• DocumentCode
    2008090
  • Title

    The artificial neural networks for real-time operation of natural gas production and sale

  • Author

    Wang, Xiao-Lin ; Xiao, Jian-zhong

  • Author_Institution
    Sch. of Econ. & Manage., China Univ. of Geosci., Wuhan, China
  • Volume
    3
  • fYear
    2010
  • fDate
    17-18 July 2010
  • Firstpage
    314
  • Lastpage
    316
  • Abstract
    Backpropagation neural networks (BPNN) is introduced in this paper to explore the non-linear relationship between planned gas supply and actual gas demand from uneven coefficients of natural gas demands of end-users with a case study of North China Branch of Sinopec, aiming to solve the imbalances between gas supply and demand and instabilities of gas operation from uncertainties of natural gas demmand fluctuation with daily or seasonal change The research indicates that BPNN could effectively build up complex non-linear map between the planned supply and actual demand in the process of natural gas production and sale in uperstream gas fields, and give the feasible guide to the real-time operation for the gas field production and sale, providing a novel intelligent method and new idea for the operation decision-making of natural gas.
  • Keywords
    backpropagation; decision making; natural gas technology; supply and demand; BPNN; North China Branch of Sinopec; actual gas demand; artificial neural networks; backpropagation neural networks; decision-making; gas field production; gas field sale; gas operation; intelligent method; natural gas demand fluctuation; natural gas demands; natural gas production; natural gas sale; nonlinear map; planned gas supply; uperstream gas fields; Backpropagation; Educational institutions; Heating; artificial neural networks; natural gas; production and sale; real-time operation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Environmental Science and Information Application Technology (ESIAT), 2010 International Conference on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-1-4244-7387-8
  • Type

    conf

  • DOI
    10.1109/ESIAT.2010.5568330
  • Filename
    5568330