ANN prediction of particle flow characteristics in a drum based on synthetic acoustic signals from DEM simulations. (31st December 2021)

• Record Type: Journal Article
• Authors: Li, Yaoyu; Bao, Jie; Yu, Aibing; Yang, Runyu
• Is Part Of: Chemical engineering science. Volume 246(2021)
• Publication Date: 2021-12-31
• Subjects: Rotating drums -- Particle flow -- Discrete element method -- Acoustic emission signal -- Artificial neural network; Chemical engineering -- Periodicals; Génie chimique -- Périodiques; Chemical engineering; Periodicals; Electronic journals; 660
• DOI: 10.1016/j.ces.2021.117012
• Languages: English
• ISSNs: 0009-2509

Abstract

Graphical abstract: Comparisons of ANN prediction of collision energy and particle size with DEM simulations. Highlights: An ANN model was proposed to predict particle flow characteristics in rotating drums. The model was based on the acoustic emission signals generated from DEM simulations. The key features of the signals were obtained through principal component analysis. Flow properties included filling level, particle size and energy distributions. ANN predictions compared well with DEM simulations. Abstract: 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 (R 2 > 0.95). Finally, the potentials and limitations of the model to the optimal operation of drums were discussed.