Tests performed on a SIRO-Pond system for improved efficiency in Bio Diesel production.

1. Raceway Bio-diesel
Effect of inserts on raceway pond efficiency
Supervisor: Dr. Richard Manasseh
Roshane Nanayakkara 7022395
Cecil Tiew Siew Hsi 4208492
Pan Sze Chung 4207416

2. Acknowledgement
First and foremost, the authors would like to take this opportunity to express our appreciation
to our supervisor of this project, Dr Richard Manasseh for his guidance. Without guidance
from him, the completion of this report would not be possible. Special thanks are given to the
CSIRO for providing the laboratory facilities for us to complete the research. We deeply
appreciate the assistance from Dr. Kurt Liffman and Dr. Peter Liovic for providing the details
and tasks for our research.
Other than that, the authors would like to express their gratitude to Dr. David Paterson who
already come back to assist and provide significant information for us in the research after
retiring. The knowledge and experience from Mr. Paterson helped a lot in the collection of
our data.
Lastly, we would like to thank Mr. Glen Bradbury as a person in charge for us at the CSIRO.
Mr. Bradbury was always been there to provide great assistance when we faced problems
with our project. His humorous and good personality has greatly reduced our stress when we
faced difficulties.
i

3. Declaration
We hereby declare that the project work entitled ―Effect of inserts on raceway pond
efficiency‖ submitted to the Swinburne University Technology, is a record of an original work
done by us under the guidance of Dr. Richard Manasseh, Senior Lecturer, Faculty of
Engineering and Industrial Sciences from Swinburne University of Technology, and this
project work has not performed the basis for the award of any Degree or diploma/associate
ship/fellowship and similar project if any, except where due reference is made in the text of
the report.
Hence, to the best of the candidate’s knowledge contains no material previously published or
written by another person except where due reference is made in the text of the report.
ii

4. Contents
Acknowledgement ................................................................................................................................. i
Declaration .............................................................................................................................................. ii
1.0 Abstract ........................................................................................................................................... 1
2.0 Introduction ..................................................................................................................................... 1
3.0 Literature Review ........................................................................................................................... 2
3.1 Bio-diesel .................................................................................................................................... 2
3.2 Micro algae ................................................................................................................................. 3
3.3 Effect of Light ............................................................................................................................. 4
3.4 Types of Algae Growth Systems ............................................................................................. 6
3.5 Photo Bioreactors ...................................................................................................................... 6
3.6 Raceway Ponds ......................................................................................................................... 7
3.7 Effect of Temperature ............................................................................................................... 7
3.8 Effect of Carbon Dioxide (CO2) On Algae .............................................................................. 7
3.9 Dimensions ................................................................................................................................. 8
3.10 Turbulence................................................................................................................................ 8
3.11 Velocity/Flow ............................................................................................................................ 8
3.12 Paddlewheels ........................................................................................................................... 9
3.13 Depth/PFD .............................................................................................................................. 10
3.14 Power ...................................................................................................................................... 10
3.15 Use of Inserts ......................................................................................................................... 10
3.16 Experimental Raceway Pond .............................................................................................. 11
4.0 Methodology ................................................................................................................................. 12
4.1 Experiments.............................................................................................................................. 12
4.2 Calculation of Time Required For Algae to Be In Darkness ............................................. 13
4.3 Reynolds number calculation................................................................................................. 15
5.0 Risk Assessment ......................................................................................................................... 16
5.1 Justifications ............................................................................................................................. 16
6.0 Apparatus and Experimental Procedure .............................................................................. 17
6.1 Apparatus:............................................................................................................................. 17
6.2 Procedure ................................................................................................................................. 18
7.0 Results and Discussion .............................................................................................................. 19
7.1 Practical Results ...................................................................................................................... 19
7.1.1 Comparison of Raceway with Insert and Raceway without Insert. ........................... 22
iii

5. 7.2 Computational Fluid Dynamics Results for 80mm water height....................................... 23
7.3 Comparison on CFD and Practical results .......................................................................... 24
7.4 Observations ............................................................................................................................ 25
7.4.1 Practical Experiment ........................................................................................................ 25
7.4.2 CFD .................................................................................................................................... 26
7.5 Discussion................................................................................................................................. 26
7.5.1 Areas of Possible Errors.................................................................................................. 27
8.0 Conclusion .................................................................................................................................... 28
8.1 Recommendations for further work ....................................................................................... 28
9.0 References ................................................................................................................................... 29
Appendix A – Experimental Results ...................................................................................................... 33
Appendix B – Readings taken for statistical Analysis ............................................................................ 49
Appendix C - Temperatures Data .......................................................................................................... 51
Appendix D – Raceway Coordinate system .......................................................................................... 52
Appendix E – Statistical Analysis Data .................................................................................................. 53
Statistical analysis for variation of velocity at 80mm water height at 50% depth (40mm) ............. 53
Statistical analysis for variation of velocity for different depths at 120mm water height ............... 54
iv

6. 1.0 Abstract
The production of bio-fuel using micro-algae potentially leaves a lower carbon footprint.
Open ponds in the shape of a raceway have been used to cultivate fresh water algae such
as Chlorella for a number of years. For maximum production of lipids through photosynthesis
the algae require exposure to CO2 and sunlight. Algae productivity has been found to
increase when exposed to flickering light where the light/dark cycle is around 8.5/4.4
seconds. To produce this flickering effect, eddy’s are formed through turbulence created by
a paddle wheel that also keeps flow at around 20 cm/s. Depth is maintained at around 30
cm. A raceway pond with modifications to reduce energy consumption and variation in
velocity has been provided by the Commonwealth Scientific and Industrial Research
Organisation (CSIRO). Computational Fluid Dynamics (CFD) simulations on these modified
raceways have already shown a potential decrease in energy consumption, which could
lower the capital cost and operating expenses of the raceway pond. The task of the research
team is to practically confirm these results while also introducing their own improvements. To
this extent, the research team has generated a model to predict the effect of turbulence
intensity of the water channel in relation to flickering light which is further discussed in
section 4.0 (Methodology).
2.0 Introduction
In a world where people are facing the effects of global warming and climatic change, the
need for greener, cleaner fuels to power our cars, ships and factories has never been
greater. This has provided a strong incentive for developing alternatives to fossil fuels and
has led researchers along different paths to produce bio fuels that leave a smaller carbon
footprint on the environment. While some researchers are still working on producing
completely new energy sources, some others are working on producing these alternative
fuels in more efficient and cost effective ways.
This paper does a study on the production of bio fuel by using micro algae in a setup called
a raceway pond which is a relatively old technique dating about 30 years. We were provided
a raceway pond by the CSIRO and we have conducted experiments on their model which
has certain modifications done to it. The pond through CFD shows a reduction in energy
consumption. The modifications reduce friction when the water takes sharp bends and also
aims to reduce large fluctuations in velocity. Our task was to practically test and confirm
these computer derived results while also looking at other ways we can improve the raceway
pond. A statistical inference was performed from a range of practical figures that were
1

