• DocumentCode
    527856
  • Title

    The quality prediction of iron ore pellets in grate-kiln-cooler system using artificial neural network

  • Author

    Feng, Junxiao ; Qiao, Yang

  • Author_Institution
    Sch. of Mech. Eng., Univ. of Sci. & Technol. Beijing, Beijing, China
  • Volume
    4
  • fYear
    2010
  • fDate
    10-12 Aug. 2010
  • Firstpage
    1906
  • Lastpage
    1909
  • Abstract
    A model of three single layer back propagation(BP) artificial neural networks has been established to predict the compression strength of final pellets and dried pellets, the same as the shatter strength of the green pellets, according to the production data from SHOUGANG Mining Company. The Levenberg-Marquardt optimization arithmetic was used to train the model with the production data. After training, the error of the prediction result is less than 3%. The developed model can meet the requirement from production with a high accuracy and a wide flexibility.
  • Keywords
    backpropagation; blast furnaces; cooling; kilns; neural nets; optimisation; production engineering computing; quality control; Levenberg-Marquardt optimization arithmetic; SHOUGANG Mining Company; blast furnace; compression strength; grate-kiln-cooler system; iron ore pellets quality prediction; shatter strength; single layer back propagation artificial neural networks; Artificial neural networks; Blast furnaces; Iron; Kilns; Predictive models; Production; Training data; artificial nerural network; grate-kiln-cooler; pellet; quality prediction;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Natural Computation (ICNC), 2010 Sixth International Conference on
  • Conference_Location
    Yantai, Shandong
  • Print_ISBN
    978-1-4244-5958-2
  • Type

    conf

  • DOI
    10.1109/ICNC.2010.5584645
  • Filename
    5584645