Calibration and Verification of Dynamic Particle Flow Parameters by the Back-Propagation Neural Network Based on the Genetic Algorithm: Recycled Polyurethane Powder

• Published in: Materials Vol. 12; no. 20; p. 3350
• Main Authors: He, Ping, Fan, Yiwei, Pan, Banglong, Zhu, Yinfeng, Liu, Jing, Zhu, Darong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 14.10.2019; MDPI
• Subjects: Adhesive strength; Agglomeration; Avalanches; Back propagation; Bonding strength; Calibration; Computer simulation; Discrete element method; Fluidizing; Genetic algorithms; Mechanical properties; Methods; Neural networks; Parameter sensitivity; Particle collisions; Particle size; Polyethylene glycol; Polyurethane resins; Process parameters; Recycling; Research methodology; Researchers; Simulation; Test procedures
• ISSN: 1996-1944; 1996-1944
• DOI: 10.3390/ma12203350

The discrete element method (DEM) is commonly used to study various powders in motion during transportation, screening, mixing, etc.; this requires several microscopic parameters to characterize the complex mechanical behavior of the particles. Herein, a new discrete element parameter calibration method is proposed to calibrate the ultrafine agglomerated powder (recycled polyurethane powder). Optimal Latin hypercube sampling and virtual simulation experiments were conducted using the commercial DEM software; the microscopic variables included the static friction coefficient between the particles, collision recovery coefficient, Johnson–Kendall–Roberts surface energy, static friction coefficient between the particles and wall, and collision recovery coefficient. A predictive model based on genetic-algorithm-optimized feedforward neural network (back propagation) was developed to calibrate the microscopic DEM simulation parameters. The cycle search algorithm and mean-shift cluster analysis were used to confirm the input parameters’ range by comparing the mean value of the dynamic angle of repose measured via the batch accumulation test. These parameters were verified by the baffle lifting method and the rotating drum method. This calibration method, once successfully developed, will be suitable for use in a variety of fine viscous powder dynamic flow conditions.