Elsevier

Fuel

Volume 253, 1 October 2019, Pages 910-920
Fuel

Full Length Article
Dynamic profiles of tar products during Naomaohu coal pyrolysis revealed by large-scale reactive molecular dynamic simulation

https://doi.org/10.1016/j.fuel.2019.05.085Get rights and content

Highlights

  • The largest model of 98,748 atoms for Naomaohu coal ever simulated by ReaxFF MD.

  • Detailed dynamic profiles of tar intermediates/structures in coal pyrolysis revealed.

  • Strong relationship between tar composition and coal pyrolysis stages observed.

  • Five- and seven-membered ring structures in tar should be the soot precursors.

  • –O–(CH2)n– linkages play a key role in low-temperature cross-linking reactions.

Abstract

Understanding dynamic profiles of tar in coal pyrolysis is vital to high quality chemical production and upgrading process of tar, which is difficult to be accessed experimentally. Using large coal models with reasonable distribution of functional groups, ReaxFF MD method can shed light on comprehensive structures and reaction details of coal tar in pyrolysis, which complements available experimental observations. In this work, a large model with 98,748 atoms of Naomaohu low-rank coal is constructed to explore tar behaviors for the first time computationally by heat-up ReaxFF MD simulations at 500–2500 K. The correspondence between the tar behaviors and the divided four pyrolysis stages observed would be very helpful for modulating the composition and yield of tar and the subsequent upgrading process. The dynamic profiles of bridge bonds, ring intermediates and the detailed structures of hydrocarbons in tar (C5–C40 fragments) are revealed, which shows that the five- and seven-membered ring intermediates in tar should be soot precursors during coal pyrolysis process. The increasing trend of –O–(CH2)n– is strongly related to low-temperature cross-linking reactions during low-rank coal pyrolysis, while the increasing trend of Car–Car plays a significant role in recombination reactions at high temperature. Moreover, the simulation also shows that the production of aliphatic hydrocarbons is favored at the primary pyrolysis stage, accompanied with high concentration of oxygenated compounds produced, while aromatic fragments are most likely generated at the secondary pyrolysis stage where the amount of phenolic products tends to decrease.

Introduction

The global energy demand is expected to increase by 40% from 2008 to 2030 with a rate of 1.5% [1]. Coal is the predominant energy resources currently worldwide and low-rank coal accounts for 45% of the total coal reserves [2], [3] in the world. Low-rank coal, commonly referred to as brown coal and sub-bituminous coal, is abundant in regions of Australia and China [4], [5], which will play a more important role in the coming decades because of the significant decrease of bituminous coal reserves [6]. As an economically attractive alternative to high-rank coal for electricity generation [5], there is an increasing focus on the development of clean technologies for low-rank coal utilization [2], [7]. It is particularly of great interest due to the ever faced energy demand and the abundant coal resources in China. However, the extensive utilization of low-rank coal has been limited by its characteristics, like high moisture content, lower calorific value, high spontaneous combustion susceptibility, and high ash yield. A deep understanding of the structure and its conversion processes at high temperature for low-rank coal will promote its effective utilization. [8].

Coal pyrolysis is the fundamental process for the manufacture of coke, tar and gases, of which the reactions are the most important and basic events occurring in the coal conversion processes of hydrogenation, gasification and combustion [9], [10]. Considering the high oxygen content and various oxygen-containing functional groups in low-rank coal [11] that leads to a complex reaction environment, understanding the pyrolysis chemistry is critical to the high efficiency and clean utilization of low-rank coal. Coal pyrolysis refers to the thermal decomposition in an inert atmosphere or in a vacuum [12], [13]. The key role of tar pyrolyzates was emphasized by many researchers in literature [14], [15], [16]. Coal tar output in China was estimated at more than 12 million tons in 2010 [1], which is the most abundant and commercially valuable product obtained from coal pyrolysis because it can be upgraded and hydro-treated to clean oil fuels [17]. Some components of coal tar will further convert to soot that is important for environmental issue [13].

