(167e) Prediction of Optimum Milling Condition and Power Consumption

Milling is one of the most important unit operations for producing powders and spends about 5% in the global energy. However its energy efficiency seems to be very as low as about 1%. Then much attention has been paid on optimization in milling operation, and there have been lots of investigations on optimization of milling condition before a few last ten years. However, most of them are conventional methods, which are based on experiments with mill devises from small to large scale. This needs lots of work to do the experiments at different scale of mills, leading to high cost and time consumption. In despite of such efforts, the milling condition determined by these conventional methods would be sometimes far from the real optimization still.

We have attempted to develop an alternative and universal method for optimizing the milling condition by a simulation work based on the Discrete Element Method (DEM). Our method is to simulate balls motion during milling to calculate the impact energy of balls in the presence of sample powder in dry and/or wet condition. We have found that the impact energy of balls is well correlated with a grinding rate constant, which was obtained from a grinding experiment using a small scale mill. This correlation can be applicable to all the relations between the grinding rate constant and impact energy of balls under different milling conditions as well as mill types and scales when the same powder sample is used for the grinding. The power consumption can be calculated on the basis of this simulation method of balls motion in the ball milling. The grinding rate constant is found to be well correlated with the net power consumption. We shall present our method for simulation the ball motion in planetary ball milling in the presence of gibbsite powder under different conditions. Thus our method for simulating the balls motion during milling can be applicable to other types of mill, and is found to be very useful for predicting the optimum condition in grinding and the net power consumption.