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The fraction of time that the flow reverses in a model with (a) bottom-up allocated SIMDEUM demands (Blokker, 2010) and (b) top-down demand allocations. The 50 % value means that 50 % of the time the flow is in one direction and 50 % in the other.
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Many researchers have developed drinking water demand models with various temporal and spatial scales. A limited number of models is available at a temporal scale of 1 s and a spatial scale of a single home. The reasons for building these models were described in the papers in which the models were introduced, along with a discussion on their poten...
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... sults heavily depend on the demand patterns that are used. The bottom-up approach to (SIMDEUM) demand allocation and the comparison to the traditional top-down demand al- location (Blokker et al., 2011a(Blokker et al., , 2010a) allowed for the study of how more realistic demands lead to different maximum flow velocities, flow direction reversals (Fig. 4) and resi- dence time in the network (Sect. 8 in Blokker, 2010). In a transition between a fully stochastic bottom-up demand al- location and a fully deterministic top-down demand alloca- tion (see Sect. 2.2), it was found that the use of specific (deterministic) demand patterns for a total of 20 different types of users, such as ...
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... specifically at locations that can receive water through multiple paths and where many flow inversions occur (Fig. 5). With SIMDEUM, the uncertainty in backtracing a contaminant becomes evident for some loca- tions; for other locations, the variability in the demand is less influential. This correlates to e.g. the changes in flow direc- tions (Fig. 4). With the help of SIMDEUM, these potential sensor locations may be determined with more ...
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... This study treats waste heat as emission-free, but charging the heat battery consumes electricity. Charging consumption profiles are generated using the SIMDEUM tool whose applications are comprehensively summarized by (Blokker et al., 2017). This study defines three scenarios of heat consumption in this neighborhood: low-demand, base-demand, and high-demand scenarios. ...
... To address these research gaps, first, the hydraulic model of a real-world case study is extended with synthetic demand data simulated with PySIMDEUM (Steffelbauer et al. 2022a), i.e., the Python implementation of the state-of-the-art stochastic enduse model SIMDEUM (Blokker et al. 2010(Blokker et al. 2017. SIMDEUM allows the generation of unique water demand patterns for each household with 1 min interval and water demands with highly varying spatial-temporal patterns across the case study are represented. ...
... Based on this information, a time series of hot and cold-water demand at 1 s resolution is generated based on probability functions for each household appliance, which is aggregated to the household level and to a temporal resolution of 1 min. For more information about the functionality of SIMDEUM and the default user profile, refer to Blokker et al. (2010) and Blokker et al. (2017). The original time series with a temporal resolution of 1 min is assumed to represent the real water demand behaviour and to simulate the observed data in a baseline scenario. ...
Model-based leakage localisation in water distribution networks requires accurate estimates of nodal demands to correctly simulate hydraulic conditions. While digital water meters installed at household premises can be used to provide high-resolution information on water demands, questions arise regarding the necessary temporal resolution of water demand data for effective leak localisation. In addition, how do temporal and spatial data gaps affect leak localisation performance? To address these research gaps, a real-world water distribution network is first extended with the stochastic water end-use model PySIMDEUM. Then, more than 700 scenarios for leak localisation assessment characterised by different water demand sampling resolutions, data gap rates, leak size, time of day for analysis, and data imputation methods are investigated. Numerical results indicate that during periods with high/peak demand, a fine temporal resolution (e.g., 15 min or lower) is required for the successful localisation of leakages. However, regardless of the sampling frequency, leak localisation with a sensitivity analysis achieves a good performance during periods with low water demand (localisation success is on average 95%). Moreover, improvements in leakage localisation might occur depending on the data imputation method selected for data gap management, as they can mitigate random/sudden temporal and spatial fluctuations of water demands.
... In this study it is assumed that the household has two inhabitants in all the scenarios. A water demand end-use model, SIMDEUM, was developed and applied to predict water demand patterns with a small-time scale and small spatial scale (Blokker et al., 2010;Blokker et al., 2017). In this study, one week of SIMDEUM patterns (time steps of 10 s) per tap is implemented in the hydraulic model to include the consumers' behavioural variability in simulations. ...
