Quantifying the Impact of Sludge Accumulation on the
Hydraulic Performance of Waste Stabilisation Ponds
Chris Murphy
Supervisors: Dr Anas Ghadouani and Dr Marco Ghisalberti
School of Environmental Systems Engineering
Faculty of Engineering, Computing and Mathematics
The University of Western Australia
June 2012
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Abstract
The treatment quality of Waste Stabilisation Ponds (WSPs) is heavily dependent on the
retention time of influent wastewater. Retention times are affected by a combination of
internal hydraulic behaviours such as mixing, recirculation and short-circuiting. These
behaviours are influenced by the build-up of sludge, resulting in decreased retention times.
Sludge build-up in these systems is inevitable, caused by the settling of suspended solids in
the water column. Managing sludge accumulation involves costly, periodic pond desludging.
Quantifying the impact of sludge accumulation on hydraulic behaviour will provide the
wastewater industry with a valuable management tool.
This study utilised Computational Fluid Dynamics (CFD) software MIKE 21 to simulate the
hydraulic behaviour of various bathymetry profiles. The model was validated using the
measured bathymetry profiles and tracer data collected from two functioning WSPs located
in the southwest of WA. Model simulations were aimed at quantifying the impact of various
sludge accumulation and distribution scenarios on hydraulic performance and were based
on real sludge distribution patterns. A number of hydraulic indices derived from the
Residence Time Distribution (RTD) curve were used to assess the hydraulic performance of
each scenario. The moment index was used in all scenarios as a measure of treatment
efficiency.
Although sludge accumulation caused a decrease in overall treatment, the rate at which
treatment efficiency decreased was attenuated by the gradual increase sludge distribution.
Isolating the impact of distribution showed that the presence of more defined channels and
ridges provided higher treatment efficiency than for flat, even distributions. The hydraulic
process of mixing was shown to be more dominant than short-circuiting when investigating
their influence on treatment efficiency. The relationship between sludge accumulation and
treatment efficiency identified in this study may be used to provide wastewater managers
with a more accurate description of efficiency compared to traditional methods. Also, the
CFD approach and methodology could be adopted and incorporated into sludge
management programs to aid strategic desludging planning and pond design improvements
both aimed at reducing operational cost.
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Acknowledgements
I would like to thank the following people for their contributions over the period of this
project.
Marco Ghisalberti for his guidance and support with the project including ideas for a minor
project direction change, interpreting of results and constructive feedback.
Anas Ghadouani for his guidance and support with the project especially through his positive
attitude, encouragement and reassurance.
Cyprien Bosserelle for all his help setting up the computer model and providing support
whenever needed without which, insanity would have surely set in.
Liah Coggins for her provision of data, answering of questions and general willingness to help
out.
Andrew Chua for his time and valued correspondence regarding project direction as well as
answering questions throughout the project.
My wife Jane for her support and understanding while completing this project.
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Table of Contents
Abstract ............................................................................................................................ 2
Acknowledgements .......................................................................................................... 3
Table of Contents.............................................................................................................. 4
List of Figures .................................................................................................................... 6
List of Tables ..................................................................................................................... 8
Abbreviations ................................................................................................................... 8
1. INTRODUCTION ....................................................................................................... 11
2. BACKGROUND ......................................................................................................... 12
2.1 Waste Stabilisation Ponds ......................................................................................... 12
2.2 Sludge Accumulation ................................................................................................. 13
2.3 Sludge Management .................................................................................................. 15
2.4 Hydraulics of WSP ...................................................................................................... 17
2.4.1 Hydraulic Indices ......................................................................................................................... 21
2.5 CFD modelling of WSP ............................................................................................... 24
2.6 Synthesis .................................................................................................................... 24
3. STUDY AIMS ............................................................................................................ 26
3.1 Aim 1 .......................................................................................................................... 26
3.2 Aim 2 .......................................................................................................................... 26
4. CHAPTER 1 .............................................................................................................. 27
4.1 Rationale .................................................................................................................... 27
4.2 Methodology ............................................................................................................. 27
4.2.1 Develop a Two-Dimensional model ............................................................................................ 27
4.2.2 HD module setup ........................................................................................................................ 28
4.2.3 AD module setup ........................................................................................................................ 29
4.3 Results and Discussion ............................................................................................... 30
4.3.1 Calibration of 2-D model ............................................................................................................. 30
5. CHAPTER 2 .............................................................................................................. 38
5.1 Rationale .................................................................................................................... 38
5.2 Methodology ............................................................................................................. 38
5.2.1 Impact of Sludge Accumulation and Distribution ....................................................................... 38
5.2.2 Data outputs and processing ...................................................................................................... 44
5.2.3 Hydraulic Assessment ................................................................................................................. 44
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5.3 Results and Discussion ............................................................................................... 46
5.3.1 Sludge Accumulation .................................................................................................................. 46
5.3.2 Sludge Distribution ..................................................................................................................... 53
5.3.3 Performance summary ............................................................................................................... 56
6. CONCLUSIONS & RECOMMENDATIONS ................................................................... 60
6.1 General conclusions ................................................................................................... 60
6.2 Recommendations ..................................................................................................... 61
7. REFERENCES ............................................................................................................ 63
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List of Figures
FIGURE 1: LAYOUT OF BRUNSWICK (LEFT) AND WAROONA (RIGHT) WSPS. THE NUMBERS REPRESENT THE ORDER OF TREATMENT
WHERE 1 IS THE FIRST TREATMENT POND. (IMAGE: COURTESY OF LIAH COGGINS). ......................................................... 12
FIGURE 2: PHYSICAL, BIOLOGICAL AND CHEMICAL PROCESSES OCCURRING WITHIN A FACULTATIVE WASTE STABILISATION POND
(TCHOBANOGLOUS AND SCHROEDER 1987) .......................................................................................................... 13
FIGURE 3: BATHYMETRIES MEASURED AT FOUR DIFFERENT PONDS; (A) MORAWA PRIMARY POND, (B) MORAWA SECONDARY POND,
(C) WAROONA PRIMARY POND AND (D) BRUNSWICK SECONDARY POND. NOTE THE VARIETY OF SLUDGE DISTRIBUTION
PATTERNS........................................................................................................................................................ 14
FIGURE 4: THE SLUDGE JUDGE (A) IS DIPPED INTO THE SLUDGE FROM A BOAT. ONCE DIPPED TO THE BOTTOM OF THE POND, THE TOP OF
THE SLUDGE JUDGE TUBE IS SEALED AND THEN REMOVED TO REVEAL THE DEPTH OF SLUDGE PRESENT (B) (IMAGES: COURTESY OF
WATER CORPORATION). .................................................................................................................................... 16
FIGURE 5: THE SONAR EQUIPPED ROV IS DRIVEN ALONG TRANSECTS OF THE POND TO DETERMINE THE BOTTOM SLUDGE PROFILE
(IMAGE: COURTESY OF LIAH COGGINS) ................................................................................................................. 17
FIGURE 6: ILLUSTRATION OF IDEAL PLUG AS WELL AS SOME MORE REALISTIC VARIATIONS (WAHL ET AL. 2010) ............................ 19
FIGURE 7: CONCEPTUAL EFFECT OF SHORT-CIRCUITING ON RESIDENCE TIME DISTRIBUTION WHERE SHORT-CIRCUITING REDUCES THE
TRAVEL TIME FROM INLET TO OUTLET (WAHL ET AL. 2010). ...................................................................................... 19
FIGURE 8: CONCEPTUAL EFFECT OF MIXING ON RESIDENCE TIME DISTRIBUTION WHERE MIXING ATTENUATES THE RESPONSE AT THE
OUTLET (WAHL ET AL. 2010). ............................................................................................................................ 20
FIGURE 9: RESIDENCE TIME DISTRIBUTION REPRESENTED AS A PROBABILITY DENSITY FUNCTION. NOTE: THE X-AXIS REPRESENTED AS
FLOW RATED TIME IS ONLY RELEVANT FOR VARIABLE FLOW SCENARIOS. FLOW WEIGHTED TIME (Φ) OF 1 IS EQUIVALENT TO THE
NOMINAL RESIDENCE TIME (TN) FOR STEADY FLOW SYSTEMS (MODIFIED FROM WAHL ET AL. 2010). ................................. 23
FIGURE 10: THE SOURCE CELL (INLET CELL) VELOCITY IS DETERMINED BY DIVIDING THE INCOMING FLOW RATE BY THE AREA OF THE FACE
OF THE CELL NORMAL TO THE FLOW DIRECTION. ...................................................................................................... 28
FIGURE 11: PROFILED BATHYMETRY OF BRUNSWICK WSP NUMBER 2 AS AT 18 AUGUST 2011. THE BATHYMETRY PROVIDES AN EVEN
AND RELATIVELY EMPTY POND THAT CAN BE USED FOR CALIBRATION. THE LOCATION OF THE INLET AND OUTLET ARE SHOWN IN
THE FIGURE. THE COLOUR REPRESENTS HEIGHT WHERE RED IS SHALLOW AND BLUE IS DEEP. ............................................. 31
FIGURE 12: RTDS FOR MANNING'S N SENSITIVITY ANALYSIS. NOTE: ONLY THE FIRST WEEK OF THE SIMULATION IS SHOWN IN THIS
FIGURE. THE MEAN RESIDENCE TIME FOR EACH MANNING’S N RTD IS SHOWN IN THE LEGEND. ......................................... 32
FIGURE 13: RTDS FOR DISPERSION SENSITIVITY ANALYSIS. ONLY THE FIRST 2 WEEKS OF THE SIMULATION IS SHOWN IN THIS FIGURE.
