A water demand end-use model was developed to predict water demand patterns with a small time scale (1 s) and small spatial scale (residence level). The end-use model is based on statistical information of users and end-uses: census data such as the number of people per household and their ages; the frequency of use; duration and flow per water-use event; occurrence over the day for different end-uses such as flushing the toilet, doing the laundry, washing hands, etc. With this approach, water demand patterns can be simulated. The simulation results were compared to measured water demand patterns on attributes such as peak flow and daily total water use, as well as on the shape of the pattern and the frequency distribution of flows and accelerations in flow. The simulation results show a good correspondence to measured water demands. Because the end-use model is based on statistical information rather than flow measurements, the model is transferable to diverse residential areas in different countries. Th...

Water temperature is often monitored at water sources and treatment works; however, there is limited monitoring of the water temperature in the drinking water distribution system (DWDS), despite a known impact on physical, chemical and microbial reactions which impact water quality. A key parameter influencing drinking water temperature is soil temperature, which is influenced by the urban heat island effects. This paper provides critique and comprehensive summary of the current knowledge, policies and challenges regarding drinking water temperature research and presents the findings from a survey of international stakeholders. Knowledge gaps as well as challenges and opportunities for monitoring and research are identified. The conclusion of the study is that temperature in the DWDS is an emerging concern in various countries regardless of the water source and treatment, climate conditions, or network characteristics such as topology, pipe material or diameter. More research is needed, especially to determine (i) the effect of higher temperatures, (ii) a legislative limit on temperature and (iii) measures to comply with this limit.

A method for optimizing sensor locations to effectively and efficiently detect contamination in a water distribution network is presented here. The problem is formulated and solved as a twin-objective optimization problem with the objectives being the minimization of the number of sensors and minimization of the risk of contamination. Unlike past approaches, the risk of contamination is explicitly evaluated as the product of the likelihood that a set of sensors fails to detect contaminant intrusion and the consequence of that failure (expressed as volume of polluted water consumed prior to detection). A novel importance-based sampling method is developed and used to effectively determine the relative importance of contamination events, thus reducing the overall computation time. The above problem is solved by using the nondominated sorting genetic algorithm II. The methodology is tested on a case study involving the water distribution system of Almelo (The Netherlands) and the potential intrusion of E. co...

In the context of household water demand generation with high time resolution (down to 1 s), two different categories of models have been proposed, aimed at representing instantaneous demand as the superimposition of pulses with constant intensity. The two categories differ in the spatial scale considered for demand generation. In detail, the models of the first category generate the demand as a whole at the household spatial scale. Those of the second category instead generate the demand at the scale of the microcomponents—that is, the various fixtures producing water demand pulses—and sum the microcontributions to obtain the total instantaneous demand of the household. The models of the first category are parameterized based on demand measurements. The models of the second category (actually, only one model exists, named SIMDEUM, standing for SIMulation of water Demand, an End-Use Model) are instead parameterized as a function of information derived from surveys, such as household occupants’ age and behavior, number and kind of household fixtures. This paper presents a review of the models for water demand pulse generation. In this review, the main differences in the modeling structure are described and the various parameterization methods are presented. Though the two categories of model operate at different spatial scales, three models—that is, two models of the first category, Poisson rectangular pulse (PRP) and PRP with correlated pulse durations and intensities (cor-PRP), and model SIMDEUM for the second category—are chosen as representative and are applied to a case study in Milford, Ohio, for which demand measurements and information about the household fixtures and occupants are all available. The merits and limits of the models are highlighted in the applications.

According to the Dutch Drinking Water Directive of 2001, the maximum temperature of drinking water should be 25°C. Occasionally, samples at the tap do exceed this limit, and with global warming, this limit may be exceeded more often. This article describes a model that predicts the temperature of the water in the drinking water distribution system (DWDS). The temperature of the soil is influenced by weather conditions such as atmospheric temperature, radiation, and environmental conditions such as soil's thermal conductivity and heat capacity. The water in the DWDS approaches the soil temperature with a rate that depends on the flow velocity and the water main's heat conductivity. In practice, the heating time required for the drinking water to reach the soil temperature is shorter than the residence time in the DWDS. Two practical test cases confirm the hypothesis that the soil temperature predicts the water temperature in the DWDS well.