Models optimize mill and crusher setpoints in real time. Variability falls and specific energy improves.
Manual setpoint tuning in processing plants led to variability in throughput and inefficient energy use. Operators struggled to adjust setpoints quickly enough to maintain optimal performance.
AI and ML models were deployed to optimize and stabilize process setpoints in real time. The system analyzed plant behavior and recommended or automated adjustments to maintain ideal operating conditions.
The site achieved higher throughput and lower specific energy consumption, driven by more stable and consistent setpoint control. Operators reported smoother operation across key circuits.
Power draw, pressure, flow, size distribution