Support Vector Machines for Environmental Informatics: Application to Modelling the Nitrogen Removal Processes in Wastewater Treatment Systems

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.

[1]  Johan A. K. Suykens,et al.  Financial time series prediction using least squares support vector machines within the evidence framework , 2001, IEEE Trans. Neural Networks.

[2]  Peter Tiño,et al.  Learning long-term dependencies in NARX recurrent neural networks , 1996, IEEE Trans. Neural Networks.

[3]  Gilles G. Patry,et al.  Identification, verification and validation of process models in wastewater engineering: a critical review , 2003 .

[4]  Sylvie Gillot,et al.  The COST Simulation Benchmark: Description and Simulator Manual , 2001 .

[5]  J. Suykens.  Nonlinear modelling and support vector machines , 2001, IMTC 2001. Proceedings of the 18th IEEE Instrumentation and Measurement Technology Conference. Rediscovering Measurement in the Age of Informatics (Cat. No.01CH 37188).

[6]  M. B. Beck,et al.  On the problem of model validation for predictive exposure assessments , 1997 .

[7]  Johan A. K. Suykens,et al.  Support Vector Machines: A Nonlinear Modelling and Control Perspective , 2001, Eur. J. Control.

[8]  George Tchobanoglous,et al.  Wastewater Engineering Treatment Disposal Reuse , 1972 .

[9]  Stefano Marsili-Libelli,et al.  Modelling, Identification and Control of the Activated Sludge Process , 1989, Lignocellulosic Materials.

[10]  M. B. Beck.  Identification, estimation and control of biological waste-water treatment processes , 1986 .

[11]  Guillaume Patry,et al.  Constructing a model hierarchy with background knowledge for structural risk minimization: application to biological treatment of wastewater , 2006, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[12]  Guillaume Patry,et al.  Statistical learning theory, model identification and system information content , 2002, Int. J. Gen. Syst..

[13]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machines , 2002 .

[14]  Guillaume Patry,et al.  Using Statistical Learning Theory to Rationalize System Model Identification and Validation Part I: Mathematical Foundations , 2003, Complex Syst..

[15]  Sun-Yuan Kung,et al.  A delay damage model selection algorithm for NARX neural networks , 1997, IEEE Trans. Signal Process..

[16]  Ulf Jeppsson,et al.  Modelling Aspects of Wastewater Treatment Processes , 1996 .

[17]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[18]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[19]  Shigeru Akamatsu,et al.  Invariant face detection with support vector machines , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[20]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machines , 2002 .

[21]  Bernhard Schölkopf,et al.  Comparing support vector machines with Gaussian kernels to radial basis function classifiers , 1997, IEEE Trans. Signal Process..

[22]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .