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Current Journal of Applied Science and Technology
32(3): 1-13, 2019; Article no.CJAST.46188
ISSN: 2457-1024
(Past name:
British Journal of Applied Science & Technology,
Past
ISSN: 2231-0843,
NLM ID: 101664541)
Modelling the Condensed Water Discharge Rate in
an Air Conditional System in South West, Nigeria
O. S. Bamisaye
1*
and P. K. Oke
1
1
Department of Production and Industrial Engineering, Federal University of Technology, Akure,
Nigeria.
Authors’ contributions
This work was carried out in collaboration between both authors. Both authors read and approved the
final manuscript.
Article Information
DOI: 10.9734/CJAST/2019/46188
Editor(s):
(1) Dr. Ahmed Fawzy Yousef, Associate Professor, Department of Geology, Desert Research Center, Egypt.
Reviewers:
(1)
Oludele Adebayo Adeyefa, University of Ibadan, Nigeria.
(2)
Halil Görgün, Dicle University, Turkey.
(3)
Nasser Mosafa Abd Elrahman Elashmawy, King Saud University, Saudi Arabia.
Complete Peer review History:
http://www.sdiarticle3.com/review-history/46188
Received 23 September 2018
Accepted 01 January 2019
Published 18 January 2019
ABSTRACT
Aims:
This work is aimed at developing an empirical model for predicting condensed water
discharge rate in an air conditional system, most especially in Nigerian offices.
Study Design: Quantitative study. Relevant data on condensate discharge rate was collected.
Place and Duration of Study: An office located within the School of Engineering and Engineering
Technology Building of the Federal University of Technology, Akure, Ondo State, Nigeria, between
November 2015 to April 2016.
Methodology: The method used consists of data collection and readings such as condensate
volume, dry bulb temperature, relative humidity, sensible heat ratio, and dew point temperatures. A
split type air-conditioning unit with a cooling capacity of 2500 W, using refrigerant and rated air flow
rate of 400 m
3
/hr was used to determine the amount of condensate rate.
Results: The result of six-month data collected showed that a total of 528 L of condensed water
was collected at the split type air conditional unit. The highest condensate discharge rate of 1.07
L/hr was recorded on 6
th
and 7
th
April 2016. The coefficient of determination, R
2
, obtained for first,
second and third order multiple linear regression model were 0.964, 0.9793, and 0.9803
respectively. The developed multiple linear regression model was used to compare the experimental
Original Research Article
Bamisaye and Oke; CJAST, 32(3): 1-13, 2019; Article no.CJAST.46188
2
and predicted values of the condensate from the air conditional unit used for the study.
Conclusion: The developed model offers ease of prediction and forecasting of the amount of
condensate discharge rate. This study confirms that relative humidity, sensible heat ratio, dry bulb
temperature, and dew point temperature are the most significant factors contributing to an increase
in condensate discharge rate.
Keywords: Air condition; regression model; reclaimed water; forecasting; modern building.
1. INTRODUCTION
The significance of using reclaimed water in air
conditioning systems is a frequent feature of
certain urban infrastructure in some part of the
world. The reclaimed water is referred to as
condensed water. Condensate is referred to the
water that settles on a cool surface because the
temperature of the surface is beneath the point at
which moisture in the air forms water droplets.
The volume of the space, the number of persons
and their activity determines the amount of
outdoor air required and the amount of moisture
in it. Condensed water is a built-in by-product of
building heating, ventilation, and air conditioning
(HVAC) systems formed from moisture in the air.
It is high-grade water therefore, it can be
collected and utilized on-site with limited
treatment. More specifically, condensed water
comes up in the evaporator area of the air-
conditional unit where evaporative cooling drives
the heat exchanger [1]. The air dehumidification
on finned evaporator coils was solved, using an
approach that describes the surface efficiency to
the enthalpy transformation of cooled moist air
[2]. According to Geshwiler [3], the occupancy
action impact the fresh air ratio and the ratio may
differ from 100% fresh air in applications like a
laundry room, and electrical room. The utilization
of high amounts of outside air, cooling load
profile and 24 hours daily operation, the cooling
coils in the laboratory facilities can furnish a
significant source of condensate water [4]. The
climate in Akure, Ondo State Nigeria is impacted
mainly by the rain-bearing southwest monsoon
winds from the ocean and the dry northwest
winds from the Sahara Desert. Maximum
daytime temperatures rarely exceed 34 °C and
low as 22 °C, a mean annual relative humidity of
about 80% also characterize the climate [5].
