Blasting Optimization Using Autonomous Fragmentation Monitoring System

A large open pit iron ore mine in Brazil, which has a heterogeneous lithology, faces a challenge in defining drill and blast patterns to achieve the expected results. The mine needed to obtain an automated representation of the fragmentation curves and correlate them to the respective blasts, lithologies and quality parameters, seeking the continuous optimization of the fragmentation process and its benefits to the subsequent comminution processes. Trials were carried out for 5 months to demonstrate the functionality of an autonomous fragmentation monitoring system that analyzes the granulometric curve of the material throughout the muckpile and makes the results available online and in real time. The system, which contains a binocular camera, a processing unit and an antenna, was installed in a shovel, to evaluate the following KPI's: monitoring failures, monitoring capacity, physical availability and reportability of oversized boulders. The system analyzed 33,060 samples, with an average capacity of 285 photos every 24 hours, 127 during the day and 158 at night, all measured, analyzed, stored, and made available on its online platform, reporting 223 alerts of P80 above 400mm (15.7 in) and 38 particle sizes above 900mm (35.4 in). The inactive and operational hours of the monitoring system corresponded, respectively, to the idle and worked hours of the shovel, according to dispatch data. There were no camera or processor failures, and rain, dust or poor internet conditions did not impact sampling and fragmentation analysis, indicating 100% physical availability of the system. The autonomous monitoring analyzed 139 times more samples compared to the currently used manual method. In addition to increasing sampling representativeness and automating the process, it increases the safety, quality, and productivity of granulometric analysis, eliminating the exposure of people in the mine and human bias in the analyses. Additional analyses were performed by correlating digitally centralized drill and blast data, data from autonomous drills and fragmentation results. Automated obtention of granulometric curves favors quick decision-making and optimization of blasts to achieve better results in productivity and cost reduction in the plant and mine operation. The autonomous fragmentation monitoring system met the need presented by the mine, proving to be a key tool in continuous improvement projects, including Mine to Plant.