doi:10.3808/jei.200600063
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Support Vector Machines for Environmental Informatics: Application to Modelling the Nitrogen Removal Processes in Wastewater Treatment Systems

     Y. H. Yang1, A. Guergachi2* and G. Khan3

  1. Citizens' Environment Watch, Toronto, ON, Canada
  2. School of Information Technology, Ryerson University, Toronto, ON, Canada
  3. Electrical and Computer Engineering, Ryerson University, Toronto, ON, Canada

    *Corresponding author. Email: a2guerga@ryerson.ca

Abstract


In order to meet the new stringent environmental regulations, it is necessary to investigate the adaptive and optimal control strategies for the biological wastewater treatment processes. Nitrogen removal is one of the essential concerns in wastewater treatment. Nitrogen removal is a nonlinear, dynamic, and time variant complex process as complicated activities of microbial metabolism are involved. The mechanistic models for nitrogen removal are complicated and still uncertain to some extent. A new machine learning approach, Support Vector Machine (SVM) was proposed as black-box modeling technique to model the biological wastewater treatment processes. LS-SVM, a simplified formulation of SVM, has been applied in this study to predict the concentration of nitrate and nitrite (NO) in the Mixed Liquor (ML) of wastewater treatment plant. Nonlinear Autoregressive model with Exogenous inputs (NARX model) can be employed with LS-SVM to extract useful information and improve the prediction performance. In this paper, the premium wastewater treatment plant simulation and optimization software, GPS-X, is used to create virtual plant layout and simulated data. The simulation results indicate that the proposed method has good generalization performance, especially when the input is fluctuated without a usual pattern. We conclude that LS-SVM with NARX modelling could be used as an alternative approach to predict the behaviour of wastewater treatment systems by further studying some essential issues such as the tuning of memory order and training data size.

Keywords: GPS-X, least-square SVMs, NARX model, nitrogen removal, support vector machines, wastewater treatment


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