Owing to the weather condition in Akure,
modelling the rate of discharge of condensed
water in an air conditional system is a good
potential opportunity that needs to be studied. All
buildings in the Federal University of Technology,
Akure are air-conditioned with a variety of
different equipment being used. In the School of
Engineering and Engineering Technology
Building, mini-split units and window units are
used. In any air conditioning system operating
with high outside dew point temperatures, there
will be a formation of condensed water generated
in the fan coils that must be disposed or diverted
out of the building. The formed condensed water
from air conditioning units is an often overlooked
source of freshwater. The consequent build-up
can provide a large amount of freshwater that
can be used to balance the use of potable water.
In most buildings, this condensate is often sent to
an open ground which can be a route to
condensate discharge piping to the nearest
sanitary drain. The evaluation of condensate by-
product for big structures during hot season
differs between 0.1 to 0.3 L/kW for every hour
the cooling system worked. It was mentioned that
for those designing buildings in hot and humid
climates, maximum condensate output during
summer months could be almost between 6 to 7
ml/s/1000 m² of the cooled area [6]. The
measure of condensate water basically
dependent on local climate, heating, ventilation
and air-conditioning design, the operation of the
building, dry bulb temperature, relative humidity,
sensible heat ratio, and dew point etc. A report
by AWE [7] shows that the amount of condensed
water can range from 11 to 38 Litres/day per
92.9 m² of air-conditioned space. Another study
by Shahid [8], provided an estimate for typical
condensate production in large buildings during
summer months in San Antonio as 0.378 to
1.135 L/h of water per ton of cooling. San
Antonio Condensate Code [9] described that big
volume of low-temperature condensate water
can be collected during the dehumidification
process in the air conditioning systems and
employed in the commercial buildings having a
big cooling capacity plant. Painter [10] developed
a predictive modelling technique for a dedicated
outdoor air handling unit with enthalpy wheel
energy recovery and also condensate production
in three locations in Texas; San Antonio,
Houston, and Dallas/Fort Worth. Lawrence et al.
[11] highlighted that a forecasting model could be
used to appraise the condensed water harvested
for a retrofit. Investigating sustainability issues
associated with the collection, storage, and
Bamisaye and Oke; CJAST, 32(3): 1-13, 2019; Article no.CJAST.46188
3
modelling of condensate water from selected air
conditioning equipment for an institutional
building sited on the Education City Campus in
Doha, Qatar was enhanced by Bryant and
Ahmed [12]. The usefulness of using condensate
as an added source of water and also the impact
of climate condition and space occupancy on the
volume of condensate generated were
investigated by Lawrence et al. [13].
International building codes, ordinances, and
standard that accompany the design and
pursuance of water-conserving practices, with
the idea of assessing condensate collection and
safeguarding human health and safety is
described by International Code Council [14].
According to American Society of Heating [15],
air conditioner condensate can be categorized
under the description of alternative on-site
sources of water and the word “reclaimed water”
is only applicable in the impression of municipal
reclaimed water. The temperature, humidity and
condensates data collected in various locations
mentioned in the available literature cannot be
used directly in Nigeria because of environmental
conditions. A split type air-conditioning unit with a
cooling capacity of 2500 W, using refrigerant and
rated air flow rate of 400 m
3
/hr was used to
determine the amount of condensate rate. An
empirical model was developed to compare the
experimental and predicted values of the
condensate from the air conditional unit used for
the study.
The objective of this study is to collect the
weather data and condensate from the air-
conditioning unit, develop an empirical model to
determine the rate of condensate and finally
validate the empirical model for the purpose of
forecasting the amount of condensate at
specific environmental conditions in Southwest,
Nigeria.
2. MATERIAL AND METHODS
2.1 Materials
The equipment used is an existing split type air-
conditional unit with a cooling capacity of 2500
W, with refrigerant R-22. Graduated measuring
cylinder (500 ml), indoor/outdoor thermo-
hygrometer (temperature measuring range: -10
°C to 70 °C, humidity measuring range: 20% to
90% and outside sensor with 3 meters cable).
These were bought at Pascal Delson Scientific
Limited, Akure. While the collector (25 litres) and
drain pipe (using 25 mm PVC adaptor)
were purchased in King’s Local market, Ondo
State.
