• DocumentCode
    3567594
  • Title

    Modeling Synthesis Processes of Photocatalysts Using Symbolic Regression α-β

  • Author

    Gonzalez-Campos, G. ; Torres-Trevino, L.M. ; Luevano-Hipolito, E. ; Martinez-De La Cruz, A.

  • Author_Institution
    Fac. de Ingeniena Mec. y Electr., Univ. Autonoma de Nuevo Leon, San Nicolás de los Garza, Mexico
  • fYear
    2014
  • Firstpage
    174
  • Lastpage
    179
  • Abstract
    Symbolic regression is an application of genetic programming and is used for modeling different dynamic processes. Industrial processes problems have been solved using this technique. In this work a symbolic regression algorithm is used for modeling the synthesis process of the oxides Bi2MoO6 and V2O5 in order to provide a model. These oxides are used on heterogeneous photo catalysis. Genetic programming, artificial neural network and linear regression are compared with symbolic regression models using statistics metrics to evaluate them.
  • Keywords
    catalysis; chemical technology; genetic algorithms; photochemistry; artificial neural network; genetic programming; heterogeneous photocatalysis; industrial processes; linear regression; photocatalysts synthesis processes; statistics metrics; symbolic regression α-ß; Artificial neural networks; Data models; Genetic programming; Linear regression; Mathematical model; Sociology; Symbolic regression; genetic programming; industrial modeling; photocatalysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Artificial Intelligence (MICAI), 2014 13th Mexican International Conference on
  • Print_ISBN
    978-1-4673-7010-3
  • Type

    conf

  • DOI
    10.1109/MICAI.2014.33
  • Filename
    7222861
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3567594