• DocumentCode
    475709
  • Title

    Modeling of the Combustion Optimizing Based on RBF Neural Networks

  • Author

    Chen, Lei ; Xie, Youcheng ; Shen, Zhongli ; Fu, Huilin

  • Author_Institution
    Coll. of Energy & Power Eng., Changsha Univ. of Sci. & Technol., Changsha
  • Volume
    2
  • fYear
    2008
  • fDate
    3-4 Aug. 2008
  • Firstpage
    95
  • Lastpage
    98
  • Abstract
    A combustion optimizing model based on RBF neural networks is set up, and the optimizations of providing coal volume and real generating electricity power are actualized. At the same time, the simulation model is established by MATLAB. The simulation research is processed. The simulation result indicates: in the stabilization state, if the boiler load, power plant coal character (the distinctness of coal heat glowing volume), combustion supplying air volume or combustion inducing air volume changes, the combustion optimizing model based on RBF neural networks can find the optimum values of providing coal volume and real generating electricity power. This result lays a strong base for optimal control and on-line prediction of the boiler.
  • Keywords
    boilers; coal; optimal control; power plants; radial basis function networks; MATLAB; RBF neural networks; boiler load; coal heat glowing volume; combustion inducing air volume; combustion optimizing model; combustion supplying air volume; online prediction; optimal control; power plant coal character; real generating electricity power; Biological neural networks; Boilers; Brain modeling; Character generation; Combustion; Educational institutions; Mathematical model; Neural networks; Power engineering and energy; Power generation; Combustion; Optimizion; RBF neural networks;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computing, Communication, Control, and Management, 2008. CCCM '08. ISECS International Colloquium on
  • Conference_Location
    Guangzhou
  • Print_ISBN
    978-0-7695-3290-5
  • Type

    conf

  • DOI
    10.1109/CCCM.2008.327
  • Filename
    4609649