Prediction of ball milling performance by a convolutional neural network model and transfer learning

This work proposed a three-phase modelling framework using the convolutional neural network (CNN) method to predict the performance a ball mill based on the externally measurable variables in the milling process. The data of the model were generated from the discrete element method under different conditions, including acous-tic emission (AE) signals, power draw and grinding rate. In the pre-training and training phases, the CNN model was able to predict particle size distributions and grinding rates with R-2 higher than 0.92. The model was then applied to the large mill system and the results showed that the model maintained its performance in the new system with limited training datasets. The transfer learning of the model was tested by comparing the model with an untrained model and the results showed the loss error (MSE) of transfer model converged to a lower level within 20 epochs while the untrained model could only converge to a larger error after 400 epochs, indicating with the pre-trained model required far less training time and data for better prediction. The potentials and limitations of the model were also discussed.(C) 2022 Elsevier B.V. All rights reserved.

Automated summary

This study proposes a three-phase modelling framework using the CNN method to predict the performance of a ball mill based on externally measurable variables. The model shows accurate predictions of particle size distributions and grinding rates in the pre-training and training phases. Application of the model in a large mill system demonstrates its robust performance with limited training datasets. The study also highlights the advantages of transfer learning and discusses the potentials and limitations of the model.