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551
AJCS 14(04):551-556 (2020) ISSN:1835-2707
doi: 10.21475/ajcs.20.14.04.p1237
Monitoring of drippers during wastewater application through statistical quality
control
Flavio Daniel Szekut1*, Delfran Batista dos Santos2, Carlos Alberto Vieira de Azevedo1, Marcio Antonio
Vilas Boas3, Márcio Roberto Klein1, Maycon Diego Ribeiro4 and Thiago Zuculotto3
1Federal University of Campina Grande, Academic Unit of Agricultural Engineering, Campina Grande, 58.429-140,
Paraíba, Brazil
2Federal Institute of Education, Science and Technology of Bahia (IF Baiano), Senhor do Bonfim, Brazil
3State University of Western Paraná (UNIOESTE/PGEAGRI), Cascavel, Brazil
4Federal University of Paraná (UFPR), Jandaia do Sul, Brazil
*Corresponding author: flaviodanielszekut@gmail.com
Abstract
The use of alternative water sources for irrigation such as wastewaters, promotes innumerous benefits, but investigations must be
conducted to minimize the negative effects of this technique. Clogging drippers are of the limitations. This study aimed to monitor
the clogging of three models of labyrinth-type drippers subjected to irrigation with wastewater from treated domestic sewage,
through statistical quality control using Shewhart
X
charts. The drippers tested were as following: Dripper Streamline 16080 model
(Netafim®); Taldrip model (Naadanjain®); and Dripper Tiran 16010 model (Netafim®). The system was installed with five lateral lines
per model of dripper on a bench at the field in the Brazilian semi-arid region. The system was evaluated every 36 h of operation at
eight collection points in each lateral line, totaling thirty-three evaluations at the end of the experiment, which corresponded to a
total of 1188 h of operation. Dripper clogging was identified by the statistical control charts with 432, 540 and 360 h for the
drippers Streamline 16080 model, Taldrip model and Tiran 16010 model, respectively, indicating the moment to apply a cleaning
process. The monitoring through statistical quality control allowed simultaneously identifying the variability of the process and the
reduction in flow rates, identifying the moment of clogging of the system and to carry out actions of unclog.
Keywords: Shewhart charts, biofilm, degree of clogging, uniformity coefficient, labyrinth-type drippers.
Abbreviations: D1_Dripper Streamline 16080 model from the brand Netafim®; D2_Dripper Taldrip model from the brand
Naadanjain®; D3_Dripper Tiran 16010 model from the brand Netafim®.
Introduction
The semi-arid regions since 1960 have the largest expansion
compared with other rainfed areas (Huang et al., 2016). In
these regions with water scarcity, the priority of the water
resource is human consumption, animal watering and then
agriculture. Irrigation in these regions uses lower-quality
water or alternative water resources. One of these
alternatives is the use of wastewater, since it is an abundant
resource that can contribute to filling the deficit between
demand and availability of good-quality water (Alobaidy et
al., 2010). The drip system is indicated for wastewater
application and its characteristic is applying the water
resource directly on the root system of the crop, which
promotes reduction of contaminants in plant shoots and for
the workers. However, clogging of the system is a problem
that can limit the application and popularization of this
technology of use of wastewaters (Li et al., 2013).
Monitoring irrigation systems for clogging control is
performed through uniformity coefficients, as used by Zhou
et al. (2015), and by the decrease of the applied flow rate or
the degree of clogging, as used by Cabral de Almeida et al.
(2013). Statistical quality control is one way of monitoring
processes (Montgomery, 2009). It started in the industry and
spread to various areas, such as the pharmaceutical sector
(Lima et al. 2006), water quality control (Kahraman & Kaya,
2009), livestock farming (Mertens et al., 2011) irrigated
agriculture (Justi et al., 2010) and irrigation with saline water
(Ferreira da Silva et al., 2016). Irrigation with wastewater can
be monitored by statistical quality control, through
Shewhart control charts. According to Gove et al. (2013), the
decisions of the chart are rapid and transparent, allowing
decision-taking and the removal of particularities from the
process. The use of statistical control to monitor the
performance of the irrigation system with wastewater is
proven by Hermes et al. (2013). These authors confirmed the
use of this tool to evaluate the capacity of the system to
maintain satisfactory uniformity conditions. The Shewhart
control chart can identify the variability of the process and
displacement of the data in relation to the mean or target,
monitoring the clogging of the drippers. In this context, the
effect of using wastewater from treated domestic sewage
552
was monitored in three models of dripper, through the
quality control chart, uniformity coefficient and degree of
clogging.
