我国低阶烟煤利用率高，而大部分的低阶烟煤中富含大量的惰质组，随着煤炭行业的逐渐发展，煤炭的高效、清洁转化利用成为了研究的热点，不同煤岩显微组分的分子结构决定了煤炭燃烧、气化、液化等过程中的转化效率，所以煤岩显微组分对煤炭转化过程的影响越来越被重视。煤作为一种复杂的混合物，无论是研究其完整的物理结构还是化学结构都是极具有挑战性的。本文探索了煤岩组分含量和分子结构特征参数之间的联系，针对四种低阶烟煤，神府大保当煤（DBD）、准东南露天煤（NLT）、准东红沙泉北露天煤（HSQ）、准东五彩湾煤（WCW），根据浮沉离心法分离出不同含量的煤岩组分，并根据傅里叶红外光谱分析（FTIR）、X射线衍射分析（XRD）、X射线光电子能谱分析（XPS）、固体核磁分析（¹³C-NMR）技术得到的谱图数据，并进行分峰拟合计算其特征参数，建立显微组分含量和特征参数的多元回归模型，总结其内在联系，实现煤岩显微组分含量的预测。

DBD煤中FTIR技术分峰拟合结果表明，芳香结构中主要以苯环三取代为主，随着镜质组含量升高，向苯环四、五取代为主转变，红外定量参数I₁、I₂和镜质组含量呈正相关，芳香度I₆和f_a-XRD与镜质组含量呈负相关，镜质组含量越多，脂肪族侧链越多，越短，支链化程度越高。XPS技术碳谱的分析结果也可支撑此结论，C-C、C-H的占比随着镜质组含量的增加呈现逐渐增大的趋势，说明在镜质组中含有大量的脂肪族结构。XRD技术分峰拟合结果表明晶格参数芳香层间距d₀₀₂，延展度L_a，堆垛高度L_c以及芳香层数N_c随镜质组含量的增加变化微小，几乎不发生变化，¹³C-NMR技术计算的特征参数总芳香碳质量分数f_a、芳香碳的质量分数f_a'随着镜质组含量的增加逐渐减少，脂肪碳的总质量分数f_al随着镜质组含量的增加逐渐增大。根据线性关系的结果最优的回归模型为V=263.16-152.033f_a-XRD+34.024 I₇-2.943f_a或者V=-31.169-152.033 f_a-XRD +34.024 I₇+2.943 f_a。

NLT分离出的各产品红外定量参数显示，I₁、I₂总脂肪度和镜质组呈现正相关关系，表明镜质组中含有较多的脂肪烃类结构，I₃也和镜质组呈现正相关，表明NLT煤有机氧含量随镜质组的增加而增加。在微晶结构参数中，和DBD煤中所展现的规律基本相似，不随镜质组的含量发生较大的变化，芳香度f_a-XRD随镜质组的增加逐渐减小。XPS分析结果表明了C-C、C-H总含量的占比随镜质组的增加而增加。¹³C-NMR结果表明脂肪碳的质量分数f_al和镜质组含量呈负相关，芳香碳的质量分数f_a'和镜质组含量呈正相关。镜质组和分子结构特征参数的最优回归模型为V=-783.643+238.451I₂+1.899f_a-XRD+9.067f_a或V=123.012+238.451I₂+1.899f_a-XRD-9.067f_al。

HSQ煤分离后各产品的红外定量参数与镜质组的相关关系结果显示，各参数的变化较为杂乱，I₁和I₂总脂肪度的变化不一致，其余红外定量参数和镜质组的相关性较低，变化规律不明朗。在XRD的参数结果中可以发现，微晶结构参数，芳香层间距d₀₀₂保持不变，但延展度L_a和镜质组呈正相关，XPS的分析结果和NMR的分析结果可以相互印证，在C-C、C-H含量较多时，脂肪碳的质量分数f_al也较大，都随镜质组含量的增加而增加。镜质组含量和结构参数的较优模型为V=84.567-126.713f_a-XRD+1.163 f_a^'-15.128 I₂。

WCW分离后各样品的红外定量参数和煤岩组分相关关系结果表明，总脂肪度I₁和I₂和镜质组呈现较强的正相关关系，富氧程度参数I₃基本不随镜质组的变化而变化，I₄和I₇分别代表脂肪族侧链的长短和多少，随着镜质组含量的增加，脂肪族侧链越多，但同时也越短、支链化程度越高。XRD拟合分析结果显示， d₀₀₂基本不发生变化，但延展度L_a、堆砌高度L_c、芳香层片数N_c随镜质组的增加呈现降低的趋势。固体核磁结果显示总芳香碳质量分数f_a、芳香碳的质量分数f_a'和镜质组呈负相关，脂肪碳的总质量分数f_al和镜质组呈正相关。镜质组含量及其结构参数的较优模型为V=-2812.198+10.888I₂-188.134 f_a-XRD +36.018 f_a +8.722 f_a^'。

