As one of the machines widely used in mining, a semi-autogenous grinding (SAG) mill can significantly improve the roughing efficiency of rock. But the SAG mill still faces the obstacles of significant energy consumption and empirical operation parameters. In order to obtain the optimal operation parameters of a SAG mill, in this paper, the discrete element method (DEM) is used to simulate the breakage process of the particles by controlling three parameters, i.e., the mill speed ratio, the mill fill level ratio, and the steel ball ratio. This method simulates the particles size, mill power, and qualified particles quality of crushed particle, which reveal the grinding strength and energy consumption of the SAG mill. In this paper, the grinding changes of a SAG mill under different parameter conditions are explored. Firstly, an experiment on the influence of a single parameter change on the mill’s operation is set up, and then the influence of three parameter changes on the mill’s operation is analyzed. These changes are characterized by particle size and mill power. Simulation results under the $\varnothing$5250 × 500 mm mill model show that the mill operates with the optimal effect when the mill is under the condition of 80% critical speed and 15% fill level; the power of the mill does not increase linearly with an increase in the mill speed ratio, but will decrease after 85% of the critical speed, and finally increase again; the optimal steel ball ratio in the SAG mill depends on the simulation time (mill actual working time) and the limitation of the rated power. The mill speed, fill level ratio, and steel ball ratio can significantly affect mill operation, and our conclusions can provide a reference for an actual situation.

The power allocation in Vanadium Redox Flow Battery (VRB) energy storage systems faces a conflict between long-term lifespan and real-time power coupling. Using a single-layer optimization method to directly address multiple objectives simultaneously may lead to conflicts among these objectives. Therefore, this paper presents a multi-objective two-layer optimization allocation strategy. Its core is hierarchical scheduling for long/short-term goals to optimize multi-attribute objectives precisely. A two-layer model comprising an initial allocation layer and an operational optimization layer is constructed to ensure the prioritization of long-term lifespan objectives based on a predefined hierarchical structure. The initial allocation layer focuses on the long-term objective of energy storage capacity lifespan, by prioritizing minimal capacity degradation. A differential evolution algorithm is then applied to perform preliminary allocation of the total power demand. The operational optimization layer aims to achieve optimal State of Charge (SOC) balance across all units and minimize power losses. An Adaptive Multi-Objective Harris Hawks Optimizer (ASAMOHHO) based on adaptive simulated annealing is established to find the Pareto optimal solution set, and ultimately determining the real-time power allocation plan for each unit. Comparative simulations with conventional methods were conducted, and the results demonstrate that the proposed strategy provides an efficient and practical solution for efficient VRB scheduling.