Results and discussion
Initial characteristics
In the process of drip irrigation with treated domestic
sewage, water quality characteristics and the internal flow
conditions of each dripper model can contribute to clogging.
Table 1 shows the characteristics of the wastewater used in
the experiment.
Based on the quality of the analyzed wastewater (Table 1),
there was an intermediate risk of clogging, according to
Nakayama et al. (2006), for total coliforms and dissolved
solids. The values of other elements indicated low risk of
clogging.
Table 2 shows the descriptive analysis of the flow rate data
obtained during the operation time of the drippers.
It should be pointed out the reduction of the mean flow rate
in relation to the initial evaluation. In addition, the minimum
values indicate the total clogging of the drippers.
At the end of the experiment, total clogging was observed in
0.6% of Streamline 16080 model drippers (D1) and 2.4% of
Taldrip model (D2) and Tiran 16010 model (D3). According to
Puig-Bargue´s et al. (2010), the number of totally clogged
drippers depends on the type of system (superficial or
subsuperficial) and the type of emitter with the application
of effluents.
The initial flow rate calculated in the first evaluation of the
brand-new system is the target to be considered in the
irrigation process, to construct the quality control charts.
The maximum values of flow rate are increments in the
outlet flow of the drippers caused by variations in the
manufacturing process or by the influence of water quality
inside the pipes, as observed by Busato & Soares (2010),
who reported increment of 1% in the flow rate of the
dripper subjected to irrigation with lower-quality water, in
700 h of use.
Hydraulic performance of the drippers
Fig 1 shows the monitoring of the drippers based on the
Christiansen’s uniformity coefficient and degree of clogging.
At the end of the system’s operation time, the degree of
clogging reached mean values of 20.74, 55.44 and 70.40%,
respectively for Streamline 16080 model, Taldrip model and
Tiran 16010 model. In studies with various drippers using
wastewater, Naji et al. (2015) observed that the operational
time and type of dripper have significant effect on the
relative flow rate.
The degree of clogging represents the variation in the flow
rate of the emitters in relation to the proposed value, which
in this case is the flow rate of the brand-new drippers.
Problems with reduction in flow rate using effluents are
characterized by drippers, in which the internal flow
structure favors the formation of biofilm, even under
conditions of treatment against clogging (Katz et al., 2014).
At the beginning of the process of using treated domestic
sewage, the reduction in the flow rate was low, followed by
a sharp increase in clogging. Li et al. (2012) reported that
such initial reduction occurs around 256 h of operation.
In labyrinth-type drippers, the water flow passes through
low-speed regions, especially in the curves and regions close
to the walls. These regions are prone to deposition of small
particles and, consequently, the formation and adherence of
biofilm. The elimination of these regions promotes better
self-cleaning capacity (Li et al., 2008).
Besides the bacterial colonization in the labyrinth, according
to Gamri et al. (2014), loose fragments of the biofilm can
cause clogging, because they are deposited in other parts of
the dripper and block the water flow.
The uniformity coefficient reached values of 77.78, 30.65
and 22.25%, respectively, for Streamline 16080 model,
Taldrip model and Tiran 16010 model at the end of the
experiment. Besides the reduction in flow rate values,
evidenced by the degree of clogging, the CUC indicates high
variability in the flow rate of the drippers along the
operation time.
The relationship between the Christiansen’s uniformity
coefficient and the degree of clogging for the three emitters
can be observed by the regression analysis in Table 3.
There was a linear fit in regression analysis between CUC
and DC for the three tested drippers, with a satisfactory
coefficient of determination (R²). Therefore, it is possible to
conclude that the reduction in flow rate is not uniform in the
system, since the uniformity coefficient evidences such
effect.
