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    Prediction of Oil Pipeline Process Operating Parameters Based on Mechanism and Data Mining

    Source: Journal of Energy Resources Technology:;2024:;volume( 146 ):;issue: 011::page 113001-1
    Author:
    Wei, Lixin
    ,
    Wang, Lan
    ,
    Zhou, Qiang
    ,
    Gao, Yuhang
    DOI: 10.1115/1.4065951
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Precisely forecasting the operational characteristics of oil pipelines is essential for developing rational design, production, and operation strategies, as well as reducing energy consumption and saving energy. Due to significant disparities in the computation outcomes of conventional mechanism models and the inadequate performance of machine learning models when handling limited sample data, their conclusions likewise lack tangible significance. In this study, a novel physics-guided neural network (PGNN) model, which integrates mechanisms with machine learning models, is introduced. The proposed model incorporates essential physical intermediate factors that impact the temperature and pressure of oil pipelines as artificial neurons within the loss function. Additionally, an adaptive moment estimate approach is employed to optimize the parameters of the model. Through a comparative analysis of various models' predictive capabilities on an oil pipeline, it was shown that PGNN has the highest level of accuracy in forecasting pipeline temperature and pressure. Furthermore, PGNN demonstrates the ability to generate satisfactory prediction outcomes even with a limited sample size. Simultaneously, the predictive outcomes of PGNN exhibit a stronger correlation with variables that have a direct impact on temperature and pressure.
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      Prediction of Oil Pipeline Process Operating Parameters Based on Mechanism and Data Mining

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4303258
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    • Journal of Energy Resources Technology

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    contributor authorWei, Lixin
    contributor authorWang, Lan
    contributor authorZhou, Qiang
    contributor authorGao, Yuhang
    date accessioned2024-12-24T19:05:14Z
    date available2024-12-24T19:05:14Z
    date copyright7/26/2024 12:00:00 AM
    date issued2024
    identifier issn0195-0738
    identifier otherjert_146_11_113001.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4303258
    description abstractPrecisely forecasting the operational characteristics of oil pipelines is essential for developing rational design, production, and operation strategies, as well as reducing energy consumption and saving energy. Due to significant disparities in the computation outcomes of conventional mechanism models and the inadequate performance of machine learning models when handling limited sample data, their conclusions likewise lack tangible significance. In this study, a novel physics-guided neural network (PGNN) model, which integrates mechanisms with machine learning models, is introduced. The proposed model incorporates essential physical intermediate factors that impact the temperature and pressure of oil pipelines as artificial neurons within the loss function. Additionally, an adaptive moment estimate approach is employed to optimize the parameters of the model. Through a comparative analysis of various models' predictive capabilities on an oil pipeline, it was shown that PGNN has the highest level of accuracy in forecasting pipeline temperature and pressure. Furthermore, PGNN demonstrates the ability to generate satisfactory prediction outcomes even with a limited sample size. Simultaneously, the predictive outcomes of PGNN exhibit a stronger correlation with variables that have a direct impact on temperature and pressure.
    publisherThe American Society of Mechanical Engineers (ASME)
    titlePrediction of Oil Pipeline Process Operating Parameters Based on Mechanism and Data Mining
    typeJournal Paper
    journal volume146
    journal issue11
    journal titleJournal of Energy Resources Technology
    identifier doi10.1115/1.4065951
    journal fristpage113001-1
    journal lastpage113001-8
    page8
    treeJournal of Energy Resources Technology:;2024:;volume( 146 ):;issue: 011
    contenttypeFulltext
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