The discrete element method has been used to simulate the particle flow in a ball mill under different operating conditions. The model was validated by comparing the simulated results of the flow pattern and input power with those measured from a same-scale laboratory mill. The impact energy of the particles under different operating conditions was analysed in detail. The results showed that the impact energy was affected by the operating conditions of the mill and can be linked to the grinding rate for a given material. The correlation between impact energy and grinding rate follows first-order grinding kinetics. Mill performance decreases with increasing mill size. Furthermore, a data-driven machine learning framework has been proposed to predict the impact energy for different operating conditions. It was found that the prediction for mills with diameters of 2000 and 3000 mm based on the training model developed by mills with diameter less than 254 mm could be achieved with an accuracy of 80% and a correlation coefficient of 0.9. Through the combination of DEM simulation and data-driven approach, the computing time required in the determination of impact energy for large scale mills can be dramatically reduced.

离散元方法已被用于模拟不同操作条件下球磨机中的颗粒流动。通过将流动模式和输入功率的模拟结果与从相同规模的实验室工厂测量的结果进行比较来验证该模型。详细分析了不同操作条件下颗粒的冲击能。结果表明，冲击能受磨机操作条件的影响，并且可以与给定材料的研磨速率相关联。冲击能和研磨速率之间的相关性遵循一阶研磨动力学。磨机性能随着磨机尺寸的增加而降低。此外，还提出了一种数据驱动的机器学习框架来预测不同操作条件下的冲击能量。结果表明，基于直径小于254 mm的轧机开发的训练模型，对直径为2000和3000 mm的轧机的预测可以达到80%的准确率和0.9的相关系数。通过结合 DEM 模拟和数据驱动方法，可以显着减少确定大型磨机冲击能所需的计算时间。