Context​

Existing simulation tools are not able to model the network (PSI) as the real state of the network (measurements) are not modeled correctly.

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Partner objective​

Create a digital twin using measured data on the entire network.

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Solution

• Create a NeurEco model for each network element (tube, pump, valve, etc).

•  Assemble these macro-elements using NeurEco to provide an overall system view.

•  Quickly solve the resulting nonlinear system at each time step, taking into account the measurements.

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Results

An accurate digital twin of the gas network enabling advance training and predictive analytics.

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Benefits to the client

• Improved OEE (overall equipment effectiveness) through reduced downtime and improved performance.

• Lower maintenance costs by predicting maintenance issues before breakdowns occur.

• More efficient supply and delivery chains resulting in increased profitability.

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