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Energy & Fuels 2008, 22, 3104–3111
Improved Low-Volatile Bituminous Coal Representation: Incorporating the Molecular-Weight Distribution Marielle R. Narkiewicz and Jonathan P. Mathews* Department of Energy and Mineral Engineering, and the Earth and Mineral Sciences Energy Institute, The PennsylVania State UniVersity, UniVersity Park, PennsylVania 16802 ReceiVed December 21, 2007. ReVised Manuscript ReceiVed April 24, 2008
A large (>22 000 atoms) molecular representation of Pocahontas No. 3 low-volatile bituminous coal was generated that contains 215 separate molecular entities, ranging between 78 and 3286 amu, creating a molecularweight distribution. Data used in this construction were based on (1) the average molecular properties and the carboxylation-oxidation molecular-weight distribution, (2) aromatic “raft” size evaluation via image analysis of lattice fringe high-resolution transmission electron microscopy images, (3) molecular-weight distribution through laser desorption mass spectrometry, and (4) physical characteristics (molecular orientation and helium density). The large-scale coal representation enabled the incorporation of molecular-weight diversity, which is an improvement to the structural modeling of coal. The large-scale model is necessary to conform to an appropriate molecular-weight distribution. A preferred orientation that is expected for a coal of this rank was imposed. The inclusion of these improvements will better enable the model to be used in application studies.
Introduction Coal is a heterogeneous solid, making it difficult to generate meaningful representative structures. There have been many simplistic representations produced; however, there have been few molecular models that can simulate behavioral processes. Some well-known molecular models have been developed to visualize coal structure in both 2D1–3 and 3D space.4–9 Given1 created one of the first well-cited models, although the goal of the author was not to create a model of coal but to illustrate the structural constraint imposed by the hydrogen distribution. He used infrared spectroscopy and proton magnetic spectroscopy to estimate the ratios of aromatic/aliphatic carbons in bituminous coal and created a structure with approximately 80 carbon atoms.1 Shinn’s2 model is also quite well-known and was used to visualize coal liquefaction behavior. The criterion for his model was a mass of 10 000 Da, which would provide a model that was large enough to illustrate diversity in composition, * To whom correspondence should be addressed: 126 Hosler Building, University Park, PA 16802. E-mail:
[email protected]. (1) Given, P. H. The distribution of hydrogen in coals and its relation to coal structure. Fuel 1960, 39, 147–153. (2) Shinn, J. H. From coal to single-stage and two-stage products: A reactive model of coal structure. Fuel 1984, 63 (9), 1187–1196. (3) Solomon, P. R. Coal structure and thermal decomposition, new approaches in coal chemistry. ACS Symp. Ser. 1981, 169, 61. (4) Spiro, C. L. Space-filling models for coal: A molecular description of coal plasticity. Fuel 1981, 60 (12), 1121–1126. (5) Spiro, C. L.; Kosky, P. G. Space-filling models for coal. 2. Extension to coals of various ranks. Fuel 1982, 61 (11), 1080–1083. (6) Carlson, G. A. Computer simulation of the molecular structure of bituminous coal. Energy Fuels 1992, 6 (6), 771–778. (7) Faulon, J. L.; Carlson, G. A.; Hatcher, P. G. Statistical models for bituminous coal: A three-dimensional evaluation of structural and physical properties based on computer-generated structures. Energy Fuels 1993, 7 (6), 1062–1072. (8) Takanohashi, T.; Nakamura, K.; Terao, Y.; Iino, M. Computer simulation of solvent swelling of coal molecules: Effect of different solvents. Energy Fuels 2000, 14, 393–399. (9) Mathews, J. P.; Hatcher, P. G.; Scaroni, A. W. Proposed model structures for Upper Freeport and Lewiston-Stockton vitrinites. Energy Fuels 2001, 15 (4), 863–873.
