Thickener Circuit

Client: Antofagasta Minerals

Project Objective

The project objective for IntelliSense.io was to model and optimise the thickener circuit in real time, identify the root cause of variability and continuously predict set-point recommendations for optimal predictive process control.

Challenges

The existing thickener circuit Expert System didn’t enable continuous set point changes based on the type of materials entering the thickener - resulting in an inability of the operations team to take pre-emptive action to minimise variance at the circuit, with action being taken only after the event. This resulted in a low underflow % solids and water recovery, and high flocculant consumption.

Intellisense.io Outcomes

The IntelliSense.io Thickener Circuit Optimisation application was implemented at the mine. The project integrated data from SCADA & other control systems (including upstream data) with advanced statistical data modelling, machine learning algorithms and first principle models to derive a digital model of the thickener circuit that can predict and simulate future performance of the circuit under various feed conditions and deliver continuous optimised control recommendations that result in;

  • Delivery of predicted material composition and mineralogy input to the thickener circuit.
  • Stable underflow % solid.
  • Online thickener circuit simulator

These recommendations were supplied as self-service dashboards and reporting allowing different types of users (operations, engineers & management) to source information based on their needs. An on premise version of the application is deployed to deliver optimisation set points continuously to existing expert / control system.

Benefits

  • Decreased variability in the thickener circuit operation.
  • Enhanced water recovery at the thickener circuit
  • Reduced equipment downtime due to stricter torque constraints.
  • Next-generation virtual sensors which replace crucial missing instrumentation.
  • Increased operational staff availability by reducing the time to collect previously siloed data.
  • Increased internal operator training through the brains.vos simulator.
  • Payback period shorter than 12 months with projected direct savings calculated at $400k in the first year alone.

 

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