Simulation and optimization of crushing chamber of gyratory crusher based on the DEM and GA

To optimize the crushing chamber of the gyratory crusher, the discrete element method (DEM) is used to explore the influence of the concave curve height, concave curve radius, eccentric angle, and mantle shaft speed on the performance of the crushing chamber in this paper, here, the DEM analysis model of iron ore particle is established based on the bonded particle model. Based on this, a prediction model of the crushing chamber performance is established through the multiple nonlinear regression, and the multi-objective optimization is performed based on the genetic algorithm (GA). The corresponding optimization values are 450 mm, 950 mm, 0.2?, and 100 rpm, respectively. Finally, the validity of the optimization results is verified by DEM simulation, and the results show that productivity and power density is increased by 36% and 26%, respectively. The optimized crushing force is approximately twice the one before optimization. Both discharge granularity and power consumption are reduced to varying degrees. ? 2021 Elsevier B.V. All rights reserved. To optimize the crushing chamber of the gyratory crusher, the discrete element method (DEM) is used to explore the influence of the concave curve height, concave curve radius, eccentric angle, and mantle shaft speed on the performance of the crushing chamber in this paper, here, the DEM analysis model of iron ore particle is established based on the bonded particle model. Based on this, a prediction model of the crushing chamber performance is established through the multiple nonlinear regression, and the multi-objective optimization is performed based on the genetic algorithm (GA). The corresponding optimization values are 450 mm, 950 mm, 0.2?, and 100 rpm, respectively. Finally, the validity of the optimization results is verified by DEM simulation, and the results show that productivity and power density is increased by 36% and 26%, respectively. The optimized crushing force is approximately twice the one before optimization. Both discharge granularity and power consumption are reduced to varying degrees.