The knowledge of the detailed composition of coal tar and their dynamic profiles during low-rank coal pyrolysis is fundamental and very important for producing high-quality tar products and the subsequent upgrading process [11], [17]. Tar is a mixture of many compounds with molecular weight range of 100–700 a.m.u and wide chemical composition, which is chiefly comprised of aliphatic hydrocarbons, phenols and aromatics that can be refined to chemicals or be upgraded to liquid fuels. Gas chromatography/mass spectrometry (GC/MS), fourier transform infrared (FTIR), high-performance liquid chromatography (HPLC), etc. are useful and effective techniques to analyze the compositions of coal tar fractions [16]. According to GC/MS results by Wang et al. [16], the phenol and its homologs of alkyl-substituted (C1 to C3) are one type of major products in volatile fractions for Shendong bituminous coal. The composition of primary tar produced in the fluidized-bed reactor by rapid bituminous coal pyrolysis at 600 °C were detected by combining GC/MS with HPLC methods by Ledesma et al. [18]. There are 27 polycyclic aromatic hydrocarbons (PAH) species identified in total, varying from 2-ring to 9-ring structures with benzeniod PAH, fluoranthene benzologues and indene benzologues. The evolution curves obtained from a thermogravemetric analyzer (TG) coupled with FTIR for 35 coals were measured at three different heating rates (10, 30 and 100 K/min) by Holstein et al. [19]. The TG-FTIR data for tar evolution reveals a generally consistent behavior for different coals, showing increasing activation energies with increasing coal rank. These observations with varied types of experimental techniques provide the general and basic understanding for coal tar products. However, each technique has its own limitation. For example, the disadvantage of GC/MS analysis is that the compounds with a boiling point higher than 300 °C are hard to be volatilized and detected, and those with a lower boiling point than solvent may be overlaid by solvent peak in chromatograms [16], which leads to incomplete identification about tar pyrolyzates.

Moreover, the dynamic evolution of coal tar with time or temperature is hardly accessible by state-of-the-art experiments. Therefore, phenomenological models such as FG-DVC [20], CPD [12], [21], and FLASHCHAIN [22], [23] were developed to predict coal devolatilization yields, providing quantitative relationships between parent coal and chemical properties of tar using the simplified network model of coal. However, the dynamic structure details and evolving trends of coal tar products during pyrolysis, are critical for modulating the nature and yield of the desired tar products, which cannot be obtained by using these models.

It is well recognized that coal has a very complicated chemical structure and coal pyrolysis is a process with radical driven reactions occurring simultaneously within extremely short time. The various steps from intermediates to final products during coal pyrolysis can hardly be disentangled experimentally, not to mention to track the dynamic evolution tendencies of functional groups and radicals. It is still a challenge to reveal a coherent comprehension about its pyrolytic degradation and kinetics performance over the entire operating range. Computational approach of the reactive molecular dynamics simulation is promising as an useful alternative in development of kinetic mechanisms for novel systems along with the rapid development of modern computing power [24], [25].

As a promising force field for reactive MD, ReaxFF developed by van Duin and Goddard et al. [26] is an empirical force field on the basis of bond-order (BO) that can smoothly describe evolving trends of chemical reactions with time. The concept of bond-order was introduced in force field development for silicon by Tersoff et al. [27] and was extended to a carbon system by Brenner et al. [28]. ReaxFF MD allows to explore reactions without any pre-defining of reaction pathways or reactive sites with acceptable accuracy when compared with Density functional theory (DFT), which demonstrates its potential in simulating very complex systems.

