RESUMEN
INTRODUCTION: The digital transition is meant as the review of processes using digital technologies and strategies to increase efficiency based on a simpler collection of representative data. The process of interest here is the leakage management in systems distributing water. OBJECTIVE: To develop a novel key performance indicator (KPI) for leakage management considering the needs of ongoing digital transition in the water sector, which is opening a new era in the management of drinking water infrastructures. METHODOLOGY: The novel KPI, named Asset Management Support Indicator, is developed starting from advanced hydraulic modelling and the physical laws governing leakage outflows, in order to be physically based and rational for increasing the efficiency of leakage management activities using representative process data. RESULTS: The Asset Management Support Indicator supports effective leakage management strategies by driving towards efficiency, as discussed and demonstrated using several real case studies. CONCLUSION: The novel indicator is consistent with the digital transition perspective and current need of increasing the efficiency of water utilities, and it is also suitable to be adopted by water authorities to benchmark their performances, because it overcomes the weaknesses of traditional KPIs.
Asunto(s)
Modelos Teóricos , Abastecimiento de Agua , Agua Potable , PresiónRESUMEN
The first step in the decision making process for proactive sewer rehabilitation is to assess the condition of conduits. In a risk-based decision context the set of sewers to be inspected first should be identified based on the trade-off between the risk of failures and the cost of inspections. In this paper the most effective inspection works are obtained by solving a multi-objective optimization problem where the total cost of the survey programme and the expected cost of emergency repairs subsequent to blockages and collapses are considered simultaneously. A multi-objective genetic algorithm (MOGA) is used to identify a set of Pareto-optimal inspection programmes. Regardless of the proven effectiveness of the genetic-algorithm approach, the scrutiny of MOGA-based inspection strategies shows that they can differ significantly from each other, even when having comparable costs. A post-processing of MOGA solutions is proposed herein, which allows priority to be assigned to each survey intervention. Results are of practical relevance for decision makers, as they represent the most effective sequence of inspection works to be carried out based on the available funds. The proposed approach is demonstrated on a real large sewer system in the UK.