Chapter
Jun 2, 2022

Hydraulic Capacity Recovery after Demand Expansion: Complex Network and Preference-Aided Multicriteria Analysis

Publication: World Environmental and Water Resources Congress 2022

ABSTRACT

Given the growth of urban population, water distribution networks (WDNs) are stressed and may lose hydraulic performance while supplying citizens. WDNs should be modified by civil engineering interventions such as water network rehabilitation, aimed at recovering the originally designed hydraulic performance. In this work, a set of expansion scenarios are evaluated under hydraulic (resilience, pressure uniformity, and water quality) and complex network (centrality, average shortest path) indexes for better understanding the impacts of demand increasing in a water network. This first analysis allows to map the risk of demand increases at each district metered area, resulting in more suitable regions for demand expansion. In a second step, rehabilitation of water network based on changes of main pipes is proposed. Structural interventions from the civil engineering point of view are proposed. Since each intervention results in economical costs as well as in recovery performance, solutions are also evaluated under hydraulic indexes, whose degrees of importance may be established by means a preference-based approach and feedback exchanges with experts in the field. In this context, a multicriteria technique is applied for ranking solutions, leading to a map of expansion-solutions for WDN managers. The complex network and WDN hydraulic analyses have been performed in Phyton programming and, respectively, by means of the packages NetworkX 2.5 and WNTR 0.3.0. TOPSIS multicriteria analysis method is lastly used to classify the expansion solutions.

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World Environmental and Water Resources Congress 2022
Pages: 1015 - 1028

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Published online: Jun 2, 2022

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Thomaz F. de F. Anchieta [email protected]
1Engineering School, Federal Univ. of Minas Gerais, Belo Horizonte, Brazil. Email: [email protected]
Gustavo Meirelles [email protected]
2Engineering School, Federal Univ. of Minas Gerais, Belo Horizonte, Brazil. Email: [email protected]
Bruno Brentan [email protected]
3Engineering School, Federal Univ. of Minas Gerais, Belo Horizonte, Brazil. Email: [email protected]
Silvia Carpitella [email protected]
4Dept. of Decision-Making Theory, Institute of Information Theory and Automation–Czech Academy of Sciences, Prague, Czech Republic. Email: [email protected]

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