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Coal quality online detection and analysis method based on regression analysis

A regression analysis and analysis method technology, applied in the direction of analyzing materials, material analysis by optical means, measuring devices, etc., can solve the problems of single detection and analysis method, inaccurate detection data, etc.

Inactive Publication Date: 2018-01-16
邓雷
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Problems solved by technology

[0008] To sum up, the problems in the prior art are: the existing detection and analysis method is single, and the detection data is inaccurate

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  • Coal quality online detection and analysis method based on regression analysis
  • Coal quality online detection and analysis method based on regression analysis
  • Coal quality online detection and analysis method based on regression analysis

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Embodiment Construction

[0065] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the examples. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0066] The application principle of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0067] The online detection and analysis method of coal quality based on regression analysis provided by the embodiment of the present invention uses the regression analysis algorithm of machine learning to automatically extract the graphic features of each area inside the coal for image feature extraction; Use machine learning to provide coal quality online detection results based on the extracted image features, and display the detection results through the display module.

[0068] Suc...

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Abstract

The invention belongs to the field of coal quality detection and discloses a coal quality online detection and analysis method based on regression analysis. Graphic features of various areas in coal are automatically extracted by regression analysis algorithm of machine learning for image characteristics extraction; and a coal quality online detection result is provided based on the extracted image characteristics by machine learning, and the detection result is displayed through a display module. the method of the invention comprises the following specific steps: a coal scanned image is acquired through data acquisition equipment with built-in near-infrared detection module and laser detection module; the coal scanned image is pretreated by the use of a pretreatment module, and the coal scanned image is divided into multiple blocks. Coal quality data information detection is carried out through the near-infrared detection module and the laser detection module, and the detection data is accurate. By component network for nonlinear prediction, the method of the invention has advantages of strong adaptability, good fault tolerance and the like. All elements analysis of coal quality can be realized, and measurement accuracy also can be enhanced.

Description

technical field [0001] The invention belongs to the field of coal quality detection, in particular to a coal quality online detection and analysis method based on regression analysis. Background technique [0002] In order to understand the quality and combustion characteristics of coal, the assay and testing work is carried out on coal samples by physical and chemical methods. Coal quality analysis is carried out in accordance with national technical standards or special test procedures. It is the basic work that provides basis for the design and operation of related equipment and processes; The specific composition of coal on the belt conveyor, so as to adjust the relevant production parameters according to the change of coal composition. For example, in coal-fired power plants, coal costs account for 80% of the total cost, so it is crucial to understand and study the impact of factors such as coal quality on production efficiency. This requires that the coal on the conv...

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Application Information

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IPC IPC(8): G01N21/359G01N21/3563G06K9/00
Inventor 邓雷湛传俊靖建中刘丽华丁洋张灿朱彤苏倩朱相英孙晋红余刚
Owner 邓雷
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