInternet at http://lammps.sandia.gov/doc/pair_reax.html, accessedOctober 11, 2013.(28) Zheng, M.; Li, X.; Guo, L. Algorithms of GPU-enabled reactiveforce field (ReaxFF) molecular dynamics. J. Mol. Graphics Modell.2013, 41, 1−11.(29) 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. EnergyFuels 1992, 6 (4), 414−431.(30) Genetti, D.; Fletcher, T. H.; Pugmire, R. J. Development andapplication of a correlation of C-13 NMR chemical structural analysesof coal based on elemental composition and volatile matter content.Energy Fuels 1999, 13 (1), 60−68.(31) Gavalvas, G. R. Coal Pyrolysis; Coal Science and Technology,Vol. 4; Elsevier Scientific: New York, 1982; pp 1−3.(32) Mathews, J. P.; Chaffee, A. L. The molecular representations ofcoalA review. Fuel 2012, 96 (1), 1−14.(33) ACD/Labs. Chemsketch. Available via the Internet at http://www.acdlabs.com/products/draw_nom/draw/chemsketch/, accessedOctober 11, 2013.(34) Accelrys. Materials-Studio. Available via the Internet at http://accelrys.com/products/materials-studio/, accessed October 11, 2013.(35) Given, P. H.; Marzec, A.; Barton, W. A.; Lynch, L. J.; Gerstein,B. C. The Concept of a Mobile or Molecular-Phase within theMacromolecular Network of CoalsA Debate. Fuel 1986, 65 (2),155−163.(36) Miura, K.; Shimada, M.; Mae, K.; Sock, H. Y. Extraction of coalbelow 350 degrees C in flowing non-polar solvent. Fuel 2001, 80 (11),1573−1582.(37) Tekely, P.; Nicole, D.; Brondeau, J.; Delpuech, J. J. Applicationof C-13 Solid-State High-Resolution NMR to the Study of ProtonMobilitySeparation of Rigid and Mobile Components in CoalStructure. J. Phys. Chem. 1986, 90 (22), 5608−5611.(38) Tersoff, J. New Empirical-Model for the Structural-Properties ofSilicon. Phys. Rev. Lett. 1986, 56 (6), 632−635.(39) Brenner, D. W. Empirical Potential for Hydrocarbons for Use inSimulating the Chemical Vapor-Deposition of Diamond Films. Phys.Rev. B 1990, 42 (15), 9458−9471.(40) Rappe, A. K.; Goddard, W. A. Charge Equilibration forMolecular-Dynamics Simulations. J. Phys. Chem. 1991, 95 (8), 3358−3363.(41) Nakano, A. Parallel multilevel preconditioned conjugate-gradient approach to variable-charge molecular dynamics. Comput.Phys. Commun. 1997, 104 (1−3), 59−69.(42) Cheng, X. M.; Wang, Q. D.; Li, J. Q.; Wang, J. B.; Li, X. Y.ReaxFF Molecular Dynamics Simulations of Oxidation of Toluene atHigh Temperatures. J. Phys. Chem. A 2012, 116 (40), 9811−9818.(43) Chenoweth, K.; van Duin, A. C. T.; Goddard, W. A. ReaxFFreactive force field for molecular dynamics simulations of hydrocarbonoxidation. J. Phys. Chem. A 2008, 112 (5), 1040−1053.(44) Yamada, T.; Phelps, D. K.; van Duin, A. C. T. First principle andReaxFF molecular dynamics investigations of formaldehyde dissocia-tion on Fe(100) surface. J. Comput. Chem. 2013, 34 (23), 1982−1996.(45) Gavalas, G. R. Coal Pyrolysis; Coal Science and Technology, Vol.4; Elsevier Scientific: New York, 1982; pp 34−38.(46) Solomon, P. R.; Hamblen, D. G.; Carangelo, R. M.; Serio, M. A.;Deshpande, G. V. General-Model of Coal Devolatilization. EnergyFuels 1988, 2 (4), 405−422.(47) Serrano, D. P.; Aguado, J.; Escola, J. M.; Rodriguez, J. M.; SanMiguel, G. An investigation into the catalytic cracking of LDPE usingPy-GC/MS. J. Anal. Appl. Pyrolysis 2005, 74 (1−2), 370−378.(48) Hodek, W.; Kirschstein, J.; van Heek, K.-H. Reactions of oxygencontaining structures in coal pyrolysis. Fuel 1991, 70 (3), 424−428.

Energy & Fuels Article

dx.doi.org/10.1021/ef402140n | Energy Fuels 2014, 28, 522−534533

(49) Chatterjee, K.; Stock, L. M.; Zabransky, R. F. The Pathways forThermal-Decomposition of Aryl Alkyl Ethers during Coal Pyrolysis.Fuel 1989, 68 (10), 1349−1353.(50) Serio, M. A.; Hamblen, D. G.; Markham, J. R.; Solomon, P. R.Kinetics of Volatile Product Evolution in Coal PyrolysisExperimentand Theory. Energy Fuels 1987, 1 (2), 138−152.

Energy & Fuels Article

dx.doi.org/10.1021/ef402140n | Energy Fuels 2014, 28, 522−534534