Prediction of rod-like particle mixing in rotary drums by three machine learning methods based on DEM simulation data

Abstract

The mixing of non-spherical particles in rotary drums exhibits significant complexity, particularly when density segregation and size segregation occur simultaneously. Three machine learning models: artificial neural network (ANN), extremely randomized trees (ERT), and particle swarm optimized support vector regression (PSO-SVR) were developed to predict the mixing time and mixing degree at the steady mixing state of rod-like particles in rotary drums. The training, validation, and test data for the machine learning models were generated from 121 discrete element method (DEM) simulations with four independent variables: revolution frequency, particle density ratio, particle size ratio, and drum length. All three models predicted the mixing degree accurately with R2≥ 0.94. The ERT and PSO-SVR models also predicted the mixing time well with R2≥ 0.88. Building machine learning models is hundreds of times faster than running DEM simulations, making these models highly promising for predicting larger-scale simulations with more complex-shaped particles.