• DocumentCode
    3215617
  • Title

    A new method of COD testing with BP neural network

  • Author

    Lv, Yanghua ; Xiang, Zhen ; Chen, Ke ; Shao, Leji

  • Author_Institution
    State Key Lab. of Modern Opt. Instrum., Zhejiang Univ., Hangzhou, China
  • Volume
    4
  • fYear
    2011
  • fDate
    29-31 July 2011
  • Abstract
    A new optical method of testing the chemical oxygen demand (COD) of wastewater with multi-wavelength at UV band is reported. The normal UV254 is based on the UV light absorption by wastewater at 254nm according to the Beer-Lambert law. But not all of the wastewater has obviously absorption at 254nm. The new method use back-propagation artificial neural network(BP network) to determine COD of the samples. After the BP network is training by a series wastewater´s samples which have similar ingredient but different COD, the sample´s absorbance at UV band(between 240~400nm) is analyzed, and the specific wavelength which is associative with sample´s COD is picked out. These wavelength can be used to calculated the COD value, and the result is close to standard chemical method. Comparing with UV254, the new method is more stability and precise, and better adaptability to varied wastewater.
  • Keywords
    backpropagation; environmental science computing; environmental testing; neural nets; ultraviolet spectra; wastewater; water pollution measurement; water quality; BP neural network; Beer-Lambert law; COD testing; COD value calculation; UV light absorption; UV254 ultraviolet spectrometry; back propagation artificial neural network; chemical oxygen demand testing; wastewater; Mathematical model; Training; Artificial Neural Networks(ANNs); UV; back-propagation; chemical oxygen demand(COD);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electronics and Optoelectronics (ICEOE), 2011 International Conference on
  • Conference_Location
    Dalian
  • Print_ISBN
    978-1-61284-275-2
  • Type

    conf

  • DOI
    10.1109/ICEOE.2011.6013482
  • Filename
    6013482