四种低阶煤大部分的分子结构特征参数和煤岩显微组分含量呈现相同的变化规律，总结了其相关关系进行，脂肪类结构参数的变化主要和镜质组含量相关，芳香类结构参数的变化主要和惰质组含量相关，这些结论给当前不同煤岩组分的分子结构充实了大量的信息，为理解其之间构效关系进行了重要的补充，对预测其反应性等方面的研究提供了进一步的理论基础，在对煤炭的高效、清洁转化利用等方面具有利用的价值。

China's low-rank bituminous coal has a high utilization rate, and most of the low-rank bituminous coal is rich in inertinite. With the gradual development of the coal industry, the efficient and clean conversion and utilization of coal has become a research focus. The molecular structure of different coal macerals determines the conversion efficiency of coal combustion, gasification, liquefaction and other processes. Therefore, more and more attention has been paid to the influence of maceral on coal transformation process. As a complex mixture, it is very challenging to study the complete physical and chemical structure of coal. This paper has explored the relationship between the content of macerals and the characteristic parameters of molecular structure. For four low-rank bituminous coals, shenfu Dabaodang coal(DBD), Zhundong Nanlutian coal (NLT), Zhundong Hongshaquanbeilutian coal (HSQ) and Zhundong Wucaiwan coal (WCW), different content of coal and rock components are separated by sink-float method. According to Fourier infrared spectroscopy (FTIR), X-ray diffraction analysis (XRD), X-ray photoelectron spectroscopy (XPS), solid-state nuclear magnetic analysis (¹³C-NMR) technology have been the chromatogram data, and points the peak fitting calculation its characteristic parameters, maceral content and characteristic parameters of linear regression model, summarizes its inner link. Realize the prediction of maceral content.

DBD FTIR technology points in coal peak fitting results showed that the aromatic structures are mainly composed of three to replace benzene ring, with higher vitrinite content, to the benzene ring four or five instead of the shift, infrared quantitative parameters I₁ and I₂, positively correlated and vitrinite content, aromaticity I₆ and fa-XRD and negatively correlated to the vitrinite content, vitrinite content, the more the more aliphatic side chains, The shorter, the more branched. XPS carbon spectrum analysis results can also support this conclusion. The proportion of C-C and C-H gradually increased with the increase of vitrinite content, indicating that there are a lot of aliphatic structures in vitrinite. XRD peak fitting results show that lattice parameters d₀₀₂, elongation L_a, stacking height L_c and number of aromatic layers N_c have little change with the increase of vitrinite content. The total aromatic carbon (f_a) and aromatic carbon (f_a') calculated by ¹³C-NMR decreased with increasing vitrinite content, while the total fatty carbon (f_al) increased with increasing vitrinite content. According to the results of the linear relationship, the optimal regression model was V=263.16-152.033f_a-XRD+34.024I₇-2.943f_a or V=-31.169-152.033f_a-XRD+34.024 I₇+2.943f_a.

The FTIR parameters of each product separated by NLT showed that the total aliphatic content of I₁ and I₂ was positively correlated with vitrinite, indicating that vitrinite contained more aliphatic hydrocarbon structures, and I₃ was also positively correlated with vitrinite, indicating that the organic oxygen content of NLT coal increased with the increase of vitrinite. In terms of microcrystalline structure parameters, the law is basically similar to that in DBD coal. There is no great change with the vitrinite content, and the aromatics f_a-XRD gradually decreases with the increase of vitrinite. XPS analysis showed that the proportion of C-C and C-H contents increased with the increase of vitrinite. ¹³C-NMR results showed that the mass fraction of fatty carbon f_al was negatively correlated with vitrinite content, while the mass fraction of aromatic carbon f_a' was positively correlated with vitrinite content. The optimal regression model of vitrinite and molecular structure characteristic parameters was V=-783.643+238.451I₂+1.899f_a-XRD+9.067f_a or V=123.012+238.451I₂+1.899f_a-XRD-9.067f_al.

