DRILL & BLAST
another,” he continues. “If you blast better, shovels digs better, trucks take the right material to the right place, rocks are the best size for where they are going, and mine sites achieve optimal blending and processing results. Advances in drilling and blasting affect energy consumption overall in the mine, for example, you can recover more material with the same amount of energy at the crusher. If fragmentation is correct, it will end recycling material through crushers. And with extremely hard rock, teams will know to blend it so performance and recovery remain high.” Machine learning, AI and cloud computing can all assist. Putt says: “Here we are witnessing faster and better geological reconciliations, interpretations and visualisations analysed with algorithms in the cloud. For example, MinePortal runs on Azure to supply mining firms with easy access to a digital twin of their operations. It integrates data sets and applies geostatistics and machine learning techniques to spot patterns and anomalies, provide reliable predictions of structure between the blast holes, and detect rock properties that inform teams how to blast more efficiently.”
SEEING THE BIGGER PICTURE Using new technology can create a better link from the drilling and blasting
process to other aspects of mining. Putt points out that where safety is concerned, improvements can be made by detecting preconditioned material, highly fractured zones and underground voids in real-time. Offering further detail, he says,
“We can now improve how we share knowledge with a single source of truth for all critical operations. So we can optimise mine-to-mill operations by correlating D&B, excavation, crushing performance and mill throughput data. Also we can use crusher and mill performance data to remove bottlenecks, reduce recycle rates and anticipate the incoming rock’s impact to overall throughput. We can improve recovery by correlating geology and assay data to blast hole drill data.” Focusing on the explosives, Putt
observes, “We are seeing more electronic detonators offering the flexibility to infinitely change timing – so that harder rock and softer rock can be matched to their appropriate burden relief. Knowing your soft and hard zones is even more important with advancements in technology. Tose technologies require this data input to be accurate. Also differential energy for explosives, different energies of explosives as they load down the blast hole based on what the geology requires to fragment correctly.”
COMBINATION APPROACH Emmanuel Schnetzler is vice president of Geoscience at DataCloud. He says, “Recent advances in cloud computing may be allowing geostatistics to crunch more data, faster, but its merging with machine learning techniques that will continue to be the most exciting development in the next decade. Tis opens an entirely new landscape for applications in the mining sector. In simplified terms, geostatistics are a set of model-driven algorithms, and machine learning can be seen as a data- driven approach. Combining them to get the best of both worlds is the holy grail of geological modelling. Tis is what we are doing at DataCloud, especially when a client asks us to integrate massive datasets and visualise their orebody in new ways to gain new insights. “A mine site is full of orebody
knowledge, evolution of rock, geochemistry, mineralogy, hyperspectral, geometallurgy and more. All this data collected is challenging to incorporate into a consistent spatial model: geostatistics can struggle with large amounts of disparate data types. On the other hand, it can be difficult to incorporate spatial constraints in machine learning models. Now they can support each other: model-driven geological features incorporated into machine learning frameworks that explicitly consider spatial
Better orebody knowledge and better feedback from downstream processing are key to improving blasting
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