The utility of coal molecular models

Fuel Processing Technology 92 (2011) 718–728

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Fuel Processing Technology j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / f u p r o c

The utility of coal molecular models Jonathan P. Mathews a,⁎, Adri C.T. van Duin b, Alan L. Chaffee c a b c

Department of Energy & Mineral Engineering, & The EMS Energy Institute, The Pennsylvania State University, 126 Hosler Building, University Park, PA 16802, USA Department of Mechanical and Nuclear Engineering & The EMS Energy Institute, The Pennsylvania State University, 136 Research Building East, University Park, PA 16802, USA Monash University, School of Chemistry, PO Box 23, Clayton, Vic 3800, Australia

a r t i c l e

i n f o

Article history: Received 9 February 2010 Accepted 22 May 2010 Available online 4 August 2010 Keywords: Coal representations Molecular modeling Char modeling Reactive force field Coal structure

a b s t r a c t There are a large number (N125) of molecular representations for coals that span the rank range over seven decades. However, their utility has mostly been in representing chemical structural features, rather than in probing physical structure or exploring the structure–behavior relationship. This paper examines the utility of coal models and reviews the existing and emerging opportunities for coal models to contribute to coals effective utilization via demystification of the structure–behavior relationship. Coal models have been used to explore the coalification pathway, including contraction with water removal. Physical evaluations have probed the density of models as a check on their accuracy. Pore size distribution and sorption have been explored in simple pores and more recent work with carbon dioxide, water and methane sorption within the porous structure of large-scale (b20,000 atoms) model. Pair distribution frequency and small angle X-ray scattering simulations have also been compared with experimental observations and offer an additional check on the constitution of the model structure. Simulated HRTEM and simulated (calculated) NMR spectra also exist. Models have been disassembled in efforts to represent the pyrolysis process, char formation, and char reactivity (including the role of ion-exchangable ions). Similar to the pyrolysis models, direct liquefaction has been explored with a pyrolysis style approach. Coal-solvent swelling, and coal-solvent solubility have also been explored. While considerable progress has accompanied improvements in computational power and software advances, it is the generation of the model that is the most significant barrier to the meaningful utility of these models. The ability to generate large-scale models (incorporation of molecular weight diversity and structural diversity) with new automation approaches, coupled with new dynamic force-fields that can simulate reactive events in complicated materials like coals, offers a new hope for the utility of coal or char molecular models to probe our understanding and aid in the scientific method rather than our current informed trial and error approach. © 2010 Elsevier B.V. All rights reserved.

1. Introduction Molecular models of coal have progressively populated the literature over the last 70 years. While they number in excess of 125 models, the initial purpose of many of these models was to aid the comprehension of coal. Models have developed over the years to capture variations in rank and maceral composition, but were relatively limited in scale until the relatively recent application of advanced computational approaches. With advances in analytical techniques, computational power and software tools, models of increasing scale, structural diversity, and incorporating physical features such as: porosity, orientation, density, pore size distribution, fractal dimensionality, etc. are beginning to emerge. The question arises when will these models be useful beyond representing constitutional features of coal?

⁎ Corresponding author. Tel.: +1 814 863 6213. E-mail address: [email protected] (J.P. Mathews). 0378-3820/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.fuproc.2010.05.037

The application of the early models to challenges of industrial relevance were limited, though the pyrolysis pathway of Fuch and Sandoff in 1942 [1] and the work of Shinn (1984) [2] in elucidating liquefaction pathways are notable exceptions. With the development of computational modeling techniques (available in the early 90's), innovative uses have emerged that capture industrially relevant properties or behaviors of coal. Limitations on computational power and model utility are progressively being removed and a wide variety of behaviors are beginning to be examined. New possibilities are emerging for coal models to contribute in a meaningful and predictive manner to many aspects of coal use. This paper examines the utility of coal models and reviews the existing and emerging opportunities for coal models to contribute to coals effective utilization and demystification of the structure–behavior relationship. Models are now being applied to a wide range of applications. These span from physical transitions (coal drying and contraction to swelling behaviors observed with solvents and gases) to chemical transitions (pyrolysis, char formation), to reactivity (including spontaneous combustion).

