Time delay neural network modeling for particle size in SAG mills

Particle size is a very important variable in semi-autogenous grinding processes. It is desirable to measure the variable efficiently or even predict its variations in advance. In this paper, the time delay neural network model is developed to predict the feed particle size of a semi-autogenous grinding mill, and the Levenberg–Marquardt algorithm is used to train the network. Results show that the model predicted values fit well with the industrial operating data. The proposed model can predict the particle size in advance and allow adequate time to take corrective actions during abnormal operations, and therefore provide a great advantage in monitoring and control of the industrial processes.

Graphical AbstractThis paper represents that the time delay neural network model was developed to predict the feed particle sizes of 0.5, 2, and 4 in. of a semi-autogenous grinding mill. A nonlinear autoregressive exogenous model was employed to forecast the time-series model in networks with 300, 350, 400, 450, and 500 iterations and performance was evaluated by mean absolute percentage error.Figure optionsDownload as PowerPoint slide