Blast design optimization system to improve mining downstream operations by adapting the explosive energy to the rock mass based on drill monitoring data

A new methodology to perform selective blast designs by adapting the explosive energy, hole by hole (or in the hole), to the rock properties as a combination of their strength properties through the X-Rock model has been developed based on drill-monitoring data from both autonomous and manual rotary drilling modes. For that, a thorough normalization process has been developed to minimize external influences in the data induced by the different rig system and the operator interventions during manual drilling operation. Principal component analysis has been used to combine drill monitoring information. Data from 48,385 blastholes, comprised in 102 production blasts at 7 different levels in one expansion of the mine have been used for the analysis. The methodology, called X-Energy, combines the X-Rock model outputs with actual blast design parameters (drill pattern, stemming and borehole length, explosive mass/density and others) and a constant variable obtained from a machine learning algorithm, to estimate size distribution and percentage of fines and coarse material, hole by hole, by combining and recalibrating the Kuznetsov (Cunningham, 1987) based equation. From it, a new shovel-independent diggability model has been developed as combination of the P80 size, the rock properties (from the X-Rock) and a constant variable obtained from a machine learning algorithm. The X-Energy application presented in this study have been implemented in Minera Escondida Limitada (MEL), to design and operate production blasts with the goal of varying the explosive density to the rock properties to obtain a more uniform size distribution and digging rates, hole by hole, based on a target size and rate, and improving slope care, preventing active faults to fail. The methodology has been applied and validated in 17 production blast (8,277 blasthole), unlocking an average of 12.8% reduction in explosives costs (explosive consumption), by combining a pattern expansion strategy and a better distribution of the explosive density within the blast. The results demonstrate how a better distribution of the explosive energy by varying its density in the hole according to the rock and blast geometry variables can reduce powder factor (explosive consumption) while improving or maintaining blasting and digging results.