Upgrading Existing Water Distribution Networks Using Cluster-Based Optimization Techniques

Abstract

Enhancing the performance of aged water distribution networks (WDNs) has become a significant global challenge. Many of these networks face issues such as deteriorated pipes, insufficient pumping heads, and increased water demands. Upgrading existing WDNs is often performed using optimization techniques, characterized by numerous decision variables, resulting in computationally intensive and time-consuming simulations. This paper proposes a novel optimal upgrading methodology for WDNs, leveraging clustering principles from graph theory. The proposed methodology involves adding a new storage tank and rehabilitating selected pipes of an existing WDN. The methodology begins with dividing the WDN into smaller subsystems based on its communication properties. The parameter ranges for adding a new storage tank are determined using a sensitivity analysis, assessing their values and impact on network resilience and water quality. Critical pipes that directly impact the WDN performance are identified and replaced for rehabilitation through three proposed scenarios, each with a distinct selection criterion. The problem is formulated as a multi-objective problem, aiming to minimize total annual costs while maximizing network resilience. The proposed methodology has proven effective in reducing the search space size and computational effort, outperforming the traditional full search space optimization approach.