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Journal ArticleDOI

Booster Disinfection of Water Supply Networks: Multiobjective Approach

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TLDR
In this article, the authors investigated the booster facility location and injection scheduling problem in water distribution networks and formulated the problem as a multiobjective optimization model with the objectives of minimizing the total disinfectant dose and maximizing the volumetric demand within specified residual limits.
Abstract
Booster disinfection is the addition of disinfectant at some critical locations of a water distribution network such that disinfectant residuals are maintained at a level greater than the minimum for public health. Compared to conventional methods that apply disinfectant only at the source, booster disinfection can reduce the total disinfectant dose. The present work investigates the booster facility location and injection scheduling problem in water distribution networks. The problem is formulated as a multiobjective optimization model. The objectives are minimization of the total disinfectant dose and maximization of the volumetric demand within specified residual limits. Multiobjective genetic algorithms are used for solving the problem. The model utilizes the theory of linear superposition in water quality modeling for calculating concentration profiles at network nodes. Unlike previous models, the present multiobjective approach does not require pruning of monitoring nodes to find feasible solutions; all demand nodes are considered as monitoring nodes. Application of the model to an example problem reveals that there is a critical point in the level of constraint satisfaction, after which the disinfectant dosage rate increases significantly in order to satisfy a few remaining constraints.

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Journal ArticleDOI

Lost in optimisation of water distribution systems? A literature review of system operation

TL;DR: This paper provides a systematic review by bringing together over two hundred publications from the past three decades, which are relevant to operational optimisation of water distribution systems, particularly optimal pump operation, valve control and system operation for water quality purposes of both urban drinking and regional multiqualityWater distribution systems.
Journal ArticleDOI

A diversified multiobjective GA for optimizing reservoir rule curves

TL;DR: Results of this work indicate that the proposed macro-evolutionary multiobjective genetic algorithm (MMGA) is highly competitive and provides a viable alternative to solve multiobjectives optimization problems for water resources planning and management.
Journal ArticleDOI

Optimal Location and Control of Pressure Reducing Valves in Water Networks

TL;DR: In this paper, the authors address the problem of optimal pressure management in water distribution systems through the introduction and regulation of pressure reducing valves, where the determination of the number, location and setting of such valves is formulated as a two criteria optimization problem and is solved with multiobjective genetic algorithms.
Journal ArticleDOI

Multiobjective Contaminant Sensor Network Design for Water Distribution Systems

TL;DR: In this study a multiobjective model for water distribution system optimal sensor placement using the nondominated sorted genetic algorithm II is developed and demonstrated using two water distribution systems of increasing complexity.
References
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Journal ArticleDOI

A fast and elitist multiobjective genetic algorithm: NSGA-II

TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
Book

Multi-Objective Optimization Using Evolutionary Algorithms

TL;DR: This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.
Proceedings Article

Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization

TL;DR: A rank-based fitness assignment method for Multiple Objective Genetic Algorithms (MOGAs) and the genetic algorithm is seen as the optimizing element of a multiobjective optimization loop, which also comprises the DM.
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