Parameter Calibration and Experimental Validation of Fermented Grain Particles During the Loading Process Based on the Discrete Element Model

Abstract

This study presents a systematic calibration of discrete element model (DEM) parameters for fermented grains (19.4% moisture) using the Hertz–Mindlin with JKR contact model to optimize robotic end-effector design in liquor distillation. By integrating cylinder lift experiments and response surface methodology, we identified three critical parameters (JKR surface energy, restitution, and rolling friction coefficients) through Plackett–Burman screening and steepest ascent optimization. The Box–Behnken design derived optimal values of 0.0429 J/m2 surface energy, 0.183 restitution coefficient, and 0.216 rolling friction coefficient. The validation results demonstrated excellent agreement between the simulated and experimental angles of repose (AORs), with a simulated AOR of 36.805° and an experimental value of 36.412°, corresponding to a 1.08% error. The geometric congruence of the deposition morphology and the notable symmetrical distribution characteristics of the left and right angles of repose confirm the robustness of the parameter calibration methodology. This study provides a theoretical basis for the kinematic parameter optimization of the end-effector distribution mechanism in fermented grain-loading robots, providing critical insights for advancing automated control in solid-state liquor distillation processes.