2.2 Selection of Environment
The facility chosen for this study was the School
of Engineering and Engineering Technology
Building which is a two storey, concrete
construction building consisting of five
classrooms, ten laboratories, and eighty-three
offices. It is a typical campus facility with heavy
use during the day with activity closing by 4 pm
in the evening. The air conditioning and
ventilation for this building is supplied through
eighty-three separate air conditioning units
(window and split type) which are all wall
mounted. Condensate removal system for this
building showed that condensate discharge at
each unit was routed via external piping from the
air-conditioning unit to open ground. An office
located on the ground floor of the facility was
selected for this study. The office selected can
accommodate at least four people; the volume of
the office selected was 33.8 m
3
.
2.3 Experimental Procedure
The performance of the indoor and outdoor unit
of the split air-conditioning system was tested if it
is working according to design capacity and the
nominal cooling rating. After the split indoor and
outdoor air-conditioning unit had been completely
tested, 25 litres container (as a collector) was
connected to the drain pipe which is sized
assuming a gravity-driven flow. Drain pipe must
be greater than or equal to 19 mm internal
diameter and must not decrease in size. The
drain line was sized in accordance with the
required standard. Care was taken to ensure
continuous horizontal slope along the discharge
path by proper installation of pipe joints to avoid
collection of condensates along the discharge
path. The drain pipe was then connected to 25
mm PVC adaptor and positioned under the
outdoor unit. The condensate collection
apparatus was incorporated with minimum
impact on the existing facility and the opening at
the drain level was capped so that the pipe would
fill with condensate water from the split air-
conditioning system installed for examination. As
soon as the condensate level reached the new
drain pipe, it was directed to the 25 litres
container. The indoor and outdoor temperature,
as well as its relative humidity, were measured
using thermo-hygrometer. The relative humidity
measurement accuracy of the device was ±3.5%
from 20% to 90% and a resolution of 0.1%. The
temperature measurement range of the device
Bamisaye and Oke; CJAST, 32(3): 1-13, 2019; Article no.CJAST.46188
4
was -10 °C to +70 °C, resolution 0.1
o
. As the
collector was filled to a given level, for a period of
an hour, the condensate reading was collected
using a measuring cylinder (500 ml) in relation to
the changes in the weather parameters. This
procedure was repeated on an hourly basis
for a period of 8hrs per day from Monday to
Friday.
2.4 Data Collection and Analysis
The data collected from the research were
analysed using a psychrometric chart and
Microsoft Excel to calculate the condensate
production rate given hourly weather conditions
from November 2015 to April 2016. The analysis
helps to determine the amount of condensed
water that can be collected in six months period
from the split air conditioning unit with a cooling
capacity of 2500 W. The weekly average indoor
condition was 23 °C and 55% relative humidity,
which fall within the comfort zone air condition.
Although the weekly average indoor condition
changes for the month of December 2015
through February 2016 to 22
°C and 41% relative
humidity. The dew point temperature range
varies from 13
°C to 25
°C. The surface
temperature of the coil was between the range of
10
°C - 12
°C, the room sensible heat factor for
the office space varies from 0.55-0.85. The mass
rate of condensed water is calculated from the
relative humidity change between the inlet and
exit states. Since the mass of the condensed
water determined is from the air conditional, the
mass flow of dry air was determined using the
relationship in equations (1) and (2).
ṁ
= ṁ
(W
− W
) (1)
ṁ
̇
(
)
(2)
where, ṁ
is mass flow of condensed water, per
unit time (kg/hr.,); ṁ
is mass flow of
dehumidified air, per unit time (kg/hr., kg/min,
kg/s); and (W
3
- W
4
) is the differences between
the moisture contents at mixed conditions (inlet
of the coil)
and the air supplied to the cooling
coil, kg/kg
(air).
The total sensible and latent heat handled by the
refrigerating equipment of the air-conditioning
system is determined using equations (3) and (4)
respectively.
Q
= C
ṁ
(t
− t
)
(3)
where, Q
TSH
is Total sensible heat (kJ); ṁ
is the
Mass flow of dehumidified air, per unit time
(kg/hr.,); (t
− t
)is Differences between the
air been cooled at different temperatures at the
evaporator, K and C
, Humid specific heat
(kJ/kgk).
Q
= ṁ
h
(W
− W
) (4)
where, Q
TLH
is Total latent heat (kJ);
ṁ
is the
Mass flow of dehumidified air, per unit time
(kg/hr.,); (W
− W
) is differences between the
moisture contents at mixed conditions (inlet of
the coil)
and the air supplied to the cooling coil,
kg/kg
(air)
; and h
is the Latent heat of
vapourization (kJ/kg).