including cross-linking and functional groups, but was small enough to be managable.2 The 2D and 3D structural models provided the field with specific interactions and behaviors that occurred on the molecular level in coal. The onset of statistical computer simulations led to a deeper investigation of molecular combinations. In the first computer simulation, Carlson6 studied the van der Waals interactions for aromatic and saturated ring molecules of bituminous coal molecular structures. The four 3D models studied were the molecular models of Given, Wiser, Solomon, and Shinn. A molecular model of anthracite was created from a combination of chemical and physical data of Pennsylvania anthracites as an aid to visualize reactions necessary for the generation of carbon material.10 Assumptions were made regarding the placement of nitrogen and oxygen in the structure, but the atomic H/C ratio of the model matched well with experimental data.10 The final structure contained five separate fragments each with approximately 81 aromatic rings per sheet.10 With improvements in computational software and in analytical approaches, further refinements in the approach of model building were possible. In a study of Upper Freeport bituminous coal, the CS2-NMP extracts were fractionated with acetone and pyridine into acetone-soluble, acetone-insoluble/pyridine-soluble, and pyridine-insoluble and then modeled with molecular mechanics and molecular dynamics.11 It was found that, the heavier the fraction, the more stable the association-dissociation structures were that formed.11 The authors suggest that interactions between hydrogen bonds and aromatic-aromatic interactions will result in a stable associated structure.11 Another (10) Pappano, P. J.; Mathews, J. P.; Schobert, H. H. Structural determinations of Pennsylvania anthracites. Prepr. Pap.-Am. Chem. Soc., DiV. Fuel Chem. 1999, 44 (3), 567–570. (11) Takanohashi, T.; Iino, M.; Nakamura, K. Simulation of interaction of coal associates with solvents using molecular dynamics calculations. Energy Fuels 1998, 12 (6), 1168–1173.
10.1021/ef700779j CCC: $40.75 2008 American Chemical Society Published on Web 07/09/2008
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noteworthy model is of Takanohashi et al.,12 which used molecular mechanics to illustrate swelling of Upper Freeport bituminous coal by means of methanol-induced volume change. They placed nine copies of the same model consisting of two molecules (C95H66N2O5 and C63H45N1S1O3) and with the same orientation within a periodic boundary cell and found it sufficient to demonstrate swelling.12 Methanol molecules were added to the system; the system was allowed to equilibrate; and the volume change was recorded. This simulation showed that swelling reaches a maximum when up to 14 methanol molecules were added to this small-scale repeated model, after which additional methanol molecules do not change the system energy.12 The swelling ratio was calculated to be 1.20.12 Although Takanohashi et al.12 developed a relatively large model (approximately 1500 carbon atoms), it used repeating units of a small structure with the same orientation, thus resulting in swelling anisotropy and greatly oversimplifying the complexity involved in coal models. Molecular models have also been used to illustrate pyrolysis and char formation.13,14 The structures discussed have served to represent structural information and, in some cases, an associated behavior; however, none of the structures have been applied to multiple processes. The small scale restricts the representation to an average molecule with a limited extent to structural diversity. The work of Shinn2 stands out because of the model scale and rationale for structural diversity as related to liquefaction studies. However, to date, there has not been meaningful molecularweight distribution incorporated into coal molecular models. A molecular-weight distribution has been included in many mathematical models that address coal behavioral issues. Such a distribution is thought to be essential for appropriate modeling behaviors, such as thermoplasticity,15,16 liquefaction,2 and pyrolysis.17 The fluidity model proposed by Solomon et al.15 predicts a molecular-weight distribution of the decomposing macromolecular network to define a solid and liquid fraction for the thermoplasticity of coal. The molecular-weight distribution, along with the chemical makeup of extracts, gave insight about the mechanism of plasticity of coal.16 Both