Flotation

Flotation is one of the most mature applications of industrial AI in mineral processing: closed-loop reinforcement-learning controllers like Imubit already write setpoints directly to plant control systems rather than just recommending them to an operator. This category covers that closed-loop control approach, the froth-camera vision models that feed it, and soft-sensor models that predict flotation performance without a physical sensor in the loop.

It also covers the plant-integration and ore-blending work - stockpile and plant-feed strategy, mill surveys, and the operational-variability challenges of connecting a model trained on historical data to a live DCS.

ABA Models

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 …

Flotation

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 …

Mill Surveys

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 …

Operational Variability & Plant Integration

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 …