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2. Discussion

2.2. Drying

2.1. Coalification

Low-rank brown coals have high moisture content — sometimes greater than 50% by weight [11]. This presents a challenge for efficient coal utilization. Kumagai et al. [12] used molecular modeling to build an understanding of coal–water interactions in Yallourn coal possessing 60% moisture. Their relatively simple model was constructed to maintain consistency with elemental analysis and 13C NMR spectroscopy data. Their model was based on monomer units, C21H20O7, illustrated in Fig. 2. The minimum energy conformation of this assembly was identified and the volume occupied by coal molecules and water molecules calculated. Upon progressive removal of water molecules, a monotonic volume reduction of the brown coal-water model was observed. The dried product occupied about one half the original volume, in close agreement to experimental observations. The potential energy of the system was also monitored and indicated considerable stabilization of the coal macromolecule beyond 80% water removal. This stabilization is also evidenced by the contraction of the coal volume beyond 80% water removal (Fig. 2.). This enhanced stabilization, observed by modeling, provided insight into why these low-rank coals do not fully rehydrate, in accord with experimental observations which indicated only ∼ 80% of the original volume is restored upon rehydration. Vu et al. proposed a structural unit (C100H80O2) for brown-coalderived fossil wood (Fig. 3) that was based on 13C-solid-state NMR, ultimate and functional group analytical data taken for a fossil sample of Podocarpus sp. collected from the Loy Yang mine, Latrobe Valley, Victoria, Australia [13]. This model comprised an 11-mer of (different) degraded lignin subunits, three of which were assembled with water into a 3D periodic cell (27.3 × 27.3 × 27.3 Å3), to achieve a density consistent with the sample. This group investigated the time development of a brown coal model system, so as to characterize for the first time molecular motion and average structural properties (e.g., molecular diffusion and average bond distances) at ambient conditions (298 K, 1 atm). Chaffee later compared the volume reduction and potential energy of this system with dehydrated models prepared by (a) water removal

There is utility of coal models in aiding demystification of the structure–behavior relationship with maturation. Several authors have examined the rank progression. Mazumdar et al. proposed five models from a lignite “parent model” to an anthracite coal showing aromatization and rind condensation with loss of oxygen [3]. While some of the models were very small (a three ring structure representing high-volatile bituminous rank) and 2D, they capture aromatic ring growth in high-rank coals as well as reducing oxygen content. Pitt generated two snake-like models for coals of 80 and 90% carbon content [4]. These aromatic and hydroaromatic conjugated chains captured changes in aromaticity, as well as loss of oxygen functionality. Spiro and Kosky proposed models for high-, intermediate-, and low-volatile bituminous coals [5]. In an approach similar to Spiro's earlier work [6], plastic space filling models (108:1 scale) were constructed. This inclusion of the third dimension enabled the orientation and stacking to be considered (along with density) in addition to ring index and other parameters. Mukhopadhya and Hatcher captured the rank transitions within a set of computational models generated to represent low-rank coalified logs (Fig. 1) [7]. This sampling approach simplified the structural generation process by avoiding the complicating issue of maceral influence. These four models aid in visualizing the coalification transitions (as determined from NMR parameters) for the Alder lignin model [8] to subbituminous coal. Iwata et al. generated 3 models for Japanese coals of carbon content 78–88% [9]. Murata et al. also used these models in their density work [10] and included a forth model of lower carbon content (72%). Progressing from lower rank to higher rank (see Fig. 1) the models show: 1) slight loss of oxygen functionality (four to three oxygen atoms) but retention of ring index, 2) slight increase in the ring index (from 1.5 to 2) and the inclusion of an additional hydroaromatic ring, 3) significant ring index increase to four with a larger model and hence lower oxygen content, despite retaining three oxygen atoms.

