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Dynamic Prediction of Natural Gas Calorific Value Based on Deep Learning

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  • Jingjing Hu

    (National Engineering Research Center of Oil and Gas Pipeline Transportation Safety, Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum−Beijing, Beijing 102249, China
    MOE Key Laboratory of Petroleum Engineering, China University of Petroleum-Beijing, Beijing 102249, China)

  • Zhaoming Yang

    (National Engineering Research Center of Oil and Gas Pipeline Transportation Safety, Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum−Beijing, Beijing 102249, China
    MOE Key Laboratory of Petroleum Engineering, China University of Petroleum-Beijing, Beijing 102249, China
    Department of the Built Environment, Aalborg University, 9220 Aalborg, Denmark)

  • Huai Su

    (National Engineering Research Center of Oil and Gas Pipeline Transportation Safety, Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum−Beijing, Beijing 102249, China
    MOE Key Laboratory of Petroleum Engineering, China University of Petroleum-Beijing, Beijing 102249, China)

Abstract

The natural gas quality fluctuates in complex natural gas pipeline networks, because of the influence of the pipeline transmission process, changes in the gas source, and fluctuations in customer demand in the mixing process. Based on the dynamic characteristics of the system with large time lag and non−linearity, this article establishes a deep−learning−based dynamic prediction model for calorific value in natural gas pipeline networks, which is used to accurately and efficiently analyze the dynamic changes of calorific value in pipeline networks caused by non−stationary processes. Numerical experiment results show that the deep−learning model can effectively extract the effects of non−stationary and large time lag hydraulic characteristics on natural gas calorific value distribution. The method is able to rapidly predict the dynamic changes of gas calorific value in the pipeline network, based on real−time operational data such as pressure, flow rate, and gas quality parameters. It has a prediction accuracy of over 99% and a calculation time of only 1% of that of the physical simulation model (built and solved based on TGNET commercial software). Moreover, with noise and missing key parameters in the data samples, the method can still maintain an accuracy rate of over 97%, which can provide a new method for the dynamic assignment of calorific values to complex natural gas pipeline networks and on−site metering management.

Suggested Citation

  • Jingjing Hu & Zhaoming Yang & Huai Su, 2023. "Dynamic Prediction of Natural Gas Calorific Value Based on Deep Learning," Energies, MDPI, vol. 16(2), pages 1-20, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:2:p:799-:d:1031052
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    References listed on IDEAS

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    1. Zhou, Daming & Gao, Fei & Breaz, Elena & Ravey, Alexandre & Miraoui, Abdellatif, 2017. "Degradation prediction of PEM fuel cell using a moving window based hybrid prognostic approach," Energy, Elsevier, vol. 138(C), pages 1175-1186.
    2. Su, Huai & Zio, Enrico & Zhang, Jinjun & Xu, Mingjing & Li, Xueyi & Zhang, Zongjie, 2019. "A hybrid hourly natural gas demand forecasting method based on the integration of wavelet transform and enhanced Deep-RNN model," Energy, Elsevier, vol. 178(C), pages 585-597.
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    Cited by:

    1. Yang, Zhaoming & Liu, Zhe & Zhou, Jing & Song, Chaofan & Xiang, Qi & He, Qian & Hu, Jingjing & Faber, Michael H. & Zio, Enrico & Li, Zhenlin & Su, Huai & Zhang, Jinjun, 2023. "A graph neural network (GNN) method for assigning gas calorific values to natural gas pipeline networks," Energy, Elsevier, vol. 278(C).

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