7. compiled after running tests on the CSIRO’s pond. This would provide the CSIRO with an
estimate of where their CFD model results should fall into.
The authors have done an exhaustive search on the physical conditions required to get the
maximum productivity from micro algae, particularly the chlorella species.
There are certain physical attributes that are accepted as industry standard and varying
them produce little or no effect on bio mass production. In this context the depth of raceway
ponds are usually kept in the range of 300mm and flow rates in the range of 15-20 cm/s. We
also found that micro algae produces more bio mass when exposed to flickering light as
opposed to constant illumination and that depending on the algae species, the required
light/darkness ratio varies.
In a raceway pond, turbulence has to be provided to achieve this flicker effect of light and
also mix the algae. Reynolds numbers above 20000 are used in practice when designing
raceway ponds. The device that provides this turbulence and also velocity to the water is a
paddlewheel.
3.0 Literature Review
A clean, renewable source of energy that is easily affordable is of major importance for the
survival of society in this day and age where the effects of global warming are being
experienced on a daily basis. Although solar energy and wind energy have been utilized for
producing electricity and are zero emission energy sources, we still need a clean energy
source that can power our engines and turbines. In this context bio-diesel is a very green
source of fuel where the net output of in its life cycle is quite low. Since the 1980’s, bio
diesel plants have opened in many countries and some cities have run buses on bio diesels
(Demirbas 2010).
3.1 Bio-diesel
Bio-diesel can be made from any oil/lipid source; the major components of these sources are
tricylglycerol molecules (Wen et al. 2009). Pure vegetable oil (virgin oils), animal fats (yellow
grease), and waste cooking oils are the main sources of oil for bio diesel presently (Wen et
al. 2009). The most familiar of virgin oils are soybean oil, rapeseed oil, mustard seed oil and
algae oil. These virgin oils have been greatly utilized during the past few years in big nations
like USA and Europe to help preserve natural resources. More and more investments are
being made into algae oil compared to other food crops because the yields of oil and fuels
from algae are much higher (10-100times) compared to competing energy crops. The annual
productivity and oil content of algae are far greater than seed crops (Campbell M 2008).
2

8. Moreover, algae can be grown practically anywhere, whereas food crops depend more on
land and labour costs in mass production. Algae are the only feedstock that has the potential
to completely replace the world’s consumption of transportation fuels compare to other
alternatives.
Table 1: Comparison of various sources of Bio Diesel (Scott S 2010)
3.2 Micro algae
Micro algae are sunlight-driven cell factories that convert carbon dioxide into potential bio-
fuels, food feeds and high-value bioactive (Chisti 2007). Studies have shown that algae can
grow in fresh drinking water, saline or brackish water, and even waste water effluent
(Kunjapur et al. 2010). Strains of micro algae are generally divided into two categories based
on whether they grow optimally in freshwater or saltwater. The level of salinity influences the
overall productivity, as well as individual production rates of lipids and carbohydrates in each
strain of algae (Kunjapur et al. 2010).
Using waste water for this application provides two significant benefits: the algae receive an
inexpensive medium rich in required nutrients and the waste water is further treated in the
process (Kunjapur et al. 2010). Not all strains of micro algae can grow in open ponds due
exposure to atmospheric air that contains contaminants. Dunaliella, Spirulina, and Chlorella
strains grow in environments exceptionally high in salinity, alkalinity, and nutrients
respectively (Lee Y K 2001). For example, the cyanobacterium, Spirulina Platensis, grows
best in highly alkaline media with a pH of up to 10; Dunaliella Salina is the most salt-tolerant
eukaryotic alga known, and produces its maximum intracellular concentrations of
commercially valuable β-carotene at salinities up to ten-fold greater than seawater and fast-
growing Chlorella species (Huntley et al. 2007).
Chemical composition of Chlorella could be dramatically altered by cultivation conditions,
from 8.7% protein and 86% lipid (oil) to 58% protein and 4.5% lipid (Huntley et al. 2006).
Algae are excellent bioremediation agents because of their ability to absorb massive
amounts of . Hence, it is reported that Chlorella sp. can be grown under 40% CO2
3

9. conditions and has commercial value (Huntley et al. 2006). The percentage of oil content
based on dry weight of Chlorella sp. is around 28-32% (Campbell 2008).
3.3 Effect of Light
Sunlight is the ultimate energy source of micro algae. Although the wavelength range of
solar radiation is very broad, only radiation of the range between 400 and 700 nm can be
used by micro algae (Janssen 2002). It has been found that when Algae are subjected to
certain light/dark cycles where the light period is characterized by a light gradient, that these
light/dark cycles will give higher productivity and biomass yield compared to algae exposed
to constant light. (Barbosa 2003) Light/dark cycles are associated with two basic
parameters: first, the light fraction, i.e., the ratio between the light period and the cycle time
and second, the frequency of the light/dark cycle. (Barbosa M 2003)
The overall biomass yield (specific growth rate over specific light absorption rate) can vary
over different light regimes. In experiments that have been carried out (Janssen 2002), it
was found that the overall biomass yield of Chlorella reinhardtii under an 8.5/4.4s light/dark
cycle was considerably lower than the yield under continuous illumination. As a result, the
dark period will have to be shorter than 4.4s for optimal light utilization efficiency (Janssen
2002). The saturating light intensity of Chlorella sp. is approximately 200 mol/sec/m2
(Janssen 2002). Micro algae often exhibit photo inhibition under excess light conditions.
(Janssen 2002) Photo inhibition is often suspected as the major cause of reducing algal
productivity (Janssen 2002).
The graph (Fig 1) obtained from the research done by Marcel Janssen shows how the
Photon Flux Density (PFD) varies with increasing depth. For raceway ponds, sunlight
impinges on the surface and is absorbed inside the culture; the photon flux density will
decrease with increasing depth (Janssen 2002).
4

10. Figure 1 – Variation of PFD with depth
Further the formulation of the correction due to non-optimal illumination is derived from
Steele’s equation
where I is the instantaneous illumination rate (W/m2) and Is is the optimal illumination rate
(W/m2). Algal production increases as a function of light intensity until an optimal intensity is
reached, and beyond that optimal value, production varies in accordance with the type of
light source. That is, algal growth curves under condition of continuous light and intermittent
light (typically 14 hours of light, 10 hours of dark) are unique and species dependent. Subject
to intermittent light, growth rates approach a constant value, which is a function of the
intermittency of the light, as the light intensity increases. (James et al. 2010) Below is a
graph that shows the variation of growth with respect to the light intensity.
5

11. Figure 2 – Variation of algae growth with respect to light intensity (James et al. 2010)
3.4 Types of Algae Growth Systems
Suspend-based open pond raceways and enclosed photo bioreactors are the two main
methods used for algal-bio-fuel production presently (Wen et al. 2009). Although photo
bioreactors (PBR) boast of higher efficiencies and smaller sizes compared to open ponds,
the higher cost of production of a photo bioreactor is the reason that open ponds are
considered as an option. There have been many attempts at using PBR’s but have failed.
Such examples can be found in Germany, Spain, China and Israel (Beneman 2008)
A major problem with open ponds is the presence of competition and predation, as it is very
difficult to maintain a monoculture of one desired strain of algae in an outdoor open
environment. (Kunjapur et al. 2010) Although it is not so much of a problem once a higher
algal density is obtained. Loss of water is considered another drawback of open pond
systems as algae concentrations can change and thus affect productivity. (Schek et al.
2008) However is must be note that loss of water will assist in increasing the concentration
of algae.
A comparison of the pond systems shows that open ponds cost $76,000 per hectare while
the raceway pond that is a special type of open pond that costs $161,000 and the closed
bioreactors cost $348,000. (Johnson et al. 1988)
Table 2: Comparison of Biomass production systems (Brennan et al 2009)
3.5 Photo Bioreactors
The three main categories most generally suitable for large-scale cultivation are
tubular/horizontal, column/vertical, and flat plate or flat panel (FP) reactors (Sierra et al.
2008). In terms of energy, closed photo bioreactors typically require energy for mixing (e.g.
6

12. pumping, or energy used to compress gas for sparing), and have much embodied energy in
the materials of construction, although this might be offset by the higher productivity of
closed systems (Scott 2010).
A photo bioreactor is a more modern design for growing algae in which the growth area is
entirely enclosed. Nutrients are added, cooling is done actively, more elaborate pumping
mechanisms are used, and active removal of waste by products is accomplished. By using a
closed system, PBRs are able to use a mono-culture of algae which allows for higher-lipid
content strains to be selectively grown. Photo bioreactors are also typically able to have a
higher concentration of algae, at approximately 28 times the biomass concentration in the
broth. This increased concentration allows for more efficient extraction from solution (Neltner
et al. 2008).
3.6 Raceway Ponds
Raceway systems are plastic lined, shallow (30 cm deep) ponds, which allow good control
over conditions (such as supply). They use paddle wheels to mix the algae. Individual
growth ponds are up to about 0.5 ha in size, although larger sizes are feasible (Beneman et
al. 1993).
Our research project is focused on the open system of algae cultivation, mainly the open
raceway pond system. This will be further discussed in the section 3.16 (Experimental
Raceway Pond).
3.7 Effect of Temperature
Microalgae grown in raceway ponds indicate sensitivity to low early morning temperatures.
This is known to be a common problem in outdoor micro algal cultures and has been
attributed to increased photo inhibition at sub-optimal low morning temperatures. (Richmond
et al 1980) A 10 to 15 increase in the morning culture medium temperature results in a
significant increase in the yield of Chlorella grown in outdoor raceway ponds. Results also
indicate that a 4 increase in the culture temperature in the morning can significantly
increase the daily biomass production, as the optimum growth temperature of this strain is
between 23 and 28 . These results suggest that this increase in productivity is mainly due
to higher photosynthetic rates at the higher temperatures while the pond oxygen is low,
rather than reduced photo inhibition (Moheimani et al. 2006).
3.8 Effect of Carbon Dioxide (CO2) On Algae
To produce lipids the micro algae must be exposed to gas. It has been experimentally
shown that Chlorella cells exposed to high partial pressures (p ) experienced
7

13. declined growth rates. can be supplied via diffusion through a gas permeable
membrane in order to provide sufficient to the entire culture. This also prevents
inhibition at high gaseous p concentrations from 1% to 5% (by volume) often leading to
maximum growth (Lee et al. 1991).
3.9 Dimensions
Oblong raceways are in the range of 10-300m in length and 1-20m in width (Ben-Amotz A).
The areas of these raceways vary from about 300- 4000m2 (Ben-Amotz A). In some sites,
there are raceways that have been constructed parallel to each other. Space between
raceways is assumed to be 3 meters, and space for the burn in the centre of the raceway is
assumed to be 2 meters (Richardson et al. 2010).
3.10 Turbulence
In practice, for the algae to experience light/dark cycles then turbulence must be provided so
that the algae are not allowed to stagnate. Local motion has important consequences to
micro algae cells. If it is too large, viscous stresses may mechanically damage the cells or
otherwise interfere with growth processes. If it is too small, vital mass transfer of nutrients
and wastes may be impeded (Thomas et al. 1990). Large-scale turbulence is important in
algal culturing in as it can intermittently mix cells in dense cultures into lighted zones for
maximum photosynthesis and growth (Thomas et al. 1990). The effects of turbulence on
organisms such as micro algae smaller than the Kolmogorov inertial-viscous length scale
depend on the stress, , where is the dynamic viscosity, p is the
density, and the rate-of-strain (Thomas et al. 1990).
Turbulent flow of the carrier fluid helps to increase the mixing of algae from different depths
(Paterson et al. 2010). Turbulent flow is characterized by viscous dissipation rates e and
kinematic viscosity of fluid v. Due to this (depending on the algae growing) there needs to be
some form of turbulent flow around the raceway ponds to obtain exposure to the light source
intermittently for the algae, and in order to achieve this effect paddle wheels are used. (Lew
2010)
In practical cases, utilization of high-intensity light would be enhanced only by inducing
turbulent streaming in culture suspension (Ketheesan et al. 2011).
3.11 Velocity/Flow
A range of velocities have been recommended by various researchers over the years in
cultivating algae. Tredici (2004) states that, in practice, mixing velocities of 30–35cm sec-1
8

14. should not be exceeded as power inputs for mixing increases as a cube function of velocity.
Stephenson (2010) also states that a mean liquid velocity of 0.3 m/s should be used in
raceway ponds for algae cultivation (Benemann 1994). Mixing also prevents settling of cells
and avoids thermal and oxygen stratification in the pond. A velocity of 5 cm/s is sufficient to
avoid that (Andersen et al. 2005). But due to frictional losses in the channel and corners, 20-
30 cm/s is used in practice (Andersen et al. 2005). Manning’s equation for open channel
velocity can be used to calculate the velocity of the flow and also calculate power required to
maintain flow (Ketheesan et al. 2011).
3.12 Paddlewheels
Paddle wheels have emerged as the preferred method for mixing high-rate ponds for the
following reasons:(1) they are high volume, low head devices (i.e. high specific speed); (2)
Their gentle mixing action minimizes damage to colonial or flocculated algae, which
improves harvest ability; (3) They are mechanically simple, requiring a minimum of
maintenance; (4) Their drive train can easily be designed to accommodate a wide range of
speeds (high turn-down ratio) without drastic changes in efficiency; (5) They do not require
intake sump, but simply a shallow depression for maximum efficiency (Welssman 1987).
People have been using paddle wheels for a number of years for the purpose of mixing the
algae and creating flow in the raceway pond. Presently paddle wheels are considered the
most efficient and cost effective method of mixing the algae. A paddle wheel is a liquid flow
creator in which a number of scoops are set around the periphery of the wheel. The can
easily create flow velocities of 20-30 cm/s.
The dimensions and specifications for paddle wheels in open raceway ponds may vary, but
for large scale operations, diameters of 1500mm have been recommended. The use of a
sump of around 100 mm depth for higher efficiency is good. The shaft and spokes should be
created from mild steel pipe and the blades from 2mm mild steel plate and also crimped to
provide greater resistance to bending (Andersen R et al. 2005). Alternatives for the paddle
wheel have been considered in the past for better power consumption and efficiency.
Impellers create similar flow rates to paddle wheels and are more energy efficient, especially
at lower pond depths. However they create dead zones of flow and a vortex that is not
suitable for the algae (Lew 2010). Airlift driven raceway reactors have also been thought
about to replace paddle wheels. Although not practically used in large-scale ponds yet
theoretically they seem to be over 80% more efficient than paddle wheels for similar flow
velocities (Ketheesan et al. 2011).
9