Fig. 1. The Hatcher et al. models of low-rank coalified logs. From Mukhopadhyay, P. H.; Hatcher, P. G., Composition of Coal. In Hydrocarbons from Coal, Am. Assoc. of Petroleum Geologists, Studies in Geology Series, Law, B. E.; Rice, D. D., Eds. 1993; Vol. 38, pp 79–118 Reprinted by permission of the AAPG whose permission required for further use. Also the coalification models of Iwata et al. (a–c) and Murata et al. (d) Reprinted with permission from Murata, S.; Nomura, M.; Nakamura, K.; Kumagaya, H.; Sanada, Y., Energy & Fuels, (1993) 7, (4), 469–472. Copyright 1993 American Chemical Society.

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Fig. 2. Monomer unit, model, experimental volume loss/gain with water removal and rehydration and simulation results. Graphs from Kumagai et al. Prepr. Pap. - Am. Chem. Soc., Div. Fuel Chem., 1999; New Orleans, 1999. Reprinted with permission of the author.

Fig. 3. Structural unit (a) and periodic box (b) containing three 11-mer units plus water. Lower boxes show locations of coal–coal hydrogen bonds with thermal drying (c) and mechanical thermal expression (d).

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at 378 K to simulate evaporative water removal (similar to Kumagai's approach) and (b) water removal at 498 K and 17.4 MPa of applied pressure to simulate water removal by the non-evaporative Mechanical Thermal Expression (MTE) process [14]. It was observed that better stabilization of the coal structure developed in the former case. This was evident through more extensive formation of electrostatic and H-bonding interactions, while the MTE system was more strained (Fig. 3). In other drying work, the Narkiewicz and Mathews largescale bituminous coal model (total mass 178,960 amu) was populated with an appropriate loading of water molecules, around 1% by mass (hydrogen bonded to oxygen functionality) and the model allowed to relax to observe volume change [15]. A volume change of 1% was determined and assessed to be reasonable for the loss of bound water [15]. 2.3. Evaluation of physical and chemical structural parameters The physical and chemical properties of a model can be evaluated, using a range of programs now available, and compared with experimental data to provide insight into the constitution of coal. The process of constructing a coal model to conform to certain physical properties, constrains model construction and provides insight into its constitution. Density is an obvious physical parameter with specialty programs/simulations being created for its evaluation in coal [16,17] and other carbonaceous materials [18]. In the POR program, the model is placed into a 3 dimensional grid and each grid cell interrogated to determine if it is within empty space or within an atomic volume [16]. Further evaluation determines if the pore space is accessible to a specific size molecule. By varying the sorbate size the fractal dimension of the model can be determined [19]. Pore size distributions can be determined for small-scale models, where the pores are mostly ultramicroporous [19], and coal models of larger scale [15]. It should be noted that determination of the volume of an early coal model was performed by immersing a physically constructed plastic representation into a volume of water in an “Eureka” style approach by Spiro [5], well before computational models were available. Simulated NMR spectra have also been used to “adjust” model structures to achieve closer agreement with experimental data [20,21]. From a consideration of NMR second moments, Jurkiewicz concluded that the Wiser model provided a good representation of the macromolecular (rigid) part of coal structure [22]. This approach can provide an informative check of coal model accuracy and also has potential, for educational purposes, to provide explanations of complex structural features. Scripting has also been used on a largescale models [23] to calculate the 12 NMR parameters of Solum et al. [24], as reported in a structural analysis paper for South African coals [25]. It is suggested that, with the combination (spectra generation coupled with quantitative analysis), improved evaluations of largescale models are possible. Computer aided design construction approaches, such as SIGNATURE [26,27], also perform a similar evaluation function for a range of chemical parameters. 2.4. Pyrolysis and char formation The first of the molecular level representations of coal, by Fusch and Sandoff published in 1942, also included a pyrolysis pathway [1]. This early representation was a central elongated aromatic “raft” interspersed with aliphatic rings and heteroatoms (C135H97O9N1S1). Although far removed from modern representations, the pyrolysis pathway involved aromatic/aliphatic carbon–oxygen bond breaking to yield low molecular weight fragments. The single and double ring products would then condense (aromatize) forming low-temperature tars and gases. The utility of the model was both to represent the structural knowledge of coal at the time and to visualize the transformations occurring with thermal decomposition. More recent

