Grinding Circuit

Digital Plant

The Grinding Circuit optimization Application is the first technology to accurately predict critical SAG Mill performance variables. It then correlates this data to give an accurate picture of conditions inside the mill.

 

This allows mines to reduce downtime in operations by reducing scheduled liner wear inspections and unnecessary changes. It also increases p80 consistency by keeping ball charge levels within the correct design ranges.

 

Predict

The Grinding Circuit Optimisation Application uses the Material Transport Model to track geometallurgical and physical properties of the material entering the SAG Mill. Virtual sensors are used to understand the current conditions within the SAG Mill and determine when liner wear inspections or changes are needed. This data is supplied to the operator in real time to allow them to make the best decision when operating SAG Mills. 

Simulate

Engineers can test various operational scenarios for training purposes in a process simulation environment. This is enabled through the creation of a digital twin to create a safe environment for testing which accurately replicates the variables present in the live operational process. This can also be done remotely through the creation of an IntelliSense.io ROC (Remote Operations Center).

Optimize

The brains.app optimization engine uses complex Machine Learning techniques to balance operational and financial optimisation by proactively providing control variables required for continuous optimization. By predicting the geometallurgical and physical properties of the material entering the process, the product liberation (P80) will be optimized using the minimum energy required at the highest throughput possible. This maximises the recoverable metal downstream and delivers process value optimization.

Benefits

  • Drastically increase recoverable metal.
  • Reduced specific energy consumption when keeping ball charge levels at a constant.
  • Better output size consistency
  • Increased P80 stability by accurately predicting the conditions inside the SAG Mill.
  • Reduces number of liner wear inspections, keeping SAG Mills running for longer.

See our Case Study with Altynalmas.

 

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