• DocumentCode
    510081
  • Title

    Coal Face Gas Emission Prediction Based on Support Vector Machine

  • Author

    Ning Yuncai ; Chen Xiang

  • Author_Institution
    Inst. of Manage., China Univ. of Min. & Technol., Beijing, China
  • Volume
    1
  • fYear
    2009
  • fDate
    7-8 Nov. 2009
  • Firstpage
    19
  • Lastpage
    22
  • Abstract
    Mine work face gas emission quantity is an important mine design basis, which also has important practical significance for guide mine design, ventilation and safety production. Mine gas emission quantity and work face multi factors have complex non-linear relationship. The paper built the work face gas emission prediction support vector machine (SVM) model. Based on data statistic of a mine work face gas emission, the paper used the model to predict gas emission. The result was accurate, which prove the model´s prediction for face gas is viable and effective.
  • Keywords
    mining industry; support vector machines; coal face gas emission prediction; data statistic; mine design; mine gas emission; safety production; support vector machine; ventilation; Artificial intelligence; Explosions; Neural networks; Prediction methods; Predictive models; Product safety; Production; Statistics; Support vector machines; Ventilation; gas emission quantity; index system; prediction; support vector machine;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Artificial Intelligence and Computational Intelligence, 2009. AICI '09. International Conference on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4244-3835-8
  • Electronic_ISBN
    978-0-7695-3816-7
  • Type

    conf

  • DOI
    10.1109/AICI.2009.217
  • Filename
    5375985