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| Development and Progress of Spectral Analysis in Coal Structure Research |
| LI Xue-ping1, 2, 3, ZENG Qiang1, 2, 3* |
1. College of Resource and Environmental Sciences, Xinjiang University, Urumqi 830046, China
2. Institute of Arid Ecology and Environment, Xinjiang University, Urumqi 830046, China
3. Key Laboratory of Oasis Ecology, Ministry of Education, Xinjiang University, Urumqi 830046, China
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Abstract Coal structure is the microscopic foundation of various coal-related research, and spectral analysis is widely used as an important method of coal structure research. Its progress in coal structure research is significant to the popularization, application and development of spectral analysis methods. The study of coal structure using spectroscopic analysis methods has become a routine method used in the coal chemical industry. Spectral analysis methods can quickly and non-destructively detect the molecular structure of coal and provide effective detection methods for changes in coal physical and chemical properties under different environmental conditions. This article introduces spectroscopic analysis methods from three aspects of coal quality, macromolecular structure, and elements in coal. It mainly reviews FTIR, Raman spectroscopy, and NMR. Its development history in the study of coal structure, the key research results obtained from its application and its significance. Synthesizing the various spectroscopic analysis methods and application status in coal structure research at home and abroad, it is found that the current research has not completely solved the problem of coal structure characteristics and property changes, lacks a summary of the common characteristics of coal structure spectral characteristics, and failed to form functional groups in coal. Unlike the database of different spectral information of elements, there is a problem that the characteristic spectral peaks and coal structure information are not equal. That is, there are characteristic peaks at a certain wavelength but cannot match the functional groups in coal, or the functional groups of coal are caused by element composition, bond energy, etc. to multiple wavelengths. Respond to the question. At this stage, the research on the structure of raw coal in its natural state is no longer sufficient to meet the problems caused by coal application. A single spectrum analysis method cannot fully analyze the coal structure characteristics, and there are few studies on the factors affecting the change of the coal structure spectrum characteristics, especially coal samples. In the future, the study of spectroscopic analysis in coal structure can start from the following aspects: the combination of spectroscopy and other methods to comprehensively describe the structure of coal, such as chemical methods, HRTEM, STM, MS and other methods are combined to analyze coal structure characteristics qualitatively and quantitatively; coal structure and spectral characteristics under various conditions. At this stage, spectral analysis methods should be used to study coal’s structural characteristics and property changes under various conditions. Solve the problems of coal in practical application. Such as oxidation, hydrogenation, pyrolysis, combustion, low temperature, liquefaction, vaporization and other treatments of coal, the analysis of process changes and product characteristics. It helps to speculate on the structure of the maternal coal, understand the nature of the coal, control the produt of coal physical chemical process, and obtain fine chemicals of coal. In addition establish a coal spectroscopy analysis feature information database, and a visual data query platforms in the background of network big data. Implementing multi-condition simulation assumptions, demonstration and exploring coal structure dynamic changes under different conditions. By uses artificial intelligence and cloud computation to realize the processing and analysis of various spectral data of coal. Enhance the mining of spectral data information to improve the validity and applicability.
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Received: 2020-12-04
Accepted: 2021-03-19
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Corresponding Authors:
ZENG Qiang
E-mail: mkszq@263.net
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