The present framework for managing long-distance oil and gas transmission pipelines is encountering difficulties as a result of the elements that are becoming more complicated, unpredictable, and time-dependent. It requires thorough system characteristics information, as well as precise beginning and boundary conditions, in order to be effective. In this chapter, we suggest using the deep learning approach to the operation and administration of the natural gas transmission system in an effort to get around these issues. The suggested approach enables efficient and reliable forecasts, particularly under unusual circumstances. The findings demonstrate that the suggested technique can operate the gas pipeline system effectively and efficiently while also making precise real-time forecasts beneficial for decreasing future operational losses.


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    Titel :

    Operation Condition Prediction for Pipeline


    Beteiligte:
    Su, Huai (Herausgeber:in) / Liao, Qi (Herausgeber:in) / Zhang, Haoran (Herausgeber:in) / Zio, Enrico (Herausgeber:in) / Zhang, Li (Autor:in) / Su, Huai (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    13.12.2023


    Format / Umfang :

    15 pages




    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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