2.5 Formulation of the Model
The formulation of the model to predict the
condensate discharge rate begins with the
identification of the contributory factors that either
enhance or inhibit the formation of condensate in
air conditioning system used to regulate the
thermal comfort of a particular space. Some of
the factors identified and used in the formulation
of the model include sensible heat ratio (SHR),
outside temperature (T), dew point (DP), relative
humidity (RH), and volume of air-conditioned
space (V
space
). Hence, the rate of condensate
discharge is a function of all these identified
factors which is mathematically expressed in
equation (5) as:
Rate of condensate discharge, CDR = f
(SHR, T, DP, RH, V
space
) (5)
The simplest form of an empirical model that can
be developed from the factors to make a
numerical prediction of the condensate discharge
rate is a first order multiple regression model
otherwise called multiple linear regression model.
This model is generally expressed as equation
(6).
=
+
+
+
+
(6)
where; y represents the rate of condensate
discharge (l/hr), (β
0
, β
1
, β
2
, β
3
, β
4
) are the
regression coefficients and (x
1
, x
2
, x
3
, x
4
)
represent relative humidity (%), outdoor
temperature (°C), sensible heat ratio and dew
point (°C) respectively, which are the factors
considered in this study. It is noteworthy that the
volume of space air-conditioned remains
constant throughout the experimental period. In
Bamisaye and Oke; CJAST, 32(3): 1-13, 2019; Article no.CJAST.46188
5
equation (6), there are five (5) regression
coefficients to be determined, hence five (5) set
of equations, to be solved simultaneously to
determine the coefficients, needs to be
developed. Based on the data obtained from the
experiment, the set of equations can be obtained
from the following mathematical expression as
equation (7).
∑(
)
+
∑(
)
+
∑(
)
+
∑(
)
+
∑(
)
=∑(
)
(7)
Where; n is the number of observations made
over the period of experiment i.e. November
2015 – April 2016. The values of the factors
considered were collected and the condensate
discharge rate for each day was obtained by
dividing the total volume of condensate collected
daily by the duration over which it was collected,
i.e. eight (8) hours. The adequacy of this model
is accessed by examining the value of the
coefficient of determination, R
2
, which indicate
the variability in the data employed. Thus,
formulating the set of equations as shown in
equation (7), and solving simultaneously, the
values of the coefficients were obtained for the
first order multiple regression model. These
values are:
β
0
= -2.087 , β
1
= 0.0033, β
2
= 0.02074, β
3
=
2.230, and β
4
= 0.01145
Therefore, the fitted first order regression
equation for the condensate discharge rate is
given in equation (8):
= −2.087 + 0.0033
+ 0.02074
+
2.23
+ 0.01145
(8)
The coefficient of determination, R
2
, obtained for
this model is 0.964. This makes the model to be
considered as a good fit to predict the rate of
condensate discharge. However, the model with
a better fit can be obtained by considering the
formulation of a second order multiple regression
equation.
The fitted second order regression equation
obtained from the experimental observations
made over the period of November 2015 – April
2016 is given as equation (9):
y = 6.45 0.476
0.328
7.04
0.127
0.000152
0.00454
−
0.06
0.00012
0.0018
0.00355
0.00004
0.16
0.00299
0.145
(9)
The coefficient of determination, R
2
, of the
second order multiple regression model is
0.9793, which is greater than the value of the first
order multiple regression model. Thus, the
second order model is considered a better fit with
higher prediction adequacy. It can be deduced
that as the degree of model equation increases,
the greater the accuracy and adequacy of the
model would be. The third order regression
equation was obtained which consists of the
cubic terms, quadratic terms and all the possible
interaction terms for the factors considered.
The fitted third order regression equation
obtained is given as equation (10):
y = 4.8 0.0227
0.87
23.3
0.125
0.001024
0.025
44.3
0.0044
0.00211
0.0144
0.00051
0.148
0.00491
0.163
0.00007
0.00026
21.7
0.000065
(10)
The coefficient of determination, R
2
, of the third
order multiple regression model is 0.9803, which
is greater than the value of that of the second
order multiple regression model. The correlation
coefficient, R, is obtained as 0.9916, which made
the third order model to be considered as the
best model to predict condensate discharge
rate.
3. RESULTS AND DISCUSSION
3.1 Total Condensate Volume
The results presented here spanned a period of
six months (November 2015 through April 2016)
and the condensate water data collected is from
Monday to Friday within the working hours, that
is, from 8 am to 4 pm. The result showed that
over the six month period, a total of 528 Litres of
condensed water was collected from split air
conditional unit of the office used for the study.
