Research on On-line Detection of Flotation Froth Ash Based on Machine Vision
This paper collects and preprocesses the foam images of different flotation stages through the slime flotation foam collection system built in the laboratory,and analyzes the characteristics of the foam images at different stages,based on the gray-level histogram and gray-level co-occurrence matrix methods.The foam texture feature parameters are extracted.Aiming at the over-segmentation and under-segmentation problems in the foam segmentation process,an improved watershed algorithm based on adaptive marker extraction is established to achieve effective segmentation of foam images.Based on this,two feature parameters of foam area and carrying capacity are extracted.Combining foam texture feature parameters,size feature parameters and foam stability,a multi-dimensional image feature vector is constructed to enhance feature sensitivity to a certain extent.On this basis,a BP neural network topology structure for predicting the ash content of clean coal is constructed.The number of hidden layer nodes of BP neural network model reaches 10,the absolute error of the ash online detection can be controlled within±1％,It has valuable reference and significance for the actual coal preparation plant to realize the automatic control in the flotation process.
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