Optimal design-for-control of self-cleaning water distribution networks using a convex multi-start algorithm

TL;DR

This paper introduces a convex multi-start heuristic for optimizing flow velocities in water distribution networks to enhance self-cleaning, addressing a complex nonconvex MINLP problem with improved robustness and scalability.

Contribution

It formulates a novel optimal design-for-control problem for WDNs and develops a convex multi-start algorithm that outperforms standard genetic algorithms in solution quality and efficiency.

Findings

The convex multi-start algorithm reliably finds high-quality solutions.

The method is scalable to large, complex networks.

It improves water quality by optimizing self-cleaning velocities.

Abstract

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.