ANFIS Direct Inverse Control of Substrate in an Activated Sludge Wastewater Treatment System

Article Preview

Abstract:

Nonlinearity of the substrate dynamics is the main inconveniences in its control. Low substrate concentration significantly affects the growth of the microorganisms responsible for treating the wastewater and too high substrate concentration level may lead to drop in the growth rate. Therefore, the control of substrate concentration is highly essential for effective and optimal operation of wastewater treatment plants. However, the control using conventional techniques is quite cumbersome and often impossible. This paper presents adaptive neuro fuzzy inference system (ANFIS) direct inverse control of substrate in an activated sludge system. The performances of the proposed controller are illustrated by tracking the substrate set-points. The simulation results demonstrate that the proposed controller can effectively and accurately control the substrate concentration level. The proposed inverse controller may serve as a valuable control strategy for the wastewater treatment plant.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

246-250

Citation:

Online since:

June 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] R. Aguilar-López, Regulation of an activate sludge wastwater plant via robust active control design, Int. J. Environ. Res., 7(2012)61–68.

Google Scholar

[2] M. Cristea and S. Agachi, Nonlinear model predictive control of the wastewater treatment plant, Comput. Aided Chem. Eng., (2006) 1365–1370.

DOI: 10.1016/s1570-7946(06)80237-3

Google Scholar

[3] F. Nejjari, B. Dahhou, A. Benhammou, and G. Roux, Nonlinear multivariable adaptive control of an activated sludge wastewater, Int. J. Adapt. Control Signal Process., 365(1998) 347–365.

DOI: 10.1002/(sici)1099-1115(199908)13:5<347::aid-acs543>3.0.co;2-8

Google Scholar

[4] A. Chirosca, G. Dumitra, M. Barbu, and S. Caraman, Fuzzy Control of a Wastewater Treatment Process, in: J. Wadata et al. (Eds. ), Intelligent Decision Technologies, SIST 10, Springer-Verlag Berlin Heidelberg, 2011, pp.155-163.

DOI: 10.1007/978-3-642-22194-1_16

Google Scholar

[5] D. Dochain, and P.A. Vanrolleghem, Dynamical Modelling and Estimation in Wastewater Treatment Processes. IWA Publishing, London, UK, (2001).

Google Scholar

[6] J. Jang, ANFIS: Adaptive-network-based fuzzy inference system, IEEE Transactions on Syst. Man Cybern., 23(1993) 665–685.

DOI: 10.1109/21.256541

Google Scholar

[7] M. A. Denai, F. Palis, and A. Zeghbib, ANFIS based modelling and control of non-linear systems: a tutorial, 2004 IEEE Int. Conf. Syst. Man Cybern., Oran, Algeria. IEEE: 3433–3438.

DOI: 10.1109/icsmc.2004.1400873

Google Scholar

[8] B. Widrow and E. Walach, Adaptive Inverse Control: A Signal Processing Approach, Reissue Edition. John Wiley & Sons, Inc., New Jersey, (2008).

Google Scholar