The correlation between the FTIR quantitative parameters of each product after HSQ coal separation and vitrinite showed that the changes of each parameter were chaotic, the changes of total aliphatic content of I₁ and I₂ were inconsistent, and the correlation between other FTIR quantitative parameters and vitrinite was low and the change rule was not clear. It could be found in the XRD results of the parameters, structural parameters of microcrystalline, aromatic layer spacing d₀₀₂ remains the same, but the range and L_a and vitrinite were positively correlated, XPS analysis and NMR analysis results could support each other, in the content of C-C and C-H is large, aliphatic mass fraction of carbon f_al is bigger also, all increase with the increase of vitrinite content. The optimal model of vitrinite content and structure parameters was V=84.567-126.713 f_a-XRD+1.163f_a^'-15.128I₂.

WCW separation after the samples infrared quantitative parameters and the maceral correlation results showed that total aliphatic I₁ and I₂ and vitrinite is a strong positive correlation, oxygen-enriched degree parameter I₃ basic does not change along with the change of vitrinite, I₄ and I₇ respectively represent the length of the aliphatic side chain and how much, with the increase of vitrinite content, the more aliphatic side chains. But at the same time, the shorter and more branched. XRD fitting analysis showed that d₀₀₂ has little change, but the elongation L_a, stacking height L_c and aromatic lamellar number N_c decrease with the increase of vitrinite. Solid state ¹³C-NMR results showed that f_a and f_a' were negatively correlated with vitrinite, while f_al was positively correlated with vitrinite. The optimal model of vitrinite content and its structural parameters is V=-2812.198+10.888I₂-188.134 f_a-XRD +36.018 f_a +8.722 f_a^'.

Most of the molecular structure characteristic parameters of the four low-rank coals and the content of the maceral showed the same variation law, and their correlations were summarized. The changes of aliphatic structure parameters were mainly related to the content of vitrinite, and the changes of aromatic structure parameters were mainly related to the content of inertinite. These conclusions enrich a lot of information on the molecular structures of different macerals, provide an important supplement for understanding the structure-activity relationship between them, and provide a further theoretical basis for research on predicting their reactivity. The efficient and clean conversion and utilization of coal has the value of utilization.

[1] 谢和平, 王金华, 王国法, 等. 煤炭革命新理念与煤炭科技发展构想[J]. 煤炭学报, 2018, 43(05): 1187-1197.

[2] 王国法, 任世华, 庞义辉, 等. 煤炭工业“十三五”发展成效与“双碳”目标实施路径[J]. 煤炭科学技术, 2021, 49(09): 1-8.

[3] 孟郅烨, 杜颖. 十四五规划下环保产业发展趋势研究[J]. 资源节约与环保, 2021(11): 140-142.

[4] 张静, 秦云虎, 王双美, 等. 东胜矿区水煤浆气流床气化用煤评价指标体系及资源分级 [J]. 中国煤炭, 2018, 44(06): 91-95.

[5] 秦云虎, 王双美, 张静, 等. 煤炭地质勘查中煤质评价指标体系建立的现状与展望 [J]. 中国煤炭地质, 2017, 29(07): 1-4.

[6] 李国玲, 秦志宏, 倪中海. 煤岩显微组分的性质研究进展[J]. 辽宁大学学报: 自然科学版, 2013, 40(01): 48-55.

[7] 门东坡, 刘文礼, 张磊, 等. 烟煤煤岩组分解离特性及分选研究[J]. 煤炭工程, 2016, 48(02): 116-119.

[8] 蒋莉. 低阶煤显微煤岩组分的粉碎解离特性及浮选试验研究[D]. 徐州: 中国矿业大学, 2015.

[9] LIN B, ZHA W, LIU T. Experimental study on molecular structure differences between the tectonic coal and primary coal in Pingdingshan coalfield[J]. Vibrational Spectroscopy, 2019, 103: 102930.

[10] 付艳红, 李振, 周安宁, 等. 煤中矿物及显微组分解离特性的MLA研究[J]. 中国矿业大学学报, 2017, 46(06): 1357-1363.

[11] 尉迟唯, 李保庆, 李文, 等. 煤的岩相显微组分对水煤浆性质的影响[J]. 燃料化学学报, 2003, 31(05): 415-419.

[12] DAI H. Status of direct coal liquefaction research in China[J]. Energy, 1986, 11(11-12): 1225-1229.

[13] 赵伟. 神府煤煤岩组分的改性及其可浮性研究[D]. 西安: 西安科技大学, 2010.

[14] 陈洪博, 张宇宏. 煤岩显微组分的分离与富集研究[J]. 煤质技术, 2012(06): 6-9.

[15] 舒新前, 王祖讷, 葛岭梅. 煤岩组分分离与选别的研究现状及前景展望[J]. 煤炭转化, 1996(01): 40-45.

[16] 宫亮. 神府煤煤岩组分浮选工艺及动力学研究[D]. 西安: 西安科技大学, 2013.

