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    Temperature and Moisture-Content Prediction (Gas-Pipeline)

     

    Regression models to predict the temperature and moisture content in natural gas pipelines

    About the customer

    A very large government-owned oil and natural gas firm with extraction facilities at source locations and underground pipelines criss-crossing the country to transfer the gas to various refining plants.

    Business Problem

    Being an expensive commodity, the client has to ensure that natural gas transfer happens at maximum efficiency with minimal transmission losses. The client articulated the issue to ORMAE and wanted help on:

    • To build regression models that can predict temperature and moisture content in the pipelines at different depths and locations.
    • The models needed to be built for extreme scalability.

    Our Approach

    ORMAE built the appropriate solution based on the client's requirements. The output included:

    • A Linear Regression model that is is trained to predict temperature and moisture content in the gas pipelines based on regression diagnostics.
    • A Random Forest Regression model to predict moisture content in the pipelines.
    • The Regression model is further aligned to an Machine Learning pipeline to predict corrosion in the pipelines.

    The Result

    ORMAE's well-architected solution answered the natural gas client's exact requirements by enabling instant access the prediction information, underlined with a proper validation report.

    Get in Touch

    Have a project in mind?

    Looking for collaboration?
    Send an email to bd@ormae.com
    for availability and enquires.

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