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 an operator.
  • Reported gains: up to ~15% recovery improvement and ~20% reagent-use reduction — real deployments, not lab results.
  • Froth-camera vision models (including ConvLSTM-based research systems) are the sensing layer that feeds these controllers real-time froth texture and bubble-size data.

TL;DR

Feed a froth camera’s real-time image stream and plant sensor data (reagent dosage, pulp density, pH, airflow) into a closed-loop reinforcement-learning controller like Imubit, and let it write setpoints back to your DCS in real time instead of relying on operators to react to trends.

How Do I Optimize Flotation With AI?

Flotation is a genuinely good fit for AI because the process is continuous, sensor-rich, and has a tight feedback loop between a setpoint change (reagent dosage, airflow, pH) and an observable outcome (recovery, concentrate grade) within minutes. That’s the exact structure reinforcement learning is good at.

The concrete path most operations take: install a froth-imaging camera above the flotation cells to capture real-time bubble size, froth speed, and color/texture — this is the visual proxy for what’s happening chemically in the pulp. Historically this fed dashboards for operators to eyeball; increasingly it feeds directly into models (ConvLSTM-style architectures are the common research approach) that predict recovery and grade several minutes ahead. Imubit’s platform builds a “Foundation Process Model” from your plant’s own historical data, then uses reinforcement learning to continuously write optimized setpoints — reagent dosage, airflow, pH — directly through your existing DCS, closing the loop without requiring a rip-and-replace of your control infrastructure. Metso/Valmet’s expert control systems occupy similar territory with a more classical (rule-based/model-predictive) approach rather than pure RL, worth evaluating if you want something less black-box.

The honest caveat: closed-loop control on a live plant is a trust-building exercise, not a weekend install. Expect an initial phase of shadow-mode operation (the model recommends, humans still act) before anyone lets an RL agent write setpoints unsupervised. The reported 15% recovery / 20% reagent-use figures are real but come from mature deployments, not day-one results.

Try It With Geocluster

Deciding whether closed-loop AI control is worth the integration effort for your specific ore body and plant means digging through vendor case studies, published recovery benchmarks, and your own historian data — research work Geocluster is designed to accelerate. Point it at your flotation question and let it do the legwork.