THE MEAN RESIDENCE TIME (T MEAN) FOR EACH DISPERSION RTD IS SHOWN IN THE LEGEND. .......................................... 33
FIGURE 14: RTD COMPARISON OF THE BRUNSWICK TRACER STUDY WITH THE CALIBRATED MODEL SIMULATION OF BRUNSWICK. ..... 35
FIGURE 15: TRACER MOVEMENT IMMEDIATELY FOLLOWING INTRODUCTION AT THE INLET. THE TRACER IS MOVING SOUTH IN A
COUNTER-CLOCKWISE DIRECTION FOLLOWING THE BOUNDARY CLOSELY. ...................................................................... 36
FIGURE 16: PROGRESSION OF THE SIMULATED TRACER IN THE SECOND BRUNSWICK WSP. THE SNAPSHOTS ARE IN 1 HOUR
INCREMENTS ORDERED LEFT TO RIGHT, TOP TO BOTTOM, STARTING AT 1 HOUR FROM INTRODUCTION OF TRACER. THE COLOUR
REPRESENTS THE TRACER CONCENTRATION WHERE RED IS HIGH AND BLUE IS LOW. THE POND LAYOUT AND ORIENTATION ARE
SHOWN IN FIGURE 11. ...................................................................................................................................... 37
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FIGURE 17: TWO PONDS WITH SIMILAR ACCUMULATION (50% OCCUPIED BY SLUDGE) AND SIGNIFICANTLY DIFFERENT DISTRIBUTIONS -
STANDARD DEVIATION OF SLUDGE HEIGHT = 0.015 AND 0.200 RESPECTIVELY. THIS FIGURE HIGHLIGHTS THE NEED TO ASSESS
THE IMPACT OF ACCUMULATION AS WELL AS DISTRIBUTION. ...................................................................................... 39
FIGURE 18: PROFILED BATHYMETRY OF WAROONA WSP NUMBER 1 AS AT 11 AUGUST 2011. COLOUR REPRESENTS DEPTH WHERE
RED IS SHALLOW AND BLUE IS DEEP. NOTE THE CHANNEL THAT RUNS FROM INLET TO OUTLET. .......................................... 40
FIGURE 19: THE SIMULATED PROGRESSION OF SLUDGE BUILD-UP OVER TIME. ORDERED FROM LEFT TO RIGHT, TOP TO BOTTOM. NOTE:
THE POND BECOMES MORE CHANNELISED. COLOUR IS USED TO HELP DEPTH VISUALISATION WHERE RED IS SHALLOW AND BLUE IS
DEEP. ............................................................................................................................................................. 41
FIGURE 20: VARYING SLUDGE HEIGHT DISTRIBUTION SCENARIOS FOR THE WAROONA WSP. THE CHANNEL BECOMES MORE
EXAGGERATED WITH INCREASING BETA VALUES. COLOUR IS USED TO HELP DEPTH VISUALISATION WHERE RED IS SHALLOW AND
BLUE IS DEEP. ................................................................................................................................................... 43
FIGURE 21: A FLOW DIAGRAM HIGHLIGHTING THE MAJOR DATA PROCESSING STEPS INVOLVED IN THE PROJECT. ............................ 44
FIGURE 22: RTD COMPARISON FOR SLUDGE ACCUMULATION SCENARIOS. HIGHER ALPHA REPRESENTS GREATER ACCUMULATION. ... 46
FIGURE 23:TREATMENT EFFICIENCY OF EACH SLUDGE ACCUMULATION SCENARIO. .................................................................. 48
FIGURE 24: (A) THE AMOUNT OF DISPERSION EXPERIENCED IN THE WSP, DERIVED FROM THE VARIANCE OF THE RTD AND (B) THE
AMOUNT OF SHORT-CIRCUITING CALCULATED AS THE 10% ARRIVAL TIME. ................................................................... 50
FIGURE 25: THE IMPACT OF INCREASING DISPERSION, (A) TO (B), IS SHOWN THROUGH THE COMPARISON OF 2-D TRACER
CONCENTRATION AT IDENTICAL POINTS IN TIME. THE COLOUR REPRESENTS THE TRACER CONCENTRATION WHERE RED IS HIGH
AND BLUE IS LOW TRACER CONCENTRATION. .......................................................................................................... 52
FIGURE 26: RTD COMPARISON OF THE SLUDGE DISTRIBUTION SCENARIOS. ............................................................................ 53
FIGURE 27: CHANGE IN TREATMENT EFFICIENCY GIVEN THE SAME VOLUME BUT VARYING DEGREES OF SLUDGE HEIGHT DISTRIBUTION.
HIGHER Β MEANS GREATER DISTRIBUTION. ............................................................................................................. 54
FIGURE 28: AMOUNT OF MIXING (A) AND SHORT-CIRCUITING (B) FOR EACH SLUDGE HEIGHT DISTRIBUTION SCENARIO. .................. 55
FIGURE 29: THE REDUCTION IN NOMINAL EFFICIENCY (V/Q) COMPARED TO THE RELATIVE TREATMENT EFFICIENCY AS INDICATED BY THE
MOMENT INDEX (FIGURE 23). ............................................................................................................................. 57
FIGURE 30: RELATIONSHIP BETWEEN SLUDGE HEIGHT STANDARD DEVIATION AND TREATMENT EFFICIENCY FOR ACCUMULATION AND
DISTRIBUTION SCENARIOS. .................................................................................................................................. 59
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List of Tables
TABLE 1: SUMMARY OF THE SENSITIVITY ANALYSIS PERFORMED AS PART OF THE CALIBRATION PROCESS. ...................................... 34
TABLE 2: COMPARISON OF FIELD TRACER STUDY AND CALIBRATED SIMULATION OF TRACER STUDY .............................................. 35
TABLE 3: ASSESSMENT TOOLS USED IN THIS STUDY AND THE INFORMATION THEY PROVIDE ........................................................ 45
TABLE 4: SLUDGE ACCUMULATION SCENARIO RESULTS FROM RTD STATISTICAL ANALYSIS ......................................................... 46
TABLE 5: RTD ANALYSIS RESULTS FROM THE SLUDGE DISTRIBUTION FACTOR (Β) SCENARIOS. ..................................................... 53
Abbreviations
BOD Biological Oxygen Demand
CFD Computational Fluid Dynamics
DHI Danish Hydraulic Institute
MI Moment Index
ROV Remotely Operated Vehicle
RTD Residence Time Distribution
WC Water Corporation
WSP Waste Stabilisation Pond
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1. INTRODUCTION
Climate change and population growth are placing increasing pressure on Australia’s natural
resources, including water. The re-use of wastewater is acknowledged through the national
research priorities as an important step towards sustainable water use (ARC 2012).
Maintaining reliable and cost-effective wastewater treatment systems is therefore critical to
achieving a sustainable water future.
Wastewater stabilisation ponds (WSP) are a safe, low-cost, low-maintenance option for
treating wastewater. These ponds are designed to remove pollutants such as suspended
sediments, pathogens, and nutrients from wastewater (Wood et al. 1998). The treatment
process relies on sufficient retention time and involves a combination of natural biochemical
and physical processes (Nelson et al. 2004). One of these processes is the settling of
suspended solids from the water column resulting in a layer of sedimented material known
as sludge (Nelson et al. 2004). The distribution of this sludge accumulation layer is rarely
uniform and varies from pond to pond. Sludge accumulation is known to have an impact on
the hydraulic performance however little is known about this relationship (Sah, Rousseau &
Hooijmans 2012). Hydraulic performance encompasses a range of hydraulic processes
including short-circuiting, lag-time, recirculation and dispersion (Persson 2000). Since
hydraulic performance is a major contributing factor of treatment efficiency, investigating
the impacts of sludge accumulation on pond hydraulic performance is an important step
forward for wastewater managers.
The interaction and complexity of the internal hydraulic processes makes accurate
prediction of performance difficult. For this reason, current methods for estimating the
efficiency of WSPs do not account for the impact of internal hydraulics. Therefore, the aim of
this project is to develop a computational model capable of reproducing experimental
results which can then be used to quantify the impact of sludge accumulation and
distribution on pond hydraulics. Computer modelling software will be used to simulate the
hydraulic response to various bathymetries. Through comparing the changes in hydraulic
performance to the changes in bathymetry, the impact of sludge distribution on WSPs can
be better understood and integrated into more accurate efficiency calculations.
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2. BACKGROUND
2.1 Waste Stabilisation Ponds
Waste stabilisation ponds (WSP) are a low cost, low maintenance process for treating
wastewater. These treatment systems typically consist of several shallow man-made ponds
operating in series (Figure 1).
Figure 1: Layout of Brunswick (left) and Waroona (right) WSPs. The numbers represent the order of
treatment where 1 is the first treatment pond. (Image: courtesy of Liah Coggins).
Treatment of wastewater occurs as constituents are removed by sedimentation or
transformed by biological and chemical processes (Nelson et al. 2004)(Figure 2). Heavy solids
settle to the bottom of the pond, lighter solids remain suspended in the water column.
Anaerobic organisms colonise the accumulated sludge layer and assist sludge removal
through digestion (Tchobanoglous and Schroeder 1987). The large surface area and shallow
construction of WSP allows atmospheric oxygen to be transferred through the surface of the
pond, preventing anaerobic conditions in the surface layer. The intermediate depths of the
pond support facultative micro-organisms capable of oxidising the organics from the
wastewater as well as the products of anaerobic digestion on the bottom of the pond
(Metcalf & Eddy 2003).
