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Solvent Extraction Settler Phase Linear Velocity Flow Monitoring and Alerts
Solvent Extraction Settler Phase Linear Velocity Flow Monitoring and Alerts

How Settler Phase Linear Velocity Flow Monitoring and Alerts could resolve plant operational issues

Mogodi avatar
Written by Mogodi
Updated over a week ago

The Settler Phase Linear Velocity Flow Monitoring and Alerts convert the volumetric flows of the organic and aqueous streams entering any SX conventional mixer-settler into linear velocities of flow in the associated settler. This is achieved by including the settler design dimensions to generate near-real time settler organic and aqueous flow regimes (turbulent/ laminar) with associated risk profiles for varying operational depths of organic and aqueous within each settler. Because most SX circuits rely upon operators to manually measure and record the depth of organic, aqueous and crud levels in the individual settlers, the Settler Linear Velocity Flow Monitoring and Risk Profiling indicates linear velocity flow regime risk profiling for 5 scenarios of organic depth in settler profiles (5cm, 10cm, 15cm, 20cm, 25cm and 30cm), which the User can use to make heuristic decisions to adjust organic depths in the settlers to minimize turbulent organic flow regimes and therefore reduce aqueous entrainment to organic phase transfer. This feature further improves the opportunities for Client Users to stabilize the Solvent Extraction circuit physically.

To access the Settler Phase Linear Velocity Flow Monitoring and Alerts Virtual Sensor, you need to first login to the IntelliSense.io Solvent Extraction App and click on the "Dashboard" icon on the left side in the Solvent Extraction App. A drop down box will appear with a list consisting of available dashboards, and you then select "SX Dashboard" and in this dashboard you will find the the Settler Phase Linear Velocity Flow Monitoring and Alerts Virtual Sensor presented in a time graph titled "E1 Settler Organic Linear Velocity".

Figure 1 shows the Organic Linear Velocity Profile Scenarios for the various organic depths for E1 Extraction Settler

For the above example at the current Organic Flow, if there is 5 cm or 10 cm of organic depth in the settler, the risk profile of aqueous entrainment to loaded organic will be high. The risk profile of aqueous entrainment to loaded organic will be moderate at 15 cm of organic depth and there will no risk of aqueous entrainment to loaded organic at 20 cm, 25 cm and 30 cm of organic depth. The interpretation can be applied to the E2 Settler Organic Linear Velocity and S1 Settler Organic Linear Velocity.

In above scenario, Settler Phase Linear Velocity Flow Monitoring and Alerts help the copper solvent extraction plant address its issue of inefficient phase separation. By providing real-time data and enabling quick interventions, the plant can maintain product quality, reduce downtime, and improve overall operational efficiency.

Here's how this helps you solve issues:

  1. Real-Time Monitoring: The flow monitoring sensors provide real-time data on the linear velocity of the organic and aqueous phases as they settle.

  2. Deviation Detection: Significant deviations are flagged as potential issues.

  3. Root Cause Analysis: The Metallurgist can access the data to understand the nature and extent of the deviation. They can then proceed with root cause analysis to determine why the phase separation process is not working optimally.

  4. Timely Interventions: With real-time data and alerts, operators can intervene promptly. They may adjust flow rates, change settling tank parameters, or perform maintenance on settling equipment to restore proper phase separation.

  5. Preventing Product Quality Issues: Efficient phase separation prevents impurities from being carried over to the extracted copper phase. This ensures the final copper product meets quality standards, reducing the need for costly reprocessing and enhancing customer satisfaction.

  6. Performance Optimization: Over time, the plant can analyze historical data from the flow monitoring sensor to identify trends and patterns. This information can guide process optimization efforts for more consistent and efficient phase separation.

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