[17] DYRKACZ G R. Adventures in maceral separation[J]. Am Chem Soc.Div Fuel Chem, 1994, 35(01): 214-218.

[18] DYRKACZ G R, HORWITZ E P. Separation of coal macerals[J]. Fuel, 1982, 61(01): 3-12.

[19] GB/T 8899-2013, 煤的显微组分组和矿物测定方法[S].

[20] 张磊, 刘文礼, 门东坡. 煤岩显微组分测定方法的研究现状及几点建议[J]. 煤炭工程, 2013, 45(04): 97-99+103.

[21] 王越, 丁华, 武琳琳, 等. 煤岩显微组分自动识别技术现状及关键问题分析[J]. 煤质技术, 2019, 34(01): 1-4+15.

[22] DAVIS. A, J. V F. Developments in automated reflectance microscopy of coal[J]. Journal of Microscopy, 1977, 109(01): 3-12.

[23] DAVIS A, K.W.KUEHN, D.H.MAYLOTTE, et al. Mapping of polished coal surfaces by automated reflectance microscopy[J]. Journal of Microscopy, 1983, 132(03): 297-302.

[24] 佘杰. 基于图像的煤岩识别方法研究[D]. 北京: 中国矿业大学(北京), 2014.

[25] 武琳琳. Gamma设置对煤岩自动测试精度影响的实验研究[J]. 煤质技术, 2018(01): 42-44.

[26] 王越, 白向飞, 张宇宏, 等. 煤岩自动测试系统图像灰度与反射率关系研究[J]. 煤炭转化, 2014, 37(02): 25-27+32.

[27] RIEPE W, STELLER M. Characterization of coal and coal blends by automatic image analysisUse of the Leitz texture analysis system[J]. Fuel, 1983, 63(03): 313-317.

[28] LESTER E, ALLEN M, CLOKE M, et al. An automated image analysis system for major maceral group analysis in coals[J]. Fuel, 1994, 73(11): 1729-1734.

[29] LESTER E, WATTS D, CLOKE M. A novel automated image analysis method for maceral analysis[J]. Fuel, 2002, 81(17): 2209-2217.

[30] DAVID P, FERMONT W. Determination of coal maceral composition by means of colour image analysis[J]. Fuel Processing Technology, 1993, 36(1-3): 9-15.

[31] 段旭琴, 王祖讷, 孙春宝. 神府煤显微组分表面性质研究[J]. 中国矿业大学学报, 2007, 36(05): 630-635.

[32] 张磊. 开滦煤显微组分分离及其富集物的特性研究[D]. 北京: 中国矿业大学(北京), 2015.

[33] NAIK A S, BEHERA B, SHUKLA U K, et al. Mineralogical Studies of Mahanadi Basin coals based on FTIR, XRD and Microscopy: A Geological Perspective[J]. Journal of the Geological Society of India, 2021, 97(9): 1019-1027.

[34] 翁成敏, 潘治贵. 峰峰煤田煤的X射线衍射分析[J]. 地球科学, 1981(01): 214-221.

[35] 宋党育, 张晓逵, 苏现波, 等. 应用XRD对煤微晶结构的研究[C]// 王兆丰. 基于瓦斯地质的煤矿瓦斯防治技术. 徐州: 中国矿业大学出版社, 2009: 67-72.

[36] 张代钧, 鲜学福. 煤结构的X射线分析[J]. 西安矿业学院学报, 1990(03): 42-49.

[37] 姜波, 秦勇, 金法礼. 高温高压实验变形煤XRD结构演化[J]. 煤炭学报, 1998(02): 78-83.

[38] 白何领, 潘结南, 赵艳青, 等. 不同变质程度脆性变形煤的XRD研究[J]. 煤矿安全, 2013, 44(11): 12-14.

[39] 周贺, 潘结南, 李猛, 等. 不同变质变形煤微晶结构的XRD试验研究[J]. 河南理工大学学报(自然科学版), 2019, 38(01): 26-35.

[40] 王丽, 张蓬洲. 煤的XRD的结构分析 [J]. 煤炭转化, 1997, 20(01): 50-53.

[41] 罗陨飞, 李文华. 中低变质程度煤显微组分大分子结构的XRD研究[J]. 煤炭学报, 2004, 29(03): 338-341.

[42] F T, L K J. Raman Spectrums of Graphite[J]. Journal of Chemical Physics, 1970, 53(3): 1126-1130.

[43] MARQUES M, SUáREZ.RUIZ I, FLORES D, et al. Correlation between optical, chemical and micro.structural parameters of high.rank coals and graphite[J]. International Journal of Coal Geology, 2009, 77(3-4): 377-382.