low- and highmolecular-weight materials were considered to accurately illustrate coal in a liquefaction study.2 Finally, a molecular-weight distribution, along with other properties, was used to infer crosslinking in the coal structure for coal pyrolysis.17 Several characterization techniques were analyzed during this research for structural elucidation of the coal, including laser desorption mass spectrometry (LDMS), high-resolution transmission electron microscopy (HRTEM), and nuclear magnetic resonance (NMR). LDMS can approximate the molecularweight distribution from cross-linked aromatic fragments. Miura et al.18 observed that the same data could be determined using (12) Takanohashi, T.; Nakamura, K.; Terao, Y.; Iino, M. Computer simulation of methanol swelling of coal molecules. Energy Fuels 1999, 13 (4), 922–926. (13) Jones, J. M.; Pourkashanian, M.; Rena, C. D.; Williams, A. Modeling the relationship of coal structure to char porosity. Fuel 1999, 78 (14), 1737–1744. (14) Mathews, J. P.; Hatcher, P. G.; Scaroni, A. W. Devolatilization, a molecular modeling approach. Prepr. Pap.–Am. Chem. Soc., DiV. Fuel Chem. 1998, 43 (1), 136–140. (15) Solomon, P. R.; Best, P. E.; Yu, Z. Z.; Charpenay, S. An empirical model for coal fluidity based on a macromolecular network pyrolysis model. Energy Fuels 1992, 6 (2), 143–154. (16) Fong, W. S.; Khalil, Y. F.; Peters, W. A.; Howard, J. B. Plastic behavior of coal under rapid-heating high-temperature conditions. Fuel 1986, 65 (2), 195–201. (17) Solomon, P. R.; Fletcher, T. H.; Pugmire, R. J. Progress in coal pyrolysis. Fuel 1993, 72 (5), 587–597.
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LDMS and matrix-assisted LDMS. Herod et al.19 found that all of the Argonne premium coals exhibit a high molecular-weight distribution in the 20 000 atoms, enabling appropriate structural diversity. An iterative approach of breaking and reforming crosslinks better accommodated the molecular-weight distribution from LDMS data. A low atomic H/C ratio (0.58 reported for Pocahontas No. 330) indicates that there should be large aromatic structures and that data are supported by the limited fraction of protonated aromatic carbons (3031 and 33%26). Pocahontas No. 3 is primarily aromatic, with the aliphatic portion mostly composed of methyl groups. For heteroatom locations, Kelemen et al.33 determined Pocahontas No. 3 coal to have 64% pyrrolic nitrogen, 33% pyridinic nitrogen, and 3% quaternary nitrogen. In this model, this ratio was adopted, which resulted in 118 pyrrolic, 61 pyridinic, and 5 quaternary nitrogen structures. In other research using X-ray photoelectron spectroscopy (XPS), it was found that the most abundant form of nitrogen in all ranks of coal is pyrrolic (80% for lignites to 55% for anthracites), pyridinic nitrogen increases with rank (10% for lignites to 40% for high-rank coal), and no rank effect was found for quaternary nitrogen.34 Stock and Muntean31 report that up to 80% of the oxygen content in Pocahontas No. 3 are present as aromatic ethers. Winans35 also reported that oxygen was found as heterocyclic ethers or furans, and in the molecular representation, this was accommodated. Pocahontas No. 3 coal has very low sulfur content, with only approximately 2 sulfur atoms for every 1000 carbon atoms;20 therefore, sulfur, a minor elemental contribution, was added to the structure as thiophenes, as determined from Stock and Obeng, to complete the desired molecular structure. George et al.36 also stated that there is an increasing trend of sulfur contained in aromatic structures with increasing rank. The aliphatic sulfides present in the precursor biomass are decomposed and can be converted into thiophenes with coalification.36 While models can be generated that conform to compositional and physical constrictions, the true test of a model is in meeting observed experimental behaviors. Incorporation of molecular diversity allows more accurate behavioral modeling, for example, solvent-extraction and solvent-swelling studies. In these processes, accurately structural representations are necessary. This model contains 22 151 atoms, with a molecular-weight distribution comprised of 215 unique molecules generated from a distribution of 777 aromatic rafts and appropriate aliphatic and heteroatom distributions. It is a significant improvement over the small-scale models traditionally generated. The raw LDMS data can be seen in Figure 2. The match between molecular-weight distributions is important for the