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work also had similar goals. The work of Mathews et al. generated models for two bituminous vitrain coals (Upper Freeport and Lewiston–Stockton seams) [28] and explored transitions to char obtained under drop-tube pyrolysis conditions (Fig. 4) [29]. The work of Jones et al. generated similar structures [30], based on an altered Shinn model [2], to meet Pittsburgh #8 data, the chars being generated in a wire-mesh reactor (Fig. 4). Both studies utilized a variety of analytic approaches to capture the structural data and probe the understanding of pyrolysis via manipulation of the original coal model structures to concur with char data (elemental, NMR, FTIR, etc.) in this visual approach. Earlier non-computational, work is also relevant for showing the state of knowledge visually [31]. The current state-of-the-art lignin pyrolysis modeling, is also applicable to coal pyrolysis modeling [32]. The two major constraints to pyrolysis studies of macromolecule coal/biopolymer models in this approach are model construction and kinetic model generation. Tools such as CompGen, an excel-visual basic application, aid in the construction process to determine distributions of component molecules to minimize difference between model and data [32]. The more transformational contribution however is the Kinetic Modelers Toolbox, an excel-based Kinetic Modeling Editor (KME) graphical interface, that facilitates reaction network construction, kinetic rate estimation, and goal seeking among other tools [33]. However, while the necessary components are determined, no atomistic models are constructed or utilized. Marzek [34] constructed models of carbonized coal (char) as a means of probing the relationship between structure and carbonization temperature. Models were based on elemental composition, XRD, TEM, Pyrolysis-Field Ionisation MS, 13C NMR and FTIR data. It was observed that chars could best be represented by a two-component model comprising both ‘oligomeric’ and ‘planar’ aggregates of aromatic ring structures that can both grow during carbonization at progressively higher temperatures. The growth of planar regions is relatively difficult since it requires that the ‘bay’ regions of adjacent aromatic moieties be approximately aligned (planar) and in close proximity so that condensation can occur (Fig. 5). In contrast, the oligomerisation of aromatic rings in non-planar configurations does not have this inherent spatial constraint and is relatively facile. It is suggested that the oligomeric aggregate model provides a good representation (Fig. 5) of the isotropic phase of semi-coke up to carbonization temperatures of 550–750 °C (depending on the original coal). Using a semi-empirical approach, Marzec demonstrated, in novel work, how chars with poorly aligned aromatic ring systems may still exhibit very low electrical resistivity because there is still substantial conjugation between the π-oribtals, when the torsion angle is ≤72°, of adjacent aromatic ring systems [34]. In an alternative approach, chars have also been directly generated utilizing a (Hybrid) Reverse Monte Carlo approach based on X-ray, neutron, and electron diffraction techniques of an “industriallygenerated char” [35]. This super-computer based approach has been utilized mostly in the generation of carbon structures and glasses including some structures at scale (≈15,000 atoms) [18,36–46]. In the coal-char work approximately 1300 carbon atoms of the char structure are placed in a graphitic structure, within a periodic cell, and the system perturbed. Atoms are selected and moved randomly. Via this approach the error is minimized between the experimental data and the simulation. The radial distribution function was the main parameter used for optimizing the fit between scattering data and simulation of the dense portion of a char. This was of interest for exploring “char” microstructure and dissolution in steel. Earlier work also examined the pair distribution function within large aromatic structures [47] as well as the rank models [48] of Spiro and Kosky [5]. Generation of char models has utility in exploring microstructure– reactivity relationships [30,49]. Recent work has attempted to directly capture structural features for coals (and also other carbonaceous materials such as chars and soots) from HRTEM lattice fringe images

722 J.P. Mathews et al. / Fuel Processing Technology 92 (2011) 718–728 Fig. 4. Structure of coal-to-char transitions derived via molecular modeling approaches (Mathews et al.) Drop-tube computational fluid dynamics generated temperature profile, SEM of coal and char particles, and molecular models of coal and chars for Lewiston–Stockton vitrinite. Also Pittsburgh #8 coal to char transitions (right hand side) for wire-mesh generated chars. This image on the right hand side was published in, Jones, J. M.; Pourkashanian, M.; Rena, C. D.; Williams, A., Fuel (1999) 78, 1737–1744. Copyright Elsevier (1999) Reprinted with permission.

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Fig. 5. “Bay” containing molecules capable of forming planar dehyrocyclization products. Model (right hand side) of the isotropic phase in low-temperature carbonized coals, illustrating the poor alignment of adjacent aromatic moieties in this ‘oligomerised aggregate’. Adapted with permission from Marzec, A., Energy & Fuels 1997, 11, (4), 837–842. Copyright 1997 American Chemical Society.

via (Fringe3D) [50]. An early example of this approach for deriving a structural model of char is shown in Fig. 6 [51]. Improvements in capturing curvature and rejecting unrealistic fringes (based on enerergetics or curvature) should result in improved representations. This approach has the potential to simplify the model construction process and enable very large structures (N100,000 atoms) to be generated with an improved alignment of aromatic moieties, stacking distribution, and structural diversity without super computer/cluster facilities. Moving forward, a hybrid construction approach utilizing the best aspects of Fringe3D and the Reverse Monte Carlo approach has potential to enable the construction of large, yet more representative, models. HRTEM images can also be simulated from atomistic models as a test of agreement, to facilitate optimization prediction for micrograph imaging, and/or as a viewing aid [40,52]. 2.5. Liquefaction, solvent swelling/extraction and molecular aggregation The utility of molecular models in conceiving the relationship between coal structure and liquefaction products was elegantly demonstrated by Shinn in 1984 [2]. Shinn constructed a large (hand-drawn) model (C661H561O74N11S6) that was consistent with elemental analysis, NMR, functional groups distributions and a range of other analytical data for similar coals (Illinois No's. 6 & 2, Indiana V or Kentucky 9 or 11). This model was assembled in a manner that

quantitatively reproduced high molecular weight products observed for this coal via short- and long-contact time dissolution (Fig. 7). The model also elucidated liquefaction pathways for the production of lower molecular weight hydrocarbons with reduced functionality that are, consequently, more amenable to downstream processing. Twostage liquefaction products were shown to be radically different from simple (one stage) thermal conversion products, with a rationale provided. Using Monte Carlo computational approaches for both the construction and simulated liquefaction of Illinois no. 6, Provine and Klein [53] examined free-radical cleavage (via initiation, propogation, and termination) and retrograde reactions in a reaction algorithm of 1,450 possible reactions. Also included was the first attempt, for coal models, of considering the local reaction environment, thus linking reaction and transport competition. Their paper presented an interesting view of what was possible in 1994 using a small lattice of 3 models, but also provided vision for large-scale “hyper lattice” simulations that would better enable the evaluation of global and physical properties such as molecular weight distribution. Kinetic parameters enabled time-dependant liquefaction to be simulated. This resulted in an array of chemical parameters for comparison with experimental data: liquid phase aromaticity, proton NMR distribution for the single and condensed ring moieties, solid carbon aromaticity, liquid phase α-protons, oxygen functionality (“solid” ethers, liquid

Fig. 6. HRTEM lattice fringe micrograph of a coal char and its derived char model Lattice fringe image is Republished with permission from Sharma, A. et al. Energy & Fuels 2002, 16, (1), 54–61, Copyright 2002 American Chemical Society. Char model image courtesy of Victor Fernandez-Alos (The Pennsylvania State University).

