Key Takeaways Acid-base accounting (ABA) itself is a standard, decades-old static/kinetic lab test — there’s no AI in the test protocol. PHREEQC, the free USGS geochemical modeling engine, is the standard open-source tool for extending raw ABA results into predictive geochemical models. The …
Key Takeaways MineMax by NTWIST is explicitly AI-driven — it sits as a supervisory layer over existing mine and plant systems, continuously learning from operational outcomes to make real-time blend and feed recommendations. Deswik Blend, BOLT, and GO are the …
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 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 Traditional mill surveys are manual, periodic snapshots — a team samples the circuit for a shift and calculates a mass balance after the fact. That’s being displaced by continuous capture: plant historians like AVEVA PI System log the same circuit variables 24/7, at far higher …
Key Takeaways The core problem: your design model (from testwork) and your actual plant behavior drift apart as ore feed varies — this is what “operational variability” means in practice. Digital twins and industrial-AI platforms are purpose-built …