Key Takeaways Throughput, recovery, flotation kinetics, and concentrate grade can each be modeled as a “soft sensor” — an ML model that continuously predicts plant performance from live sensor/historian data instead of a static simulation. …
Key Takeaways Crusher circuit optimization is one of the more measurable AI wins on this list: reported gains of 1-5% throughput and 10-15% energy reduction from AI setpoint optimization. Imubit’s Closed Loop AI Optimization uses reinforcement learning to write optimal setpoints directly to …
Key Takeaways Fleet dispatch is one of the longest-established “AI-adjacent” systems in mining — Modular Mining’s DISPATCH (now under Komatsu) and Wenco FMS (Hitachi) have run truck-shovel optimization for decades. What’s new is the AI layer on top: Modular’s Adaptive …
Key Takeaways Flotation is one of the most mature applications of industrial AI in mineral processing — this isn’t speculative. Imubit’s closed-loop AI uses reinforcement learning to write optimal setpoints directly to the plant’s existing control system, not just recommend them to …
Key Takeaways AI-based thickener control is a published, validated strategy — reinforcement-learning (proximal policy optimization) controllers have reported 10-15% flocculant savings in the literature. There’s no single dominant named “AI thickener” product yet — this capability …