Water conservation measures are increasing in response to regulatory requirements addressing the need for lower environmental footprint and in response to water shortages. In households with lead service lines (LSLs), lowering consumption can adversely impact lead release as it will increase stagnation. Using a lead dissolution model and data from extensive pilot studies on excavated LSLs, the impact of adaptation to different water conservation strategies on dissolved lead contamination at the kitchen tap is assessed under three water qualities and three LSL lengths (3, 14 and 30 m) using hydraulic and water quality modelling. Consumers' behavioural variability is also assessed based on integration of EPANET and results of the stochastic water demand model SIMDEUM. Demand reduction increased the dissolved lead concentrations (Pbdiss) at the end of the LSL with mean values ranging from 28.4 to 63.3 μg/L (without corrosion control) and from 4.6to 9.9 μg/L with corrosion control (addition of orthophosphate and pH adjustment). Adding orthophosphate (1 mg P/L) to the water reduces the mean Pbdiss values at the kitchen tap from 7.1 μg/L to 1.2 μg/L for a high water demand scenario and from 31.2 to 4.9 μg/L for a low water demand scenario. Finally, the Integrated Exposure Uptake Biokinetic (IEUBK) model is used to predict the potential blood lead levels (BLLs) for children aged 0-84 months. Results showed that the orthophosphate addition of only 1 mg P/L can significantly decrease the proportion of children with a BLL >5 μg/dL, from 82 % to 17 %, under the most extreme water conservation scenario studied, using the 90th percentile of Pbdiss concentrations during usage at kitchen tap. Wide variations of Pbdiss concentrations at the kitchen tap were calculated at times of use over a week (up to 155 μg/L in lower demand scenarios, without corrosion control) showing evident limitations of single random daytime sampling.
... 30 Blokker et al. provided a review of the applications of SIMDEUM, which include water quality and quantity modeling, design and research for the drinking water distribution system, and the prediction of future demands. 40 Time series of water demands for each fixture were generated based on 10 second time intervals for 60 weeks. Because of the size of the input file, two independent 30 week simulations were run, where the final values of the first simulation were the inputs for the second simulation, creating a 60 week simulation without repetition of demand patterns. ...
Water age is often used as a proxy for water quality in drinking water systems, but the highly stochastic nature of water demands in premise plumbing systems, and nonlinear relationship...
... Additionally, novel use cases led to further developments and add-ons to the SIMDEUM software package (e.g., SIMDEUM hot and cold water use package or SIMDEUM's discharge pattern simulator for computing thermal energy and nutrients recovery potentials). A detailed overview of the many applications for SIMDEUM can be found in [3]. ...
Water demand is a crucial input parameter in water distribution system analysis because it can fluctuate over various temporal and spatial scales. In the past, researchers developed stochastic models that can provide realistic consumption patterns for simulations to account for those demand dynamics. Parameters for stochastic models are usually retrieved by fitting these models on smart water meter data. The stochastic demand model SIMDEUM uses an entirely different approach by generating highly realistic water demands based on (country-specific) statistical information only, without the need for measurements. While this approach makes SIMDEUM widely applicable in the water sector, its widespread usage within the community has been hindered due to its software implementation and availability. We produced pySIMDEUM, an open-source and object-oriented implementation of the SIMDEUM software in the popular and freely available programming language Python. The pySIMDEUM software package is not only publicly available for usage within the water field — it is also intended to build the cornerstone of a widespread pySIMDEUM community of active developers. We want to use the WDSA/CCWI conference to address interested researchers or practitioners in the water sector and invite them to contribute to the software package as active part of the pySIMDEUM community. We will show SIMDEUM’s history and past applications, the mathematical approach behind SIMDEUM and pySIMDEUM, where to download and install the pySIMDEUM package, the structure of the program, and a minimal example of how easily pySIMDEUM can be used to generate realistic stochastic water demand patterns from scratch. Furthermore, we will highlight possible future applications of the new pySIMDEUM tool. These applications include automatic parametrisation of pySIMDEUM parameters on smart meter data, coupling stochastic demands directly with hydraulic solvers, or how to enable city-scale stochastic demand simulations.
... In this context, we aim to demonstrate how numerical simulations can contribute in estimating lead consumption under realistic water usage scenarios. We account for lead temporal variability by incorporating SIMDEUM [2,3], a stochastic drinking water demand model, in our framework. For this purpose, we perform simulations that are described in Section 2. In premise plumbing, water quality computations are afflicted by the presence of short pipes. ...
... Information about the points-of-use, together with the composition of the household (number of adults and children, attitude towards water consumption) are fed to SIMDEUM [2,3], a stochastic drinking water demand model. This program generates realistic water consumption patterns at each usage point. ...
