Use of surrogate modelling for multiobjective optimisation of urban wastewater systems

Copyright © IWA Publishing 2009. The definitive peer-reviewed and edited version of this article is published in Water Science and Technology Volume 60 Issue 6, pp. 1641–1647 (2009), DOI: 10.2166/wst.2009.508 and is available at www.iwapublishing.comSimulation 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

Comparison of control strategies for multi-objective control of urban wastewater systems

In recent years much attention has been paid to integrated management and control of urban wastewater systems. With the application of integrated system modelling tools, overall system performance can be improved to a great extent in terms of receiving water quality, through development of optimal control strategies. Most studies to date, however, have used a single objective to demonstrate the potential benefits. Control of urban wastewater systems is actually a multiple objective optimisation problem, involving balancing different, possibly conflicting objectives required by stakeholders with different interests. This paper compares three different control strategies for multi-objective optimal control of the urban wastewater system, including one global control strategy and two integrated control strategies. A popular multiple objective evolutionary algorithm, NSGA II, is applied to derive the Pareto optimal solutions for the three strategies. The comparative results show the benefits of application of integrated control in achieving an improved system performance in terms of dissolved oxygen and ammonium concentrations in the receiving river. The simulation results also illustrate the effectiveness of NSGA II in deriving the optimal control strategies with different complexities

Multiobjective optimisation of urban wastewater systems using ParEGO: a comparison with NSGA II

Commercial and research-based simulation models are now available to represent the performance and control of the sewer network, wastewater treatment plant and receiving water as a whole. To improve overall system performance, these models can be combined with optimisation methods to derive optimal control strategies. The popular evolutionary algorithms (EAs) have been proven to be a powerful method in developing optimal control strategies; however, the high computational requirements of these methods impose a limit on their application due to the complexity of the system. This paper explores the potential of a surrogate based multi-objective optimisation method, ParEGO, for real time control of urban wastewater systems. An existing integrated model is used to evaluate the multiple objectives. This method is compared with NSGA II by using two performance indicators: the hypervolume indicator and the additive binary ε ε-indicator. Comparative results show that ParEGO is an efficient and effective method in deriving optimal control strategies for the multiple objective control problems. It is suggested that ParEGO can greatly improve the computational efficiency, particularly for complex systems

Optimizing Maritime Terminal Infrastructure Subject To Uncertainty

This paper describes a hydroinformatic model for generating a Pareto set of LNG terminal layouts that are subject to uncertainty using a multi-objective genetic algorithm. The NSGAII is used to select parameters that propagate through a bespoke LNG terminal design algorithm which includes a Monte Carlo simulator to estimate the uncertainty in each concept. This allows the trade-off between cost and risk to be explored at the earliest stage of design. The results of a case study indicate that nearshore terminals typically have lower capital costs but higher maintenance costs and more uncertainty. The paper concludes that in the example site used, locating the terminal 1000m offshore results in an optimal compromise between cost and risk

Multiple objective optimal control of integrated urban wastewater systems

Copyright © 2008 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in Environmental Modelling and Software. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Environmental Modelling and Software, Vol. 23 Issue 2 (2008). DOI: 10.1016/j.envsoft.2007.06.003Integrated modelling of the urban wastewater system has received increasing attention in recent years and it has been clearly demonstrated, at least at a theoretical level, that system performance can be enhanced through optimized, integrated control. However, most research to date has focused on simple, single objective control. This paper proposes consideration of multiple objectives to more readily tackle complex real world situations. The water quality indicators of the receiving water are considered as control objectives directly, rather than by reference to surrogate criteria in the sewer system or treatment plant. A powerful multi-objective optimization genetic algorithm, NSGA II, is used to derive the Pareto optimal solutions, which can illustrate the whole trade-off relationships between objectives. A case study is used to demonstrate the benefits of multiple objective control and a significant improvement in each of the objectives can be observed in comparison with a conventional base case scenario. The simulation results also show the effectiveness of NSGA 11 for the integrated urban wastewater system despite its complexity

The impact of new developments on river water quality from an integrated system modelling perspective

Copyright © 2009 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in Science of the Total Environment . Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Science of the Total Environment, Vol. 407 Issue 4 (2009). DOI: 10.1016/j.scitotenv.2008.10.033New housing areas are a ubiquitous feature of modern life in the developing and developed world alike built in response to rising social, demographic and economic pressures. Inevitably, these new developments will have an impact on the environment around them. Empirical evidence confirms the close relationship between urbanisation and ambient water quality. However, what is lacking so far is a detailed and more generalised analysis of environmental impact at a relatively small scale. The aim of this paper is to quantify the impact of new developments on river water quality within an integrated system modelling perspective. To conduct the impact analyses, an existing integrated urban wastewater model was used to predict water flow and quality in the sewer system, treatment plant and receiving water body. The impact on combined sewer overflow (CSO) discharges, treatment plant effluent, and within the river at various reaches is analysed by 'locating' a new development on a semi-hypothetical urban catchment. River water quality is used as feedback to constrain the scale of the new development within different thresholds in compliance with water quality standards. Further, the regional sensitivity analysis (RSA) method is applied to reveal the parameters with the greatest impact on water quality. These analyses will help to inform town planners and water specialists who advise them, how to minimise the impact of such developments given the specific context

Imprecise probabilistic evaluation of sewer flooding in urban drainage systems using random set theory

publication-status: Publishedtypes: ArticleCopyright © 2011 American Geophysical UnionUncertainty analysis is widely applied in water system modeling to quantify prediction uncertainty from models and data. Conventional methods typically handle various kinds of uncertainty using a single characterizing approach, be it probability theory or fuzzy set theory. However, using a single approach may not be appropriate, particularly when uncertainties are of different types. For example, in sewer flood estimation problems, random rainfall variables are used as model inputs and imprecise or subjective information is used to define model parameters. This paper presents a general framework for sewer flood estimation that enables simultaneous consideration of two types of uncertainty: randomness from rainfall data represented using imprecise probabilities and imprecision from model parameters represented by fuzzy numbers. These two types of uncertainties are combined using random set theory and then propagated through a hydrodynamic urban drainage model. Two propagation methods, i.e., discretization and Monte Carlo based methods, are presented and compared, with the latter shown to be much more computationally efficient and hence recommended for high-dimensional problems. The model output (flood depth) is generated in the form of lower and upper cumulative probabilities, which are best estimates given the various stochastic and epistemic uncertainties considered and which embrace the unknown true cumulative probability. The distance between the cumulative probabilities represents the extent of imprecise, incomplete, or conflicting information and can be reduced only when more knowledge is available. The proposed methodology has a more complete and thus more accurate representation of uncertainty in data and models and can effectively handle different uncertainty characterizations in a single, integrated framework for sewer flood estimation

DEVELOPMENT OF PARETO FRONT USING ACCELERATED CONVERGENCE GENETIC ALGORITHM AND NEURAL NETWORKS

Ph.DDOCTOR OF PHILOSOPH