15. 3.13 Depth/PFD
In practice the depths usually used are in the range of 300mm (0.3m). The reason higher
depths are not used is because light intensity/photon flux density decreases with increasing
depth. According to experiments recorded by Janssen (2002) (Fig 1) for a 300mm pond with
a photon flux density (PFD) of 1000 micro mole per m2/s, the PFD gets to nearly 0 at a
300mm depth. (Janssen 2002) Borowitzca (in Andersen 2005) mentions an equation derived
by Oswald that links pond depth with Algae concentration. d = 6000/C (Andersen et al.
2005). Field observations have also shown that continually mixed cultures allow light
penetration to about ⅔ its depth (Borowitzca in Andersen R et al. 2005). Using higher
depths means using more energy to create flow as well. Therefore using a depth of 300mm
is considered an industry standard. (Andersen R et al 2005)
Figure 3 – Variation of flow velocity and area of pond with depth. (Borowitzca M in Andersen
R 2005)
3.14 Power
The hydraulic power required for maintaining flow can be used as a measuring tool to check
the efficiency of the pond when we add improvements to the design. The electrical power
used by the motor powering the paddlewheel will also give us an indication of the efficiency
of the system.
3.15 Use of Inserts
The use of inserts in raceway ponds is something that has been implemented very recently
and is still in an initial research stage. The main objective of using inserts in raceway ponds
is to deflect the flow to the outer edge of the bend before the start of the bend, in order to
maximize the turning circle and so minimize centrifugal effects (Paterson et al. 2010). This
10

16. would also decrease the power consumption of the paddlewheel as it can operate at a lower
speed and still maintain the same mean velocity around the raceway pond.
Inserts used in a raceway pond can have a multitude of shapes and sizes. Some examples
are islands and asymmetric inserts. The figures below are some examples of how this
inserts look like when implemented in an algae raceway pond.
Figure 5 (Ben-Amotz C)
Figure 4 (Ben-Amotz B)
3.16 Experimental Raceway Pond
The modified raceway pond under consideration is one that has been modified by the
CSIRO’s own staff. The raceway pond built by the CSIRO is designed in the shape of an
automotive raceway circuit, where the water moves around in a rectangular and shallow
pond. This artificial pond used in the cultivation of algae is lined with plastic. There are
presently two identical ponds built side by side at the CSIRO site. This enables us to use
one as a control during experiments. Each pond contains a paddle wheel to provide motive
force and keeps the algae circulating around the pond. It has changes made to reduce loss
of kinetic energy around hairpin bends and also to keep the channel cross-sectional area
perpendicular to the flow direction constant in order to keep the flow speed uniform
(Paterson et al. 2010). These include fixing semi circular islands/inserts and also varying the
depth near the hairpin bends. Presently the tests performed on the design have been done
through CFD (Computational Fluid Dynamics) which have given favourable results. The task
of our team is to practically see if the results agree with the theoretical ones. What should be
noted here is that in the computer simulation the length and width of the pond has been
given as 96m x 5m, which is much larger than the dimensions of the test pond. The test
pond is approximately 1.5m long and 1m wide.
11

17. 4.0 Methodology
4.1 Experiments
The parameters our team has observed are:
 Flow rate:
 At 15 different location around the insert in the raceway pond
 At 3 different depths in the raceway pond
 At 70mm to 120mm water depth of the raceway pond at 10mm intervals
 The motor speed controller was varied from 1.5Hz – 3.5 Hz which were increased
in 0.5 Hz intervals.( These are not directly the paddle wheel speeds but increases
the current frequency)
 Testing the flow rate in identical ponds with and without the addition of inserts.
The diagram (Figure 6) below shows the approximate positions of the points numbered 1-15.
The flow rate of the water was estimated initially by using sponges. The time it took for
the sponges to travel one lap around the pond was measured using a stopwatch. This is
called particle tracking velocimetry. Using this technique we were able to determine the
flowpath of the sponge which corresponded to the highest velocity. The path of the sponge
where the flow was highest was observed as depicted by the red line in (Figure 11)
Figure 6 - Top View of Raceway
12

18. Figure 7 – Side View of Raceway Pond
Figure 8 – Water Level Height/Probe Depth Variation
The intention of the team was to practically prove that the CFD results can be obtained in
real life and to determine if the changes made to the standard raceway pond can help
reduce energy consumption. This would help reinforce the idea that producing bio diesel via
micro algae may become cost effective and viable method.
4.2 Calculation of Time Required For Algae to Be In Darkness
The distribution of turbulence intensity in a channel is an indicator of a flow’s ability
to maintain sediment in suspension. After an in-depth discussion with our project supervisor,
it was decided that calculating the turbulence intensity will help us calculate the time the
algae will be in the dark zone, as a time less than 4.4 seconds is preferred by Chlorella.
(Janssen 2002) The following calculations made are based on exceedingly crude
assumptions, but it is imperative that some numbers are formulated for this area where
further research has been conducted. Data from numerous studies have been used to
calibrate a theoretical model for the downstream turbulence intensity. (Wren 2000)
13