The norm for dissolved lead in drinking water is lowered in the Netherlands per 2023, from 10 to 5 µg/L. The effect is that: (a) homeowners and the Dutch water utilities want to find problematic lead components and remove them; and (b) the utilities want to know if copper service mains with lead soldering are expected to lead a norm exceedance. This calls for an improved sampling protocol (collected at the tap) to trace dissolved lead. We help solve this problem using hydraulic and water quality simulations in EPANET. With short time steps, low flow rates, and short pipe lengths, a water quality time step of 1 second is not short enough. As EPANET cannot work with timesteps smaller than 1 second, we devised a work-around to ensure that the numerical solution does not result in a large error. A household is defined as a system of pipes with various locations where water is used. The water demand patterns at each tap are generated using SIMDEUM. A few (sections of the) pipes are assigned to contain lead wherefrom dissolution occurs. The benchmark scenario involves a two-person household with average Dutch water consumption and with solely the service line containing lead. Next, fourteen scenarios are considered wherein either the geometry of the system, the water consumption, or location/extent of the lead releasing surface(s) are varied individually. Of the sampling protocols considered, the results show that “profile sampling” is the most promising. In such a protocol, twenty consecutive samples of 300 ml are collected following a minimum of six hours of prolonged stagnation. Irrespective of the scenario (with a lead component), at least one of the twenty samples is guaranteed to possess a high concentration of lead, owing to our choice of a fine sample volume. Just two days of profile sampling can potentially unveil: (1) the location of the lead releasing component, (2) the volume of the lead releasing region, and (3) the saturation concentration of lead dissolution. The key conclusions (for the current assumptions) are as follows. A lead service line potentially leads to an exceedance of the new norm, necessitating immediate action. The results also provide evidence that one week of proportional sampling is insufficient. Presently, it is considered to be the gold standard for measuring weekly intake of dissolved lead, however, it is susceptible to the stochastic nature of water demand leading to variations exceeding 50%. Lastly, a profile sampling protocol possesses the best opportunity to detect dissolved lead in the household drinking water network in a robust manner. In the future, a bespoke experimental facility (“PilotCity indoor installation”) will be used to validate the assumptions surrounding the lead dissolution and advection/diffusion model.
... According to [21], the model allows a designed survey to apply at the large area and produce significant estimates on micro-component (households or individuals), also no requirement to increase the sample size. For example, MCA has been practiced for hydraulic network modelling (leakage detection), water quality modelling, an adaptation of water supply systems for drinking, future water demand projection and more related water resources investigations [22], [23]. The model also makes it possible to predict the effect of technology advancement, societal change and water regulation in the coming 25 years [24]. ...
Water is one of the cornerstones in human survival and a necessity for socio-economic development. Due to that, effective management of water demand especially in the rural area becomes vital. The objective of this study is to conduct a water demand study at Kenyir Lake, Terengganu using Micro-Component Analysis (MCA). Water demand is also being assessed using National Water Services Commissions (SPAN) Guidelines, parallel to water guidelines applied for Malaysia’s building and comparison purposes. MCA considers detailed parameters of water use activities of the respective study area, whereas SPAN Guidelines use the fixed values of average daily water demand according to the type of building. The available dataset was interpreted using MCA and SPAN Guidelines, resulting in total daily water demand at Kenyir Lake of 1249.8 m ³ and 1254.1 m ³ respectively. Estimated water demand using SPAN Guidelines is found to be higher than MCA. The value differences occur due to the average water demand value in the guideline that is limited to a specific type of premises and does not comply with premises there. The estimated water demand can be adopted as a baseline for water companies and the government to improve the demand and supply of water.
... Usually, the studies on this topic are statistical reports, often from official public entities (e.g., NRCan, 2011). The temporal pattern studies deal with how the total water or energy consumption varies over time, providing information regarding peak demand and seasonal patterns relevant for the design of the water and electrical infrastructures (Anda et al., 2013;Blokker et al., 2017;Butler, 1991;Rathnayaka et al., 2017). End-use distribution studies provide insight into how water and energy are effectively used (AWWA, 2016;Barreto, 2008;Bonnet et al., 2002;DeOreo et al., 1996a;Froehlich et al., 2009;Hall et al., 1988;Lee et al., 2012;L. ...
