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  • Estimation of Chemical Oxygen Demand by Ultraviolet Spectroscopic Profiling and Artificial Neural Networks

    Author(s)
    Fogelman, S
    Blumenstein, M
    Zhao, HJ
    Griffith University Author(s)
    Zhao, Huijun
    Blumenstein, Michael M.
    Fogelman, Shoshana
    Year published
    2006
    Metadata
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    Abstract
    A simple method based on the mathematical treatment of spectral absorbance profiles in conjunction with artificial neural networks (ANNs) is demonstrated for rapidly estimating chemical oxygen demand (COD) values of wastewater samples. In order to improve spectroscopic analysis and ANN training time as well as to reduce the storage space of the trained ANN algorithm, it is necessary to decrease the ANN input vector size by extracting unique characteristics from the raw input pattern. Key features from the spectral absorbance pattern were therefore selected to obtain the spectral absorbance profile, reducing the ANN input ...
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    A simple method based on the mathematical treatment of spectral absorbance profiles in conjunction with artificial neural networks (ANNs) is demonstrated for rapidly estimating chemical oxygen demand (COD) values of wastewater samples. In order to improve spectroscopic analysis and ANN training time as well as to reduce the storage space of the trained ANN algorithm, it is necessary to decrease the ANN input vector size by extracting unique characteristics from the raw input pattern. Key features from the spectral absorbance pattern were therefore selected to obtain the spectral absorbance profile, reducing the ANN input vector from 160 to 10 selected inputs. The results indicate that the COD values obtained from the selected absorbance profiles agreed well with those obtained from the entire absorbance pattern. The spectral absorbance profile technique was also compared to COD values estimated by a multiple linear regression (MLR) model to validate whether ANNs were better and more robust models for rapid COD analysis. It was found that the ANN model predicted COD values closer to standard COD values than the MLR model.
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    Journal Title
    Neural Computing And Applications
    Volume
    15
    DOI
    https://doi.org/10.1007/s00521-005-0015-9
    Subject
    Cognitive and computational psychology
    Publication URI
    http://hdl.handle.net/10072/13804
    Collection
    • Journal articles

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