application of this model to future studies, where other phenomena and coal behavioral properties can then be investigated. The raw data (Figure 2) show a sharp peak around 250-400 amu followed by peaks between 500 and 800 amu, although smaller, indicating that most of the molecular fragments included in the molecular representation should be between these ranges. This is similar to the data of Herod et al.,19 in which it was reported that, for all of the Argonne Premium coals, there were corresponding series of peaks in the 200-500 mass range; however, the authors also reported another series of peaks in the 1000-5000 mass range, which our raw data did not contain. The generated data is sensitive to laser power. We obtained the LDMS at a laser power just over the ionization desorption minimum. While there is uncertainty in the quantitative nature of LDMS, it is used here as a basis for molecular-weight (33) Kelemen, S. R.; Gorbaty, M. L.; Kwiatek, P. J. Quantification of nitrogen forms in Argonne premium coals. Energy Fuels 1994, 8 (4), 896– 906. (34) Wojtowicz, M. A.; Pels, J. R.; Moulijn, J. A. The fate of nitrogen functionalities in coal during pyrolysis and combustion. Fuel 1995, 74 (4), 507–516. (35) Winans, R. E. Pyrolysis fast-atom-bombardment tandem massspectrometry characterization of coals. J. Anal. Appl. Pyrolysis 1991, 20, 1–13. (36) George, G. N.; Gorbaty, M. L.; Kelemen, S. R.; Sansone, M. Direct determination and quantification of sufur forms in coals from the Argonne premium sample program. Energy Fuels 1991, 5 (1), 93–97.
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Figure 2. Mass spectrometry ion chromatogram of the frequency of molecular-weight ranges versus the mass for Pocahontas No. 3, obtained from the Penn State Coal Sample Bank.22
Figure 3. Proposed molecular representation of Pocahontas No. 3 coal. The heteroatoms are rendered as spheres for ease of visualization.
inclusion. Figure 2 also shows a total molecular-weight range from the smallest fragments around 78 amu (benzene) to much larger fragments around 2500 amu (aromatic sheets). To ensure that the molecular representation has a similar weight distribution, some fragments were altered (through adding or breaking bonds) to match the LDMS data as discussed earlier. The physical characteristics of the model are also important, ensuring that both bulk parameters and diverse components are accurately represented. The physical structure of such a high-rank coal should have a slight preferential orientation, as implied by optical birefringence.37 To force alignment, stress was applied on
two opposing faces of a well-dispersed model to force alignment. To complete this, the periodic boundary structure underwent a geometry optimization, in which the cell was reduced with the application of an external stress in the y direction, forcing a slight compression in the y direction. The stress was then removed, and the geometry optimization was performed. Because coal is naturally in a strained state, no global energy optimization was attempted. (37) Cody, G. J.; Larsen, J. W.; Siskin, M.; Cody, G. D. S. Correlation of optical birefrigence with coal rank. Structural implications. Energy Fuels 1989, 3 (5), 551–556.
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Figure 4. Different view of the model with the hydrogen omitted to improve visualization. The heteroatoms are rendered as spheres for ease of visualization.
The simulated helium density was calculated using the POR program.28 The helium density of Pocahontas No. 3 is 1.34 g/cm3 (dmmf).38 Presented in this paper is the largest coal model generated to date, incorporating a rationale for a molecular-weight distribution. The model has a total molecular mass of 178 960 amu, including 215 high- and low-molecular-weight structural components and an average number molecular weight of 832 amu. The purpose of this paper is to outline the methods used to generate and evaluate the large, molecularly diverse representation of Pocahontas No. 3 bituminous coal.
Results and Discussion Model Construction. The Cerius2 software was used to build and combine all fragments, as well as to add oxygen, nitrogen, and sulfur atoms and to build and break bonds required by crosslinking. Beyond this, energy minimizations were completed on the structure to ensure a low-energy configuration. The resulting (38) Huang, H.; Wang, K.; Bodily, D. M.; Hucka, V. J. Density measurements of Argonne premium coal samples. Energy Fuels 1995, 9 (1), 20–24.