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Fig. 7. The Shinn model of bituminous coal (upper left); its dissolution products with short-contact times (upper right) and with more severe processing (lower left); the products of 2-stage liquefaction (lower right). Modified from the image published in Shinn. J. H., Fuel 1984, 63, 1187–1196 Copyright Elsevier (1984), Reprinted with permission.

ethers, and hydroxyl groups), likewise sulfur functionality, gas and liquid yields, with rough agreement with the analytic data. From this cacophony of results, the initial stages were controlled by initiationhydrogen transfer-termination chemistry; with ether groups producing liquid products, and thioether groups producing gas products. Retrograde reactions were progressively more important once 30% conversion was achieved; this was also the point where 100% conversion for the rigid portion of the coal was reached. As the era of computer simulation developed, Takanohashi and colleagues began to investigate coal swelling and extractability [54,55]. Using molecular dynamics to evaluate structure–potential energy relationships, they were able to determine the volume change

(swelling ratio) in a variety of solvents and demonstrate close agreement with experimental data for Upper Freeport coal. A detailed consideration of the energetics, indicated that it was electrostatic interactions between coal molecules that were disrupted by the solvent, facilitating swelling (Fig. 8) [54]. The initial stages of South African coal swelling was also simulated by van Niekerk et al. [56] based on proposed models of inertinite-rich and vitrinite-rich coal [23]. Pyridine, NMP, and CS2/NMP swollen coals were created by a model reconstruction approach to include an appropriate number of solvent molecules. Energetics were examined, including analysis of the hydrogen bond distribution through an automated Perl scripting approach, necessary because of the model scale: N14,000 atoms. Coal–

Fig. 8. Equilibrium swelling of an Upper Freeport pyridine soluble model with 14 methanol molecules and the volume/energy relationship. Republished with permission from Takanohashi et al. Energy & Fuels 1999, 13, 922–926. Copyright 1999 American Chemical Society.

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Fig. 9. Model (center), theoretical pyridine soluble extract (left), and residue of a South African vitrinite-rich coal (right). Carbon atoms are green, oxygen red, nitrogen blue, and sulphur atoms are yellow (hydrogen not shown). Image courtesy of Daniel van Niekerk from Structural elucidation, molecular representation and solvent interactions of vitrinite-rich and inertinite-rich South African coals. Ph.D. Thesis, The Pennsylvania State University, 2008.

coal non-bonding interactions were influenced with the solvent addition, with the coal-hydroxy group providing the dominant site for solvent–coal interaction. Also of note, is the ability to predict theoretical solubility parameters, via the Painter et al. approach [57], for molecules within the macromolecular assembly [58]. This visual aid to, a priori, predict the extraction “potential” (potential as transportation issues are not included) for a given solvent has utility in direct coal liquefaction studies. The simulation provided visual confirmation of the compositional (maceral) influences on solubility (Fig. 9). Along with density calculations, the aggregation of structural fragments can be explored [59]. Here, the stacking and relative contributions of bonding (cross-links, hydrogen bonding, etc.) can be studied more easily than via an experimental approach. With a molecular dynamics approach, the influence of heat (energy) on aromatic association can be explored [59]. At temperatures between 350 and 400 °C the volumes of the aggregated molecules increased dramatically, and irreversibly, in agreement with differential scanning calorimetry observations. This thermal stabilization results from the relaxation of strained structures [59].

