Showing papers by "Francois Clemens published in 2001"

Wastewater System Optimization using Genetic Algorithms

Abstract: The optimisation of complete wastewater systems receives more and more attention, since it has become clear that the current approach of optimising the subsystems of sewerage and wastewater treatment separately often results in sub-optimal solutions. This optimisation, however, shows to be very troublesome in practice due to its complexity. The complexity of a wastewater system optimisation problem is related to the many variables involved (multi-dimensional search) and to the optimisation goal, defined by an objective function. Optimisation with respect to multiple objectives, e.g. overall costs as well as environmental performance, is often suggested but increases the complexity. One of the optimisation or search techniques which is capable of dealing with such a complex surrounding is a genetic algorithm (GA). This paper discusses the potential of the use of genetic algorithms for optimisation of wastewater systems. The definition of the objective function and the characteristics of the GA, especially mutation probability, proved to be key-elements for a successful application of a GA for this type of problems. Finally, it is concluded that GAs are capable of solving the very complex optimisation problems related to improvement of total wastewater systems.

A Design Method for Monitoring Networks in Urban Drainage

Abstract: When designing a measuring network several questions must be answered. Basic properties like defining sampling interval and the exact locations for measuring sites are mostly based upon expert's judgement. In this paper a design method for measuring networks aiming at obtaining information for model calibration is proposed. This method is based upon the application of a hydrodynamic model for initial design. Based on a rain-max approach for the sampling fiequancy, a first estimate is obtained. Based on a mathematical analysis of the model parameterization and information content of potential measuring locations it is possible to judge measuring networks objectively. Moreover, by applying some optimization technique (e.g. a genetic algorithm) an automated optimization of the measuring network is possible.

Wastewater System Optimisation Using Genetic Algorithms

Abstract: The optimi.~ation of complete wastewater systems receives more and more attention, since it has become clear that the current apptczeh of optimising the subsystems of sewerage and was~water treatment separately often results in sub-optimal solutions. This optimisation, however, shows to be very troublesome in practice due to its complexity. The complexity of a wastewater system opthuisafion problem is related to the many variables involved (multi-dimensional search) and to the optimisation goal, defined by an objective fuuetien~ Optimi.xation with respect to multiple objectives, e.g. overall costs as well as environmental performance, is often suggested but increases the complexity. One of the optimisation or search techniques which is capable of dealing with such a complex surrounding is a genetic algorithm (GA). This paper discusses the potential of the use of genetic algorithm~ for opfimisation of wastewater systems. The definition of the objective function and the characteristics of the GA, especially mutation probability, proved to be key-elements for a successful application of a GA for this type of problems. Finally, it is concluded that GAs are capable of solving the very complex opfimisation problems related to improvement of total wastewater systems.

Calibration and Verification of Hydrodynamic Models in Urban Drainage

Abstract: Calibration of models is mostly focussed on obtaining an 'optimal set of parameter values'. Analysis of the properties of these parameters and the analysis of the differences between model and reafity after parameter optimization are in most cases neglected. In this paper some techniques for residue analysis and their applications are discussed and demonstrated by an example.