Critical mineral mining operations, vital for clean energy supply chains, struggle with daily uncertainties including equipment failures, variable ground conditions, and planning variances. These disruptions trigger ad-hoc cross-departmental meetings by engineers, geologists, metallurgists, surveyors, etc. The decisons made are often suboptimal and sometimes results in persistent shortfalls against production targets. Despite abundant departmental data on uncertainties and outcomes, no integrated platform exists to synthesize this information for enterprise-wide, real-time optimization while upholding safety.
Our mining projects aim to develop a unified AI agent to address these gaps. We use a combination of Large Language Models, deep neural nets and reinforcement learning algorithms to train on decades of critical mineral mining data. The developed agent will be able to ingests streaming data from all mine functions—geological, operational, and processing—in real time. It dynamically models evolving conditions to deliver precise, actionable recommendations, such as:
- Optimized trucking routes and allocations to cut idle time and emissions;
- Adaptive mining sequences for optimum ore extraction;
- Prioritized haulage road gradients and slope stability intuitions to enhance safety and efficiency;
- Tailored reagent dosing and throughput for processing plants to boost yields and minimize waste.
- Resource allocation and reallocations during operational uncertainties
By enabling consistent target achievement and beyond, the agent will drive operational resilience, cost savings, and sustainability in domestic critical mineral production.