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Figure 2: Physical, biological and chemical processes occurring within a facultative Waste Stabilisation Pond
(Tchobanoglous and Schroeder 1987)
The accumulation of sludge in the anaerobic zone results in reduced pond efficiency due to
decreased pond volume (Nelson et al. 2004). Sludge accumulation is also known to have an
impact on the hydraulic processes occurring within the pond, however the relationship has
not been explored in any detail (Alvarado et al. 2011; Olukanni & Ducoste 2011). The
hydraulic processes occurring within WSP play a significant role in overall treatment
efficiency (Olukanni & Ducoste 2011; Persson 2000; Torres et al. 1997). The quality of
treatment is dependent on the ponds ability to retain the entering wastewater for a
designated period of time. The hydraulic mechanisms involved in moving the wastewater
from inlet to outlet include advection and dispersion. The combination of internal hydraulic
behaviour such as mixing, short-circuiting and recirculation result in preferential flow-paths
and dead zones which act to reduce the effective volume of the pond. The reduced effective
volume means that entering wastewater is more likely to spend less time in the system and
therefore exit the pond with lower than expected quality.
2.2 Sludge Accumulation
Sludge accumulation in WSP is due to the sedimentation of bio-solids including algae and
bacteria. The distribution of this accumulating material can be highly spatially variable
(Figure 3) and differ significantly from pond to pond (Pena & Mara 2000; Abis & Mara 2003;
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Nelson et al. 2004; Picot et al. 2005; Alvarado et al. 2011). The accumulation rate is
determined by the non-biodegradable portion of the settled solids that either enter the
system or get produced by microorganism biological activity (Saqqar & Pescod 1995).
Figure 3: Bathymetries measured at four different ponds; (a) Morawa primary pond, (b) Morawa secondary
pond, (c) Waroona primary pond and (d) Brunswick secondary pond. Note the variety of sludge distribution
patterns.
Although it is widely accepted that sludge accumulation plays an important role in the
hydraulic performance (Persson 2000; Olukanni & Ducoste 2011; Torres et al. 1997), the
extent of these effects have not been established or quantified (Sah, Rousseau & Hooijmans
2012). As a result, current methods of estimating retention times are based on the nominal
pond volume. The nominal volume is the volume in the pond not occupied by sludge. This
method of estimating retention time takes no account of the hydrodynamic realities of such
systems where sludge accumulation and its distribution are likely to result in dead zones and
short-circuiting (Nelson et al. 2004; Picot et al. 2005), both likely to further reduce retention
times (Lloyd, Vorkas & Guganesharajah 2003).
(a)
(c)
(b)
(d)
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2.3 Sludge Management
Sludge management is an integral part in the efficient functioning and treatment within
WSP. The accumulation of sludge can impact performance by altering the ponds hydraulics
due to a decrease in the ponds effective volume and changes to the bottom surface (Pena &
Mara 2000). Periodic sludge removal is required to maintain a ponds ability to effectively
treat the entering wastewater. However, the cost of desludging operations is significant,
making the frequency of removal a major factor in the long term sustainability of the WSP.
Reported sludge accumulation rates range from 0.021 m3/person/year up to 0.148
m3/person/year (Pena & Mara 2000; Abis & Mara 2003; Picot et al. 2005). For ponds with
average temperatures above 20°C, using a value of 0.04 m3/person/year is suggested
(Nelson et al. 2004). A study of 19 WWTP in south France concluded the average cost for
desludging ranged from 38 €/m3 to 62 €/m3 depending on whether the pond is drained prior
to desludging (Picot et al. 2005).
The population of Western Australia is circa 2.4 million (ABS 2012), putting the value of
desludging at $5 Million per year for Western Australia alone. With wastewater managers
under ever increasing budget pressures, identifying the ponds with the highest desludging
priority is critical to achieving maximum benefit at minimum cost.
Wastewater managers monitor sludge levels in WSP in order to estimate efficiency and
predict when a pond may require desludging. Sludge height measurement has traditionally
been performed using either the white towel test or the sludge judge method. The white
towel test, described by Mara (2004) involves dipping a white towel into the pond and the
sludge depth is estimated from the markings on the towel. The sludge judge method uses a
transparent tube, open at each end, dipped to the bottom of the pond and then sealed at
the top (Figure 4). When the tube is removed from the pond, the contents remain inside and
the sludge height can be determined.
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Figure 4: The sludge judge (a) is dipped into the sludge from a boat. Once dipped to the bottom of the pond,
the top of the sludge judge tube is sealed and then removed to reveal the depth of sludge present (b)
(Images: courtesy of Water Corporation).
The sludge height measurement methods described above are time consuming, pose health
and safety risks and the accuracy of the measurement itself is subjective. In light of these
issues, recent theses by Morgan (2010) and Coggins (2011), worked towards providing the
industry with a new sludge height measuring tool. A remotely operated vehicle (ROV) fitted
with a sonar device was trialled (Figure 5) and results compared to manual measurements.
The comparison of sludge heights collected from sludge judge surveys and those obtained
from sonar measured heights showed significant and high correlation (R2 = 0.98)(Coggins
2011). The sonar fitted ROV has since been used by Water Corporation to profile a number
of ponds across Western Australia (pers comm. Chua 2012).
(a) (b)
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Figure 5: The sonar equipped ROV is driven along transects of the pond to determine the bottom sludge
profile (Image: courtesy of Liah Coggins)
2.4 Hydraulics of WSP
Hydraulic performance is a broad term encompassing a range of hydraulic processes
including short-circuiting, lag-time, recirculation and dispersion (Persson 2000). In WSP, the
hydraulic performance is critical to the quality of wastewater treatment. In particular,
hydraulic efficiency has been identified as a fundamental factor controlling the performance
of WSP (Pena & Mara, 2000; Persson 2000; Lloyd et al. 2003; Agunwamba 2006). The
hydraulic efficiency of WSP can be thought of as a measure of how effective the pond is at
maintaining plug flow (Persson, Somes & Wong 1999). Plug flow is considered to be the
optimal flow from a hydraulic point of view since all fluid parcels reside around the nominal
residence time. However, the flow in WSP is not homogenous, but rather, moves in eddies
and gets recirculated (Persson 2000). A common method for evaluating hydraulic
performance is the use of tracer studies (Torres et al. 1997; Pena & Mara 2000; Persson
2000). After introducing a tracer slug at the inlet of the WSP, the outlet is monitored for
tracer concentration and a residence time distribution function (RTD) can be obtained.
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Direct comparison of RTDs is possible for assessing hydraulic performance only after
normalising for volume and tracer mass. The area under the raw RTD represents tracer mass.
Once normalisation is performed, the RTD becomes a dimensionless function with time
along the x-axis such that the area under curve is equal to one (Equation (1)). These
functions are known as exit age curves or E curves.
∫ ( )
(1)
The E curve is related to the time series concentration as follows:
( ) ( )
∫ ( )
(2)
WSP residence time describes the travel time from inlet to outlet. The RTD is the distribution
of times that exiting fluid particles have spent in the system. The corrected, or normalized,
RTD is essentially a probability density function of residence time (Teixeira & Siqueira 2008).
The distribution aids in describing the manner and extent of deviation from ideal flow
(Werner & Kadlec 1996). The mean residence time, tmean, of a tracer particle is defined as the
centroid of the RTD while the variance is the spread of the RTD. Figure 6 shows the
concentration-time distribution in response to a spike tracer input and steady flow
conditions. Each response has a similar mean detention time but significantly different
hydraulic efficiency due to their differences in the range of detention periods experienced by
individual parcels of water.
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Figure 6: Illustration of ideal plug as well as some more realistic variations (Wahl et al. 2010)
The nominal residence time, indicated at the top of Figure 6 as tn is calculated from V/Q.
Such calculations assume the pond is making use of the entire volume by uniformly
distributing the flow. As discussed earlier, traditional estimates of efficiency are based on
this number. Thus, these estimates are based on ideal plug flow conditions. In reality, dead
zones or re-circulating zones reduce the effective volume of the WSP creating preferential
flow paths and effectively shorten the average residence time (Wahl et al. 2010). This short-
circuiting shifts the centre of the distribution towards the origin and below the theoretical
residence time (Figure 7).
Figure 7: Conceptual effect of short-circuiting on residence time distribution where short-circuiting reduces
the travel time from inlet to outlet (Wahl et al. 2010).
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The shape of the RTD is generally related to the mixing i.e. the deviation from plug flow
(Wahl et al 2010). A common tank-in-series model is used for measuring the degree of plug
flow where treatment ponds can be considered as reactors that are modelled by a sequence
of tanks-in-series (Fogler 1992). In a continuously stirred tank reactor (CSTR) all parcels have
an equal probability of leaving the basin at any given time (Wahl et al. 2010). The RTD for a
single CSTR is an exponential function. As the number of CSTRs-in-series (N) increases, the
spread of the RTD decreases, hence the more plug-flow-like the flow is (Figure 8) (Wahl et al.
2010; Persson 2000). Measures of N are
(3)
where tn is the nominal retention time (V/Q) and σ2 is the variance.
Figure 8: Conceptual effect of mixing on residence time distribution where mixing attenuates the response at
the outlet (Wahl et al. 2010).
