An Integrated Cellular Automata Model Improves the Accuracy of Secondary Fragmentation Prediction - René Gómez; Camila San Martin; Raúl Castro (Applied Sciences)
Fine material in caving mining can impact dilution, inrushes, and downstream processing. This work describes the application of a new model to improve the accuracy of the prediction of fine material in block caving mining by coupling a stress model and a fragmentation model, integrating the shear strain effect. This combination seeks to offer a better representation of the secondary fragmentation process than previous models have attained. This approach was implemented through a flow simulator using cellular automata to model the gravitational behavior of broken material during extraction. Physical experiments were then replicated in the flow simulator to couple both models and estimate rock fragmentation under stress. Adding the shear strain effect to the new model showed demonstrable improvements in fine fragmentation estimations, optimizing the results under different confinement conditions. The errors obtained did not exceed 6.8% for 0.8 MPa of confinement, 6.5% for 3 MPa, and 3.8% for 5 MPa, also maintaining a low margin of error for medium and coarser fragments, such as d50 and d80. This improvement in predicting the appearance of fine material supports more accurate planning and the implementation of more focused measures to be taken at drawpoints.