Pipeline System Optimisation Application 2018-08-16T14:29:55+00:00

Pipeline System Optimisation Application

The Pipeline System Optimisation Application more accurately predicts the demand water volume compared to current expert systems. It supplies this data to Operators to enable them to make the best decision even during busy schedules. This allows mines to implement the most energy efficient pumping schedule on a continuous basis.

Sense

The Pipeline System Optimisation Application integrates directly with existing data historian systems.  Virtual Flow Sensors are generated using an asset specific library of mathematical models to determine missing engineering parameters in real time. This data is assimilated into a data lake that enables a complete understanding of the Pipeline System in real time.

Predict

The Pipeline System Optimisation Application algorithm simulates pipeline operation in real time. By modelling the finite combination of pump station configurations against the Target Flow Rate, an optimised pumping schedule can be generated to deliver the required water through the pipeline.

Optimise

The brains.app optimisation engine uses Machine Learning techniques to balance operational and financial optimisation by proactively providing control variables required for continuous optimisation. Pipeline stability and optimisation is delivered via the implementation of an Optimised Pump on/off schedule. The Dynamic Pumping Schedule is delivered directly into the SCADA control system and supports the Operator by continuously implementing the most energy efficient pumping schedule.

Benefits

  • Continued transport of demand water volume at minimum energy consumption.
  • Optimised system performance during high variable flow demand (Summer Months) and low variability (Winter Months).  
  • Increase pump life expectancy by reducing the number of pumps switches.
  • Reduce risk of tanks being overfilled or emptying.

Request a Live Demo

Request a live demo today and see how you can benefit from taking an AI first approach to mining optimisation.

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