Modelling tumbling ball milling based on DEM simulation and machine learning

Tumbling ball milling is a critical comminution process in materials and mineral processing industries. It is an energy intensive process with low energy efficiency. It is important that ball mills and the milling process are properly designed and operated. To achieve this, models at different scales are needed to provide accurate prediction of mill performance under various conditions. This study aimed to develop a combined discrete element method (DEM) and machine learning (ML) modelling framework to link mill design, operation parameters with particle flow and mill efficiency. A scale-up model was developed based on DEM simulations to link mill size ratio, rotation rate, and filling level with power draw and grinding rate. Then, an ML model using the Support Vector Machine (SVM) algorithm was developed to predict the angle of repose (AoR) and collision energy based on various operation conditions. The ML model was trained by the data generated from the DEM simulations and able to predict the AoR and collision energy. In the process monitoring, an artificial neural network (ANN) was firstly proposed to predict internal particle flow properties of a rotating mill based on acoustic emission (AE) signal generated using the DEM. Main features of AE signals and power draw were fed into the ANN to predict flow properties such as particle size distributions, collision energy distribution and filling levels. Further, a convolution neural network (CNN) was used to replace the ANN to extract more efficient features of AE signals non-linearly based on different local frequency ranges in a ball milling process partially filled with steel balls and grinding particles. Last, a physics-informed ML model was developed based on continuous convolution neural network (CCNN) to learn particle contact mechanisms provided by DEM data at different rotation speeds. The ML model coupled with DEM simulation can accelerate DEM simulation to accurately predict particle flow in a long time series. In summary, this work has demonstrated that combining physics-based numerical models DEM to ML models not only improves the efficiency and accuracy of predictions of complicated processes but also provides more insight to the process and makes predictions more transparent