Abstract
Proper monitoring of natural gas transmission in a pipeline network is necessary to ensure balance in gas supply and demand. Physical correlations such as hydraulic and thermodynamic equations, although has been applied widely in many use cases to simulate gas flow throughout pipelines, have their own limitation in forecasting future trends. For that matter, data-driven technique is needed. In this paper, deep neural network with multi-layer Long Short-Term Memory (LSTM) cells is implemented to forecast future values of gas operational conditions in receiving facility (Sink) of a gas transmission network. Past flowrate data in all network end-nodes are used as input with additional historical pressure data in the sink itself. The model successfully learns from data and able to forecast pressure and flowrate condition in the sink at some time in the future with acceptable error.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