The existence of dead zones creates areas of dead volume that have little or no interaction
with the water flowing through the system (Persson 2000).
A combination of these hydraulic features effectively reduces the pond volume such that the
mean retention time (tmean) is less than the nominal retention time (tn). The effective volume
ratio, e, is defined by Thackston, Shields & Schroeder (1987):
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(4)
where Vtotal is the total volume of the system and Veffective is the total volume minus the dead
volume. In other words, treatment ponds with similar ratios of volume to flow rate will have
similar nominal residence times but may have very different measured residence times
depending on hydraulic performance. Analysing hydraulic performance therefore requires
the use of hydraulic indices to describe and quantify the efficiency. These hydraulic indices
are commonly extracted from RTDs (Wahl et al. 2010).
2.4.1 Hydraulic Indices
Hydraulic indices are usually divided into two categories: short-circuiting and mixing
indicators. Short-circuiting is related to the advection of the fluid inside the system, forcing
parts of the fluid to leave the system via preferential flow-paths earlier than the nominal
retention time, tn (Texeira & Siqueira 2008). Mixing is related to the random spreading of the
fluid inside the system. Mixing, in these systems, refers to the joined action of turbulent
diffusion and other effects that can cause the spreading or retention of tracer, such as re-
circulation and dead zones (Figure 6) (Texeira & Siqueira 2008).
An assessment of commonly used short-circuiting and mixing indices found that most, if not
all indices, cannot completely account for the phenomena they are supposed the represent
(Texeira & Siqueira 2008). However, the assessment found that the best short-circuit
indicator, based on three criteria including how well it represents the designated
phenomena, was t10, the time it takes for the first 10% of tracer mass to arrive at the outlet.
The 10% arrival time is directly related to the advection of the tracer front, it shows small
statistical variability and gives some indication of the amount of fluid that leaves the system
via preferential flow-paths. For the mixing indices, two were recommended and the
preference for one over the other is determined by the expected amount of mixing present.
Systems with low levels of mixing should use the Morril index, otherwise, the dispersion
index is shown to be more closely related to physical phenomena of mixing (Texeira &
Siqueira 2008).
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The hydraulic efficiency index (λ) is a commonly used metric (Persson, Somes & Wong 1999)
that combines the flow uniformity index (1 – 1/N) and effective volume (e) as:
(
)
(5)
This index was formulated in an attempt to reflect the two basic features in the
hydrodynamic performance of detention system. First, the ability to distribute the inflow
evenly across the detention system and second, the amount of mixing or re-circulation, i.e.
deviations from plug flow (Persson, Somes & Wong 1999). Since both components have a
value between 0 and 1, the index is designed to give each an equal weighting on the overall
efficiency. This consideration has led to the hydraulic efficiency index (λ) becoming one of
the most common tools for assessing efficiency (Wahl et al. 2010). Despite its common use,
the hydraulic efficiency index is not exempt to criticism of inaccuracy. Tracer responses are
often characterised by positively skewed distributions with long tails. The calculation of
means and standard deviations of an RTD can therefore vary significantly depending on the
selected end point of the tracer measurement (Persson, Somes & Wong 1999).
A relatively new hydraulic index, the moment index, attempts to overcome many of these
weaknesses (Wahl et al. 2010). The moment index provides a hydraulic efficiency index that
avoids reliance on mixing and short-circuiting indices and operates independent of the
influence from tail effects (Wahl et al. 2010). Moment analysis of the normalized RTD is a
tried and tested technique used to describe the distribution (Kadlec & Knight 1996; Werner
& Kadlec 1996; Holland et al. 2004; Min & Wise 2009; cited in Wahl et al. 2010). Through the
use of moment analysis and a nominal divide, the moment index assigns more weight to the
more severely pre-mature residence times when quantifying hydraulic efficiency (Figure 9)
(Wahl et al. 2010).
∫ ( ) ( ́( ))
(6)
Where ́( ) is the fraction of tracer mass recovered at time t.
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Figure 9: Residence time distribution represented as a probability density function. NOTE: the x-axis
represented as flow rated time is only relevant for variable flow scenarios. Flow weighted time (ϕ) of 1 is
equivalent to the nominal residence time (tn) for steady flow systems (modified from Wahl et al. 2010).
The moment index is shown to be a reliable measure of hydraulic performance in treatment
wetlands and has demonstrated more sensitivity than existing indices in detecting slight
variations in such systems (Wahl et al. 2010). One major benefit of using the moment index
is the strong correlation between it and the treatment efficiency. The moment index has
been shown to have a higher correlation (R2 = 0.94) to pollutant reduction than the effective
volume index and hydraulic efficiency index (Wahl et al. 2010). This strong correlation
implies the index is a good predictor of treatment.
To summarise, pond hydraulics include a range of complex processes that are central to the
efficiency of WSP. Understanding and measuring these various processes provides an
indication of how well the system is making use of the available volume. A review of the
various hydraulic indices has identified those most appropriate for understanding the
treatment efficiency of WSP. The moment index should be used to determine overall
treatment efficiency while the 10% arrival time and variance should be used to understand
the short-circuiting and dispersion processes respectively.
tn
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2.5 CFD modelling of WSP
The application of Computational Fluid Dynamics (CFD) to wastewater treatment systems
has become an important, low-cost tool for a better description and improved
understanding of such systems. A number of studies describe the usefulness of CFD models
as tool for assessing wastewater treatment system hydraulics (Wood et al. 1998; Salter et al.
2000; Vega et al. 2003; Shilton, Kreegher & Grigg 2008; Olukanni & Ducoste 2011; Sah et al.
2011). These studies also show that CFD models are very useful in the design of new systems
and improving the effectiveness of existing systems.
A review of the various WSP modelling studies performed so far was undertaken by Sah,
Rousseau & Hooijmans (2012). The comprehensive review covers 35 peer reviewed papers
and 4 conference papers on modelling WSPs. The review found that due to the inherent
complexities of the system, the models would focus on specific aspects whilst ignoring other
key processes and interactions (Sah, Rousseau & Hooijmans 2012). For example, some might
look at only the hydrodynamics or only the biochemical processes. The review also
highlighted the limited validation of CFD models on full-scale systems. The idea of
quantifying the impact of sludge accumulation and its distribution is not visited by any of
these studies.
One study used velocity measurements from a field study to develop and calibrate a two-
dimensional model (Somes, Bishop & Wong 1999). The results from this study showed that
the two-dimensional model, MIKE 21, was capable of reproducing the measured flow
distributions. The model was found to be relatively insensitive to changes in bed resistance
due the low velocities in the pond (Somes, Bishop & Wong 1999). Eddy viscosity was the
calibration factor with the most influence on flow patterns. This was due to the flow being
dominated by inertia (Somes, Bishop & Wong 1999).
2.6 Synthesis
Previous studies have shown that WSP treatment efficiency is often hydraulically
compromised (Olukanni & Ducoste 2011; Torres et al. 1997). It is well understood that
sludge accumulation impacts treatment efficiency through decreasing the effective pond
volume and therefore mean residence time. However, the implications of short-circuiting
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and dead zones are unknown until a tracer study is performed. If the effects of spatial sludge
distribution on hydraulic efficiency were quantified, sludge distribution patterns would
become useful tools in determining the hydraulic efficiency. The most frequently used
method for evaluating hydraulic performance is based on the interpretation of RTDs. This
interpretation is centred on the comparison of hydraulic indices extracted from these
functions. CFD has been tested for its use and relevance in modelling WSP retention times
(Olukanni & Ducoste 2011; Shilton, Kreegher & Grigg 2008) and has demonstrated a good
representation of pond hydrodynamics. The use of CFD presents a low-cost, reliable method
of determining the hydraulic impacts of sludge accumulation and distribution. If the impact
of these features are quantified it will provide the wastewater industry with a breakthrough
management tool.
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3. STUDY AIMS
3.1 Aim 1
Develop and calibrate a 2-Dimensional computer model capable of accurately reproducing
tracer response curves of waste stabilisation ponds. The use of computation fluid dynamics
allows fast, inexpensive analysis of pond performance. The calibrated computer model can
be used to assess the impact of varying pond properties, such as bathymetry, on treatment
efficiency.
3.2 Aim 2
Quantify the impact of sludge accumulation and distribution on the hydraulic performance
of waste stabilisation ponds. Sludge accumulation is known to cause a decrease in pond
efficiency; however the details of this relationship are not well established. Further to this, it
is unknown whether the nature of accumulation i.e. the distribution, has a significant impact
of pond hydraulics. Understanding how these features are impacting performance will allow
wastewater managers to apply more appropriate efficiency calculations and employ
improved sludge management practices.
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4. CHAPTER 1
4.1 Rationale
The first step of this project was to develop a 2-D computational model capable of
replicating the hydraulic processes occurring in WSPs. Developing and calibrating a
computational model will allow for the fast and accurate assessment of WSP performance.
Computational simulation provides the ability to obtain RTD curves for various bathymetric
scenarios in a much shorter time frame than physical tracer studies. Sufficient time, effort
and thought must go into the calibration phase of the modelling to ensure accuracy and
confidence in model outputs. The calibrated model is intended for use in achieving Aim 2.
