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Coal component analysis method based on coal spectroscopic data

A spectral data and component analysis technology, which is applied in color/spectral characteristic measurement, biological models, biological neural network models, etc., can solve the problems of large redundancy and high data dimension of spectral data presentation, and achieve fast and accurate coal mining The effect of compositional analysis methods

Inactive Publication Date: 2018-09-04
NORTHEASTERN UNIV
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Problems solved by technology

This makes the spectral data of coal present the characteristics of high data dimensionality and large redundancy.

Method used

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  • Coal component analysis method based on coal spectroscopic data
  • Coal component analysis method based on coal spectroscopic data
  • Coal component analysis method based on coal spectroscopic data

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Embodiment Construction

[0030] The specific implementation manners of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0031] This embodiment provides a coal composition analysis method based on coal spectral data, such as Figure 7 shown, including:

[0032] Step 1, coal spectral data acquisition;

[0033] The coal mine samples collected in this embodiment are from Fushun, Guizhou Zhijin, Shanxi Datong, Henan Jiajinkou, Shaanxi Shendong, and Yimin coal mining areas in China. These coal mining areas are distributed in various regions from south to north in China, and there are a total of 100 coal samples, including anthracite, bituminous coal and lignite. The SVC HR-1024 portable surface object spectrometer of Spectra Vista Company in the United States is used as the spectral data acquisition instrument. Spectral range of the instrument: 350~2500nm. Built-in memory: 500scans (scanning). Weight: 3kg, number of channels: 1024. Spectral resol...

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Abstract

The invention provides a coal component analysis method based on coal spectroscopic data. The method comprises the following steps: collecting coal spectroscopic data; performing coal component prediction by using a coal component analysis model, and ensuring that the input of the model adopts the collected spectroscopic data and the output is a coal component. The method establishes the coal component analysis model through the spectroscopic data and coal industrial analytic determination results, obtains spectroscopic characteristic data through extraction of a convolutional neural network,outputs coal components corresponding to the coal spectroscopic data determined by coal industrial analysis through a spectroscopic characteristic data extreme learning machine, and adopts a weight and deviation value of an artificial bees colony optimized extreme learning machine during the predication process, so as to obtain an optimized coal component analysis model. The coal component analysis model is integrated with a spectrum technology and is applied to the coal industrial analysis field, and a novel, rapid and accurate coal component analysis method is provided.

Description

technical field [0001] The invention belongs to the technical field of coal composition analysis, and in particular relates to a coal composition analysis method based on coal spectral data. Background technique [0002] Coal is the main source of energy. In 2017, the proven exploitable reserves of coal mines in the world were about 850 billion tons, and countries with rich reserves include the United States (245 billion tons), Russia (150 billion tons), and China (120 billion tons). With the development of industry, the global demand for coal quality continues to increase. High-quality coal has a significant impact on production efficiency and environmental pollution issues. Therefore, before using coal, industrial analysis of coal is essential. Traditional coal mine analysis methods mainly use chemical analysis methods. Although its accuracy is high, this method has the disadvantages of high cost and time-consuming. Therefore, how to quickly and accurately determine t...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N21/25G06N3/04G06N3/00G06N99/00
CPCG06N3/006G01N21/25G06N3/045
Inventor 黎霸俊肖冬毛亚纯宋亮何大阔
Owner NORTHEASTERN UNIV
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