This figure indicates the amount of reclaimed
water source that is not in use. Higher
condensed water volumes were collected during
the months of November, March, and April.
Comparatively, lower condensate water volumes
were collected during the months of December,
January, and February. As the average relative
humidity increased, there was a corresponding
increase in the amount of condensate collected.
The total volume of condensate water produced
per month in the office selected for the study is
shown in Fig. 1.
3.2
The Relative Humidity and Outside
Dry Bulb Temperature against the
Number of Days
The value of the temperature can be described
as dry bulb temperature of the air which
maintains the mean value of 30
period of the days inspected. According
the minimum temperature recorded was 25
day 129 (28
th
– 04 –
2016) while the maximum
temperature of 31º
C was recorded on days 17
to 19
th
respectively (24
th
to 26
th
–
From Fig. 2, it can be inferred that the
temperatures of th
e air vary over the range of
6º
C between days 1 to day 130, over the period
of data inspections. The result of the relative
humidity, RH, for the numbers of days
considered showed a wide range of variations
with the average value of 55% between the
period
considered. The lowest RH obtained was
22% on day 34 (17/12/2015) and a maximum
value of RH was 72% which was recorded on
days 102 and 104 respectively (22
/03/2016). The low value of RH is an indication of
the high amount mass of saturated air
period and low amount mass of water vapour
present in unit mass of air at this period, while
the high amount of RH would have resulted from
the low amount of mass of saturated air and the
amount of mass of water vapour present in a unit
Fig. 1. Condensate volume from November 2015 through April 2016
Bamisaye and Oke; CJAST, 32(3): 1-13, 2019
; Article no.
6
The Relative Humidity and Outside
Dry Bulb Temperature against the
The value of the temperature can be described
as dry bulb temperature of the air which
maintains the mean value of 30
ºC over the
period of the days inspected. According
to Fig. 2,
the minimum temperature recorded was 25
ºC on
2016) while the maximum
C was recorded on days 17
th
–
11 – 2016).
From Fig. 2, it can be inferred that the
e air vary over the range of
C between days 1 to day 130, over the period
of data inspections. The result of the relative
humidity, RH, for the numbers of days
considered showed a wide range of variations
with the average value of 55% between the
considered. The lowest RH obtained was
22% on day 34 (17/12/2015) and a maximum
value of RH was 72% which was recorded on
days 102 and 104 respectively (22
nd
and 24
th
/03/2016). The low value of RH is an indication of
the high amount mass of saturated air
over this
period and low amount mass of water vapour
present in unit mass of air at this period, while
the high amount of RH would have resulted from
the low amount of mass of saturated air and the
amount of mass of water vapour present in a unit
amount of
air. Fig. 2. shows the plot of the
relative humidity and outside dry bulb
temperature against the number of different
days the experiment was carried out
respectively.
3.3 Mass of the Dehumidified Air with the
Total Latent Heat and Total Sensible
Heat
The weekly result of the mass of dehumidified
air, total latent heat and total sensible heat
handled by the refrigerating equipment of the air
conditioning system is shown in
the dehumidification process, the sensible heat
transfers by conv
ection from the air to the
surface, and the latent heat transfer occurs
because of the condensation on the surface. It
was observed that the sensible heat adds more
heat to the moist air in order to increase its
temperature to form moisture on the coil tha
latent heat. At a point in Fig. 3, the latent heat
appreciates over the sensible heat and later
decreases. The sensible and latent heat was
high during the cooling season and low during
harmattan because of low moisture found in
conditioned space, mo
isture from human
respiration, perspiration, and evaporation of
moisture from clothing.
Fig. 1. Condensate volume from November 2015 through April 2016
; Article no.