4.2 Methodology
4.2.1 Develop a Two-Dimensional model
The impact of various bathymetries on the hydraulic performance of WSPs was analysed
using CFD. The numerical model used in this study was the Danish Hydraulic Institute’s (DHI)
depth averaged, two-dimensional modelling system MIKE 21. The model simulates variations
of water level and flows (depth and flux) in response to a variety of forcings (DHI 2011a). The
depth and flux are resolved on a square or rectangular grid covering the area of interest. The
main inputs of the model are bathymetry, bed resistance, eddy viscosity, wind, water level
and/or discharge boundary conditions (Somes, Bishop & Wong 1999). MIKE 21 consists of
four fundamental sub-modules for environmental hydraulic simulations: hydrodynamics
(HD), advection-dispersion (AD), ecological process/water quality (ECO Lab) and Mud
Transport (MT). This study utilised the HD and AD module. The HD module uses the
conservation of mass and momentum equations integrated over the vertical to describe the
flow and water level variations in two horizontal dimensions (DHI 2011b). The equations are
solved by implicit finite difference techniques. The AD module solves the advection-
dispersion equation for dissolved or suspended substances in two dimensions. Required AD
information on horizontal velocities u and v and water depth h are provided by the HD
module (DHI 2011c).
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4.2.2 HD module setup
The bathymetry of the modelled area was formed on a 1 x 1 m grid. The simulation period
was set from 1 January 2004, 12:00 AM to 2 March 2004, 12:00 AM (5 days warm up plus 56
days of tracer simulation), which allows adequate time to cover the experimental tracer
recovery period. Each simulation time step for both flow and conservative transport
modelling was 30 s. The boundary of the pond was assigned a land value and a zero flux
condition was applied. Inflow and outflow were simulated using the source and sink pair
option. Two cells corresponding to the location of the inlet and outlet pipes were selected
and a constant flow rate applied. The applied flow rate for the model was equal to the
average measured flow rate (423 kL/day), obtained from Water Corporation, for the period
of the tracer studies. The velocity applied to the inflow was calculated using the known flow
rate and the depth of the cell representing the inflow such that
(7)
where A is the surface area of the exit side of the cell (Figure 10).
Figure 10: The source cell (inlet cell) velocity is determined by dividing the incoming flow rate by the area of
the face of the cell normal to the flow direction.
The direction of velocity was normal to the associated boundary. In this study, if the water
depth at a cell was less than 0.01 metre, the cell was considered to have become dry. Once
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the depth was greater than 0.1 metre, the cell was regarded as in a flooded condition
subject to flow.
The flow regime in these wastewater systems is generally regarded as laminar to sub-
laminar due to such low velocities. Turbulence generated by flow velocity is therefore not
considered as a dominant controlling factor in surface water flow and solute transport in
these systems (Min & Wise 2009). Since the depth of the pond is similar to the grid size,
shear effects are the ones responsible for the transfer of momentum and constituents (DHI
2011c). Therefore it was assumed that the impact of eddy viscosity on flow dynamics and
solute transport in the WSP is negligible. This assumption is also made in a similar study
involving simulating short-circuiting flow in a constructed wetland (Min & Wise 2009). The
initial surface elevation of water within the pond was set as a constant value of 0 m. For flow
resistance, expressed in the form of Manning’s roughness coefficient, a constant value of
n=0.02 was initially set. This value represents the base n value for a straight uniform channel
with sand grain size of 0.4mm with no irregularity, variation in channel cross section,
obstruction, vegetation or meandering (Arcement & Schneider 1989). Precipitation and
evaporation during the field tracer study was negligible compared with the flow through the
WSPs, circa 3% and 2% of total flow respectively (BOM 2012), and set to zero in the model.
According to average wind speed data (BOM 2012) monitored at Harvey approximately
20km away from the Brunswick WSP, daytime mean wind speed was between 2.5 and 3.9
ms-1 for the duration of the tracer experiment. The wind direction during this period varied
significantly with no predominant conditions. It was deemed appropriate to set the wind to
zero for all modelling simulations as there were no significant wind events that persisted for
more than 12-24 hours and there was no predominant wind direction. The results from HD
module simulation, such as flow velocity and water depth, are used as input data for the
tracer transport simulation in the AD module.
4.2.3 AD module setup
The simulation of tracer movement through the WSPs was carried out using the conservative
solute transport option of the MIKE 21 AD module. The initial concentration of tracer was set
to zero and a time series tracer concentration profile was created at the inlet source cell.
The dispersion coefficients are one of the most important parameters in the advection-
dispersion simulation (DHI 2011c). Dispersion is a general term referring to the scattering of
fluid particles. The term encompasses effects of random-type processes, such as molecular
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motion and turbulence as well as the effects of shear (DHI 2011c). The module requires a
maximum and minimum allowable dispersion coefficient. The selection of these coefficients
was based on the recommended estimate formulation of DHI:
D≈1.0 U h
(8)
where D is the dispersion coefficient (m2s-1), U is depth-integrated velocity of the flow (ms-1)
and h is the flow depth (m). An isotropic, non-dimensional proportionality factor of 0.6 was
for in this study. The proportionality factor was based on those used in a similar study that
cited the main reasons for this value selection was to avoid simulation instabilities of input
and output concentration profiles (Min & Wise 2009). Since local current velocity is not
constant with respect to time, the water velocity at a particular time step was used to
determine the depth-integrated velocity (U). U was determined using the velocities within
the pond at the time step associated with tracer introduction. Tracer was introduced to the
system after 5 days of the hydrodynamic module starting (12:00 AM 6 January 2004). Using
the local current velocity (x and y components) at the inlet cell, Equation (8) provided the
maximum dispersion coefficient for Dx and Dy of 0.0035 m2s-1. The initial minimum
dispersion value of 0.00003 m2s-1 used for calibration was based on the minimum velocity
within the pond determined by the hydrodynamic module.
4.3 Results and Discussion
4.3.1 Calibration of 2-D model
Calibration of the model utilised data collected from the Brunswick WSP located in the
southwest of WA. The Brunswick pond was chosen for calibration because of its flat, even
distribution of sludge (Figure 11). It was assumed the impact of the bottom profile of
Brunswick would have negligible impact on the hydrodynamics and therefore simplify the
calibration process.
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Figure 11: Profiled bathymetry of Brunswick WSP number 2 as at 18 August 2011. The bathymetry provides
an even and relatively empty pond that can be used for calibration. The location of the inlet and outlet are
shown in the figure. The colour represents height where red is shallow and blue is deep.
Sludge profiling of the second Brunswick WSP was performed using the sonar fitted ROV
technique in August 2011 (Coggins 2011). The profiling data from the sonar device contained
many replicated coordinates since the boat passes over the same location more than once.
Matlab® software was used to extract only one z coordinate for each x-y location. The sludge
height data was imported into MIKE Zero bathymetry editor and a pond boundary was
created around the sludge data. Interpolation converted the sludge height data to a
continuous (1 m x 1m resolution) 2-D sludge profile that can be used by MIKE 21 Flow
model. A tracer study in the Brunswick pond was also performed between August and
October 2011 (Coggins 2011). Data from the tracer study was used to calibrate the model
parameters.
The results from the calibration simulations were compared to those of the field study. The
field tracer study allowed the construction of an RTD. Statistical analysis of the RTD obtained
the experimental mean residence time and variance that were used for calibration. The
parameters adjusted during calibration were bed resistance in the form of a Manning’s n
number and the dispersion coefficients.
N
Inlet
Outlet
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Manning’s n
Figure 12: RTDs for manning's n sensitivity analysis. NOTE: Only the first week of the simulation is shown in
this figure. The mean residence time for each manning’s n RTD is shown in the legend.
Statistical analysis revealed that the effect of changing Manning’s n was minimal on the
mean residence time i.e. of the order ~ hours. Extremely high (n = 0.05) and low (n = 0.01)
bed resistance values (Arcement & Schnieder 1989) resulted in a similar mean residence
times, however the RTD curves indicate different internal behaviours (Figure 12).
Visual inspection of the RTDs provides an indication of the impacts of various bed resistance
values. Increasing bed resistance caused the time of initial tracer recovery to be slowed. The
peak of the response curve shifts towards zero as resistance decreases, indicating faster
movement through the pond. The width of the peak i.e. the dispersion, does the opposite,
increasing with greater resistance. This analysis indicates that the selection of Manning’s n
will influence the internal hydraulic behaviour but will not significantly alter the mean
residence time. Therefore, selection of the manning’s n value for calibration required a
balance between the timing of the initial response and the width of the initial peak by
comparing the shape of the field tracer RTD versus simulated RTD.
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Dispersion
Figure 13: RTDs for dispersion sensitivity analysis. Only the first 2 weeks of the simulation is shown in this
figure. The mean residence time (t mean) for each dispersion RTD is shown in the legend.
Despite the model requirement to input both maximum and minimum dispersion
coefficients, it is the minimum dispersion value that determines the impact of dispersion on
the model outputs. The initial minimum estimate of 3 x 10-5 m2s-1 was altered to determine
the sensitivity. Statistical analysis of the RTDs shows that small changes to the minimum
dispersion coefficient caused significant variation to the mean residence time (Figure 13).
Visual Inspection of the RTDs provided valuable insight into the changes caused by modifying
the dispersion coefficient. All the scenarios show the same pattern of initial response where
a large initial peak is followed by two smaller peaks. The timing of these peaks is also very
similar. The lowest dispersion value shows the lowest peak concentration, however the
residual concentration after the initial peaks remains consistent through most of the
simulation. Conversely, the highest dispersion scenario shows the largest peak
concentration, however the residual concentration drops relatively fast following the initial
peaks. This analysis shows that the dispersion has significant effect on the mean residence
time through the size of the initial peak and the shape of the RTD tail. Calibration of the
dispersion coefficient required a balance between matching the height of the initial peak
and matching the characteristics of the tail.