19. (2)
where u’/u* = Turbulence Intensity, y = height, d = depth, u’ = instantaneous velocity and u* =
mean shear (friction) velocity.
The turbulent flow over a rough bed shows nearly the same characteristics of turbulence
intensity as those over a smooth bed (Nagakawa 1975). Where u, v, w are the downstream,
vertical and lateral directions, it is U’ that plays a significant role in creating turbulence while
V’ and W’ do not make much of a contribution (Nagakawa 1975). These assumptions were
used when making calculations for the raceway bed.
According to Nagakawa (1975) the mean friction velocity is given by the equation
(3)
Where g=acceleration due to gravity, h=water depth and S=slope of the bed
Usually slopes are in the range of 1/100 (Mulbry 2008) Therefore using g=9.81 m/s2, h=0.3
m, S=0.01 the mean friction velocity was calculated as
Using d = 0.3 m and varying y in steps of 0.01 m the average value of Turbulence intensity
(T.I) was found to be 1.43 and the mean instantaneous velocity (u’)= 0.243 m/s
Variation of Turbulence Intensity with
Increasing Depth
2.5
2
Intensity/Velocity
1.5 y = 2.3e-3.333x
1
0.5 y = 0.391e-3.333x
0
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35
Depth(m)
Turbulence Intensity Instantaneous Velocity
Expon. (Turbulence Intensity) Expon. (Instantaneous Velocity)
Figure 9 – Variation of turbulence intensity/shear velocity with depth
14

20. The mean instantaneous velocity was used to calculate the mean instantaneous angular
velocity of an eddy from the equation (where v = u’ = instantaneous velocity and r =
depth (d/2)
It was assumed that a circular eddy the diameter of the depth of the pond would be formed
during calculations.
Using the equation , the period for the algae to make one whole cycle within the
depth was found to be 2.14s.
Field observations have indicated that light is permitted to penetrate two thirds (2/3) of the
actual culture depth. (Borowitzka)
Using simple trigonometric calculations the angle covered by the algae during the dark 1/3
was found to be 2.8 rad.
(4)
The time the algae spend in the dark period is below the maximum of 4.4s. Therefore it can
be seen that the present flow rate and depth are acceptable to be used with the chlorella
species.
4.3 Reynolds number calculation
The turbulence of the water can be calculated by finding the Reynolds Number
Re = VD/μ (1) where Re- Reynolds Number, V- velocity of Water, D- Hydrodynamic
Diameter, μ– Kinematic Viscosity
Reynolds number for channel flow= ReCHANNEL = ρRhu/µ where ρ is the density of the liquid,
Rh is the hydraulic radius, u is the mean velocity of the liquid and µ is the viscosity of the
liquid
Hydraulic radius = Rh = A/P, where A is the cross sectional area of flow and P is the wetted
perimeter, i.e. the perimeter of the channel that is in direct contact with the water.
Density of sea water, ρ = 1029kg/m3
15

21. Mean velocity, u = 0.20m/s
Viscosity of sea water, µ = 1.08x10-3 Pa.s
Cross sectional area of flow, A = 0.3m deep x 0.429m wide = 0.1287m2
Wetted perimeter, P = 0.3m deep + 0.429m wide + 0.3m deep = 1.029m
Hydraulic radius, Rh = 0.1287m2/1.029m = 0.125m
ReCHANNEL = 1029x0.125x0.20/ (1.008x10-3) = 23819
As ReCHANNEL is greater than 1000 therefore flow in the paddlewheel pond is turbulent. (Lew
S 2010)
5.0 Risk Assessment
 Process of identifying the risks associated with each of the hazards so that
appropriate control and measure can be implemented based on the probability that
hazards may occur.
Hazard or Risk Priority Recommended solution
Break down of the Detailed operating instruction for safe operation of
raceway ponds 5 ponds before experiment.
Paddlewheel gears
and blades catching on Place a protective cage over any moving parts of the
hands of operator 4 paddlewheel.
Motor of the
paddlewheel 3 Set a speed limit for the motor avoid overheat.
Electric controls near Waterproof containment box for all electrical
water 3 equipment to avoid short circuit.
Power box next to Waterproof containment box for all electrical
water tanks 3 equipment to avoid short circuit.
Weight of the water Ensure the joints and links are able to supports the
and structure for ponds 3 heavy weight of the ponds.
Table 3 – Risk Assessment Analysis
5.1 Justifications
 The priority number stands for the importance of the case.
16

22.  Detailed operating instructions of the instrument for safe operation have to be given
to the operator by the advisor before the experiment. For health and safety reasons
this is the major priority.
 There is a low possibility of the operator catching the paddle wheel gears and blades
by using their hands when the paddle wheel is still operating. A priority of 4 for this is
given for this case, because at the worst, the operating paddle wheel gears and
blades might cause laceration of the hands of the operator.
 A priority of 3 is given for both electrical hazards as electrocution is possible.
However, the likelihood of this situation occurring is low as the operator does not
need to touch the electricity boxes unless to stop the motors and at the same time
the water has to reach the boxes. Safety switches are attached to all electric circuits.
Moreover, a speed limit for the motor has to be set to avoid overheating of the motor.
 Ensure the joints and links of the structure are able to support the heavy weight of
the ponds. Because the system might cause death or serious injury if the system
collapses. The heavy metal supports of the structure have reduced the likelihood of a
system collapsed happening.
6.0 Apparatus and Experimental Procedure
6.1 Apparatus:
1. FP111-FP211 Global Water Flow Probe details
Figure 10 – Global Water Flow Probe
a. Range: 0.3-19.9 FT/S (0.6-6.1M/S)
b. Accuracy: 0.1 FT/S
c. Averaging: True digital running average updated once per second
d. Display: LCD, Glare and UV Protected
e. Sensor Type: Turbo-Prop propeller with magnetic pickup
f. Instrument weight: FP111: 2 Lbs. FP21: Lbs
17

23. g. Approx. Length: FP222 3’ to 6’ FP211 5’ to 15’
h. Materials: Probe: PVC and anodized aluminum with stainless steel water bearing
i. Computer: ABS/Polycarbonate housing with polyester overlay
j. Power: Internal Lithium, Approx 5 year life Non-Replaceable
k. Operating Temp.: -20 to 70 Celsius
2. Paddlewheel Controller – ABB ACS350-01E-04A7-2 x 2 (set the input frequency for
motor)
3. Switch Box – B&R
4. Electrical Switch – Clipsal WHB340
5. Electrical Meter – 56SB8 IP60 Closed Position
6. Motor – ABB 50Hz 1420r/min 0.75kW 1.13/10 A 10.75cos
7. Vacuum – Euro clean Electrolux 240V
8. Paddlewheel
9. Ruler
10. 1cm3 cube sponges (check the characteristic for water flow)
11. Timer (obtain the motor period)
12. String attach to the steel (check the characteristic for water flow)
13. Metal with marking (mark the location of the points)
14. Ladder (location of flow meter is too high to be reach)
15. Water pipe (fill the raceway)
6.2 Procedure
Raceway Pond with Insert
1. Plug in the cable and switch on the power supply connect to the controller for the
raceway pond
2. Fill the water into raceway pond until 60mm
3. Make sure blade in flow meter spin smoothly at the begin of each test
4. Set the flow meter on top of point 1
5. Set 1.5Hz on controller (Input for motor speed)
6. Press the ―START‖ button on the controller to initiate the paddle wheel, the paddle
wheel should start rotate slowly (There are ―EMERGENCY‖ button on left and right
hand side of the raceway pond to cut down the power supply immediately if anything
go wrong)
7. Wait 30second, let the value in flow meter get stable
8. Check the flow characteristic by throwing the sponges and wood pieces into the
water (Sponges and wood pieces will float along the raceway. By tracking the tiny
18

