Assessing load in ball mill using instrumented grinding media

[Display omitted] •An instrumented grinding media was designed to mimic the motion state of ordinary grinding balls.•Grinding efficiency index was proposed to evaluate the grinding effect.•Time-domain features were combined with sample entropy in feature extraction.•Recognition accuracy of ball mill load based on PSO-SVM classification model achieved as 96.67%. Monitoring mill load is vital for the optimization and control of grinding process. This study proposed the use of an instrumented grinding media to assess solid loading inside a ball mill, with size and density of the instrumented ball comparable to that of the ordinary grinding media. The acceleration signal was captured by an embedded triaxial accelerometer. The signal was first detrended by a complete ensemble empirical mode decomposition and then reconstructed using a correlation coefficient method. The filling ratio, particle to ball ratio, time domain features and sample entropy are features extracted from the signal, providing input to a support vector machine (SVM) learning model. Grinding experiments with different loads were conducted. The typical loading level was classified according to grinding efficiency index and associated power consumption. Different methods were adopted to determine the optimal values of parameters in the SVM model, including particle swarm optimizer (PSO), genetic algorithm (GA), and grid search (GS). The results showed that the accuracy of particle swarm optimizer can reach 96.67%. This study demonstrates the potential of using instrumented grinding media for real-time characterization of mill feed and operation monitoring.