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Wastewater BOD Forecasting Model for Optimal Operation Using Robust Time-Delay Neural Network

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Advances in Neural Networks – ISNN 2005 (ISNN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3498))

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Abstract

Due to the lack of reliable on-line sensors to measure water quality parameters, it is difficult to control and operational optimization in the wastewater treatment plants (WWTPs). A hybrid time-delay neural network (TDNN) modeling method with data pretreatment is applied for BOD forecasting model in wastewater treatment. PCA is combined with robust expectation-maximization (EM), which reduces the influence of noise, outliers and missing data. The principal components are used as inputs to time-delay neural networks to predict the effluent BOD value. The simulation results using real process data show an enhancement in speed and accuracy, compared with a back propagation neural networks.

This paper is supported by the Chinese National Hi-Tech Development Program (2004AA412030), and the National Natural Science Foundation of China under Grant 60074019.

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Authors and Affiliations

  1. Automation Research Center, Northeastern University, Shenyang, 110004, China

    Lijie Zhao & Tianyou Chai

  2. Information Engineering School, Shenyang Institute of Chemical Technology, 110142, China

    Lijie Zhao

Authors
  1. Lijie Zhao
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  2. Tianyou Chai
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. The Key Laboratory of Optoelectric Technology & Systems, Ministry of Education, China

    Xiao-Feng Liao

  3. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

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© 2005 Springer-Verlag Berlin Heidelberg

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Zhao, L., Chai, T. (2005). Wastewater BOD Forecasting Model for Optimal Operation Using Robust Time-Delay Neural Network. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_163

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  • DOI: https://doi.org/10.1007/11427469_163

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25914-5

  • Online ISBN: 978-3-540-32069-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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