24. wood pieces and sponges along the raceway pond, the water characteristic is clear
to notice)
9. Record down the lowest, min and highest velocity for the fluid flow on the flow meter
10. Record the time for motor rotate for 10 periods
11. Press the ―STOP‖ button after obtain the result
12. Repeat steps 5 to 8 with 2.0Hz, 2.5Hz, 3.0Hz and 3.5Hz.
13. Repeat steps 4 to 10 with different points 2 until points 15
14. Repeat steps 4 to 11 with water depth 70mm, 80mm, 90mm, 110mm and 120mm
15. Repeat every steps again to obtain the second set of readings for comparison
16. Use the vacuum to clear the water inside the pond
Raceway Pond without Insert
Follow procedure for raceway pond with insert for points P1, P2, P3, P13, P14 and P15 only.
7.0 Results and Discussion
7.1 Practical Results
Particle tracking velocimetry was performed using approximately 1cm3 sponge pieces to
track where the flow was highest. The red line shows the path of the highest velocity.
Figure 11 - Observation from particle tracking velocimetry
The speed of the paddlewheel could be controlled using the motor speed controller. The bar
charts below (Figure 12 & 13) give the velocity taken at points 1-15 for 5 speed controller
values at water heights 80mm and 120mm. The velocity values are in m/s. The Figure 14
shows how paddle wheel RPM changes on different days when all other variables are kept
constant.
19

25. Velocity at points 1 - 15 for varied paddle
wheel speeds at 80mm water height
1
Velocity (m/s)
0.8 1.5
0.6
2
0.4
0.2 2.5
0 3
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
3.5
Points
Figure 12 - Velocity at points 1 - 15 for varied paddle wheel speeds at 80mm water height
Velocity at points 1 - 15 for varied paddle
wheel speeds at 120mm water height
1
Velocity (m/s)
0.8 1.5
0.6
2
0.4
0.2 2.5
0 3
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
3.5
Points
Figure 13 - Velocity at points 1 - 15 for varied paddle wheel speeds at 120mm water height
Variation of Paddle wheel RPM vs
Controller Hz for 80mm height for
readings on different days
40
30
y = 13.294x - 5.7062 Reading 1
RPM
20
Reading 2
10 Linear (Reading 1)
y = 11.807x - 5.9908
0 Linear (Reading 2)
0 1 2 3 4
Hz
Figure 14 - Variation of Paddle wheel RPM vs Controller Hz for 80mm height for readings on
different days
20

26. Since 2 readings per depth isn’t adequate to do a statistical analysis on the data, 10
repetitions were done at the depth of 80mm in the positions P2, P5, P8, P11, P14. Another
set of 10 readings were taken at a water height of 120mm at depths 25mm, 60mm, 95mm
measured from the top of the water surface. To minimize the error due to varying
paddlewheel speeds for the same depth, the velocities were divided by the paddle speed to
normalize it. Below is a plot of the average value of the 10 readings with upper control limits
and lower control limits calculated at 95% confidence interval. The values of the initial two
sets of readings have also been included in the plot for comparison purpose.
Statistical Analysis of Points 2, 5, 8, 11, 14
compared with Initial 2 Readings
0.019
Normalized Velocity (ms-1/RPM
0.017 Point 2
0.015 Point 5
Point 8
0.013
Point 11
0.011
Point 14
0.009 R1
0.007 R2
0 2 4 6 8 10 12 14 16
Points
Figure 15 - Statistical Analysis of Points 2, 5, 8, 11, 14 compared with Initial 2 Readings
Average Velocity at Points 5, 8, 11 vs
Probe Depth
0.08
Normalized Velocity ms-1/RPM
0.07
0.06
0.05
0.04 P5
0.03 P8
0.02
0.01 P11
0
0 20 40 60 80 100
Depth(mm)
Figure 16 - Average Velocity at Points 5, 8, 11 vs Probe Depth
21

27. 7.1.1 Comparison of Raceway with Insert and Raceway without Insert.
Due to time constraints only velocities at points 1,2,3 and 13,14,15 were taken from the
raceway without inserts. Two sets of readings were taken from the raceway without insert for
depths 55mm to 95 mm. 55mm in the raceway without inserts correspond to 80 mm in the
raceway with inserts as it has a slope of maximum height 25mm the raceway without inserts
doesn’t have. This was done to make the area of water the paddle pushed to be constant.
Readings are in m/s.
Raceway with Insert and Raceway
without Insert compared
0.03
0.025
Velocity m/s
0.02
R1
0.015
R2
0.01
0.005 R1,I
0 R2,I
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Points
Figure 17 - Raceway with Insert and Raceway without Insert compared
22

28. 7.2 Computational Fluid Dynamics Results for 80mm water height
When the CFD tests were performed a velocity of 0.3 m/s was provided to the fluid. The
assumption is that the paddle will provide a constant velocity that moves the water at 0.3 m/s
Figure 18 - Variation of horizontal velocity component of raceway at 0.3 m/s
Figure 19 - Vector Plot of Horizontal Velocity Component at given speed of 0.3 m/s
23

29. Figure 20 - Variation of vertical velocity Component at a given horizontal velocity of 0.3 m/s
Figure 21 - Variation of velocity with depth at location of points 1-15
7.3 Comparison on CFD and Practical results
CSIRO used two different CFD models to compare with our practical values. Below are the
results they obtained by using the Rigid Free Surface: Steady state Solution model and the
Deformable Free Surface: Time- averaged solution models at depths 80mm and 120mm for
the raceway with the insert.
24

30. 0.200
Velocity comparison: 80mm pond depth case Experimen
t
0.150
Velocity (m/s)
Rigid FS:
steady-
0.100
state soln
0.050
Deformabl
e FS: time-
0.000 avgd soln
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Point Label (refer "Raceway" sheet for point probe locations)
Figure 22 - Velocity comparison: 80mm pond depth case
Velocity comparison: 120mm pond depth case
0.300
Velocity (m/s)
0.250
0.200
0.150
0.100 Experiment
0.050 Rigid FS: steady-state soln
0.000
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Point Label (refer "Raceway" sheet for point probe locations)
Figure 23 - Velocity comparison: 120mm pond depth case
7.4 Observations
7.4.1 Practical Experiment
 In Figure 11 it can be seen that flow of water is highest closer to the insert at points
P1, P4, P7 while it gets slower closer to the points P3, P6, P9. This can also be
compared with (figures 12 & 13) to prove that velocities are highest in the region
closer to the insert.
 Point 3 (P3) has the lowest velocity from all the points.
 Points 8-15 have very similar velocities which show how well the insert smoothens
out the flow.
25

