Expanded
Summary
Modeling temperature in the drinking water distribution
system
E. J . M i r ja m Blokk e r and E.J . Pi e te rs e - Qui ri j ns
http://dx.doi.org/10.5942/jawwa.2013.105.0011
According to the Dutch Drinking Water Directive, the
maximum temperature of drinking water should be 25ºC.
Occasionally, samples at the tap exceed this limit. With
climate change, this limit may be exceeded more often. This
article describes a model that predicts water temperatures
in drinking water distribution systems (DWDSs). Soil
temperature is influenced by weather conditions including
atmospheric temperature and radiation and environmental
conditions such as the soil’s thermal conductivity and heat
capacity. DWDS water approaches soil temperature at a rate
that depends on flow velocity and the main’s heat
conductivity. In practice, the heating time required for
drinking water to reach the soil temperature is shorter than
the residence time in the DWDS. Two practical examples
confirm the hypothesis that soil temperature predicts water
temperature in the DWDS.
To limit regrowth of microorganisms, the World Health
Organization recommends a maximum temperature of
drinking water at the customer’s tap of 25ºC. In the
Netherlands, this recommendation is all the more important because drinking water is distributed without a
residual disinfectant. Within the regular sampling program of Dutch water companies, the 25ºC limit is sporadically exceeded.
In addition to meeting regulatory requirements, temperature in the DWDS is important because it influences
the complex of physical, chemical, and biological processes within the DWDS—such as adsorption of chemicals, chlorine decay, and formation of biofilm. Although
many processes in the distribution network depend on
temperature, the applied hydraulic and quality models
suggested in the literature usually assume a constant
temperature. A model that predicts the water temperature
in the distribution network may therefore contribute to
assessing the consequences of higher temperatures and
ensure the delivery of high-quality drinking water.
This article describes a model that predicts the temperature of the water in an actual distribution network
under realistic conditions, such as sandy soils and under
pavement, using actual weather data.
soil and drinking water is transferred by conduction and
convection.
Soil temperature and drinking water temperature are
modeled in two phases, with the first being the temperature
of the soil as a function of weather and soil parameters. In
the second phase, the drinking water temperature is modeled as a function of soil temperature, flow velocity, and
pipe material.
This article describes the soil temperature model and
available input data in numeric terms. A sensitivity analysis showed that the thermal properties of the various soil
types need to be studied further. The soil temperature
model was validated with available measurements of
temperatures at several depths. It was found that the
model predicts the soil temperatures at different well
depths (Figure 1).
Subsequently, this article describes water temperature
and the available input data in numeric terms. When the
heating time is shorter than the residence time, drinking
water will reach the wall temperature during the residence
time in the DWDS. A sensitivity analysis showed the
importance of the conductivity of the main’s material. For
heat-conductive pipes (e.g., cast iron), convection is the
limiting process because the heating time depends on the
flow rate of the water. For heat-insulating (plastic) pipes,
conduction is the limiting process because the heating time
depends on the pipe thermal conductivity and pipe diameter. In a 110-mm polyvinyl chloride pipe, the heating time
for drinking water from 10 to 25ºC is shorter than 15 h.
The model was tested in a case study of an actual DWDS
in which the water temperature model was incorporated
in EPANET-MSX software (www.epa.gov/nrmrl/wswrd/
dw/epanet.html/#extension). The results show that, in this
network, the water temperature at almost all nodes reaches
the temperature of the surrounding soil, even if the entire
network is of polyvinyl chloride. In practice, the heating
MODEL FOR WATER TEMPERATURE IN DWDSs
Water temperature in a DWDS is influenced by the
temperature of the surrounding soil and DWDS characteristics. The system surrounding a water main can be
assumed to be divided into four layers: the atmosphere,
the roughness layer, the soil surface, and the soil. The
roughness layer represents the layer between the atmosphere and soil surface in which air properties can be
changed by vegetation or buildings, for example. Energy
is transferred by radiation and convection. The water
main is located inside the soil, and the energy between
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35
FIGURE 1 Modeled and measured data in Breda, the
Netherlands, at a 100-cm depth
22
Model
Measured
Temperature—C°
21
20
19
18
17
16
July
August
September
Month
FIGURE 2 Modeled soil temperatures at –1 m and
measured temperature in Zandvoort, the
Netherlands, at two locations in summer 2008
22
Model –1 m
Location 3
Location 4
CONCLUSIONS
Temperature—C°
21
20
19
18
17
16
15
September
October
Month
time required for the drinking water to reach the soil temperature is shorter than the residence time in the DWDS.
Based on these observations, the following hypothesis
was formulated: the soil temperature model can be used
to predict water temperature in the DWDS. The water
temperature model is not always required.
TESTING THAT SOIL TEMPERATURE PREDICTS
DRINKING WATER TEMPERATURE
To verify the hypothesis, two cases were used to compare model predictions and measurements.
Test case 1. In 2008, the temperature of the drinking
water was measured for two months at several loca-
36
tions in the DWDS in Zandvoort, the Netherlands. In
Figure 2, the modeled soil temperature at –1 m and the
measured drink- ing water temperatures at two locations are shown together. The figure shows that the
variation over time is very similar for the modeled and
measured temperatures.
The deviation between model and measurement could
be the result of several model assumptions. The burial
depth of the pipes is not known exactly. The moisture
content is unknown, which may cause uncertainty in the
soil temperature. In Zandvoort, the cast-iron pipes are
cement-lined, therefore acting less strongly as heat conductors. This insulation was not taken into account.
The practical approach that was followed leads to an
estimated water temperature that is up to 1.5ºC higher
than the measured data. This small deviation is acceptable for most temperature-dependent processes.
Test case 2. Sampling data between 2004 and 2006
from Dutch water companies were collected. The temperature was measured after flushing and a stable temperature was reached. The temperature was modeled with
2005–06 local weather data from a specific meteorological Dutch site at –1 m in clay (α = 0.3 × 10–6 m2/s)
and sand (α = 1.2 × 10–6 m2/s). A strong relation between
average modeled soil temperature and the sample temperature at the tap was indicated. Soil temperature is a
much better predictor for drinking water temperature
than the atmosphere temperature.
With the use of a micrometeorology model, it is possible to predict the soil temperature at various depths as
a function of weather and environmental conditions.
Comparison of the modeled and measured water temperature in two test cases clearly illustrates that the predicted temperature of the soil surrounding the DWDS is
indicative of water temperature in the main. Therefore
the soil temperature model can be used as a model to
predict the water temperature in the DWDS. In most
cases, the residence time of the drinking water in the
DWDS is longer than the time required for heat transfer
between soil and drinking water.
The soil temperature model can predict the water temperature that will be experienced at the tap. The water
temperature model in EPANET-MSX calculates the temperature at each location in the distribution network,
assuming a constant soil temperature over 24 h. Coupling
a water temperature model with a temperature-dependent
model (describing, for example, chlorine decay, biofilm
formation, and adsorption) can provide the basis for predicting these phenomena in a water distribution network.
Corresponding author: E.J. Mirjam Blokker is a
scientific researcher at KWR Watercycle Research
Institute, Groningenhaven 7, 3433 PE Nieuwegein,
the Netherlands; [email protected].
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