[44] GUEDES A, VALENTIM B, PRIETO A C, et al. Micro.Raman spectroscopy of collotelinite, fusinite and macrinite[J]. International Journal of Coal Geology, 2010, 83(4): 415-422.

[45] 唐志锟, 赵泉, 周君龙, 等. 拉曼光谱在快速鉴别煤炭种类中的应用[J]. 中国口岸科学技术, 2021, 3(06): 42-48.

[46] 苏现波, 司青, 宋金星. 煤的拉曼光谱特征[J]. 煤炭学报, 2016, 41(05): 1197-1202.

[47] 李霞, 曾凡桂, 王威, 等. 低中煤级煤结构演化的FTIR表征[J]. 煤炭学报, 2015, 40(12): 2900-2908.

[48] PING A, XIA W, PENG Y, et al. Construction of bituminous coal vitrinite and inertinite molecular assisted by 13C NMR, FTIR and XPS[J]. Journal of Molecular Structure, 2020, 1222: 128959.

[49] CHEN Y, MASTALERZ M, SCHIMMELMANN A. Characterization of chemical functional groups in macerals across different coal ranks via micro.FTIR spectroscopy[J]. International Journal of Coal Geology, 2012, 104: 22-33.

[50] 常海洲, 王传格, 曾凡桂, 等. 不同还原程度煤显微组分组表面结构XPS对比分析[J]. 燃料化学学报, 2006, 34(004): 389-394.

[51] JIANG J, YANG W, CHENG Y, et al. Molecular structure characterization of middle.high rank coal via XRD, Raman and FTIR spectroscopy: Implications for coalification[J]. Fuel, 2019, 239: 559-572.

[52] 余晓露, 白帆, 李志明, 等. 衰减全反射——显微傅立叶变换红外光谱原位分析煤有机显微组分[J]. 石油实验地质, 2012, 34(06): 664-670.

[53] 于春梅, 张楠, 滕海鹏. FTIR和Raman技术在煤结构分析中的应用研究[J]. 光谱学与光谱分析, 2021, 41(7): 2050-2056.

[54] JIANG J, ZHANG S, LONGHURST P, et al. Molecular structure characterization of bituminous coal in Northern China via XRD, Raman and FTIR spectroscopy[J]. Spectrochim Acta A Mol Biomol Spectrosc, 2021, 255: 119724.

[55] DUN W, GUIJIAN L, RUOYU S, et al. Investigation of Structural Characteristics of Thermally Metamorphosed Coal by FTIR Spectroscopy and X.ray Diffraction[J]. Energy & Fuels, 2013, 27(10): 5823-5830.

[56] YUAN L, LIU Q, MATHEWS J P, et al. Quantifying the Structural Transitions of Chinese Coal to Coal.Derived Natural Graphite by XRD, Raman Spectroscopy, and HRTEM Image Analyses[J]. Energy & Fuels, 2021, 35(3): 2335-2346.

[57] 周贺, 潘结南, 李猛, 等. 不同变质变形煤微晶结构的XRD试验研究[J]. 河南理工大学学报:自然科学版, 2019, 38(1): 26-35.

[58] 苏现波, 司青, 王乾. 煤变质演化过程中的XRD响应[J]. 河南理工大学学报:自然科学版, 2016, 35(04): 487-492.

[59] 马汝嘉, 张帅, 侯丹丹, 等. 陕西凤县高煤级煤分子结构模型的构建与结构优化[J]. 煤炭学报, 2019, 44(06): 1827-1835.

[60] GENG W, NAKAJIMA T, TAKANASHI H, et al. Analysis of carboxyl group in coal and coal aromaticity by Fourier transform infrared (FT-IR) spectrometry[J]. Fuel, 2009, 88(1): 139-144.

[61] 刘大锰. 烃源岩显微组分的显微傅利叶红外光谱研究[J]. 岩石学报, 1998, 14(002): 222-231.

[62] 朱亚明, 赵雪飞, 高丽娟, 等. 煤系针状焦微晶结构的XRD与Raman分峰拟合定量研究[J]. 光谱学与光谱分析, 2017, 37(6): 1919-1924.

[63] 陈丽诗, 王岚岚, 潘铁英, 等. 固体核磁碳结构参数的修正及其在煤结构分析中的应用[J]. 燃料化学学报, 2017, 45(10): 1153-1163.

[64] 郭德勇, 叶建伟, 王启宝, 等. 平顶山矿区构造煤傅里叶红外光谱和13C核磁共振研究[J]. 煤炭学报, 2016, 41(12): 3040-3046.