model can be seen in Figures 3 and 4. The structure shown is a highly cross-linked molecular representation that includes the appropriate aromatic and aliphatic carbon components, as well as heteroatoms, shows orientation, and is the largest structure of coal constructed to date. A small portion of fragments is represented in two-dimensional form in Figure 5 to illustrate some random structural parameters (hydrogen has been omitted to aid viewing). The scale of the model is 110 × 70 × 110 Å, which has a volume of 847 000 Å.3 Model Evaluation. The model was created and compared to the review data of Stock and Muntean.31 Stock and Muntean31 observed the following elemental composition: C100H57.5O1.1N1.3S0.2, while the model presented here possesses the following elemental composition: C100H58.2O1.0N1.3S0.2. As seen, the model compares very well with the literature review data. The atomic H/C ratio reported in experimental studies is 0.58,30,31 and the model data gave a ratio of 0.58. Solum et al.26 described techniques used to derive 12 parameters (determined through CP-MAS NMR) that relate to the carbon structure in several coals, and once established,
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the model calculated 44 of 100 carbon atoms represented as bridgeheads, while both Stock and Muntean31 and Solum et al.26 reported 34. Different catenation approaches account for some of the differences between the model and experimental data. Incorrect labeling of the aromatic sheets may have also occurred during the lattice fringe image analysis, resulting in an askew bridgehead carbon number. For example, if two intermediatesized sheets were situated in close proximity, it may have been incorrectly assigned as one large aromatic sheet, resulting in a larger bridgehead number. However, if the bridgehead frequency was adjusted through breaking large aromatic sheets, the molecular-weight distribution would then have been altered, resulting in less structural diversity. The approach used here was to have structures ranging from a benzene ring to a 9 × 9 aromatic raft cross-linked to agree with LDMS molecular-weight distribution. Closer matching to the HRTEM fringe frequency and careful selection of structures in the naphthalene to 3 × 3 range should improve this discrepancy. The elemental composition as well as several chemical parameters of the model match well with literature review data. Outside the chemical constitution, the molecular-weight distribution and physical parameters were considered just as important to accurately represent the coal structure. Beyond the chemical parameters, the physical description of Pocahontas No. 3 is important in accurately portraying the molecular structure of the coal. Three physical parameters were incorporated into the molecular representation; porosity, density, and (for a first time) orientation. The stress simulated on the model created a more microporous coal than initially generated. The porosity was evident when the structure was viewed in virtual reality using Crystal Eyes39 3D analysis. The stress forced the fragments much closer together, creating a pore map that was microporous. Stress was employed to force an orientation. The model was analyzed using the POR program to determine porosity and simulated helium density. POR reported a helium density of 1.30 g/cm3 for the molecular representation. This number is close to the reported helium density of 1.34 g/cm3 for Pocahontas No. 3 (dmmf basis).38 The stress simulated on the model served to force alignment of the larger aromatic fragments. To measure the relative angle of orientation of the fragments in the model, the HRTEM feature in Cerius2 was employed. As can be seen from this image (Figure 6), there is a slight preferential alignment or stacking of the larger fringes present, as expected. To illustrate the versatility of the structure, the model was evaluated using several force fields (Dreiding, Universal, cvff, and pcff), which all generated similar results of energy values. This demonstrates the validity of the model and that it can be used for different applications. Incorporating molecular diversity was an objective during model generation. Laser desorption data of Pocahontas No. 3 (Figure 2) was compared to the molecular weight of the fragments present in the model. They are compared in Figure 7. The overall shape and distribution of both model and experimental data are reasonable, given the constraint of starting
Figure 5. Two-dimesional depiction of 14 fragments randomly selected that are found in the Pocahontas No. 3 molecular representation.
