Multiobjective Evolutionary Computation Approach for Redesigning Water Distribution Systems to Provide Fire Flows

Kanta, Lufthansa; Zechman, Emily M.; Brumbelow, Kelly

doi:10.1061/(asce)wr.1943-5452.0000156

Cited by 30 publications

(14 citation statements)

References 27 publications

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“…Alternatively, water quality is included as a constraint to a single-objective model in a form of minimum (and maximum) disinfectant concentrations at the network nodes [87,143]. In the latter, water quality presents a sole objective, which is either water quality benefit (being maximised) [63,131], water quality deficiencies (being minimised) [92,97,144] or water quality reliability (being maximised) [78].…”

Section: Design For Water Qualitymentioning

confidence: 99%

“…Water quality benefit is expressed as a function of the length of renewed and/or lined old pipes, as aged pipes are considered to cause the development of microorganisms and water discolouration [63,131]. Water quality deficiencies can be represented by a performance function on disinfectant residual reflecting governmental regulations [92], water age [97], or the risk of water discolouration due to the potential material after daily conditioning shear stress [144]. Water quality reliability is based on disinfectant residual [145] and/or water age [78].…”

Section: Design For Water Qualitymentioning

confidence: 99%

“…Water quality (e.g., disinfectant, sedimentation, discolouration) deficiencies or water age at customer demand nodes, water discolouration risk, velocity violations (causing sedimentation/discolouration) [92,120] (water quality deficiencies), [97] (water age), [144] (water discolouration), [95] (velocity violations)…”

Section: Objectivesmentioning

confidence: 99%

“…Optimisation methods used to solve WDS design problems including water quality considerations were LP [129], GA [53,87,100,120,143] and differential evolution (DE) [27] for single-objective models, and SMGA [63,131], NSES [92], ε-NSGA-II [97], NSGA-II and SPEA2 integrated with a heuristic Markov-chain hyper-heuristic (MCHH) [144] and ant colony optimisation (ACO) [78] for multi-objective models. These algorithms were mainly linked with a network simulator EPANET to solve network equations.…”

Section: Design For Water Qualitymentioning

confidence: 99%

See 3 more Smart Citations

Lost in Optimisation of Water Distribution Systems? A Literature Review of System Design

Mala-Jetmarova

Sultanova

Savić

2018

Water

139

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Optimisation of water distribution system design is a well-established research field, which has been extremely productive since the end of the 1980s. Its primary focus is to minimise the cost of a proposed pipe network infrastructure. This paper reviews in a systematic manner articles published over the past three decades, which are relevant to the design of new water distribution systems, and the strengthening, expansion and rehabilitation of existing water distribution systems, inclusive of design timing, parameter uncertainty, water quality, and operational considerations. It identifies trends and limits in the field, and provides future research directions. Exclusively, this review paper also contains comprehensive information from over one hundred and twenty publications in a tabular form, including optimisation model formulations, solution methodologies used, and other important details.

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Section: Design For Water Qualitymentioning

confidence: 99%

Section: Design For Water Qualitymentioning

confidence: 99%

Section: Objectivesmentioning

confidence: 99%

Section: Design For Water Qualitymentioning

confidence: 99%

See 2 more Smart Citations

Lost in Optimisation of Water Distribution Systems? A Literature Review of System Design

Mala-Jetmarova

Sultanova

Savić

2018

Water

139

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“…age, the more important relationship is that water age increases with capital cost. This is because larger pipe diameters cause slower flow through the network, resulting in higher water ages (Kanta et al 2012). In the space of operating cost and water age, a clear tradeoff relationship can be observed.…”

Section: Lower-dimensional Objective Tradeoffsmentioning

confidence: 99%

Optimal Design of Water Distribution Systems Using Many-Objective Visual Analytics

Kapelan

Kasprzyk

et al. 2013

J. Water Resour. Plann. Manage.

150

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This study investigates the use of many-objective optimization for water distribution system (WDS) design or rehabilitation problems. The term many-objective optimization refers to optimization with four or more objectives. The increase in the number of objectives brings new challenges for both optimization and visualization. This study uses a multi-objective evolutionary algorithm termed the epsilon Nondominated Sorted Genetic Algorithm II (ε-NSGAII) and interactive visual analytics to reveal and explore the tradeoffs for the Anytown network problem. The many-objective formulation focuses on a suite of six objectives: (1) capital cost, (2) operating cost, (3) hydraulic failure, (4) leakage, (5) water age and (6) fire fighting capacity. These six objectives are optimized based on decisions related to pipe sizing, tank siting, tank sizing, and pump scheduling under five different loading conditions. Solving the many-objective formulation reveals complex tradeoffs that would not be revealed in a lower-dimensional optimization problem. Visual analytics are used to explore these complex tradeoffs and identify solutions that simultaneously improve the overall WDS performance but with reduced capital and operating costs. This study demonstrates that the many-objective visual analytics approach has clear advantages and benefits in supporting more informed, transparent decision making in the WDS design process.

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Water Quality and Fire Protection Trade‐Offs in Water Distribution Networks

Gibson¹,

Karney²,

Guo³

2019

Journal AWWA

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Multiobjective Evolutionary Computation Approach for Redesigning Water Distribution Systems to Provide Fire Flows

Cited by 30 publications

References 27 publications

Lost in Optimisation of Water Distribution Systems? A Literature Review of System Design

Lost in Optimisation of Water Distribution Systems? A Literature Review of System Design

Optimal Design of Water Distribution Systems Using Many-Objective Visual Analytics

Water Quality and Fire Protection Trade‐Offs in Water Distribution Networks

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