ReaxFF MD simulation has been applied in a wide variety of systems and materials [29], [30], [31], [32], [33], [34], especially showing its advantage in simulating combustion and pyrolysis processes with complex coal models [35], [36], [37], [38], [39]. Castro-Marcano et al. [36] constructed a coal model containing greater than 50,000 atoms for Illinois No. 6 coal, the largest when published, and investigated structural transformations and reactions associated with coal pyrolysis using the ReaxFF MD simulation at 2000 K. A detailed list of products, elemental composition and molecular weights for coal tar pyrolyzates were obtained, indicating that ReaxFF MD approach combined with the large-scale coal molecular representation is a useful computational method for investigating detailed composition of tar products during coal pyrolysis. More importantly, GMD-Reax [40], the graphic processing unit (GPU) enabled ReaxFF MD program was created to make it practical for complex coal pyrolysis simulations performed at desktop workstation with a single GPU attached. In addition, the code of VARxMD (Visualization and Analysis of Reactive Molecular Dynamics) [41] was developed to generate detailed chemical reactions directly and automatically from ReaxFF MD simulation trajectories for examining the complexity of reaction network. With the aid of GMD-Reax and VARxMD [42], ReaxFF MD simulations of the large-scale coal molecular model uncover the overall pyrolysis stage and the underlying chemical reactions for Liulin coal pyrolysis in the previous work [38], [39], [43], which provides new insight into the tar product investigation during low-rank coal pyrolysis.

To our best knowledge, most of experimental techniques are hardly accessible to obtain the dynamic profiles of tar products in terms of time or temperature, not to mention the detailed structures, elemental composition and functional groups. The large-scale ReaxFF MD simulations with coal models containing ∼100,000 atoms by using high performance computing and cheminformatics-based reaction analysis has the capability to reveal the dynamic evolutions of large tar products as well as their structures and reactions with time or temperature without any pre-defining reaction pathways. With the intention of modulating the composition and yield of the desired tar products, the pyrolysis of a low-rank Naomaohu coal was simulated in the paper to investigate the dynamic generation of coal tar. A large coal model of 98,748 atoms was constructed that allows for describing the distribution of different functionality types in Naomaohu coal structure. The coal model that is among the largest was simulated with ReaxFF MD. For practical computational cost, the slow heat-up ReaxFF MD simulation at 500–2500 K at a heating rate of 2 K/ps were performed to explore the product behaviors of coal tar comprehensively in coal pyrolysis. The model construction and simulation details can be found in Section 2. Reasonable evolution trends of tar yields, the temporal tendencies of functional groups and ring structures, as well as H/C and O/C ratios in tar were obtained and described in Section 3. The conclusion and some discussions were summarized in the last section.

Section snippets

Experimental analysis of coal sample and model construction

Quantitative analysis of functional groups and bridge bonds in coal is a very difficult task because of the limited accessibility of the reagent to the coal structure [44]. Naomaohu coal (NMH coal for short) is a low-rank coal from Xinjiang province in China. The proximate and ultimate analysis is listed in Table 1, as well as its 13C NMR parameters [45] obtained by resolving overlapping peaks reported as Supplementary materials Table S1, which provides the detailed information for the coal

Evolution of bridged bonds and pyrolysis stages

Tar is a mixture of many compounds with broad distribution of molecular weights. But coal tar is never explicitly defined experimentally to our best knowledge [12], [13]. The same definition of coal tar in our previous work [30] is adopted in this work that the fragments of C14–C40 are considered as heavy tar and the C5–C13 fragments as light tar. Accordingly, the C40+ are considered as the large fragments that cannot be evaporated at high temperature.

The spectrum evolution profiles of weight

Conclusion

Tar is the most abundant and commercially valuable product obtained from coal pyrolysis because it can be upgraded to clean oil fuels. With the intension of producing the tar pyrolyzates with high quality and optimizing the upgrading process, this work focuses on the comprehensive structures and reaction details of tar products in NMH low-rank coal pyrolysis, which is investigated computationally by combining GPU-based ReaxFF MD simulation and cheminformatics based reaction analysis. The

Author contributions

The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.

Declaration of Competing Interest

The authors declare no competing financial interest.

Acknowledgements

This work was supported by the National Key Research and Development Plan of China under Grant [2016YFB0600302]; the National Natural Science Foundation of China under Grant [21606231]; and China’s State Key Laboratory of Multiphase Complex Systems under Grant [MPCS-2017-A-03]. The authors are very grateful for the GPUs provided by NVIDIA. The authors thank Dr. Guangrui Liu from Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences for providing the 13C-NMR data

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