ANN prediction of particle flow characteristics in a drum based on synthetic acoustic signals from DEM simulations

Rotating drums are widely used in industries for particle mixing, granulation and grinding. Linking internal particle flow condition with externally measured variables is crucial to online process monitoring and control. This work proposed a modelling framework to use an artificial neural network (ANN) model for quick prediction of particle flow based on the acoustic emission (AE) signals generated from the discrete element method (DEM) simulations. In total 131 DEM simulations were conducted under different conditions (i.e., different particle size distributions and filling levels). The AE signals on the drum surface were then obtained based on the simulated particle–wall collisions. Through FFT transformation and principal component analysis (PCA), 5 principal components (PCs) were obtained and, together with power draw, fed into the ANN model to predict to the unmeasurable internal flow conditions, including filling level and the distributions of particle size and internal collision energy. The back propagation neural network was adopted in the model. After being trained with 90 datasets, the ANN model was able to predict those internal variables with reasonable accuracy (R² > 0.95). Finally, the potentials and limitations of the model to the optimal operation of drums were discussed.