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The formation of discrete regions in a water distribution system, referred to as district metered areas (DMAs), can be a pragmatic approach to diagnose both system and leakage characteristics. Their application, however, has historically been limited in the North American context owing in part to their costly implementation and operational challenges. To both overcome these barriers and to demonstrate the benefits of DMAs, a leakage testing programme was undertaken in Ontario, Canada. Novelty arises from the development and deployment of a mobile testing unit specifically designed to collect minimum night flow (MNF) and pressure data into temporarily configured DMAs. Moreover, activation of a pressure reducing valve facilitated the direct testing of pressure modulation on leakage reduction. The mobile unit was deployed in 22 DMAs across eight water systems with results indicating a clear relationship between MNFs and system characteristics for well-performing DMAs. MNF benchmarks were quantified to enable an evidence-based assessment of leakage performance at the DMA level in typical Canadian water systems. This project established the proof-of-concept of the mobile unit for providing both accurate and reliable measurements of key leakage performance characteristics and for predicting leakage reduction following system interventions.

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The provision of self-cleaning velocities has been shown to reduce the risk of discolouration in water distribution networks (WDNs). Despite these findings, control implementations continue to be focused primarily on pressure and leakage management. This paper considers the control of diurnal flow velocities to maximize the self-cleaning capacity (SCC) of WDNs. We formulate a new optimal design-for-control problem where locations and operational settings of pressure control and automatic flushing valves are jointly optimized. The problem formulation includes a nonconvex objective function, nonconvex hydraulic conservation law constraints, and binary variables for modelling valve placement, resulting in a nonconvex mixed integer nonlinear programming (MINLP) optimization problem. Considering the challenges with solving nonconvex MINLP problems, we propose a heuristic algorithm which combines convex relaxations (with domain reduction), a randomization technique, and a multi-start strategy to compute feasible solutions. We evaluate the proposed algorithm on case study networks with varying size and degrees of complexity, including a large-scale operational network in the UK. The convex multi-start algorithm is shown to be a more robust solution method compared to an off-the-shelf genetic algorithm, finding good-quality feasible solutions to all design-for-control numerical experiments. Moreover, we demonstrate the implemented multi-start strategy to be a fast and scalable method for computing feasible solutions to the nonlinear SCC control problem. The proposed method extends the control capabilities and benefits of dynamically adaptive networks to improve water quality in WDNs.

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