Classification of flotation froth based on CCM texture feature

The surface texture of flotation froth is closely related to flotation production conditions. In order to conduct accurate classification and recognition to flotation production conditions, a method of BP neural network classification based on CCM texture feature extraction is proposed. First, the feature statistics which are extracted to characterize the froth texture condition are treated as texture feature parameters. Then, a principal component analysis is used to extract two characteristics which contain the main contents of information, COR and ASM. Finally, according to the peculiarity of the classification data, the standard BP neural network with 140 nodes in input layer, 14 nodes in hidden layer and 4 nodes in output layer, is used to classify the froth flotation state. Experimental results are given to demonstrate that the method can accurately classify the froth flotation state and obtain better clustering quality of flotation froth. It will provide guides for improving the operation of flotation.