Hydraulic monitoring through statistical quality control
Fig 2 shows the monitoring of the flow rate of the drippers
along the operation time using the Shewhart quality control
chart for samples in subgroups. The calculated control limits
indicate tolerance of the irrigation process around the
proposed target. Points outside these limits indicate that the
process is not under statistical control and show the
variability of the flow rates.
The flow rate reduction observed in the control charts points
to the influence of the wastewater in the drip system and its
causes include the formation and fixation of biofilm. For Yan
et al. (2010), the beginning of the formation and adhesion of
the biofilm occurs at 96 h of operation, inducing the clogging
process.
Water quality, when it interferes with the quality of the
irrigation process, is detected by the statistical control.
Hermes et al. (2015) observed points outside the control
using effluent from cassava processing at 555 h, but there
were no points outside these limits using clean water.
For the dripper Streamline 16080 model (Figure 2a), from
the sample 13 on, corresponding to 432 h of operation, the
irrigation process with domestic sewage effluent exceeded
the lower control limit, indicating the interference of
clogging in the application quality of the drippers. For this
operation time, the reduction in flow rate in relation to the
target was equal to 3.87%.
The quality control chart demonstrates, for the flow rates of
the dripper Taldrip model (Figure 2b), the loss of quality of
the process from the sample 16 on, 540 h of operation. At
this time, the reduction in flow rate was equal to 4.35%.
For the dripper Tiran 16010 model (Figure 2c), the loss of
quality in the process occurred at 5.46% of reduction in the
target flow rate, corresponding to 360 h of operation.
Among the three drippers, Tiran 16010 model obtained the
553
Table 1. Physicochemical and biological characterization of the wastewater used
Physicochemical parameters
Wastewater
Electrical conductivity (mmho cm-1 at 25 ºC)
2139.0
pH
7.6
Aluminum (mg L-1)
0,09
Calcium (mg L-1)
48.0
Sodium (mg L-1)
234.7
Magnesium (mg L-1)
37.2
Potassium (mg L-1)
60.6
Total Iron (mg L-1)
0.08
Chloride (mg L-1)
388.7
Silica (mg L-1)
6.2
Total Dissolved Solids at a 180 ºC (mg L-1)
1160.0
Biological Parameters
Total Coliforms (CFU)
10112.0
Fig 1. Hydraulic performance, Christiansen’s uniformity coefficient (CUC) and degree of clogging (DC) for drippers Streamline 16080
model (a), Taldrip model (b) and Tiran 16010 model (c).
Table 2. Descriptive statistics of the flow rates of the drippers.
Dripper
Initial Flow Rate (L h-1)
Mean ( L h-1)
Standard Deviation
Coefficient of Variation (%)
Minimum ( L h-1)
Maximum ( L h-1)
D1
1.45
1.32
0.212
15.96
0.00
1.65
D2
1.57
1.30
0.400
30.58
0.00
1.71
D3
1.90
1.41
0.521
36.64
0.00
2.10
D1 - Streamline 16080 model; D2 - Taldrip model; D3 - Tiran 16010 model.
Fig 2. Shewhart quality control charts for the monitoring of flow rates of the drippers Streamline 16080 model (a), Taldrip model (b)
and Tiran 16010 model (c).
554
Table 3. Regression analysis between Christiansen’s uniformity coefficient (CUC) and the degree of clogging (DC).
Dripper
Equation
R²
D1
CUC = 99.07 - 0.94DC
0.92
D2
CUC = 101.60 - 1.08DC
0.95
D3
CUC = 103.30 - 0.89DC
0.87
D1 - Streamline 16080 model; D2 - Taldrip model; D3 - Tiran 16010 model. F test at 5% significance level.
