Article pubs.acs.org/EF
Pyrolysis of Liulin Coal Simulated by GPU-Based ReaxFF MD with Cheminformatics Analysis Mo 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. 1 Zhongguancun 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, surpassing CPU implementations, was employed to explore the initial chemical mechanisms and product distributions in pyrolysis of Liulin coal, a bituminous coal from Shanxi, PRC. The largest coal model ever used in simulation via ReaxFF MD, the Liulin coal molecular 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 effects on the product profile and the initial chemical reactions of the Liulin coal model pyrolysis. The generation rates of C14−C40 compounds and gas tend to equilibrate within 150−250 ps, indicating that the simulation should allow most of the thermal decomposition reactions complete and the simulated product profiles are reasonable for understanding the chemical reactions of the Liulin coal pyrolysis. The product (gas, tar, and char) evolution tendencies with time and temperature observed in the simulations are fairly in agreement with the experimental tendency reported in the literature. In particular, the evolution trends of three representative products (naphthalene, methyl-naphthalene and dimethyl-naphthalene) with temperature are very consistent with 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 the chemical reaction network in ReaxFF MD simulation. The generation and consumption of HO· and H3C· radicals with time and temperature are reasonable and consistent both with the evolution of H2O and CH4, and with the detailed chemical reactions obtained as well. The amount of six-membered ring structures was observed to decrease with time and temperature, because of their conversion into 5-membered rings or 7−9-membered rings or even-larger-membered ring structures that will further open and decompose into small fragments. This work demonstrates a new methodology for investigating coal pyrolysis mechanism by combining GPU-enabled high-performance computing with cheminformatics analysis in ReaxFF MD.
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INTRODUCTION Coals have complex carbonaceous structures and represent a majority (70%) of China’s total energy consumption in 2009.1 Pyrolysis is the initial and fundamental step in most coal conversion processes, accounting for up to 70% of the weight loss suffered by the coal;2 this process controls the swelling, char reactivity, and physical structure of coal. The pyrolysis products are generally related with the ignition, temperature and flame stability in coal combustion. Therefore, mechanism investigation of coal pyrolysis will assist in improving the efficiency and cleanliness of coal conversion and utilization.3−6 Coal pyrolysis refers to the thermal decomposition of coal in an inert atmosphere or in a vacuum at a specific temperature.3 Coal undergoes a rapid loss of moisture and volatiles, followed by a myriad of coupled complex reaction pathways when pyrolysis occurs. However, the heterogeneous nature of coal and the complexity of the process have made it very difficult to determine the mechanisms of coal pyrolysis, even with state-ofthe-art experimental approaches.2 The progress of molecular modeling based on the development of realistic atomistic representations of coal in recent years7 provides a useful approach for better understandings of the coal molecular structure and its initial mechanism for the effective utilization of coal in the pyrolysis system. © 2013 American Chemical Society
As a method for investigating chemical bonding with high accuracy, density functional theory (DFT)8,9 is computationally intensive and expensive, resulting in the fact that it has little applicability for large-scale coal models necessary to capture the complexity of coal pyrolysis. Classical molecular dynamics (classical MD or MD) has the ability for modeling large-scale system with millions of atoms10 but could not be used to explore chemical reactions due to its physical elastic collision between atoms with static bonds and fixed partial charges.11 The Reactive Force Field (ReaxFF), developed by van Duin et al.,11 is a general bond-order potential and can be used to fully address the chemistry of dynamic bonds and polarization effects. It is able to describe the evolving of formation, transition, and dissociation of chemical bonds in a molecular system when combined with molecular dynamics (ReaxFF MD). ReaxFF MD is an atomistic-scale approach with accuracy close to DFT but with very much reduced computational costs.12,13 Since the parameters of ReaxFF are derived from DFT11 on transition-state energy, geometry data, and heat of formation of small molecules, it can be used to investigate Received: October 28, 2013 Revised: December 7, 2013 Published: December 9, 2013 522
dx.doi.org/10.1021/ef402140n | Energy Fuels 2014, 28, 522−534
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Article
the complex chemical reactions and understand the initial mechanisms. In this paper, ReaxFF MD simulation using GMD-Reax was employed to perform pyrolysis reaction on a molecular representation for Liulin bituminous coal to examine the initial decomposition mechanisms and product distributions under different temperature conditions. The Liulin coal model was constructed to contain 28 351 atoms and its pyrolysis simulation details using ReaxFF molecular simulation are described, followed by the analysis of pyrolysis product distributions and thermal decomposition mechanisms obtained by using a C++ program VARMD created for revealing the detailed chemical reactions. The strategy that combines GPUenabled high performance computing with cheminformatics analysis of the simulation trajectory in ReaxFF MD suggests a practical approach for revealing the detailed chemical reactions in pyrolysis simulation of more-realistic coal models, which is useful for the optimization of coal conversion processes.
