Abstract
The potential costs and benefits of a combination of asset management actions on the water distribution network are predicted. Two types of actions are considered: maintenance actions and renewal actions. Leak detection and reparation of failures on connections and pipes define the set of potential maintenance actions to be carried out. Renewal actions concern connections, pipes, and meters. All these actions represent the model’s decision variables in order to determine a trade-off between two objectives: (i) the maximization of the water efficiency rate and (ii) the minimization of the total cost of actions to be carried out on the water system. The assessment of objective functions is ensured by an artificial neural network (ANN) trained on a French mandatory database «SISPEA». A non-dominated sorting genetic algorithm (NSGA-II) is coupled to the ANN to reach the set of compromised solutions representing potential actions to achieve. Applied to a real water distribution system in the southeast of France, the proposed decision model indicates that the improvement of water efficiency rate (WER) in the short term requires increasing operation expenditures (OPEX), which represent 99% of the total cost. Results show the existence of a threshold effect that implies to use the budget in a certain way to improve performance. A potential solution can be chosen by the decision maker among the generated Pareto front with regard to the constraint on the budget and the targeted WER.
Highlights
Water utility performance monitoring is widely addressed in the literature
This paper focuses on the prediction of two key performance indicators (KPIs) considered as objective functions: (1) the water efficiency rate considered as a benefit and (2) the total cost obtained by the sum up of operation expenditures (OPEX) and capital expenditures (CAPEX)
The values of upper limits for decision variables were defined as the following: The limit of leak detection rate corresponds to a total inspection of the entire network each month (12 per year), renewal rate of pipes and connections is limited to 5% (5 times the actual rate) per year considering an average lifespan for asset of 50 years, and the renewal rate of meters is limited to 10% which corresponds to a lifespan for meters of 10 years on average
Summary
Water utility performance monitoring is widely addressed in the literature. IWA initiative carried out by Ref. [1] to build key performance indicators (KPIs) led to the emergence of national mandatory databases in several countries in order to improve the management of water utility and ensure transparency against stakeholders and users. KPIs are generally measured on an ex-post basis in order to assess the ability of conducted policy to achieve planned goals; otherwise, corrective actions can be planned in the case of a mismatch. This way of management could be expensive in terms of time and money. One possible improvement to avoid this mismatch is the use of a decision-aiding model to predict. A possible shortfall concerns the absence of data collection at the scale of the water utility, which renders it difficult to train and fit a prediction model. In the last 2 decades, we observe the development of sensors technologies and information and communications technology (ICT) that encourage water utility to install smart devices in order to monitor water systems in real
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