In flotation plants, flow estimation is often conducted using camera systems that track bubble velocity in a limited visual field. While helpful, this approach offers only a partial view of cell behaviour and may not reflect broader flow dynamics. To improve measurement coverage and decision-making, fibre optic-based acoustic sensors were installed to detect axial strain changes in piping and infer eddy current activity—offering a potential soft-sensing alternative to cameras. The goal was to determine whether the acoustic sensor signals correlate meaningfully with existing camera-based velocity estimates. A strong correlation would validate the sensors for broader use in process monitoring and control. Key technical uncertainties included signal variability across flotation cells, potential time lags, data quality issues, and the interpretability of acoustic signals in relation to real flow patterns.
Opti-Num conducted a rigorous data correlation study using time-aligned measurements from acoustic sensors and camera-derived velocity estimates.
These tools were designed to support both technical interpretation and operational decision-making.
The analysis provided the client with a robust evidence base to evaluate fibre optic acoustic sensors as viable soft-sensing tools for flotation systems:
• Improved Flow Visibility – In cells where strong correlation was observed, acoustic sensors offered more comprehensive flow estimation than limited visual zones.
• Reduced Sensor Dependency – Acoustic sensing reduced reliance on camera systems, especially in hard-to-monitor or visually obstructed environments.
• Process Control Potential – Where reliable, the data opens up new control strategies across flotation lines.
• Scalability and Flexibility – The method can be extended to assess additional cells, configurations, or operational states.
• Data-Driven Recommendations – Reports included clear guidance on which cells showed strong vs. weak correlation, allowing for phased rollout or sensor recalibration where needed.
The project successfully laid the foundation for the broader use of distributed acoustic sensing in critical process applications—offering a hybrid approach to soft sensing that combines statistical rigour with operational insight.
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