chemical reactions without any predefining of reactive sites or reaction pathways, providing a new and promising approach for molecular simulation of complex system with chemical reactions.13 In addition, the accuracy and efficiency of ReaxFF molecular dynamics (ReaxFF MD) simulation has been demonstrated by applications in a wide variety of materials.14−17 Especially, ReaxFF MD has been utilized in several studies to explore initial reactive mechanisms and kinetics associated with combustion and pyrolysis processes of important compounds. Simulation of pyrolysis and combustion of important compounds including n-dodecane by Wang et al.,18 6dicyclopropane-2,4-hexyne by Liu et al.,19 and n-heptane by Ding et al.20 yielded reasonable results consistent with the experimental data and their reaction mechanisms were proposed accordingly. In 2009, Salmon et al. reported ReaxFF MD simulation of the thermal decomposition of two large models, namely, a macro-model containing 2692 atoms for Morwell brown coal21 and the algaenan Botryococcus braunii race L biopolymer model that consists of 2966 atoms,22 both of which reproduced some reactions observed in offline experiments, showing that such computation is useful in providing molecular-based kinetic models for pyrolysis processes. CastroMarcano et al.23 carried out ReaxFF MD simulation on a largescale model of Illinois No. 6 coal char with 7458 atoms in order to examine the complex char combustion chemistry and also to discuss the role of sulfur within the model.24 Very recently, Zhang et al.25 combined ReaxFF method with DFT to investigate the reaction mechanism of coal pyrolysis and hydrogen production in supercritical water (SCW), which revealed the cooperative effects between SCW and coal. ReaxFF MD was also employed in our recent work26 to perform simulation of chemical reactions in pyrolysis of a bituminous coal model with 4976 atoms to examine the nascent decomposition mechanisms. These applications have shown the capability and great potential of ReaxFF to handle complex chemistry of larger molecular systems with chemical reactions in coal pyrolysis. Although ReaxFF MD allows direct and fast thermolysis modeling for larger-scale molecular systems than DFT methods, it is still a computationally intensive approach, ∼10−50 times slower than classical MD.13 It would take 54 days to perform a million-time-step simulation for PETN crystal system with 16 240 atoms27 on a processor (Dell Precision T7500 desktop system) using LAMMPS FORTRAN codes. Obviously, larger coal models allow for more-detailed description in terms of coal structure which would be closer to the real-world coal structure but have to suffer much longer simulation time when ReaxFF MD is applied in coal pyrolysis.23 The computational challenge to simulating a large-scale coal model comparable to real coal motivated us to have GMD-Reax created.28 It is the first graphic processing unit (GPU)-enabled ReaxFF molecular dynamics program with significantly increased computational capability of ReaxFF MD for larger system size and longer time scale by taking advantage of a single GPU attached to a desktop workstation. In terms of the simulation time per time step, averaged over 100 steps, GMDReax achieved a high speed computation, i.e., 4 times faster than van Duin et al.’s FORTRAN codes in LAMMPS27 on 8 CPU cores for simulated system with 27 283 atoms.28 With GMD-Reax, it was proven to be practical to simulate pyrolysis of large coal models containing 10 000−30 000 atoms with ReaxFF MD on a desktop workstation in an effort to investigate
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METHODS
Experimental 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 in Table 1.
Table 1. Proximate and Ultimate Analysis of Liulin Coal Ultimate Analysis (wt % daf) C H O N S
88.4 4.8 5.2 0.94 0.46 Proximate Analysis (wt %)
moisture ash volatile
0.66 11.32 20.64
Solid-state 13C NMR spectroscopy has been shown to be an important tool in characterization of coal structure.29 13C NMR has been used to quantify the average carbon skeletal structure of coal with 12 parameters that describe the aromatic and aliphatic regions of the coal matrix.30 The 13C NMR spectra of Liulin coal are presented in Figure 1. The structural parameters of Liulin coal obtained directly from the 13C NMR spectrogram or by resolving overlapping peaks in
Figure 1. 13C NMR spectra of Liulin coal. 523
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the spectra are listed in Table 2, where fa′ is the percentage of carbon with sp2 hybridization in aromatic carbon fa among the total carbon;
Table 2. Structural Parameters of Liulin Coal Samples Obtained from 13C NMR structural parameter
value
fa fal faC fa′ faH faN faP faS faB fal* falH falO
0.76 0.24 0.044 0.73 0.40 0.34 0.004 0.11 0.19 0.065 0.122 0.036
Figure 2. Unimolecular model of Liulin coal constructed based on the Wiser model. experimental results in the literature,35−37 relatively small molecules with molecular weights of