CJAST.46188
air. Fig. 2. shows the plot of the
relative humidity and outside dry bulb
temperature against the number of different
days the experiment was carried out
3.3 Mass of the Dehumidified Air with the
Total Latent Heat and Total Sensible
The weekly result of the mass of dehumidified
air, total latent heat and total sensible heat
handled by the refrigerating equipment of the air
-
conditioning system is shown in
Fig. 3. During
the dehumidification process, the sensible heat
ection from the air to the
surface, and the latent heat transfer occurs
because of the condensation on the surface. It
was observed that the sensible heat adds more
heat to the moist air in order to increase its
temperature to form moisture on the coil tha
n the
latent heat. At a point in Fig. 3, the latent heat
appreciates over the sensible heat and later
decreases. The sensible and latent heat was
high during the cooling season and low during
harmattan because of low moisture found in
isture from human
respiration, perspiration, and evaporation of
Bamisaye and Oke; CJAST, 32(3): 1-13, 2019; Article no.CJAST.46188
7
Fig. 2. The plot of relative humidity and outside dry bulb temperature against the number of
days
Fig. 3. The total latent heat and total sensible heat against a mass of the dehumidified air
3.4 Results of the Model Developed
In order to establish the reliability of the models
as being capable of predicting the condensate
discharge rate, the rate values obtained from the
model based on the prevailing factor values, are
compared to true experimental data to determine
their percentage disparity. Fig. 4a - 4c shows the
plot of the experimental values of the condensate
discharge rate and the predicted values from the
first order, second order and third order models
respectively. As seen in Fig. 4b., the second
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
604.58
383.097
614.583
457.19
200.74
223.625
143.75
210.43
276.563
384.95
351.67
28.25
139.79
32.479
172.35
422.23
381.38
410.977
604.41
505.03
442.31
407.975
329.76
490.792
Total Latent Heat and Total Sensible Heat(KJ/hr)
Mass of the Dehumidified Air(kg/hr)
QTSH(kJ/hr)
QTLH(kJ/hr)
Bamisaye and Oke; CJAST, 32(3): 1-13, 2019; Article no.CJAST.46188
8
order model predicted values almost relatively
match with the experimental values of the
condensate discharge. However, the model is
limited to being able to give reliable prediction
when there is drastic fluctuation in the
condensate discharge and when the condensate
discharge was near zero or zero as observed
between 25 – 45 days of data collection. This
was also observed in the behaviour of the first
order model in Fig. 4a.
The third order model in Fig. 4c seems to be best
appropriate as it can predict the intensification of
the water, where there are zero values when
compared with the experimental values of the
condensate discharge. The correlation of
determination for the third order model is higher
than that of the first and second order, the third
order model is considered in this work as the
best option to predict the rate of condensate
discharge from air conditioning systems.
Fig. 4a. Comparison between experimental and 1
st
order model value of condensate rate
Fig. 4b. Comparison between experimental and 2
nd
order model value of condensate rate
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
0 20 40 60 80 100 120
140
Rate of Condensate discharge, l/hr
Number of days
Experimental
2nd order model
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
0 20 40 60 80 100 120 140
Rate of Co ndensate discharge, l/hr
Number of days
Experimental
1st order model
Bamisaye and Oke; CJAST, 32(3): 1-13, 2019; Article no.CJAST.46188
9
Fig. 4c. Comparison between experimental and 3
rd
order model value of condensate rate
3.5 Model Validation
The data obtained during the month of 19
th
- 23
rd
September 2016 was used to validate the
accuracy of the model developed under this
study. It was intended to examine if the model
can reproduce the similar results for the weather
conditions in the year 2016. Some of the data
identified include sensible heat ratio (SHR),
outside temperature (T), dew point (DP) and
relative humidity (RH). For the validation of the
model, the averages of data obtained from the
experiment were fitted into the regression
equations of 1
st
, 2
nd
, and 3
rd
order. The results of
the regression model were compared to the
experimented values in other to test for their
correlations. The values of (x
1
, x
2
, x
3
, x
4
)
represent relative humidity (%), outdoor
temperature (ºC), sensible heat ratio, and dew
point (ºC) respectively, which are the factors
considered in this study. The experimental and
predicted values of the condensate rate are
shown in Table 1.
Fig. 5a - 5c shows the plot of the experimental
values of the condensate discharge rate and the
predicted values from the first order, second
order and third order models respectively. The
graphical presentation of the model predictions
indicated that the third order model in Fig. 5c
seems to be best appropriate as its predicted
values relatively close to the experimental values
of the condensate discharge. The first order
model did not give a good match with the
experimental values obtained, but rather under-
predict the values and the second order model
shows a little improvement but not too close to
the experimental values. The correlation of
determination for the third order model is higher
than that of the second order, the third order
model is considered in this month of September
as the best option to predict the rate of
condensate discharge from air conditioning
systems. In September, the rainy season was
still on, hence the relative humidity, RH, was
high, ranging from (65 – 85) %, the outdoor
temperature ranges from (24 – 30)°C, the
Sensible Heat Ratio, SHR, was also high (0.83 –
0.85) and the dew point, DP, was high ranging
from (20 – 23)°C. During this period, the
condensate discharge rate, CDR, was high,
ranging from (0.9 – 1.27) L/hr.
