Review of applications for SIMDEUM, a stochastic drinking water demand model with a small temporal and spatial scale

Blokker, Mirjam; Agudelo-Vera, Claudia; Moerman, Andreas; Thienen, P. van; Pieterse-Quirijns, Ilse

doi:10.5194/dwes-10-1-2017

Cited by 30 publications

(19 citation statements)

References 24 publications

(40 reference statements)

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“…The initial application of SIMDEUM® was to aid the design of self-cleaning water supply networks but it has since been developed to enable use in several other applications (Blokker et al, 2017). SIMDEUM WW® is one such development that describes wastewater discharge, including thermal and nutrient loads (Pieterse-Quirijns et al, 2012).…”

Section: Elíasmentioning

confidence: 99%

Developing a stochastic sewer model to support sewer design under water conservation measures

Bailey

Arnot

Blokker

et al. 2019

Journal of Hydrology

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Developing a stochastic sewer model to support sewer design under water conservation measures Bailey, O.; Arnot, T. C.; Blokker, E. J.M.; Kapelan, Z.; Vreeburg, J.; Hofman, J. A.M.H. Abstract: Population growth and climate change place a strain on water resources; hence, there are growing initiatives to reduce household water use. UKWIR (2016) have a stated aim to halve water abstraction by 2050. This will significantly reduce inflow to sewer systems and increase wastewater concentration. This work presents a new stochastic sewer model that can be used to predict both hydraulic and pollutant loading for various water saving scenarios. The stochastic sewer model is based on integration of the stochastic water demand model SIMDEUM® with the InfoWorks ICM® (Sewer Edition) hydraulic model and software. This model has been developed using foul sewer networks, i.e. where household discharges are the dominant inflow; however, it could also be used in combined sewage systems where rainwater flows would add to © 2019 Manuscript version made available under CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/ 2 the stochastic dry weather flow (DWF). The stochastic sewer model was tested and validated on several real catchments in the Wessex Water area of the UK. Calibration was carried out using metered consumption data. The stochastic sewer model gives an accurate prediction of the diurnal patterns of sewage discharge at a household level and was validated using real flow measurements within the catchment. The results obtained indicate that this model can be used to accurately predict changes in flow velocity and pollutant concentrations because of water conservation. A preliminary study for the impact of low water use on this validated network model has been conducted and it was found that overnight and daytime flow was reduced by up to 80 % whereas evening flows remained largely similar. Extended stagnation times were observed in the street scale pipes (150 mm) in the low water use scenario.

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Section: Elíasmentioning

confidence: 99%

Developing a stochastic sewer model to support sewer design under water conservation measures

Bailey

Arnot

Blokker

et al. 2019

Journal of Hydrology

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“…Empirical CDF (pipes: 50mm < diameter < 300mm) Looped Branched(2011, 17Vs) Branched(Alg1, 5Vs) Branched(Alg1, 10Vs) course, not surprising since the coarser spatial and temporal demand details result in a less accurate computation of flows in each pipe segment compared to the bottomup SIMDEUM demand model (Blokker et al 2010b;Blokker et al 2011), where each household connection is assigned a unique demand with an order higher temporal resolution; see al (Blokker et al 2016) for a review of SIMDEUM applications. This is in line with analysis in Blokker et al (2010a) that employing stochastic enduser models in hydraulic simulations is more accurate in modelling flow velocities at detailed temporal and spatial scales.…”

Section: Figmentioning

confidence: 99%

Decreasing the Discoloration Risk of Drinking Water Distribution Systems through Optimized Topological Changes and Optimal Flow Velocity Control

Abraham

Blokker

Stoianov

2018

J. Water Resour. Plann. Manage.

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In this paper, a new mathematical framework is proposed for maximising the selfcleaning capacity (SCC) of drinking water distribution systems by controlling diurnal peak flow velocities in pipes under normal operation. This is achieved through an optimal change of the network connectivity (topology). We propose an efficient algorithm for network analysis of valve closures, which allows enforcing favourable changes in flow velocities for maximising the SCC by determining an optimal set of links to isolate in forming a more branched network, while satisfying hydraulic and regulatory pressure constraints at demand nodes. Multiple stochastic demands from an end-use demand model are generated to test the robustness in the improved SCC for the modified network connectivity under changing demand. We use an operational network model to demonstrate the efficacy of the proposed approach.

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“…Pieterse-Quirijns et al [52] and Blokker et al [53] investigated the flow rates measured on a per second basis for the hot and cold water supply systems in two offices (255-2000 employees), two business hotels (80-192 rooms), and two nursing homes (124-260 beds). The measurement periods ranged from 28 to 47 days, and the measurement results showed that the peak flow rates were only fractions (0.417-0.755) of the design flow rates given in existing guidelines.…”

Section: Measurement Datamentioning

confidence: 99%

A Review of Demand Models for Water Systems in Buildings including a Bayesian Approach

Wong

Mui

2018

Water

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Instantaneous flow rate estimation is essential for sizing pipes and other components of water systems in buildings. Although various demand models have been developed in line with design and technology trends, most water supply system designs are routinely and substantially over-sized to keep failure risks to a minimum. Three major types of demand models from the literature are reviewed in this paper: (1) deterministic approach; (2) probabilistic approach; and (3) demand time-series approach. As findings show some widely used model estimates are much larger than the field measurements, this paper proposes a Bayesian approach to bridge the gap between model-based and field-measured values for the probable maximum simultaneous water demand. The proposed approach is flexible to adopt estimates as its prior values from a wide range of existing water demand models for determining the Bayesian coefficients for reference models, codes, and design standards with relevant measurement data. The approach provides a useful method not only for evaluating the corresponding demand values from various design references, but also for responding to the call for sustainable building design.

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Review of applications for SIMDEUM, a stochastic drinking water demand model with a small temporal and spatial scale

Cited by 30 publications

References 24 publications

Developing a stochastic sewer model to support sewer design under water conservation measures

Developing a stochastic sewer model to support sewer design under water conservation measures

Decreasing the Discoloration Risk of Drinking Water Distribution Systems through Optimized Topological Changes and Optimal Flow Velocity Control

A Review of Demand Models for Water Systems in Buildings including a Bayesian Approach

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