• DocumentCode
    2914329
  • Title

    Research of sludge compost maturity degree modeling method based on classify support vector machine for sewage treatment

  • Author

    Tian, Jingwen ; Gao, Meijuan ; Zhou, Hao

  • Author_Institution
    Beijing Union Univ., Beijing
  • fYear
    2007
  • fDate
    18-20 Nov. 2007
  • Firstpage
    1122
  • Lastpage
    1127
  • Abstract
    Because of the complicated interaction of the sludge compost components, it makes the judging system of sludge compost maturity degree appear the non-linearity and uncertainty. According to the physical circumstances of sludge compost, a sludge compost maturity degree modeling method based on support vector machine (SVM) is presented. We select the index of sludge compost maturity degree and take the high temperature duration, moisture content, volatile solids, the value of fecal bacteria, and germination index as the judgment parameters. We construct the structure of SVM network that used for the maturity degree judgment of sludge compost, and use the genetic algorithm (GA) to optimize SVM parameters. With the ability of strong self-learning and well generalization of SVM, the modeling method can truly judge the sludge compost maturity degree by learning the index information of sludge compost maturity degree. The experimental results show that this method is feasible and effective.
  • Keywords
    environmental science computing; genetic algorithms; learning (artificial intelligence); sewage treatment; sludge treatment; support vector machines; genetic algorithm; maturity degree judgment; self-learning; sewage treatment; sludge compost maturity degree modeling method; support vector machine; Artificial neural networks; Microorganisms; Moisture; Organisms; Pathogens; Sewage treatment; Soil; Support vector machine classification; Support vector machines; Temperature;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Grey Systems and Intelligent Services, 2007. GSIS 2007. IEEE International Conference on
  • Conference_Location
    Nanjing
  • Print_ISBN
    978-1-4244-1294-5
  • Electronic_ISBN
    978-1-4244-1294-5
  • Type

    conf

  • DOI
    10.1109/GSIS.2007.4443447
  • Filename
    4443447