2.6. Carbon dioxide sequestration and coalbed methane One of the newest areas for utility in coal models has been CO2 sequestration and enhanced coalbed methane simulations. Early work was focused on methane capillary condensation behavior in slit-shaped pores of parallel displaced surfaces within an anthracite and other coal representations [60]. Pore filling was achieved with a Grand Canonical Monte Carlo approach and molecular dynamics used to determine diffusion coefficients at various temperatures so as to explore energy and density transitions as a proxy for phase change (freezing/melting) of methane. Bulk methane froze at a higher temperature than the confined methane in the cases of a graphitic pore, and for simulations with anthracite and other non-uniform coal pore “surfaces”. A large-scale Pocahontas model [15] was utilized to visualize the CO2, CH4, and H2O

loadings with a Monte Carlo sorption simulation [15]. Gas loadings, to meet experimental observations, were achieved with random placement coupled with rotational and transitional movement of the sorbate molecules. Configurations were accepted or rejected until an appropriate loading and minimal change in overall energy was observed. Pore size distributions and the physical blocking of porosity by stationary water molecules were quantified. Anisotropic swelling was observed for CO2 and greater swelling for CO2 (twice as many molecules) was observed than for CH4. The visualization illustrates the complexity of gas sorption phenomena and aids in considering sequestration issues such as sorption location, loading, swelling/contraction issues relevant to coal. High energy wide angle pair distribution function and pore size distribution (from small angle X-ray scattering), with and without gas loading was compared with experimental data [61]. This approach, coupling dynamic experiments with modeling has provided a significant improvement in confidence for modeling behaviors over broad length-scales and with high gas pressures. Tambach et al., utilizing a bituminous model by Spiro [5], extended the modeling simulation with Monte Carlo Metropolis algorithm to populate the model, then used molecular dynamics to evaluate the energetics and sorption locations of CH4 and CO2 [62]. A statistical sampling of 11,200 grid cells distributed across the model identified locations of preferential sorption for each gas. Competitive sorption was also evaluated and showed preferential sorption of CO2. Investigations utilizing a single gas molecule probing the coal model allowed the heat of adsorption to be calculated, these values were consistent with the expected trend of CO2 being more stable than CH4 and within the expected range for physisorption (8–42 kJ/mol).

2.7. Spontaneous combustion of coal The spontaneous combustion of coal is a problem of considerable practical significance. Because spontaneous combustion involves chemical reactions higher level (ab initio or semi-empirical) modeling

Fig. 10. Reaction profiles for the reaction of molecular oxygen with 9,10-dihydroanthracene (a) and guaiacyl-carboxyilic acid. Taken from Florez, E.; Montoya, A.; Chamorro, E.; Mondragon, F. In Molecular modeling approach to coal spontaneous combustion, 12th International Conference on Coal Science, Cairnes, Australia, 2003; Cairnes, Australia, 2003 (b), as models for the prevalent benzylic carbons in bituminous coal and brown coal, respectively.

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approaches have been employed. The computational power required to facilitate these higher order calculations limits the size of models that can be considered. Studies to date have focused only on the regions (functional groups) of the coal structure that are likely to be involved. Benzylic positions are well known in coal models and are expected to be one of the more highly reactive groups. Florez et al. [63] investigated the interaction of molecular oxygen with 9,10dihydroanthracene as a model for this functional group in coal. They observed a two-step process, progressing through a peroxide intermediate leading to a quinone reaction product (Fig. 10a). The overall reaction was exothermic by approximately −110 kcal/mol with reaction energy barriers of 25 and 27 kcal/mol, respectively, for the two steps. After oxidation at the C9 position, subsequent oxidation at the C10 position was investigated. This second oxidation reaction was found to be more highly exothermic (−200 kcal/mol) and the energy barrier of this second oxidation was just 5 cal/mol. In other words, the second reaction was activated by the first in a way that would lead to increasing heat release, or spontaneous combustion in the case of real coal. Zhang and Chaffee [64] carried out a similar study where the guaiacyl-carboxylic acid (Fig. 10b) was used as a model for the prevalent type of benzylic carbon in brown coal. It can be seen that the oxidation of the guaiacyl carboxylic acid progresses through a number of exothermic steps with low energy barriers until the aliphatic side-chain is completely excised. Again, the activation of the latter steps, as a consequence of the heat release in the earlier steps, provides a coherent molecular level understanding of how spontaneous combustion can progress within brown coal. 2.8. Reactive force fields The development of accurate, atomistic-scale models creates exciting opportunities for application of atomistic-scale simulations on coal chemistry, which can provide unique insight in the complex chemistry associated with structural modifications and reactions. To perform such simulations we require computational methods that can include large (N1000 atoms) systems to capture coal complexity, and can simulate the dissociation and formation of chemical bonds. While quantum mechanics based methods are too computationally expensive for reasonable scale simulations, the quality and versatility improvements of reactive force fields (RFFs) over the last 10 years has positioned the field for realistic dynamic coal-chemistry simulations. RFF development started with the Kelires and Tersoff formulation for silicon [65], based on bond order concepts introduced by Pauling [66]. These concepts were extended to carbon-based systems by Brenner [67]. Since then, a number of RFF-schemes have been developed [68–73]. Of these RFF schemes, the ReaxFF potential [72] has arguably the highest relevance for coal systems. ReaxFF has demonstrated a high reliability for both reaction barriers and reaction

