Development of a fast simulation model to facilitate multi-objective optimisation of the integrated urban wastewater system

Dirk Muschalla and Manfred Ostrowski

ABSTRACT

This paper presents a new multi-objective evolution strategy in combination with an integrated pollution-load and water-quality model to optimize an urban wastewater system. The developed optimisation algorithm combines the advantages of the Non-Dominated Sorting Genetic Algorithm and Self-Adaptive Evolution Strategies and contains further developments improving convergence and diversity. The identification of a good spread of solutions on the Pareto-optimum front and the optimization of a large number of decision variables equally demands numerous simulation runs. In addition, evaluation of criteria with regard to the frequency of critical concentrations in the river and peak discharges to the receiving water requires continuous long-term simulations. Therefore, a fast operating integrated simulation model is needed providing the required precision of the results.

For this purpose, different methodologies are used: identification of the dominating effects, which determine the key pollutants, processes and also the necessary timeframe of simulation; model reduction techniques like boundary relocation and sub-model elimination; computing time effective coupling techniques for the sub-models; consequent application of fast programming languages and compilers. In addition, an application of distributed computing systems within the optimization algorithm is discussed.

The integrated simulation model and the multi-objective optimization algorithm were implemented in a modular common software shell. The functionality of the optimization and simulation tool has been validated by analyzing a real catchment area including sewer system, WWTP, water body and natural river basin. For the optimization/rehabilitation of the urban drainage system, both innovative and approved measures have been examined and used as decision variables. As objective functions, investment costs and river water quality criteria have been used.


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