Fig 3. Layout of the irrigation system installed on the bench at the field, with control head and pumping system.
shortest time of operation without exceeding the quality
control limits. The operation time under statistical control
was different between the drippers and Taldrip model stood
out with the longest time. As observed, this dripper
obtained, at the end of the experiment, degree of clogging
and Christiansen’s uniformity coefficient of 55.44% and
30.65%, respectively (Figure 1b). Although the statistical
control was maintained for a longer operation time, the
clogging was severe along the use of treated sewage. The
dripper Streamline 16080 model obtained the second
longest operation time under statistical control and, at the
end of the 1188 h, the lowest degree of clogging among the
tested drippers. According to these characteristics, there
was a combined monitoring of the variability of the process
and the displacement of the flow rate in relation to the
target mean, constituting an effective form to control the
hydraulic performance in irrigation systems that use lower-
quality water. In this context, it can be concluded that the
monitoring of dripper clogging using the quality control
chart indicates the application of an unclogging process from
432, 540 and 360 h of operation for the drippers Streamline
16080 model, Taldrip model and Tiran 16010 model,
respectively. With the application of an effective unclogging
process, the hydraulic performance could be normalized and
the flow rates could be within the limits of tolerance of the
statistical control. In studies on water quality, Smeti et al.
(2007) concluded that the statistical control allows the
investigation of the process and the application of corrective
actions before quality problems accumulate. The quality
control allowed to observe not only the variability of the
process through the uniformity coefficients, but also the
displacement of the flow rate in relation to the proposed
mean or target, evidenced by the degree of clogging. Thus,
quality control charts constitute a combined form of
monitoring flow rate in irrigation systems for the diagnosis
of problems related to dripper clogging.
Materials and methods
Experiment conduction
The experiment was carried out on a test bench installed at
the field, in order to be under the influence of the Brazilian
semi-arid climate.
The bench was built at the National Institute of the Semi-
Arid Region (INSA), located in the municipality of Campina
Grande-PB, Brazil, at geographic coordinates of 7º 16’ 20’’ S
and 35º 56’ 29’’ W and altitude of 550 m. Accorging to
Köppen’s classification, the climate of the region is tropical,
with rains in the autumn and drought periods in the rest of
the year, referred to as As.
The wastewater used in the experiment came from an
anaerobic sewage treatment station (STS), which operates
with the sewage produced by the INSA.
The main clogging components of the water were
physicochemically and biologically characterized, at the
Reference Laboratory in Desalination (LABDES) of the
Federal University of Campina Grande - UFCG.
Three models of in-line labyrinth-type drippers were
selected for the experiment, for being prone to clogging by
lower-quality water. Another point considered in the choice
was that these drippers are used in systems of the Brazilian
semi-arid region, especially in the region of the
municipalities of Mossoró-RN and Petrolina-PE.
The selected drippers were: Streamline 16080 model from
the brand Netafim®, referred to as D1, with nominal flow
rate of 1.60 L h-1 at pressure of 100 kPa, at spacing of 0.30 m
between emitters; Taldrip model from the brand
Naadanjain®, referred to as D2, with flow rate of 1.70 L h-1 at
pressure of 100 kPa and spacing of 0.20 m; and Tiran 16010
model from the brand Netafim®, referred to as D3, with flow
rate of 2.00 L h-1 at pressure of 100 kPa and spacing of 0.40
m.
The installed system had a controller with 120-mesh disc
filter (IRRITEC®); opening valve; hydrometer (LAO®);
glycerin-filled manometer (GE®) and pressure controller
(BERMAD®) to control the inlet pressure, fixed at 100 kPa.
After irrigation, return gutters conveyed the wastewater to a
tank in a recirculation procedure.
Figure 3 shows a layout of the bench. The drippers were
installed at the same level along a length of 10.00 m, the
minimum distance for the model of dripper with longest
spacing to have twenty-five emitters, the value
recommended by the Brazilian norm ABNT/NBR ISO
926:2006, for tests with emitters. The bench was 2.00-m
wide and 1.50-m high.
555
Five lateral lines were evaluated for each model of dripper.
In each lateral line, eight points of collection were selected,
following a hydraulic distribution of choice. The volumes
were sampled in the first dripper, in the second one, at 1/7
of number of drippers, 2/7, 3/7, 4/7, 5/7, 6/7 and in the last
dripper, according to the methodology of Denículi et al.