Knowing water and energy consumption patterns sets the baseline for understanding their drivers and assessing the performance of potential measures to increase efficiency and/or reliability. These patterns can vary substantially depending on the building characteristics, on the building users and use, on the cultural, social, economic, environmental context in which the building is located, among many other factors. This article presents a general methodological framework for characterizing water and energy consumption patterns in buildings based on the evaluation of the characteristics of the equipments and appliances, as well as the type of users and the activities developed in each type of room. This allows estimating water and energy use, by end use per square meter and by roomtype. The methodological framework proposed was applied to the buildings of the Paricarana Campus of Federal University of Roraima (UFRR), Brazil, providing one of the few examples in the literature reporting water and energy consumption in university buildings in tropical climates. Universities, in most cases, represent large water and energy consumers with distinctive consumption drivers and patterns which have received limited attention when compared to other types of buildings (e.g., residential). The findings have shown that teaching rooms and administration rooms are the main consumers, representing 48% and 49% of the institution's energy and water consumption, respectively. Air conditioning is the biggest energy consumption (63%), while personal use represents 72% of the total water consumption in a building. The toilets represent a large water consumption in a university building (46.40%). Comparing different building uses, the central library is the highest consumer, due to the longest operating time and the highest occupational density. The methodological proposal intends to be a useful tool to support managers and decision-makers to understand the dynamics of consumption and then propose effective practices to reduce water and energy uses, as well as providing reference data for comparison with other educational institutions.
... We thus expect that the activities that will drive changes in the urban water cycle during COVID-19 are the key water use activities carried out by people, e.g., toilet flushing, showering, dish washing (Almeida et al., 1999). Modelling water consumption that is disaggregated at this appliance scale is referred to as "end-use modelling" (Blokker et al., 2010), however, so far, no megacity scale application of such a framework has been performed (Blokker et al., 2017). ...
Due to the COVID-19 pandemic, citizens of the United Kingdom were required to stay at home for many months in 2020. In the weeks before and months following lockdown, including when it was not being enforced, citizens were advised to stay at home where possible. As a result, in a megacity such as London, where long-distance commuting is common, spatial and temporal changes to patterns of water demand are inevitable. This, in turn, may change where people's waste is treated and ultimately impact the in-river quality of effluent receiving waters. To assess large scale impacts, such as COVID-19, at the city scale, an integrated modelling approach that captures everything between households and rivers is needed. A framework to achieve this is presented in this study and used to explore changes in water use and the associated impacts on wastewater treatment and in-river quality as a result of government and societal responses to COVID-19. Our modelling results revealed significant changes to household water consumption under a range of impact scenarios, however, they only showed significant impacts on pollutant concentrations in household wastewater in central London. Pollutant concentrations in rivers simulated by the model were most sensitive in the tributaries of the River Thames, highlighting the vulnerability of smaller rivers and the important role that they play in diluting pollution. Modelled ammonia and phosphates were found to be the pollutants that rivers were most sensitive to because their main source in urban rivers is domestic wastewater that was significantly altered during the imposed mobility restrictions. A model evaluation showed that we can accurately validate individual model components (i.e., water demand generator) and emphasised need for continuous water quality measurements. Ultimatly, the work provides a basis for further developments of water systems integration approaches to project changes under never-before seen scenarios.
... Similarly, reviews on end use (micro-component) analysis have elaborated on myriad methods and techniques of quantifying household water consumption through disaggregation (e.g. Blokker et al., 2017;Cominola et al., 2015;House-Peters and Chang, 2011) with some going into factors influencing consumption (e.g. Arbu es et al., 2003;Bich-Ngoc and Teller, 2018;Parker and Wilby, 2013;Russell and Fielding, 2010). ...
Rapid population growth and economic prosperity among other factors are exacerbating existing water stress in the east and southeast regions of England, hence, the water sector is increasingly shifting focus from the expansion of water sources and increased abstraction to demand-side management (DSM) strategies aimed at improving household water efficiency and reducing per capita consumption. A crucial component of water DSM strategy is a good understanding of household water use patterns and the myriad factors that influence them. Smart metering, conflated with innovative techniques and groundbreaking ancillaries continue to support DSM strategies by providing quasi-real-time data, offering powerful insights into household water consumption patterns and delivering behaviour-changing feedback to consumers. This paper presents a comprehensive review of the current state of household water consumption and their determinants as reported in the literature. The paper also reviews the methods and techniques for measuring and understanding consumption patterns and discuss prominent DSM instruments utilised in the household water demand sector globally along with their relative impact on per capita consumption (PCC). The review concludes that while disaggregation remains a very effective means of revealing consumption patterns at micro-component levels, the process is time-consuming and costly, relying on high-resolution data, specific hardware and software combination, making it difficult to incorporate into the utility’s routine DSM framework. A future research is proposed, that may focus on an alternative, scalable consumption pattern recognition approach that can easily be incorporated into the utility’s DSM strategy using medium resolution smart-meter data.
