Genetic algorithms in real time control applied to minimize transient pollution from urban wastewater systems

Real time control aims at optimization of the urban wastewater system performance under dynamic loading from rain. This paper presents a novel approach to control the whole system: sewer system, treatment plant and receiving water with the aim to achieve minimum effects of pollution. The application of nonlinear model predictive control by means of a genetic algorithm reveals excellent results with hypothetical problem sets. The methodology makes it possible to optimize the system performance directly with respect to water quality parameters and to avoid the traditional indirect and artificial performance criteria, such as permissible annual overflow volume. The relevance of this novel approach is illustrated by the fact that no stringent correlation has been found in the investigation between the reduction of overflow volume and the increase of oxygen concentration in the receiving water.

[1]  J. Eheart,et al.  Using Genetic Algorithms to Solve a Multiobjective Groundwater Monitoring Problem , 1995 .

[2]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[3]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[4]  Poul Harremoës,et al.  Correlation of combined sewer overflow reduction due to real-time control and resulting effect on the oxygen concentration in the river , 1998 .

[5]  W. Gujer,et al.  Activated sludge model No. 3 , 1995 .

[6]  S. Ranjithan,et al.  Using genetic algorithms to solve a multiple objective groundwater pollution containment problem , 1994 .

[7]  L. L. Rogers,et al.  Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling , 1994 .

[8]  W. Rauch,et al.  Minimizing acute river pollution from urban drainage systems by means of integrated real time control , 1996 .

[9]  Poul Harremoës,et al.  Impact of rain runoff on oxygen in an urban river , 1996 .

[10]  Poul Harremoës,et al.  The importance of the treatment plant performance during rain to acute water pollution , 1996 .

[11]  I. Takács.  A dynamic model of the clarification-thickening process , 1991 .

[12]  Q. J. Wang.  The Genetic Algorithm and Its Application to Calibrating Conceptual Rainfall-Runoff Models , 1991 .

[13]  Poul Harremoës,et al.  Immediate and delayed oxygen depletion in rivers , 1982 .

[14]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[15]  Fons Nelen.  On the Potential of Real Time Control of Urban Drainage Systems , 1993 .

[16]  Poul Harremoës,et al.  Oxygen and pH fluctuations in rivers , 1978 .

[17]  Ghassan Chebbo,et al.  Storm water quality modelling, an ambitious objective? , 1998 .

[18]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[19]  W. Verstraete,et al.  Selection of one-dimensional sedimentation: models for on-line use , 1995 .