(1980).
The evaluations consisted in the collection of volumes of
water using collectors for a period of 4 min in each selected
point. Then, the values were measured in graduated
cylinders for the calculation of the flow rate per dripper.
The first evaluation of the system, brand-new, was
performed with good-quality water and represents the initial
flow rate, considered as the historic mean/quality target for
the construction of the control charts. Subsequently,
wastewater was applied and the evaluations were
performed every 36 h of operation. The system remained
turned on for 12 h a day.
Hydraulic performance of the drippers
The monitoring of the irrigation systems for prevention or
remediation of problems that affect the applied water depth
is performed based on the hydraulic performance of the
emitter (Patil et al., 2013). Uniformity coefficient and degree
of clogging are widely used for performance assessment.
Thirty-three evaluations were performed, corresponding to
the time of 1188 h, for the probable clogging of the system
of 1000 h, observed by Liu & Huang (2009). For each
evaluation, the Christiansen’s uniformity coefficient (CUC)
and degree of clogging were calculated according to
Equations 1 and 2.
qn
qq
CUC
n
ni i
1100
(1)
Where:
i
q
- Flow rate of the tested emitter, L h-1;
q
- Mean flow rate of the emitters, L h-1: and,
CUC - Christiansen’s uniformity coefficient, %.
1001
initial
used
q
q
DC
(2)
Where:
used
q
- Mean flow rate of the drippers, when used, L h-1;
initial
q
- Mean flow rate of the drippers, when new, L h-1;
and,
DC - Degree of Clogging, %;
Quality control charts
For the quality control chart, the data are plotted around the
mean of the process. For the process of irrigation with
lower-quality water and risk of clogging, the mean would
indicate an already altered flow rate. Thus, a historic
mean/target was fixed, determined by the first evaluation of
the brand-new drippers.
This historic mean considered the use of the drippers with
good-quality water, without alterations in the flow rates
along the operation period. Thus, it is possible to observe
the change of flow rate in relation to the mean and the
variability of the samples in the irrigation with lower-quality
water.
In the Shewhart
X
statistical control chart, three lines are
drawn; a medium line (mean of the process or target) and
the lines identifying the lower and upper control limits.
Variations in the process that exceed the lower and upper
control limits, according to Equations 3 and 4, characterize a
process outside the statistical quality control, indicating that
sources of variability are acting.
3UCL
(3)
3LCL
(4)
Where:
UCL
- Upper Control Limit;
LCL
- Lower Control Limit;
- Standard deviation of the population; and,
- Mean of the process.
The Shewhart
X
chart can be considered as resistant to
small deviations of normality. In addition, these deviations
cause increase in the Type I error, false alarms. However,
with the increase in the size of the sample, this difference
decreases (Korzenowski and Werner, 2012). False alarms for
the monitoring of irrigation systems indicate an anticipated
clogging.
Each evaluation is a subgroup for the construction of the
control chart. Each subgroup is formed by forty values of
flow rate, eight collection points in each of the five lateral
lines. Thus, based on the central limit theorem, the flow rate
values can be considered as in a normal distribution.
Conclusion
The monitoring of the hydraulic performance of the drippers
subjected to irrigation with wastewater from treated
domestic sewage, using the statistical quality control charts,
determines operation times of 432, 540 and 360 h for the
application of unclogging processes in the drippers
Streamline 16080 model, Taldrip model and Tiran 16010
model, respectively. The statistical quality control charts
indicate the variability and the reduction in the flow rate of
the drippers, simultaneously, allowing to identify the
moment of clogging of the system.
Conflict of interests
The authors have not declared any conflict of interests.
Acknowledgments
To the National Institute of the Semi-Arid (INSA), the Federal
Institute of Education, Science and Technology of Bahia (IF
Baiano) and the Federal University of Campina Grande
(UCFG), for the logistic and infrastructure support; to the
Coordination for the Improvement of Higher Education
Personnel (CAPES), for granting the scholarship; and to the
National Council for Scientific and Technological
Development (CNPq), for the financial support through the
project nº. 94/2013 MEC/SETEC/CNPq.
556
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