Application of the combination on data-driven and discrete element model: Particle segregation prediction and classification in rotary drums

Abstract

With the complexity and variability of particle systems in industrial scenarios and the huge cost of repetitive testing, predicting and classifying particle systems in advance have a certain necessity. Combined with discrete element method (DEM) and data-driven model, this work develops a prediction and classification model for particle flow characteristics based on the long-short term memory (LSTM) method. On the basis of the generated macroscopic and microscopic particle flow characteristic information, the LSTM model was constructed and then described particle flow information based on optimal model parameters determined by hyperparameter optimization. Compared of predicted results with the DEM simulation validated the interpolation and extrapolation ability of the established model. And it has better prediction accuracy and performance comparing with traditional prediction methods. In addition, for the classification of multiple working condition categories, the LSTM model trained from fused dataset obtained the best results with the accuracy of more than 95 % in all cases. With the combination of DEM model and data-driven approach, the rapid prediction of particle flow characteristics and parameter optimization in various industrial applications will be achieved.