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Calibration Summary
The model was found to be relatively insensitive to changes in resistance when compared to
minor variation to the dispersion coefficients. The minimum dispersion coefficient was
adjusted until the RTD showed similar initial peak concentration and tail characteristics. The
manning’s n was then adjusted to best match the timing of initial concentration response
and width of the initial peak.
Manning’s n was therefore set at a value of 0.02 while dispersion coefficients were varied.
The best representation of the field tracer mean residence time and variance were produced
using a minimum allowable dispersion coefficient of 0.00035 m2s2 and a manning’s n of 0.02
(Figure 14). The maximum allowable dispersion was set to 0.001 m2s2 for all scenarios. Table
1 summarises the results of the model calibration
Table 1: Summary of the sensitivity analysis performed as part of the calibration process.
Parameter Sensitivity of model output to change in value Value selected
Bed resistance
(Manning’s n)
The time to the initial concentration readings at
the outlet ranged from 4 to 18 hrs with n = 0.01
to 0.05 respectively. Despite the significant range
of initial response times, the mean residence
time only ranged from 6.99 to 7.93 days for the
same range of n.
0.02
Minimum dispersion
coefficient
Dispersion coefficients ranging from 1x10-5 to
1x10-4 m2s-1 altered the mean residence time
from 11.36 to 3.24 days respectively. Lower
dispersion coefficients resulted in more
immediate mixing and therefore higher
concentrations in the tail.
3.5x10-5 m2s-1
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Figure 14: RTD comparison of the Brunswick tracer study with the calibrated model simulation of Brunswick.
The comparison of simulated and experimental RTDs suggests the model is capable of
reproducing experimental results. The experimental results produced a peak concentration
three hours after tracer introduction. This relatively fast response is indicative of direct
short-circuiting from inlet to outlet aided by surface currents, or the effect of wind on the
surface (Coggins 2011). The model simulation could not match such a quick response with
any combination of modelling parameters suggesting earlier short-circuiting assumptions are
correct. Despite the initial peak of the simulated RTD being slightly delayed the general
shape is well replicated. The tails are also very similar with the exception of a few anomalies
towards the end of the experimental tail. A summary of how the calibrated model outputs
compared to the field study are shown in Table 2.
Table 2: Comparison of field tracer study and calibrated simulation of tracer study
Field Study Calibrated Simulation
Nominal residence time (Tn, days) 8.22 8.22
Mean residence time (Tmean, days) 7.04 6.99
Variance (2, days2) 7.45 6.52
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Additionally, the calibrated model was able to replicate the phenomena observed during the
field tracer study. Field observations noted that the tracer followed a counter-clockwise path
around the pond and remained very close to the boundary (Figure 15).
Figure 15: Tracer movement immediately following introduction at the inlet. The tracer is moving south in a
counter-clockwise direction following the boundary closely.
The simulated tracer concentration obtained from the model output was imported to
Matlab® for visualisation purposes. The following series of images shows the progression of
tracer through the pond (Figure 16).
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Figure 16: Progression of the simulated tracer in the second Brunswick WSP. The snapshots are in 1 hour
increments ordered left to right, top to bottom, starting at 1 hour from introduction of tracer. The colour
represents the tracer concentration where red is high and blue is low. The pond layout and orientation are
shown in Figure 11.
1 hr 2 hr
3 hr 4 hr
5 hr
7 hr
6 hr
8 hr
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5. CHAPTER 2
5.1 Rationale
The calibrated model resulting from Aim 1 of this study was used to assess the impact of
sludge accumulation and distribution on the hydraulic performance of WSPs. The use of CFD
allows for a quick and reliable comparison of various bathymetry scenarios to be performed.
With all modelling parameters except bathymetry held constant, changes to the hydraulic
performance can be assumed to be a direct consequence of the bathymetry changes.
Quantifying these changes will help wastewater managers to better understand the
consequences of sludge build-up and uneven sludge distribution and to take such features
into account when determining the efficiency of these systems.
5.2 Methodology
5.2.1 Impact of Sludge Accumulation and Distribution
The impact of sludge accumulation and distribution on pond hydraulics was determined
through testing the response of a variety of hydraulic indicators to changing bathymetry. To
determine the impact that sludge build-up has on pond hydraulics, it was necessary to break
up the methodology into two categories, the impact of sludge accumulation and the impact
of sludge distribution. The rationale behind this is that two ponds with similar accumulation
(in terms of % of pond occupied by sludge) may have significantly different patterns of
distribution (Figure 17).
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Figure 17: Two ponds with similar accumulation (50% occupied by sludge) and significantly different
distributions -standard deviation of sludge height = 0.015 and 0.200 respectively. This figure highlights the
need to assess the impact of accumulation as well as distribution.
The testing and analyses of various bathymetric profiles were based on the Waroona pond.
The Waroona pond was chosen because it provided a realistic, uneven bathymetry that
could be used to understand the impact of sludge patterns on the hydrodynamic behaviour
of the pond (Figure 18). Additionally, using actual profiled accumulation over an arbitrary
accumulation pattern provides the study with more meaningful and realistic results.
50% occupied by sludge
50% occupied by sludge
Sludge height (m)
Sludge height (m)
st. dev. = 0.015
st. dev. = 0.200
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Figure 18: Profiled bathymetry of Waroona WSP number 1 as at 11 August 2011. Colour represents depth
where red is shallow and blue is deep. Note the channel that runs from inlet to outlet.
Sludge Accumulation
Traditional efficiency calculations are based solely on the effective volume of the pond. The
efficiency, e, is calculated using the effective volume of the pond, V, and the average flow-
rate, Q:
(9)
This calculation assumes a linear relationship between average sludge height and efficiency.
The aim of assessing the impact of sludge accumulation on pond hydraulics is to better
understand this relationship between sludge height and pond efficiency. To perform this
assessment, the original Waroona sludge height values were altered using Matlab® software
to represent the build-up of sludge over time (Figure 19). A sludge height factor (α) was
applied to the height measurements (Equation (10))
( )
(10)
Sludge height factors used in this study were α = 0.01, 0.25, 0.5, 0.75, 1.0, 1.2, 1.3 and 1.5.
e V
Q
Inlet
Outlet
N
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Figure 19: The simulated progression of sludge build-up over time. Ordered from left to right, top to bottom.
Note: the pond becomes more channelised. Colour is used to help depth visualisation where red is shallow
and blue is deep.
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Sludge Distribution
The aim of assessing the impact of sludge distribution on pond hydraulics is to determine
whether the presence of significant sludge height variation has more impact than sludge
with an even distribution. The ROV profiled bathymetry of the Waroona pond was modified
using Matlab® software by applying an exaggeration factor (β) to each sludge height
measurement (Figure 20). The exaggeration factor was applied to the difference between
the sludge height and mean sludge height for each height measurement:
( ) ( )
(11)
Exaggeration factors of β = 0.1, 0.3 and 0.5 - 1.5 were applied to the bathymetry.
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Figure 20: Varying sludge height distribution scenarios for the Waroona WSP. The channel becomes more
exaggerated with increasing beta values. Colour is used to help depth visualisation where red is shallow and
blue is deep.
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5.2.2 Data outputs and processing
All data processing, manipulation and visualisation were performed in Matlab® (Figure 21).
Hydraulic performance indicators were extracted from the MIKE output files as well as the
mean and standard deviation of sludge heights. The movement of tracer was visually
represented in a 2-D plane to provide an additional analysis tool.
Figure 21: A flow diagram highlighting the major data processing steps involved in the project.
5.2.3 Hydraulic Assessment
Treatment efficiency
The moment index, proposed by Wahl et al. (2010), is shown to provide the best correlation
to pollutant reduction (R2=0.94) compared to other common efficiency indices. The moment
index is therefore used for and referred to as the treatment efficiency indicator for the
purpose of this study.
Mixing and short-circuiting
Many hydraulic performance indices will focus on either mixing or short-circuiting process
(Teixeira & Siqueira 2008). Since both are required for a full understanding of the
hydrodynamic properties, one index for each process was used for analysis. The dispersion
index, σ2, and the 10% arrival time, t10, were used to determine the mixing and short-
circuiting properties of each scenario, respectively. The dispersion index is based on the
Extract hydraulic indicators from RTD
Extract mean and standard
deviation from sludge
heights
Visualise the tracer
movement in the pond
Manipulate sludge height
values for bathymetric
scenarios
MIKE 21
Output
Matlab
software
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variance of the RTD, therefore by definition, takes account of all phenomena that can cause
mixing (Teixeira & Siqueira 2008). The 10% arrival time is directly related to the advection of
the tracer front and is shown to be capable of detecting variations in the intensity of the
phenomenon (Teixeira & Siqueira 2008).
Residence Time Distribution shape
Visual interpretation of the RTD is an important analysis tool. The shape of the RTD provides
a qualitative assessment of the hydraulic performance.
Performance assessment
In order to gain a well-rounded insight into the change of hydraulic performance due to
changes in sludge patterns, a combination of hydraulic indices were used to make both
qualitative quantitative assessments of performance. Table 3 provides a list of the hydraulic
performance assessment tools used and a summary of each for quick reference.
Table 3: Assessment tools used in this study and the information they provide
Hydraulic Assessment
Tool
Summary of Assessment Tool
Moment index (MI)
The moment index is an overall hydraulic performance indicator that places a
higher inefficiency value on parcels that leave the pond more prematurely.
