Wastewater treatment plant design: Optimizing multiple objectives

Abstract

The high costs associated with the design of wastewater treatment plants (WWTPs) motivate the research in the area of modelling their construction and the water treatment process optimization. This work addresses different methodologies, which are based on defining and simultaneously optimizing several conflicting objectives, for finding the optimal values of the state variables in the plant design. We use an evolutionary many-objective optimization algorithm with clustering-based selection that proved effective in handling challenging optimization problems with a large number of objectives. The obtained results are promising and with physical meaning. It is shown that the overall WWTP design can be improved by coming up with appropriate formulation of the optimization problem and solving approach.