Particle-scale numerical study on screening processes

The present study aimed to increase the understanding of the industrial screening process by using the discrete element method simulation (DEM) and machine learning modelling. Thus, the study focused on understanding the fundamentals of the complicated screening processes by investigating the process model with different controlling factors through particle-scale analysis. The particle-scale analysis was also linked to several macroscopic models and screening processes such as percolation of particles under vibration, the local passing of particles from the screen, choking of screening, non-spherical shaped particles contact detection and packing and machine learning modelling. The computational and theoretical analyses as well as machine leaning helped to clarify the use of particle-scale analysis and screening processes in several areas. The outcomes of this thesis include: (i) the percolation of particles under vibration and the machine learning modelling of percolation velocity to predict the size ratio threshold; (ii) a better understanding of screening process based on local passing of inclined and multi-deck screen and physics informed machine learning modelling to predict the particles passing; (iii) a logical model to predict the choking judgement of screen while combining the numerical results and machine learning and (iv) a novel contact force model for non-spherical particles by Fourier transformation and packing. The research in this thesis is useful for the fundamental understanding of the effect of particles’ contact force, operational conditions, particle properties, percolation and sieving on the screening process. Moreover, the novel process models based on artificial intelligence modelling, DEM simulation, and physics laws can help the design, control and optimisation of screening processes

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