energies. Furthermore, ReaxFF parameters have been reported for a wide range of materials, including hydrocarbons [74,75], hydrocarbon combustion [72,76,77], ceramics [78], metals and metal oxides [74,79] and metal hydrides [75]. Recent applications of ReaxFF to algaenan [76], lignite [80] and phenolic polymers [81] have demonstrated the applicability of ReaxFF to study the pyrolysis of coal and complicated organic polymers. Furthermore, recently developed parallel ReaxFF schemes [82,83] provide a sufficiently large atom budget (N1,000,000 atoms) to capture highly complex coal structure. Fig. 11 demonstrates the feasibility of ReaxFF integration with the coal and char models. This figure shows the initial configuration, rate of oxygen consumption and char fragmentation observed during a high-pressure, high-temperature ReaxFF molecular dynamics simulation of a char portion (taken from Fig. 4). Such simulations enable mapping of the influence of the local char structure on reactivity. By linking current RFF-methodology, accurate char- or coal models, RFFderived data on reaction probabilities/diffusion constants in meso- or macroscale coal models will greatly enhance our design strategies for the utilization of coal in coal-to-liquids, gasification, and combustion systems. 3. Summary and future prospects Considerable effort, over a seven-decade period, has been invested in the creation of models (of various scale) that capture many of the diverse structural components and arrangement of coal. Many of these modelers achieved their goal with the model creation but others have, through a variety of innovative approaches, applied coal models to unraveling the complexities of coal structure and structure– behavior relationships. These simulations have involved studies of coalification (chemical and physical transformations), maceral differences, coalbed methane release, carbon dioxide sequestration, drying and compression, liquefaction, pyrolysis, char formation, gasification, and combustion. Approaches have varied from the manual manipulation of bonds, to large-scale simulations capturing bond-breaking and bond-forming processes. With increasing accessibility of computational power, it is important to note the potential for expansion of simulation methods to explain coal behaviour in a variety of applications. The flow between the model creation process and the utility of coal models (computational simulation) is illustrated in Fig. 12. Simulations can be considered successful if “modeling results generally agree with experimental results, and provide additional insight beyond that from experiments alone” [84] and if “the models have been utilized... to investigate the mechanisms of pyrolysis, … including the formation of the products and its control by experimental conditions and catalysts” [85]. These and other reviews that have considered the utility of coal models, or the future application [86,87] have provided insight into the desire for coal science

Fig. 11. (Left) Initial char-structure used in a high-temperature (T = 3500 K), high-pressure ReaxFF simulation of char combustion. (Right) oxygen uptake and char fragmentation as a function of time, as observed during the ReaxFF molecular dynamics simulation.

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Fig. 12. Flow of data, coal model generation, and simulation for the utility of coal models. Modified and expanded from Shinn [88].

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