Intelligent detection model of flotation tailings ash based on CNN-BP

The tailings ash is an important production index of flotation systems. It not only reflects the current operating conditions of flotation system and clean coal recovery, but also has important significance for intelligent flotation control. The existing image-based detection method of flotation tailings ash has the problems of incomplete feature extraction and insufficient model precision. In order to solve the above problems, an intelligent detection method of flotation tailings ash based on convolutional neural network (CNN) - back propagation (BP) is proposed. An intelligent detection model of flotation tailings ash is constructed by combining CNN preliminary prediction and BP neural network compensation prediction. The pulp image feature data is extracted through CNN to preliminarily predict the tailings ash. The image gray feature data and color feature data are used as input to the BP compensation model. The difference between the preliminary prediction value and the actual value is used as output. Finally, the preliminary prediction value and the compensation prediction value are added to obtain the flotation tailings ash. The experimental results show that when the rotor of the magnetic stirrer is small, the rotation speed is 500 r/min, and the light intensity is 12 750 Lux, the pulp is fully stirred, and the image quality is the best. Compared with the CNN model and extreme learning machine (ELM) model, the CNN-BP model has the highest prediction precision, the smallest error fluctuation range. The prediction error is within the range of −2% to +2%. The root mean square error (RMSE) of the CNN-BP model is 0.7705, the determination coefficient is 0.9974, and the mean absolute error (MAE) is 0.5572%. This indicates that its high precision, good effect and strong generalization can meet the requirements of on-site production testing.