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Temperature is one of the most important factors affecting chlorine decay rates in drinking water systems. In this article, temperature effect on chlorine decay rates in raw and treated waters was studied. Results show that temperature affects differently the fast and slow decay phases, the latter being more sensitive to temperature variations, as higher values of the activation energy parameter were obtained. Accordingly, an improvement to the temperature dependent two reactant model (a parallel second order model), in which the activation energies of each decay phase are distinct, is proposed and successfully used for chlorine decay modelling. In waters from transport and distribution systems, however, the fast decay phase is mostly negligible. In such cases, a single phase second-order model in which the activation energy parameter is given by the slow phase reaction, is likely to describe temperature effect on chlorine decay accurately.

Water utilities collect, store and manage a vast set of data using a large set of information systems (IS). For Infrastructure Asset Management (IAM) planning those data need to be processed and transformed into information. However, information management efficiency often falls short of desired results. This happens particularly in municipalities where management is structured according to local government model conventions. Besides the existing IS at utilities' disposal, engineers and managers take their decisions based on information that is often incomplete, inaccurate or out-of-date. One of the main challenges faced by asset managers is integrating the several, often conflicting, sources of information available on the infrastructure, its condition and performance, and the various predictive analyses that can assist in prioritizing projects or interventions. This paper presents an overview of the IS used by Portuguese water utilities and discusses how data from different IS can be integrated in order to support IAM.

The use of adequate decay models for simulating chlorine residuals can effectively aid in chlorine management in water supply systems. In this paper, wall decay in a full-scale water supply system is assessed and modelled using the traditional first-order (FO) model and the recent EXPBIO model. The EXPBIO model was successfully implemented in EPANET-MSX for the first time and predicted chlorine residuals with high accuracy. However, in the tested conditions (chlorine residuals ≥0.55 mg/L and small wall decay rates), the FO and the EXPBIO models described chlorine wall decay with similar accuracy. The results suggest that in systems of large diameter pipes and of high disinfectant concentrations, the simpler FO model can be used for the modelling of chlorine residuals without significant loss of accuracy. Further research is needed to identify in which conditions (chlorine levels, wall decay rates) the EXPBIO model performance may exceed that of the FO model.

Enhancing energy efficiency of water supply systems by recovering part of the excessive pressure is currently an issue of growing interest for water companies. The installation of micro hydro plants for energy recovery can be profitable in sites with excessive pressure, though requiring proper technical and economical evaluation. This paper presents a methodology for assessing the energy recovery potential in water supply systems under high seasonal demand variation. The methodology is based on the calculation of head and flow rate conditions that maximize energy production for a specific energy recovery technology, given available head and flow rate ranges. The methodology is applied to the inlet of a storage tank of a water transmission system using hourly collected data over one year. Results show that, in systems of high variability of flow rate, the installation of turbomachines in parallel is necessary for maximizing energy recovery and that the developed methodology returns lower, but more realistic, energy production estimates than other approaches based on average head and flow rate data.

Water age is frequently used as a surrogate for water quality in distribution networks and is often included in modelling and optimisation studies, though there are no reference values or standard performance functions for assessing the network behaviour regarding water age. This paper presents a novel methodology for obtaining enhanced system-specific water age performance assessment functions, tailored for each distribution network. The methodology is based on the establishment of relationships between the chlorine concentration at the sampling nodes and simulated water age. The proposed methodology is demonstrated through application to two water distribution systems in winter and summer seasons. Obtained results show a major improvement in comparison with those obtained by published performance functions, since the water age limits of the performance functions used herein are tailored to the analysed networks. This demonstrates that the development of network-specific water age performance functions is a powerful tool for more robustly and reliably defining water age goals and evaluating the system behaviour under different operating conditions.

The current paper proposes a novel methodology for near–real time burst location and sizing in water distribution systems (WDS) by means of Multi–Layer Perceptron (MLP), a class of artificial neural network (ANN). The proposed methodology can be systematized in four steps: (1) construction of the pipe–burst database, (2) problem formulation and ANN architecture definition, (3) ANN training, testing and sensitivity analyses, (4) application based on collected data. A large database needs to be constructed using 24 h pressure–head data collected or numerically generated at different sensor locations during the pipe burst occurrence. The ANN is trained and tested in a real–life network, in Portugal, using artificial data generated by hydraulic extended period simulations. The trained ANN has demonstrated to successfully locate 60–70% of the burst with an accuracy of 100 m and 98% of the burst with an accuracy of 500 m and to determine burst sizes with uncertainties lower than 2 L/s in 90% of tested cases and lower than 0.2 L/s in 70% of the cases. This approach can be used as a daily management tool of water distribution networks (WDN), as long as the ANN is trained with artificial data generated by an accurate and calibrated WDS hydraulic models and/or with reliable pressure–head data collected at different locations of the WDS during the pipe burst occurrence.