This index overcomes statistical problems associated with long distribution
tails. The moment index has shown strong correlation to pollutant treatment
thus, is used in this study to estimate treatment efficiency.
Dispersion index (2)
The dispersion index is commonly used mixing index because it has been
shown to be a good representation of the mixing process and shows high
statistical reproducibility.
10% arrival time (T10)
This index was chosen because of it shows a good correlation to the physical
phenomenon of short-circuiting. The index is also capable of detecting
variations of short-circuiting intensity and shows low statistical variability.
RTD shape
The shape of the RTD is a fundamental tool when it comes to assessment of
hydraulic performance. Despite this study aiming to quantify hydraulic
performance, understanding the causes behind any hydraulic change is
critical to the analysis. Examining the RTD shape provides a tool for
qualitative assessment of the hydraulic performance.
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5.3 Results and Discussion
5.3.1 Sludge Accumulation
Residence Time Distribution analysis
The RTD distributions for the sludge accumulation scenarios are shown in Figure 22. All
distributions have been normalised such that the y-axis is dimensionless and the area under
the curve is equal to one in accordance with E-curve RTDs (Section 5.2.2). The RTD analysis
results for each curve are provided in Table 4.
Figure 22: RTD comparison for sludge accumulation scenarios. Higher alpha represents greater accumulation.
Table 4: Sludge accumulation scenario results from RTD statistical analysis
Alpha 0.01 0.25 0.50 0.75 1.0 1.2 1.3 1.5
tnominal (days) 29.1 26.1 23.0 19.9 16.7 14.2 13.0 10.7
tmean (days) 12.6 11.7 11.5 13.2 12.4 15.4 16.1 20.1
σ2 (days2) 9.8 9.7 10.2 11.3 11.4 13.3 13.4 14.6
t10 (days) 3.8 3.8 3.2 2.7 2.1 2.2 2.8 3.4
Moment index 0.41 0.41 0.44 0.54 0.56 0.67 0.71 0.83
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Visual analysis of the RTD shows a complex relationship between accumulation and internal
hydraulic processes. The RTD peaks begin moving towards the origin as sludge accumulates,
however after α=1.0, the peaks begin to move away. The t10 index captures this
phenomenon well, showing exactly the same trend.
The tracer mass contained within the tail of the RTDs looks to follow a simple trend where
increased accumulation results in extended tails. An extended RTD tail indicates the
presence dead zones (Thackston, Shields & Schroeder; Torres at al. 1997). Dead zones are to
be expected, given more defined channelling of the pond associated with sludge
accumulation (Wahl et al. 2010). All scenarios prior to and including α=1.0 have a very
similar tail shape indicating similar amount of dead space. Following these scenarios, α=1.2
and α=1.5 show distinct jumps in tail concentrations indicating the presence or more dead
space.
Combining the observations of short-circuiting and dead zones provides some insight into
the internal behaviour of the system. The increase in the presence of dead zones after α=1.0
corresponds to the point at which short-circuiting reduces. This suggests that parcels of
water entering the system are being mixed into dead zones rather than moving preferential
to the outlet.
CHRIS MURPHY
48 | P a g e
Treatment efficiency
Figure 23:Treatment efficiency of each sludge accumulation scenario.
Figure 23 presents the results of two sets of efficiency data, the moment index and relative
moment index. The ‘Moment Index’ is the direct measure of the moment index derived from
the RTD. The ‘Relative Moment Index’ incorporates the decrease in pond nominal volume as
sludge accumulates, such that
( ) ( ) ( )
(12)
The idea of a ‘relative’ measure is necessary when comparing the impacts of accumulation
on treatment efficiency within the same pond. The moment index will indicate how efficient
the pond is, given the available volume. Two ponds may have the same treatment efficiency
but very different treatment capacities due to the difference in volume. The relative
moment index accounts for this and allows the direct comparison of results for the pond in
question.
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6
Sludge height factor (α)
Treatment Efficiency
Moment Index
Relative Moment Index
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The relative moment index indicates that the treatment efficiency is decreasing with
accumulation, as would be expected. Additionally, it may be implied that the presence of
more defined channels and ridges may lead to decreased effective volume and more
premature exiting of water parcels. However, results show that the rate of this efficiency
decrease seems to be attenuated by the amount accumulation rather being made
progressively worse. Analysing the unadjusted moment index provides some insight into this
attenuation. The moment index results indicate that as the pond accumulates sludge, the
manner in which the pond is using the available volume is providing increased treatment
efficiency. Remembering that the accumulation scenarios are not flat, even distributions but
rather based on the Waroona pond bathymetry indicates the distribution of sludge could be
a contributing factor for this attenuation of inefficiency. In these scenarios, as accumulation
increases, the standard deviation of sludge height values also increases. Further
investigation of the impact of sludge distribution on treatment efficiency can be found in the
results and discussion of Section 5.3.2.
CHRIS MURPHY
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Mixing and short-circuiting
Figure 24: (a) The amount of dispersion experienced in the WSP, derived from the variance of the RTD and
(b) the amount of short-circuiting calculated as the 10% arrival time.
Once again, the changing pond volume for each accumulation scenario necessitates a
‘relative’ measure of both mixing and short-circuiting. The relative measure allows for direct
comparison of results by factoring in the loss of volume as increases (Equation (12)).
Figure 24(a) shows a similar pattern to that shown for the moment index where the amount
of dispersion is increasing with increased accumulation. However, relative to the initial pond
volume scenario, dispersion is decreasing. It is likely the increasing accumulation and
distribution is leading to greater velocity gradients between the channel and non-channel
zones due to rapid sludge height variation. Increased velocity gradients result in greater
shear and therefore greater dispersion (DHI 2011c). This process is highlighted by the time
0
2
4
6
8
10
12
14
16
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6
2
(day
s2)
Sludge height factor (α)
Mixing DispersionRelative Dispersion
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6
S (tn/t10)
Sludge height factor (α)
Short-circuiting Short-circuitingRelative Short-circuiting
(a)
(b)
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51 | P a g e
series of spatial tracer distribution shown in Figure 25. The tracer can be seen to spread over
the entire pond very rapidly in the scenario with high dispersion.
With reference to Figure 25, it is important to note that the depth of the pond decreases
from (a) to (b) and that the snapshots in time represent tracer concentration rather than
mass. The (b) scenario shows a much more efficient use of the volume of the pond
compared to scenario (a).
Analysis of the relative short-circuiting i.e. taking account of the changing volume, indicates
the amount of short-circuiting increases with accumulation until a threshold value is
reached, after which short-circuiting begins decreasing. The effect of this hydraulic property
does not have much impact of the treatment efficiency. This leads to the conclusion that
treatment efficiency is dominated by the process of dispersion for this particular system.
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Figure 25: The impact of increasing dispersion, (a) to (b), is shown through the comparison of 2-D tracer
concentration at identical points in time. The colour represents the tracer concentration where red is high
and blue is low tracer concentration.
1 day
Alpha = 0.25 (low dispersion)
Alpha = 1.30 (high dispersion)
3 days 5 days
11 days 9 days 7 days
1 day
(b)
(a)
3 days 5 days
7 days 9 days 11 days
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5.3.2 Sludge Distribution
The RTD distributions for the sludge accumulation scenarios are shown in Figure 26. All
distributions have been normalised such that the y-axis is dimensionless and the area under
the curve is equal to one in accordance with E-curve RTDs (Section 5.2.2). The RTD analysis
results for each curve are provided in Table 5.
Figure 26: RTD comparison of the sludge distribution scenarios.
Table 5: RTD analysis results from the sludge distribution factor (β) scenarios.
Beta 0.1 0.3 0.5 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5
tnominal
(days) 17.2 17.1 17.0 16.9 16.8 16.8 16.7 16.7 16.7 16.6 16.6 16.5
tmean
(days) 10.9 11.8 12.3 12.6 13.1 13.2 12.4 12.8 13.7 14.5 15.1 15.6
σ2
(days2) 10.0 10.8 11.4 11.4 11.8 11.8 11.4 11.6 11.9 12.5 13.0 13.6
t10
(days) 2.1 2.4 2.2 2.0 2.0 2.0 2.1 2.2 2.3 2.3 2.2 2.2
Moment
index 0.51 0.54 0.55 0.56 0.58 0.58 0.56 0.57 0.60 0.62 0.63 0.62
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Visual analysis of the RTDs shows similar time to peak concentrations but varying width. The
width (dispersion) becomes larger with increasing β, suggesting that sludge distribution is
causing dispersion. The concentration contained in the tails is greater with increasing
distribution. This observation indicates that more exaggerated distribution results in more
dead space and is in agreement with the results of the accumulation scenarios (Section
5.3.1)
Treatment efficiency
Figure 27: Change in treatment efficiency given the same volume but varying degrees of sludge height
distribution. Higher β means greater distribution.
The moment index calculation showed a general trend of increasing with sludge height
distribution (Figure 27). Exaggeration of the peaks and troughs is therefore providing
improved treatment efficiency compared to more even distributions. This result is counter-
intuitive as the presence of more defined channels suggests preferential flow paths and
dead zones, both likely to reduce hydraulic efficiency (Lloyd, Vorkas & Guganesharajah
2003).However, these results support those found for the sludge accumulation scenarios
(Section 5.3.1) providing further validation of this phenomenon.
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6
MI
Exaggeration factor (β)
Moment Index
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Mixing and short-circuiting
Figure 28: Amount of mixing (a) and short-circuiting (b) for each sludge height distribution scenario.