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
1.4
0 20 40 60 80 100 120 140
Rate of C o nde nsate discha rge, l/hr
Number of days
Experimental
3rd order model
Table 1.
The data obtained for
DATE
X
1
(%)
X
2
(
o
C)
X
3
19/09/2016 77 27
0.84
20/
09
/2016 76 27
0.83
21/
09
/2016 75 27
0.83
22/
09
/2016 78 27
0.83
23/09/2016 78 26
0.83
Fig. 5a.
The plot between experimental and 1
Fig. 5b.
The plot between experimental and 2
Bamisaye and Oke; CJAST, 32(3): 1-13, 2019
; Article no.
10
The data obtained for
September 19
th
- 23
rd
X
4
(
o
C)
Experimental
1
st
order
model
2
nd
order
model
0.84
22 1.270 0.85218 0.90756
0.83
22 1.213 0.82658 0.87219
0.83
23 1.095 0.83473 0.88464
0.83
23 1.113 0.84463 0.89751
0.83
22 1.180 0.81244 0.90975
The plot between experimental and 1
st
order model value of condensate rate
The plot between experimental and 2
nd
order model value of condensate rate
; Article no.
CJAST.46188
3
rd
order
model
0.99518
0.96095
0.96492
0.96405
0.98678
order model value of condensate rate
order model value of condensate rate
Fig. 5c.
The plot between experimental and 3
3.6 Discussion
The regression analysis of data collected during
the month of the study showed significant
improvement in the accuracy of the model in
predicting the condensate production. The study
is in line with [10] and [11] that use of
prediction model technique co
uld be used to
estimate the condensed water collected for an air
handling unit.
The adequacy of this model is
accessed by examining the value of the
coefficient of determination, R
2
, which indicate
the variability in the data employed. The
coefficient of determination, R
2
, of the first,
second and third order are 0.964, 0.9793, 0.9803
respectively.
The characteristics of the
condensate discharge rate over the period of 130
days, from November 2015 –
April 2016, is a true
reflection of the seasonal variatio
n in Southwest,
Nigeria. In November, the rainy season was
almost rounding off, hence the relative humidity,
RH, was high, ranging from (52 –
68) % between
the 1
st
day (2
nd
November) –
21
November). The outdoor temperature ranges
from (26 – 31)º
C, the Sensible Heat Ratio, SHR,
was also high (0.76 –
0.85) and the dew point,
DP, was high ranging from (20 –
period, the condensate discharge rate, CDR, was
high, ranging from (0.6 –
1) l/hr. However, this
value decreases rapidly to near
December. In December, Harmattan, dry season
sets in and RH drops to (22 –
decreases to (0.55 –
0.59) and DP decreases
also to (12 – 17)º
C. The near zero CDR
Bamisaye and Oke; CJAST, 32(3): 1-13, 2019
; Article no.
11
The plot between experimental and 3
rd
order model value of condensate rate
The regression analysis of data collected during
the month of the study showed significant
improvement in the accuracy of the model in
predicting the condensate production. The study
is in line with [10] and [11] that use of
a
uld be used to
estimate the condensed water collected for an air
The adequacy of this model is
accessed by examining the value of the
, which indicate
the variability in the data employed. The
, of the first,
second and third order are 0.964, 0.9793, 0.9803
The characteristics of the
condensate discharge rate over the period of 130
April 2016, is a true
n in Southwest,
Nigeria. In November, the rainy season was
almost rounding off, hence the relative humidity,
68) % between
21
st
day (30
th
November). The outdoor temperature ranges
C, the Sensible Heat Ratio, SHR,
0.85) and the dew point,
23)ºC. In this
period, the condensate discharge rate, CDR, was
1) l/hr. However, this
zero l/hr in
December. In December, Harmattan, dry season
36) %, SHR
0.59) and DP decreases
C. The near zero CDR
persisted from 1
st
Dec. 2015 (22
nd
2016 (46
th
day).
