Identification of Coal and Gangue by Feed-forward Neural Network Based on Data Analysis

ABSTRACT

While coal is the major power source around the globe, gangue is unwanted in power plants. Thus, separating gangue from coal is a crucial part in the preprocessing step of mining. With the development of the computational technologies, it is possible to find one way to enhance the effect of gangue separation. By establishing a coal-gangue separation system based on the difference between coal and gangue in their surface texture and grayscale feature, this paper proposes a method of combining image feature extraction and artificial neural network, to identify gangue. In addition, this method will enable robots, instead of human, to pick the gangue. Ultimately, the automated separation of coal-gangue and increased efficiency of raw coal sorting and quality of coal can be achieved if the method proposed in this paper can be applied in coal industry.

KEYWORDS:

• Automation separation
• coal
• coal beneficiation
• gangue
• image recognition
• neural network