Using a Genetic Algorithm to Solve a Bi-Objective WWTP Process Optimization

Lino Costa (), Isabel A. C. P. Espírito Santo () and Edite M. G. P. Fernandes ()
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Lino Costa: University of Minho
Isabel A. C. P. Espírito Santo: University of Minho
Edite M. G. P. Fernandes: University of Minho

A chapter in Operations Research Proceedings 2010, 2011, pp 359-364 from Springer

Abstract: Abstract When modeling an activated sludge system of a wastewater treatment plant (WWTP), several conflicting objectives may arise. The proposed formulation is a highly constrained bi-objective problem where the minimization of the investment and operation costs and the maximization of the quality of the effluent are simultaneously optimized. These two conflicting objectives give rise to a set of Pareto optimal solutions, reflecting different compromises between the objectives. Population based algorithms are particularly suitable to tackle multi-objective problems since they can, in principle, find multiple widely different approximations to the Pareto-optimal solutions in a single run. In this work, the formulated problem is solved through an elitist multi-objective genetic algorithm coupled with a constrained tournament technique. Several trade-offs between objectives are obtained through the optimization process. The direct visualization of the trade-offs through a Pareto curve assists the decision maker in the selection of crucial design and operation variables. The experimental results are promising, with physical meaning and highlight the advantages of using a multi-objective approach.

Keywords: Chemical Oxygen Demand; Activate Sludge; Total Suspend Solid; Multiobjective Optimization; Biochemical Oxygen Demand (search for similar items in EconPapers)
Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:spr:oprchp:978-3-642-20009-0_57

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DOI: 10.1007/978-3-642-20009-0_57

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