31.  Set of 10 readings taken for statistical analysis and initial two readings taken show a
similar pattern where the readings at points 2 and 5 are higher than the readings at
points 8, 11 & 14. (Figure15)
 Velocity patterns for heights 80mm and 120mm are very similar although 80mm has
a higher average velocity due to less water being pushed through.
 Velocities at point 5 and 8 decrease with increasing depth while velocities at point 11
increase with increasing depth. (Figure 16)
 It can be observed from (Figure 15) that flow velocities have increased when average
temperature of the day readings were taken were higher. (Appendix C )
 Raceway with insert shows less variation in velocity across the breadth of the
channel compared to the raceway without insert. (Figure 17)
7.4.2 CFD
 The Horizontal velocity component plot (Figure 18) and vector plot of velocity (Figure
19) show a similar pattern to the flow pattern drawn using particle tracking
velocimetry. (Figure 11)
 The variation of velocity in Horizontal velocity component plot (Figure 18) and Figure
12 show similar values when compared.
 The variation of velocity with change in depth( Figure 21 and Appendix E) shows a
similar pattern to the practical results obtained.
 The comparison of practical results with the two models used by the CSIRO’s CFD
models show that the Deformable FS model is very accurate in predicting flow
values. (Figures 22 & 23)
7.5 Discussion
When conducting this experiment the team set out with a few objectives
 Compare CFD Results with Practical Results
 Analyze the advantages of having the insert in the raceway
 Measure the power savings obtained by using the insert
 Investigate the effect of flickering light on the algae in the raceway pond
Of these Objectives the team was able to get experimental values that could be analyzed to
prove the first two points, while the third can be deduced from the initial two results.
Due to time constraints and the demands of the CSIRO for us to provide more velocity data
over a larger range than initially anticipated, the goal of investigating the effect of light
flickering on algae could not be accomplished.
26

32. The main objective of the team was to provide as much experimental data as possible to the
CSIRO so that they could use them to compare with their CFD models. We have managed
to provide the CSIRO with two sets of readings for water heights of 70 – 120 mm. From
further readings we took at 80mm and 120mm water level height, we also have provided a
statistical analysis on the range their CFD model results should fall into.
When comparing the experimental and CFD results it can be seen that the two give similar
results in most instances. This confirms that the CSIRO’s CFD calculations are consistent
with practical estimates.
There is much less variation in velocity across the breadth of the channel in the raceway with
the insert when compared to the one without. This shows that the flow of the fluid is
smoother around the insert and that the insert reduces the reverse flow around hairpin
bends.
Because there is considerably less reverse flow around hairpin bends it can be seen that by
using an insert it saves energy that would otherwise be lost to make the water flow in the
right direction.
7.5.1 Areas of Possible Errors
 The flow measurements were taken in feet per second (ft/s) and multiplied by the
factor 0.31 to convert it to m/s. This would result in a conversion error.
 The paddlewheel is assumed to cover the full breadth of the channel. However in the
test pond there was around a 5mm clearance on both sides.
 As observed, in general, flow velocity increased with increase in average
temperature. Therefore even when water height and paddle input speed was kept
constant there could be a change in flow velocity.
 The paddle wheel speed was always changing due to the axle bearing heating up
and causing friction. Usually the paddlewheel took a while to get to optimum speed,
ran well for about 1-2 hours and then started slowing up due to the bearing heating
up. This played a major role in giving varied results.
 Particles of dirt that was in the water sometimes got stuck in the impellor of the
velocity probe and would slow it down. This would also result in varied results.
 The readings for Paddle RPM were taken manually. Therefore there would be
experimental errors involved with the RPM readings.
 The probe was supposed to be at 50% - 60% of the water depth. However due to
difficulty in accessibility, the probe position was estimated at some instances.
Therefore there would be experimental errors in the position of the probe as well.
27

33.  The readings were taken 30 seconds after resetting the probe. However sometimes
the readings were taken before or after 30 seconds due to the velocity stabilizing
issues.
8.0 Conclusion
Throughout the practical experiments conducted on the raceway pond made by the CSIRO,
we can conclude that the insert regulates flow around hairpin in a much smoother manner. It
reduces reverse flow that is common around hairpins. Apart from that, there is also less
variation across the breath of the channel where the insert is present. This results in less
loss of energy from the system due to less hydrodynamic losses.
The team is able to state with 95% confidence that the range of normalized velocities for the
points P2, P5, P8, P11 and P14 at a water level height of 80mm, the CFD should be in the
range plotted in the Figure * with an allowance for an extra +/- 30% outside the range due to
experimental errors.
The team is able to state with 95% confidence that the range of normalized velocities versus
depth for the points P5, P8 and P11 at a water level height of 120mm, the CFD should be in
the range plotted in the Figure * with an allowance for an extra +/- 30% outside the range
due to experimental errors.
When establishing this statistical claim, we have made the assumption that the 5 points
tested are applied to all 15 points in the raceway system.
8.1 Recommendations for further work
Some of the recommendations we can offer are that for future research into the algae
raceway systems, the fluctuations of velocity should be measured at closer time intervals.
This can help investigate the influence of turbulence intensity on the algae. Besides that, a
raceway that is ideally scaled and practically proportionate should be used in further
experiments. Ideally, any future raceway designs should incorporate two inserts on either
end.
On top of that, the inserts should also be varied to have different radii to investigate the
effect it has on the flow around hairpins. Another point worth nothing is the apparatus used
to measure the flow in the raceway pond. Smaller diameter probes should be used to allow
for the analysis of velocities at shallower depths in the raceway pond.
28

34. 9.0 References
A Richmond, A Vonshak, 1980, Environmental limitations in outdoor production of algal
biomass
Aditya M. Kunjapur and R. Bruce Eldridge, 2010, Photo bioreactor Design for
Commercial Bio-fuel Production from Microalgae
Ami Ben-Amotz, Large scale open algal ponds
Anna L. Stephenson, Elena Kazamia, John S. Dennis, Christopher J. Howe, Stuart A.
Scott, Alison G. Smith, 2010, Life-Cycle Assessment of Potential Algal Biodiesel
Production in the United Kingdom: A Comparison of Raceways and Air-Lift Tubular
Bioreactors
Ayhan Demirbas, M Fatih Demirbas, 2010, Algae Energy, Algae as a new source of
biodiesel, Springer
B. Ketheesan, N. Nirmalakhandan, 2011, Development of a new airlift-driven raceway
reactor for algal cultivation
Beneman John R, Oswald W J, 1993, Systems And Economic Analysis Of Microalgae
For Conversion Of CO2To Biomass
Brian Neltner, Jeff Tester, 2008, Algae Based Biodiesel
Daniel G. Wren, Sean J. Bennett, Brian D. Barkdoll, and Roger A. Kuhnle, 2000, Studies
in Suspended Sediment and Turbulence in Open Channel Flows, Research Report No.
18
David A. Paterson, Pratish Bandopadhayay, Kurt Liffman, 2010, Design Of Energy-
Efficient Algal Ponds
29