In J
anuary, the dryness persisted, however CDR
began to increase from near zero as at the 46
day to 0.689 l/hr on 18
th
Jan. (56
th
(35 –
58) %, outdoor temperature (28
SHR (0.55 –
0.8) and DP (17
thereafter nosedived to near
zero CDR towards
the end of the month at the 61
January). In February, there have been signs
that the rainy season is approaching, though the
dryness persisted until 18th February (79
with RH (28 – 32) %, SHR (0.55 –
13)º
C, however there was exception to 68
days (3
rd
– 5
th
Feb.), the CDR slightly increase to
(0.28 –
0.35) l/hr with improved RH (40
SHR (0.56 - 0.67) and DP (17 –
19)
After the 79
th
day, as the month of March is
approaching, there was a rapid increase in the
CDR. Since the months of March and April were
rainy season period, the RH, SHR and DP have
increased to between (57-
72) %, (0.79
and (22 – 25)
o
C respectively with outdoo
temperature ranging from (25
–
making the CDR recorded between 86
Feb.) – 130
th
day (29
th
April) to be in the range of
(0.623 –
1.07) l/hr. Over the period of
experimental observation in this study, the
highest condensate dischar
ge rate of 1.07 l/hr is
recorded on the 113
th
day (6
th
April) and 114
day (7
th
April) of 2016. In general, the most
significant factors contributing to an increase in
; Article no.
CJAST.46188
order model value of condensate rate
day) – 4
th
Jan.
anuary, the dryness persisted, however CDR
began to increase from near zero as at the 46
th
th
day) with RH
58) %, outdoor temperature (28
– 30)ºC,
0.8) and DP (17
– 22)ºC; and
zero CDR towards
the end of the month at the 61
st
day (25
th
January). In February, there have been signs
that the rainy season is approaching, though the
dryness persisted until 18th February (79
th
day)
0.8), DP (12 –
C, however there was exception to 68
– 70
th
Feb.), the CDR slightly increase to
0.35) l/hr with improved RH (40
– 47)%,
19)
ºC.
day, as the month of March is
approaching, there was a rapid increase in the
CDR. Since the months of March and April were
rainy season period, the RH, SHR and DP have
72) %, (0.79
– 0.85)
C respectively with outdoo
r
–
31)ºC. Thus,
making the CDR recorded between 86
th
day (27
th
April) to be in the range of
1.07) l/hr. Over the period of
experimental observation in this study, the
ge rate of 1.07 l/hr is
April) and 114
th
April) of 2016. In general, the most
significant factors contributing to an increase in
Bamisaye and Oke; CJAST, 32(3): 1-13, 2019; Article no.CJAST.46188
12
the rate of condensate discharge is increase in
the RH, SHR, and DP. The study agrees with the
findings of [4] and [6], they confirm that amount
of condensate water largely dependent on local
climate, heating, ventilation and air-conditioning
design, dry bulb temperature, relative humidity,
and sensible heat ratio.
4. CONCLUSION
The study was carried out to develop an
empirical model for predicting condensed water
discharge rate in an air conditional system in
other to ascertain the volume of useful water that
is wasted, most especially in Nigerian offices.
The analysis showed that over the six month
period of the 8 hours daily operation of the air
conditioning unit, a total of 528 L of condensed
water was collected from the 2500 W split air
conditioning unit of the office space (33.8 m
3
)
used for the study. The analysis of the data
collected suggested a multiplying factor for
determining the amount of condensate
production possible from such systems in order
to effectively use it for different purposes such as
toilet flushing and as a distilled water for
laboratory uses. The regression model of the
first, second and third order was developed
based on data collected from the period of
November 2015 - April 2016. It can be deduced
that as the degree of the model equation
increases, the greater the accuracy and
adequacy of the model. The results of correlation
analysis for the model equations showed that
dew point temperature, sensible heat ratio,
relative humidity have a strong correlation with
the hourly condensate production rate. This
study shows that condensate from air
conditioning unit has a potential for water
sustainability that should be tapped instead of
leaving it to simply drained off into the open
grounds as waste and consequently disfiguring
and destroying the surface of the structure.
5. RECOMMENDATIONS
In promoting reclaimed water source and water
sustainability in Nigeria, it is thereby
recommended that further research needs to be
conducted on condensed water discharge rate
for two consecutive years to give a perfect
scenario for water sustainability. Further studies
should also be done in other to detect more
factors that can determine condensate discharge
rate from an air-conditioning unit. The further
studies can help to reduce the cost being spent
per month on water supply.
ACKNOWLEDGEMENTS
Authors hereby acknowledge the technical
contributions of Engr. A.Y. Oyerinde of the
department of Production and Industrial
Engineering of Federal University of Technology
Akure, Nigeria; during the course of this study.
COMPETING INTERESTS
Authors have declared that no competing
interests exist.
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