UBC Theses and Dissertations
Optimal design of water distribution networks under uncertainty Moosavian, Seyed Abdolnaser
Generally, the use of the water distribution network (WDN) modeling is divided into two main categories, WDN design, and WDN hydraulic analysis, which itself is employed as part of WDN design. The former generally identifies components of the network, considering the system cost and the ability of the network to satisfy consumer demands for water availability, pressure, and quality. The latter evaluates the distribution of nodal pressures and pipe flows under a specified network design and known or estimated water consumption levels. WDN analysis is often embedded within design algorithms, where, for every potential design considered, the hydraulic energy and continuity equations that govern the system are solved. If water demands and the physical characteristics of the network design are known with certainty, deterministic approaches for solving these equations may be used. If some information is uncertain, non-deterministic approaches are used for identifying the Probability Density Function or the fuzzy membership functions of the pressure and flow conditions at all locations in the network. This dissertation is divided into three main parts, 1) the application and development of evolutionary algorithms for single- and multi-objective optimization of WDNs, 2) the introduction of new frameworks and performance surrogates as objectives in the optimization of WDNs, and 3) the advancement of an efficient gradient-based technique for fuzzy analysis of WDNs under uncertainty. First, efficient Evolutionary Algorithms (EAs) are compared and advanced for reducing the computational burden of single- and multi-objective design of WDNs, respectively. We investigate and identify the most appropriate operators and characteristics of EAs for optimization of realistic-sized networks. Based on the experiences and capabilities of EAs obtained, a new EA is introduced for single-objective optimization of WDNs which is faster and more reliable than other popular algorithms presented in the literature. Next, two different frameworks are introduced for implementing many objectives in the optimization of realistic-sized WDNs. Both approaches can distinguish appropriate design solutions with minimum cost and maximum hydraulic and mechanical reliability. Finally, a fuzzy method is introduced for analysis of WDNs under uncertainty. The proposed technique significantly reduces the CPU time of uncertainty analysis of large-scale networks.
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