Use of surrogate modelling for multiobjective optimisation of urban wastewater systems

Simulation models are now available to represent the sewer network, wastewater treatment plant and receiving water as an integrated system. These models can be combined with optimisation methods to improve overall system performance through optimal control. Evolutionary algorithms (EAs) have been pr...

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Bibliographic Details
Published in:Water science and technology 2009-01, Vol.60 (6), p.1641-1647
Main Authors: Fu, G, Khu, S-T, Butler, D
Format: Article
Language:English
Subjects:
Algorithms
Business metrics
Cities
Computer Simulation
Computing time
Efficiency
Evolutionary algorithms
Genetic algorithms
Integrated approach
Maximum likelihood method
Models, Theoretical
Multiple objective analysis
Optimal control
Optimization
Receiving waters
Simulation
Stochastic models
Systems simulation
Time Factors
Waste Disposal, Fluid - methods
Wastewater treatment
Wastewater treatment plants
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Summary:Simulation models are now available to represent the sewer network, wastewater treatment plant and receiving water as an integrated system. These models can be combined with optimisation methods to improve overall system performance through optimal control. Evolutionary algorithms (EAs) have been proven to be a powerful method in developing optimal control strategies; however, the intensive computational requirement of these methods imposes a limit on their application. This paper explores the potential of surrogate modelling in multiobjective optimisation of urban wastewater systems with a limited number of model simulations. A surrogate based method, ParEGO, is combined with an integrated urban wastewater model to solve real time control problems. This method is compared with the popular NSGA II, by using performance indicators: the hypervolume indicator, additive binary epsilon-indicator and attainment surface. Comparative results show that ParEGO is an efficient and effective method in deriving optimal control strategies for multiple objective control problems with a small number of simulations. It is suggested that ParEGO can greatly improve computational efficiency in the multiobjective optimisation process, particularly for complex urban wastewater systems.
ISSN:0273-1223
1996-9732
DOI:10.2166/wst.2009.508