Goal:
Use AI to optimize ore blending, mill throughput, and energy use while improving recovery and reducing waste.
Impact Keywords:
Approach:
LunarTech Lab created an integrated optimization platform that connects pit, haul, and processing data.
Data Integration Layer:
Unified data from haul-truck telemetry, ore assays, and mill sensors into a single, real-time dataset.
Ore-Blending Optimization:
Machine-learning models predicted recovery outcomes for each ore blend and suggested optimal feed combinations.
Adaptive Process Control:
Reinforcement learning dynamically adjusted mill and flotation parameters to stabilize throughput.
Predictive Maintenance Hooks:
Embedded asset-health monitoring for crushers and conveyors to prevent downtime.
Operational Dashboards:
Provided plant managers with live recovery KPIs, anomaly alerts, and cost-per-tonne tracking.
Summary:
The AI-driven optimization increased ore recovery by 7 %, reduced energy use per tonne by 12 %, and boosted overall plant efficiency within three months of deployment.
