• DocumentCode
    1803437
  • Title

    Modeling of thermodynamic properties of substances by neural networks

  • Author

    Lilja, Reijo ; Hamalainen, Jari J.

  • Author_Institution
    Tech. Res. Centre of Finland, Finland
  • Volume
    6
  • fYear
    1999
  • fDate
    36342
  • Firstpage
    3927
  • Abstract
    A new method based on neural networks was developed for the modeling of thermodynamic properties of substances. When applied to the mixture of air and H2O, the preset accuracy of 1% was obtained at every test point and the neural networks proved to be 5000 times faster than a conventional iterative algorithm. Large tables characteristic of previous interpolation methods are not needed. The neural network models enable new process simulation applications
  • Keywords
    digital simulation; interpolation; neural nets; physics computing; production engineering computing; thermodynamic properties; air-water mixture; neural networks; process simulation applications; thermodynamic property modeling; Analytical models; Automation; Function approximation; Interpolation; Iterative algorithms; Neural networks; Numerical simulation; Temperature; Testing; Thermodynamics;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1999. IJCNN '99. International Joint Conference on
  • Conference_Location
    Washington, DC
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-5529-6
  • Type

    conf

  • DOI
    10.1109/IJCNN.1999.830784
  • Filename
    830784
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1803437