基于质谱数据和统计分析的煤转化方法学研究

Coals are rich in aromatic ring, alkyl groups and heteroatom containing compounds, and the compositions of coals are very complex. The key step to improve the efficiency of clean utilization for coals is understanding the correlation between molecular structure and reactivity. The analytical instrument and method are becoming the bottleneck to cognize the molecular structure of coals. Supercritical solvent extraction is the project of the study. The influences of coal molecular structure on the solubility and transformation of coal will be investigated. Fine separation of soluble fraction is realized using fractional extraction, preparative chromatography and high performance liquid chromatography equipped with a fraction collector. Ultrahigh resolution mass spectrometry (MS) equipped with different ion sources can well analyze molecules with different polarities and very close molecular mass in the coal extracts. Infrared spectroscopy and nuclear magnetic resonance spectroscopy provide information of molecular structure. The combination of various analytical methods provides information of molecular structure (carbon number, degree of unsaturation, distribution of heteroatom, distribution of alkyl chain and etc.) with high confidence. Based on the amounts of data from ultra high resolution MS, statistical analyses such as cluster analysis and principal component analysis are used to compare the difference. The parameters with high correlation in coal conversion are re-classified to optimize the selection of coal sample and the process of conversion. The study will build methodology for the optimization of coal chemical conversion under the guidance of analytical chemistry.

煤中富含芳环类、脂类和含杂原子的化合物等，种类繁多，成分十分复杂。提高煤的高效洁净利用的关键在于掌握煤的分子结构和反应性的关系。而分析仪器和分析方法的滞后日益成为认知煤分子结构的瓶颈。本项目以煤的超临界萃取获得的可溶组分为对象，研究煤分子结构对溶出及形态转变的规律的影响。采用分级萃取、中压制备色谱和配备馏分收集器的高效液相色谱对液体产物做到精细分离。利用配备不同离子源的超高分辨率质谱分析萃取物中不同极性和质量数十分接近的煤分子，配合红外光谱和核磁共振波谱，提供高置信度的煤分子结构信息（碳数、不饱和度、杂原子分布、脂链分布等）。对不同产物的大量质谱数据采用统计分析（聚类分析、主成分分析等）的方法进行差异性比较，把煤转化路线中相关性高的参数重新归类，优化原料煤的选择及煤转化过程中的参数。超高分辨率质谱和统计分析的结合，将构建以分析为先导，指引并优化煤化学转化过程的研究方法。

煤的精细高附加值利用的关键在于掌握煤的分子结构和反应性的关系。而分析仪器和分析方法的滞后日益成为认知煤分子结构的瓶颈。本项目采用高分辨质谱等分析方法获得具有高置信度的煤分子结构信息，并对不同煤转化产物的质谱数据采用统计分析的方法进行差异性比较，继而优化煤的化学转化过程。对国内主要产煤区的中低阶煤在不同溶剂热溶/萃取条件下得到的可溶组分进行聚类分析，发现聚类的优先级顺序从高至低依次为：质谱离子源>柱色谱富集>热溶/萃取溶剂>煤种。对不同分子量区间内的煤可溶有机组分进行分析，从200 Da到300 Da，增加的碳主要在芳核上，而从300 Da到400 Da，增加的碳对芳核和脂链均有贡献。在获得全分子信息的同时，利用质谱碰撞诱导解离技术获得煤分子芳核的信息。发现煤有机质分子中的芳核存在2种聚集状态：群岛结构和岛屿结构。得到杂原子在芳核和脂链上的分布特征，继而评价催化剂对不同分子位置的杂原子的脱除差异。本项目通过合适的质谱分析方法结合统计分析加深了对煤分子结构的认知，为煤的精细转化提供了必要的理论基础，有着积极的应用前景。