• DocumentCode
    2222213
  • Title

    Forecasting NOx emissions in power plant using rough set and QGA-based SVM

  • Author

    Zhou, Jian-guo ; An, Yuan-yuan

  • Author_Institution
    Sch. of Bus. Adm., North China Electr. Power Univ., Baoding, China
  • Volume
    4
  • fYear
    2010
  • fDate
    20-22 Aug. 2010
  • Abstract
    NOx emissions prediction research is to the benefit of NOx emissions control. Studying the NOx emissions under new situation in coal-fired plant is of great significance. This paper introduces Quantum Genetic Algorithm (QGA) to optimize the parameters of SVM. Our experiment results demonstrate that using the QGA-SVM model will achieve better prediction than the individual SVM model.
  • Keywords
    coal; genetic algorithms; nitrogen compounds; power plants; power system control; rough set theory; support vector machines; NO; SVM; coal-fired plant; emissions control; power plant; quantum genetic algorithm; rough set; support vector machines; Positron emission tomography; Support vector machines; NOx Emissions; Quantum Genetic Algorithm (QGA); Rough Set; Support Vector Machines (SVM);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advanced Computer Theory and Engineering (ICACTE), 2010 3rd International Conference on
  • Conference_Location
    Chengdu
  • ISSN
    2154-7491
  • Print_ISBN
    978-1-4244-6539-2
  • Type

    conf

  • DOI
    10.1109/ICACTE.2010.5579299
  • Filename
    5579299
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2222213