As the sludge progresses from an almost flat distribution (β=0.1) to one with clearly defined
channels and ridges (β=1.5), the amount of dispersion increases while the amount of short-
circuiting fluctuates. Similar to the accumulation results, the relationship between dispersion
and exaggeration factor (β) mirrors the relationship between treatment efficiency and β. The
amount of short-circuiting peaks at β=0.3 and troughs at β=0.8 before levelling off around
β=1.4. The amount of short-circuiting appears to have little influence on the treatment
efficiency results. Again, the analysis of the hydraulic properties using these two indicators
shows dispersion to be the dominant property influencing treatment efficiency.
0.0
5.0
10.0
15.0
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6
Day
s^2
Exaggeration factor (β)
Degree of Dispersion
0.00
0.50
1.00
1.50
2.00
2.50
3.00
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6
T10
(d
ays)
Exaggeration factor (β)
Degree of Short-circuiting
(a)
(b)
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56 | P a g e
5.3.3 Performance summary
Sludge accumulation and the characteristics of its spatial distribution have a significant
impact on hydraulic performance of WSPs. The two major hydraulic processes occurring in
WSP systems, mixing and short-circuiting, were both effected by the changing bathymetric
scenarios used in this study. Mixing, as described by the dispersion index (2) was the
dominant process controlling treatment efficiency in these systems.
Increasing the distribution of sludge improves the treatment efficiency of the system. The
presence of more defined peaks and troughs is causing increased dispersion, likely due to
increased shear. The ability of shear to transport water parcels out of the main channels and
into the dead zones appears to be a mechanism contributing to improved treatment
efficiency. This process reduced the amount of short-circuiting and better utilised the
available pond volume.
It is important to note that the build-up of sludge still decreases the overall treatment
efficiency. This reduction in efficiency due to decreasing nominal volume (V/Q) is offset to
some extent by the increasing sludge distribution. Efficiency reduction as calculated using
nominal volume change is therefore quite different from that determined by the moment
index (Figure 29).
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57 | P a g e
Figure 29: The reduction in nominal efficiency (V/Q) compared to the relative treatment efficiency as
indicated by the moment index (Figure 23).
With current design and layout, the empty Waroona pond begins its life at an efficiency of
0.41, already less than half of what could be expected from the same available space.
Although the initial treatment efficiency is low, the results show a relatively slow decline in
efficiency with accumulating sludge. The final accumulation scenario (α=1.5) represents a
total pond volume reduction of 64% compared to initial (α=0.01) conditions. During the
same time, treatment efficiency only reduced by 22%. This indicates that the rate of
efficiency decline is almost 3 times slower than the rate calculated from nominal efficiency
(Figure 29).
These results highlight two key management issues. The first is the rate of reduction in
treatment efficiency. Although decreasing, it is at a much slower rate than what is predicted.
Therefore, the operating life of these systems may be longer than previously expected.
Secondly, the initial treatment efficiency (0.41) is nowhere near the expected or nominal
value of 1. The severe initial underperformance shown by these results demonstrates the
need to reconsider pond design and layout in order to achieve maximum initial
performance.
y = -0.4253x + 0.999
y = -0.0626x + 0.3955 R² = 0.8781
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6
Sludge height factor (α)
Efficiency Comparison
Nominal volume efficiency (V/Q)
Relative treatment efficiency
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58 | P a g e
The Waroona pond began operating in the 1980’s and has not yet been desludged (pers.
Comm Chua 2012). If we assume the pond has taken 25 years to reach current levels (α=1),
we can make quantitative predictions of treatment efficiency based on accumulation rates.
The analysis above indicates that postponing desludging for a further 12.5 years (until α=1.5
is reached) will result in a 5% decrease in effluent quality relative to current treatment. The
difference between desludging now and desludging in 12.5 years would seem to have little
impact on treatment efficiency of this system. However, the environmental cost of a slight
decrease in treatment could be significant. Placing a monetary value on treatment quality
would be required to appropriately asses the cost-benefit relationship associated with the
timing of desludging.
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Additionally, the standard deviation of individual sludge heights within the pond was
calculated for all scenarios of both accumulation and distribution. The aim of this calculation
was to establish whether the standard deviation of sludge height could be used as an
indicator for treatment efficiency. Such an indicator would eliminate the reliance on CFD and
could be derived directly from the ROV data. Figure 30 presents the relationship between
sludge height standard deviation and treatment efficiency.
Figure 30: Relationship between sludge height standard deviation and treatment efficiency for accumulation
and distribution scenarios.
The two scenario sets, accumulation and distribution, show a similar relationship between
sludge height standard deviation and treatment efficiency. The mean sludge height changes
dramatically with accumulation while the distribution scenarios maintain a consistent mean
sludge height. Thus, the slight difference in relationship identified in Figure 30 suggests that,
despite showing some indication, sludge height standard deviation is not sufficient to
completely describe treatment efficiency. CFD modelling therefore remains the best method
for estimating treatment efficiency.
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0 0.05 0.1 0.15 0.2
Mo
men
t in
dex
Sludge height standard deviation
Treatment efficiency and standard deviation relationship
Accumulation scenarios
Distribution scenarios
Chris Murphy
60 | P a g e
6. CONCLUSIONS & RECOMMENDATIONS
6.1 General conclusions
The conclusions below are presented in a format designed to addresses the specific aims of
this study.
Aim 1 - Develop and calibrate a 2-Dimensional computer model capable of accurately
reproducing tracer response curves for waste stabilisation ponds.
The calibrated CFD model proved to be capable of accurately reproducing the tracer
response of WSPs. The use of 2-Dimensional computer modelling allowed fast, inexpensive
analysis of the hydraulic performance of multiple bathymetric profiles. Combining the sonar
equipped ROV technology with CFD, as this study has done, delivers advancement to the
sludge management process.
Aim 2 - Quantify the impact of sludge accumulation and distribution on the hydraulic
performance of waste stabilisation ponds.
CFD generated tracer response curves for a range of bathymetric scenarios provided some
insight into the impacts of sludge accumulation and distribution hydraulic performance. The
reduction in pond efficiency due to sludge accumulation is dependent on the evenness of
distribution. Treatment efficiency is shown to improve with the increasing sludge
distribution. Efficiency calculations based on the reduction in nominal volume fail to account
for the effects of this sludge distribution. In the early stages of WSP operation, nominal
efficiency calculations are likely to overestimate the treatment efficiency. Following a period
of sufficient sludge build-up and distribution these same calculations (V/Q) are likely to
underestimate the treatment efficiency.
Wastewater managers must consider the impact of sludge accumulation and distribution
when making sludge management decisions. Assessment should always include available
volume, however considering sludge height distribution instead of mean sludge height
provides a more accurate indication of treatment efficiency. Improving new pond design and
retrofitting existing of ponds aimed at increasing the initial treatment efficiency may
significantly extend the life of these systems.
Chris Murphy
61 | P a g e
6.2 Recommendations
The conclusions of this study have led to some key recommendations as well as providing
motivation for areas of further study. The key recommendations of this study are:
1. Combine the use of the already developed sonar ROV with the CFD framework
presented in this study to assess the condition and performance of WSPs - Given an
estimated accumulation rate, the predictive capabilities of CFD could be used to
project efficiency decrease. Wastewater managers could generate a database of
pond profiles including current efficiency estimates and projected efficiency
decrease. Essentially providing a desludging prioritising and management tool.
2. Use the moment index to determine treatment efficiency. This hydraulic index shows
good correlation to pollutant reduction. With the aid of CFD, moment index
estimates can be derived quickly from the resulting RTD response of the pond.
3. Consider the impact of sludge height standard deviation rather than mean sludge
height when estimating the treatment efficiency. This recommendation is only
relevant when not using computer modelling to determine efficiency. Traditional
estimates of efficiency (V/Q) do not reflect the impacts of sludge unevenness.
4. Undertake design and layout modifications to WSPs to increase the initial treatment
efficiency. Current designs were shown to be highly inefficient even when the pond
was completely void of sludge. The CFD framework of this study should be used to
assess the treatment efficiency of the proposed modifications.
Industry involvement is fundamental to the advancement of study in this area. Access to
wastewater facilities, support of staff and sharing of knowledge and ideas is essential for
studies such as these to proceed.
Chris Murphy
62 | P a g e
Following on from the recommendations, potential further study recommendations are:
1. Further assess CFD and the impacts of sludge accumulation and distribution using a
variety of pond distribution patterns. Distribution pattern vary between ponds, a
variety of different distributions will allow for more accurate description of the
impacts. Two specific objectives of this recommended study could be:
Further calibrate and validate the existing 2-D model with the aid of field
tracer studies. Field tracer RTDs can be compared to simulated RTDs similar to
the methodology of this study.
Examining the impact of accumulation and distribution on a variety of ponds
to determine whether the relationships established by this study are globally
true.
2. Use the computational model and analysis tools presented in this study to assess
pond design. A combination of configurations could be tested for their impact on
treatment efficiency. These include:
Inlet / outlet position and orientation
Pond shape
Baffle systems
The aim of this recommended study would be to optimise the initial treatment
efficiency of these systems.
The involvement of Water Corporation has been fundamental to the investigation of this
study topic to date. The conclusions and recommendations of this study highlight the
importance of continued industry involvement. Wastewater managers such as Water
Corporation have the potential to see significant financial benefit through adopting the
recommendations of this study and supporting future studies.
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