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    BME’s connected AI-powered blasting: design and risk takeaways for mine planners

    June 26, 2026|

    Reviewed by Joe Ashwell

    BME’s connected AI-powered blasting: design and risk takeaways for mine planners

    First reported on International Mining – News

    30 Second Briefing

    BME, part of Omnia Group, is pushing “connected” AI-enabled blasting by integrating its advanced explosives with digital platforms to turn blasting from simple rock breakage into a data-rich upstream process. Using blast design, initiation and monitoring tools linked across the value chain, the company is targeting tighter fragmentation control, reduced energy use in crushing and milling, and improved vibration and flyrock management. For mine planners and geotechnical teams, this means blast parameters increasingly feed directly into real-time optimisation of loading, hauling and downstream plant performance.

    Technical Brief

    • For other mines, the main implication is the need for rigorous, standardised drill-and-blast data collection to unlock similar AI workflows.

    Our Take

    BME’s push into AI-enabled, connected operations builds on its recent work on resilient reagent supply chains highlighted in the June 2026 webinar with BME Metallurgy and Omnia Holdings, signalling a strategy to integrate digital intelligence from blasting through to downstream processing performance.

    The new AI focus aligns with BME’s investment in electronic detonator capacity at Kalgoorlie, where tighter control of blast timing already generates high-frequency data that can feed machine‑learning models for fragmentation, vibration and dilution prediction on hard‑rock sites.

    Within our 1207 Mining stories, BME appears repeatedly in safety and operational excellence pieces, and coupling that 40‑plus years of blasting expertise with AI suggests its competitive edge is likely to be in decision-support tools that are tightly grounded in field data rather than generic analytics platforms.

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    Prepared by collating external sources, AI-assisted tools, and Geomechanics.io’s proprietary mining database, then reviewed for technical accuracy & edited by our geotechnical team.

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