Figure 6. Simulation of HRTEM using Cerius2 on the molecular representation of Pocahontas No. 3 coal.
the parameters were used to determine the aromatic cluster size for each sample.26 Specifically, the amount of bridgehead carbons determined was used in equations of linear or circular catenation to determine the cluster size of aromatics in the structure.26 Literature data of the average aromatic carbons per cluster is 20 (determined by NMR).26 Because of the fact that the model uses neither circular nor linear catenation (parallelogram catenation is used instead), the aromatic carbons per cluster were manually counted in the molecular representation, yielding 22 aromatic carbons per cluster. This further demonstrates the closeness of the results of the model to literature experimental data. The chemical constitution was also evaluated in the model and compared to literature results of Stock and Muntean31 and Solum et al.26 (see Table 2 in the text below). In comparison to the experimental data, the molecular representation matches in many respects. The aromatic and aliphatic carbon components match well to Stock and Muntean.31 Contrary to this, the model matches closer with Solum et al.,26 with respect to the protonated and nonprotonated aromatic components. The largest discrepancy is seen in the bridgehead carbon component (faB), where
Table 2. Comparison of the Chemical Constituentsa of the Proposed Model and the Data of Stock and Muntean31 and Solum et al.26 carbon structural parameters of Pocahontas No. 3 coal model name
fa (%)
faH (%)
faN (%)
faP (%)
faS (%)
faB (%)
fal (%)
fal* (%)
falH (%)
falO (%)
Stock and Muntean31 Solum, Pugmire, and Grant26 proposed model
89.5 86.0 91.0
30.0 33.0 35.0
59.5 53.0 56.0
5.3 2.0 3.0
19.5 17.0 10.0
34.5 34.0 44.0
10.5 14.0 9.0
7.9 6.0 7.0
2.6 8.0 2.0
0.0 0.0 0.0
a f , aromatic carbon; f , protonated aromatic carbon; f , nonprotonated aromatic carbon; f , phenolics; f , alkylated ethers; f , bridgehead carbon; a aH aN aP aS aB fal, aliphatic carbon; fal*, CH3 and C aliphatic carbon; falH, CH2 and CH aliphatic carbon; and falO, bonded to oxygen.
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Figure 7. Comparison of the molecular-weight distribution of the model (color bars) to the molecular-weight distribution from the LDMS graph (black transparency) of Pocahontas No. 3.
with 777 fragments. The initial molecular-weight distribution of the model did not match the raw data well. To match the molecular-weight distribution of the model to the LDMS raw data, the fragments of the model were altered by either building bonds between fragments to create higher molecular-weight structures or breaking bonds to create smaller molecular-weight structures. The breaking and building of bonds was performed in an iterative approach, continually comparing the results to the raw LDMS data until an appropriate match was made. Both distributions show a molecular-weight component consistent with benzene and fragments between 250 and 700 amu. The proposed model had a total molecular mass of 178 960 amu, with 215 structural fragments spanning the molecularweight distribution from 78 amu (benzene) to 3286 amu (large aromatic sheet), with a mean molecular weight of 832 amu. Summary and Conclusions Coal is complex! It is challenging to analyze and provide representative structures. However, a model can replicate many of the physical and analytical properties specific to a certain rank and type of coal. A large, diverse structure of bituminous coal was created on the basis of the combination of multiple analytical techniques. Structural diversity was attained through the union of HRTEM lattice fringe analysis and LDMS (39) RealD. The Premier Digital 3D Experience, http://www.realdcorporate.com/scientific/ (accessed May 1, 2007).
molecular-weight distribution. Data obtained from literature that included the chemical constitution of the coal and the physical characteristics (porosity, helium density, and orientation) were used to define the Pocahontas No. 3 coal structure. The model generated contains 22 151 atoms, making it the largest coal model to date, with a diverse molecular-weight distribution to accurately represent the intricacy of the coal structure. When compared to the literature-reported experimental data, the model matches well with respect to the H/C ratio, helium density, elemental analysis, average aromatic rings per cluster, and general chemical constitution, as well as illustrating diversity through a molecular-weight distribution and including orientation anisotropy. The model generated (shown in Figures 3 and 4) was consistent with analytical data: microporosity, helium density, and molecular-weight distribution. Because of the large, molecularly diverse structure, the application of the model for many coal behavioral processes should extend much further beyond this research. Because it consists of several criteria encompassing the chemical and physical attributes of this type of coal, it will serve future research. Acknowledgment. We thank the U.S. Department of Energy, who funded this research under Grant number DE-FG26-02NT41556. We also thank the reviewers who helped refine this paper. EF700779J
