Key Takeaways
- AI flotation control 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 about 15% recovery improvement and about 20% less reagent use, from real deployments rather than 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
AI flotation control works in two layers. A froth camera and plant sensors (reagent dosage, pulp density, pH, airflow) describe what the cells are doing. Then a closed-loop reinforcement-learning controller such as Imubit writes setpoints back to your DCS in real time, instead of waiting for operators to react to trends.
What Is AI Flotation Control?
Flotation separates valuable minerals from waste by attaching them to air bubbles in a tank of slurry. Reagents make the target minerals water-repellent, so they rise with the froth and overflow as concentrate. Recovery and concentrate grade depend on reagent dosage, airflow, pH, froth depth and the ore coming in. All of these change during a shift.
AI flotation control means a model, rather than a person, decides how to adjust those levers. It watches the process continuously and changes setpoints to keep recovery and grade on target. Flotation suits this well because the process is continuous and sensor-rich. It also has a tight feedback loop: a setpoint change shows up in recovery or grade within minutes. That is exactly the structure reinforcement learning is good at.
How Do I Optimize Flotation With AI?
Most operations follow the same path, from sensing to prediction to closed-loop control.
Sensing the froth with cameras
Start by installing froth-imaging cameras above the flotation cells. They capture bubble size, froth speed, colour and texture in real time. That picture is the visual proxy for what is happening chemically in the pulp. For years this fed dashboards that operators eyeballed. Today it increasingly feeds models directly.
Predicting recovery and grade
Next, models turn froth images and sensor readings into predictions of recovery and grade several minutes ahead. ConvLSTM-style architectures are a common research approach, because they learn both the image features and how they change over time. A model that only predicts, without acting, is a flotation soft sensor. It is often the first AI step a plant takes.
Closing the loop on AI flotation control
The final step lets the model act. Imubit’s platform builds a “Foundation Process Model” from your plant’s own historical data. It then uses reinforcement learning to write optimised setpoints for reagent dosage, airflow and pH through your existing DCS. As a result, you close the loop without replacing your control infrastructure. Imubit reports more than 90 closed-loop industrial deployments.
Metso and Valmet expert control systems cover similar ground with a more classical approach. They rely on rules and model-predictive control rather than pure reinforcement learning. They are worth evaluating if you want something less black-box.
What to Expect From AI Flotation Control in Practice
Closed-loop control on a live plant is a trust-building exercise, not a weekend install. Expect an initial shadow-mode phase: the model recommends, and humans still act. Only after that does anyone let a reinforcement-learning agent write setpoints unsupervised. The reported 15% recovery and 20% reagent figures are real, but they come from mature deployments, not day-one results.
Results also depend on what arrives at the cells. Feed that swings in grade, hardness or clay content makes any controller’s job harder. That is why flotation projects often pair with ore blending optimization upstream and plant variability monitoring across the circuit.
FAQs
How does AI improve flotation recovery?
AI improves flotation recovery by adjusting reagent dosage, airflow and pH continuously as the ore changes, instead of at fixed intervals. The model learns from plant history how those settings affect recovery and grade. It then keeps the circuit closer to its best operating point than manual control usually can.
What data does AI flotation control need?
It needs a continuous stream of plant data: reagent flows, pH, airflow, pulp density and froth camera features, logged in a plant historian. It also needs lab assay results for recovery and grade, because those are the ground truth the model learns from. Gaps or noise in that data are the most common blocker.
Is AI flotation control safe to run on a live plant?
Yes, when introduced in stages. Most sites start in shadow mode, where the model only recommends setpoints and operators decide. Closed-loop control follows once the team trusts the model’s behaviour. The controller writes through the existing DCS, so normal alarms and operator overrides stay in place.
What is a froth camera in flotation?
A froth camera is an industrial camera mounted above a flotation cell. Image analysis software measures bubble size, froth speed, colour and texture from its video. Those measurements act as a real-time proxy for flotation performance, and they are a key input for AI flotation control.
Try It With Geocluster
Deciding whether closed-loop AI control is worth the integration effort for your ore body and plant means digging through vendor case studies, published recovery benchmarks and your own historian data. That research work is what Geocluster is designed to accelerate. Point it at your flotation question and let it do the legwork, or read how the Geocluster Research Harness works.