
First reported on MINING.com
30 Second Briefing
AI is reshaping mining operations by enabling real-time data analysis and predictive maintenance, crucial for meeting rising demands for critical minerals like lithium and copper. Technologies such as sensor-laden quadruped robots patrol crusher corridors, identifying potential issues through vibration and temperature monitoring, while remote metallurgists optimise mill performance from afar. The integration of AI not only enhances operational efficiency but also shifts workforce roles towards higher-skill oversight, with predictive models reducing unplanned downtime and improving safety by minimising human exposure to hazardous environments.
Technical Brief
- Sensor-laden quadruped robots patrol crusher corridors, monitoring vibration and temperature to identify potential issues early.
- Remote metallurgists optimise mill performance by providing guidance from off-site locations, reducing the need for on-site presence.
- AI-driven real-time analytics enable in-shift adjustments, cutting water use, energy draw, and reagent consumption.
- Data quality remains a crucial factor, with AI models flagging unlikely values and missing tags to ensure reliable outputs.
- Autonomy and remote tools decrease the need for personnel to enter hazardous zones, enhancing safety and operational efficiency.
- Culture and trust are vital for successful AI integration, with leaders focusing on care to encourage adoption and confidence in new tools.
- The research highlights the importance of time as a unifying concept, with faster decision-making leading to improved safety and cost performance.
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