• DocumentCode
    176800
  • Title

    BTP prediction of sintering process by using multiple models

  • Author

    Jialin Wang ; Xiaoli Li ; Yang Li ; Kang Wang

  • Author_Institution
    Sch. of Autom. & Electr. Eng., Univ. of Sci. & Technol. Beijing, Beijing, China
  • fYear
    2014
  • fDate
    May 31 2014-June 2 2014
  • Firstpage
    4008
  • Lastpage
    4012
  • Abstract
    Burning Through Point (BTP) state is a very important parameter for sintering process. A lot of researches for the modeling of BTP have been made, but the precise model is not very easy to find, so the prediction based on modeling cannot be carried out effectively. This paper presents fuzzy neural network structure and multiple model algorithms which can process incomplete, ambiguous information and gives an effective model for sintering process. The simulation result is made to show the effectiveness of the proposed algorithm.
  • Keywords
    combustion; fuzzy neural nets; production engineering computing; sintering; BTP prediction; burning through point state; fuzzy neural network structure; incomplete ambiguous information processing; multiple model algorithms; sintering process; Decision support systems; BTP; Fuzzy Neural Network; Multiple Model Algorithm; Sintering Process;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control and Decision Conference (2014 CCDC), The 26th Chinese
  • Conference_Location
    Changsha
  • Print_ISBN
    978-1-4799-3707-3
  • Type

    conf

  • DOI
    10.1109/CCDC.2014.6852882
  • Filename
    6852882