Research on online detection of particle size in fine-grained coal classification overflow

Real time online detection of the particle size of the overflow in the selection and classification of fine-grained coal can be carried out, and the classification parameters can be adjusted to reduce the content of coarse particles in the overflow and improve the total clean coal recovery rate. The current research generally limits the detection of overflow particle size to around 180 μm, and the upper limit of slurry volume concentration is 10%. It cannot meet the requirements of overflow particle size detection for fine-grained coal classification cyclones with coarse particle size, wide particle size range, and high volume concentration. A set of ultrasonic online particle size detection system has been developed to improve the upper limit of coal particle size and slurry volume concentration detection. Based on the ultrasonic attenuation model, a coal particle size detection model suitable for on-site conditions of fine-grained coal classification with coal particle size of 44.5-600 μm and slurry volume concentration of 0-40% is constructed. A coal particle size distribution prediction model is established using a BP neural network optimized by particle swarm optimization algorithm, achieving the prediction of the particle size distribution of the overflow slurry in a fine-grained coal classification cyclone. The simulation results based on the coal particle size detection model show that the ultrasonic attenuation value decreases first and then increases with the increase of coal particle size, and increases with the increase of ultrasonic frequency and slurry volume concentration. The ultrasonic online particle size detection system and coal particle size distribution prediction model are respectively used to detect the distribution of overflow particle size (actual value is 150.0, 215.0, 315.0 μm) in a hydraulic classification cyclone of a certain mine. The results show that the relative errors of the measurement values of the detection system are 10.87%, 9.81%, 8.48%, and the relative errors of the predicted values of the prediction model are 9.27%, 6.05%, and 6.92%. It indicates that the research have achieved accurate detection of overflow particle size in fine-grained coal classification.