Loading [MathJax]/extensions/MathZoom.js
Genetic Hybrid Predictive Controller for Optimized Dissolved-Oxygen Tracking at Lower Control Level | IEEE Journals & Magazine | IEEE Xplore

Genetic Hybrid Predictive Controller for Optimized Dissolved-Oxygen Tracking at Lower Control Level


Abstract:

A hierarchical two-level controller for dissolved-oxygen reference trajectory tracking in activated sludge processes has been recently developed and successfully validate...Show More

Abstract:

A hierarchical two-level controller for dissolved-oxygen reference trajectory tracking in activated sludge processes has been recently developed and successfully validated on a real wastewater treatment plant. The upper level control unit generates trajectories of the desired airflows to be delivered by the aeration system to the aerobic zones of the biological reactor. A nonlinear model predictive control algorithm is applied to design this controller. The aeration system itself is a complicated hybrid nonlinear dynamical system. The lower level controller (LLC) forces the aeration system to follow these set-point trajectories, minimizing a cost of energy due to pumping of the air and accounting for system operational limitations such as the limits on the allowed frequency of switching of the blowers and on their capacity. The predictive control is also applied to design the LLC based on a piecewise-linearized hybrid dynamics of the aeration system. Casting the mixed-integer nonlinear optimization problem under heterogeneous constraints due to the limits on the blower switching frequency into the approximated mixed-integer form is done at a cost of introducing large number of auxiliary variables into the lower level predictive controller optimization task. This paper derives another nonlinear hybrid predictive control algorithm for the LLC. It is directly based on the nonlinear hybrid dynamics and logical formulation of the switching constraint. A genetic algorithm is derived with dedicated operators allowing for efficient handling of the switching constraint and nonlinear hybrid system dynamics. The efficiency of the control algorithm is validated by simulation based on real data records.
Published in: IEEE Transactions on Control Systems Technology ( Volume: 17, Issue: 5, September 2009)
Page(s): 1183 - 1192
Date of Publication: 28 April 2009

ISSN Information:

References is not available for this document.

Select All
1.
M. A. Brdys, M. Grochowski, T. Gminski, K. Konarczak and M. Drewa, "Hierarchical predictive control of integratedwastewater treatment systems", Control Eng. Pract., vol. 16, no. 6, pp. 751-767, Jun. 2008.
2.
G. J. Haarsma and K. J. Keesman, "Robust model predictive dissolvedoxygen control", Proc. 9th Forum Appl. Biotechnology, pp. 2415-2426, 1995.
3.
C. F. Lindberg and B. Carlsson, "Nonlinear and set-point controlof the dissolved oxygen concentration in an activated sludge process", Water Sci. Technol., vol. 34, no. 3/4, pp. 135-142, 1996.
4.
C. K. Yoo, H. K. Lee and I. B. Lee, "Comparison of process identificationmethods and supervisory control in the full scale wastewater treatment plant", Proc. 15th IFAC World Congr., 2002-Jul.-2126.
5.
W. Chotkowski, M. A. Brdys and K. Konarczak, "Dissolved oxygen control foractivated sludge processes", Int. J. Syst. Sci., vol. 36, no. 12, pp. 727-736, Oct. 2005.
6.
A. Sanches and M. R. Katebi, "Predictive control of dissolvedoxygen in an activated sludge wastewater treatment plant", Proc. ECC, 2003.
7.
R. Piotrowski, M. A. Brdys, K. Konarczak, K. Duzinkiewicz and W. Chotkowski, "Hierarchical dissolved oxygencontrol for activated sludge processes", Control Eng. Pract., vol. 16, no. 1, pp. 114-131, Jan. 2008.
8.
R. Piotrowski and M. A. Brdys, "Lower-level controller forhierarchical control of dissolved oxygen concentration in activated sludgeprocesses", Proc. 16th IFAC World Congr., 2005-Jul.-48.
9.
R. L. Woods and K. L. Lawrence, Modeling and Simulation of Dynamic Systems, NJ, Upper Saddle River:Prentice-Hall, 1997.
10.
A. Bemporad and M. Morari, "Control of systems integratinglogic dynamics and constraints", Automatica, vol. 35, no. 3, pp. 407-427, Mar. 1999.
11.
K. Deb, Multi-Objective Optimization Using Evolutionary Algorithms, NY, Chichester:Wiley, 2001.
12.
M. Henze, W. Gujer, T. Mino, T. Matsuo, M. Wentzel, G. Marais, et al., "Activated sludge model no. 2d", Water Sci. Technol., vol. 39, no. 1, pp. 165-182, 1999.
13.
Users Guide.
Contact IEEE to Subscribe

References

References is not available for this document.