35. Donna A. Johnson, Joseph Weissman, Raymond. Goebel, 1988, An Outdoor Test
Facility for the Large-Scale Production of Microalgae, Presented at the Institute of Gas
Technology Energy from Biomass and Wastes XII
E. Sierra, F.G. Aci´en, J.M. Fern´andez, J.L. Garc´ıa, C. Gonz´alez, E. Molina, 2007,
Characterization of a flat plate photo bioreactor for the production of microalgae
Hiroji Nagakawa, Iehisa Nezu & Hiroshi Ueda, September 1975, Turbulence of open
channel flow over smooth and rough beds
J. C. Welssman, R. P. Goebel, 1987, Design and Analysis of Microalgal Open Pond
Systems for the Purpose of Producing Fuels, A Subcontract Report
James W. Richardson, Joe L. Outlaw, and Marc Allison, 2010, The Economics of
Microalgae Oil
John R. Benemann J, 2008, Photobioreactors – Comparative Economics, Presented at
5th Annual World Congress on Industrial Biotechnology, Chicago
Liam Brennan, Philip Owende, 2009, Biofuels from microalgae—A review of
technologies for production, processing, and extractions of biofuels and co-products
Marcel Janssen, 2002, Cultivation of microalgae: effect of light/dark cycles on biomass
yield
Maria J. Barbosa, Marcel Janssen, Nienke Ham, Johannes Tramper, René H. Wijffels,
2003, Microalgae Cultivation in Air-Lift Reactors: Modeling Biomass Yield and Growth
Rate as a Function of Mixing Frequency
Mario R. Tredici, 2004, Handbook of Micro algal Culture
30

36. Mark E. Huntley and Donald G. Redalje, 2006, CO2 Mitigation and renewable oil from
photosynthetic microbes: a new appraisal
Mark E. Huntley and Donald G. Redalje, 2006, CO2 Mitigation and renewable oil from
photosynthetic microbes: a new appraisal
Matthew N Campbell, 2008, Biodiesel: Algae as a Renewable Source for Liquid Fuel
Micheal A Borowitzka, Culturing Micro Algae in Outdoor Ponds, Algal Culturing
Techniques, Elsevier Academic Press.
Navid Reza Moheimani, Michael A. Borowitzka, 2006, Limits to Productivity of the Alga
Pleurochrysis Carterae(Haptophyta) Grown in Outdoor Race way Ponds
Scott C. James Aand Varun Boriah, 2010, Modeling Algae Growth in an Open-Channel
Raceway, Journal Of Computational Biology Volume 17, Number 7, Pp. 895–906
Simon Lew, 2010, Experimental Investigation into Raceway Ponds and the use of
Impellers as a potential paddlewheel replacement for water propulsion
Stuart A Scott, Matthew P Davey, John S Dennis, Irmtraud Horst, Christopher J Howe,
David J Lea-Smith and Alison G Smith, 2010, Biodiesel from algae: challenges and
prospects
Walter Mulbry ,Mulbry, Shannon Kondrad, Carolina Pizarro, Elizabeth Kebede-
Westhead, 19/5/2008, Treatment, Treatment of dairy manure effluent using freshwater
algae: Algal productivity and recovery of manure nutrients using pilot-scale algal turf
scrubbers
31

37. William H. Thomas and Carl H. Gibson, 1990, Effects of small-scale turbulence on
microalgae
Yuan-Kun Lee & Hong-Soon Tay, 1991, High CO2partial pressure depresses productivity
and bioenergetic growth yield of chlorella
Yuan-Kun Lee, 2011, Microalgae mass culture systems and methods: Their limitation
and potential
Yusuf Chisti, 2007, Biodiesel from microalgae
Zhiyou Wen, Michael B. Johnson, 2009, Microalgae as a Feedstock for Biofuel
Production, PUBLICTION 442-886
32

38. Appendix A – Experimental Results
First set of result with the insert
26/8/2011
Depth:60mm
Raceway Velocity Paddle Speed (Hz)
Location Category (ft/s) 1.5 2 2.5 3 3.5
P1 Min - - - - -
Max - - - - -
Avg - - - - -
P2 Min - - - - -
Max - - - - -
Avg - - - - -
P3 Min - - - - -
Max - - - - -
Avg - - - - -
P4 Min 0 0.8 0.9 0.9 1.3
Max 1.1 1.5 1.3 1.5 1.5
Avg 0.6 1 1.1 1.3 1.4
P5 Min 0 0 0.4 0.4 0.4
Max 0.6 0.6 0.9 0.9 0.9
Avg 0.2 0.4 0.7 0.6 0.6
P6 Min 0 0 0 0 0
Max 0.2 0.4 0.6 0.2 0
Avg 0 0 0 0 0
P7 Min 0 0.6 0.6 0.6 0.6
Max 0.9 0.9 0.9 1.1 0.3
Avg 0.5 0.7 0.8 0.9 0.9
P8 Min 0 0 0 0 0
Max 0.6 0.6 0.8 0.8 0.8
Avg 0.1 0.3 0.5 0.4 0.3
P9 Min 0 0 0 0 0
Max 0.4 0.4 0.4 0.4 0.4
Avg 0 0.1 0.1 0.1 0.1
P10 Min 0 0 0 0 0
Max 0.4 0.4 0.4 0.6 0.6
Avg 0 0.2 0.2 0.1 0.1
P11 Min 0 0.2 0.2 0 0.2
Max 0.4 0.6 0.8 0.8 0.6
Avg 0.2 0.4 0.6 0.3 0.5
P12 Min 0 0.4 0.4 0.4 0.4
Max 0.8 0.8 0.9 0.8 0.9
Avg 0.2 0.5 0.7 0.6 0.6
P13 Min - - - - -
Max - - - - -
Avg - - - - -
P14 Min - - - - -
Max - - - - -
Avg - - - - -
P15 Min - - - - -
Max - - - - -
Avg - - - - -
Duration Time(s) 36.4 24.8 18.7 15.2 -
for 10revs RPM 16.484 24.194 32.086 39.473 -
Table A1 – 60mm Depth on 26/8/2011
33

39. 26/8/2011
Depth:70mm
Raceway Velocity Paddle Speed (Hz)
Location Category (ft/s) 1.5 2 2.5 3 3.5
P1 Min - - - - -
Max - - - - -
Avg - - - - -
P2 Min - - - - -
Max - - - - -
Avg - - - - -
P3 Min - - - - -
Max - - - - -
Avg - - - - -
P4 Min 0.9 1.1 1.1 0.9 1.3
Max 1.1 1.5 1.7 1.9 1.9
Avg 1.1 1.4 1.5 1.6 1.6
P5 Min 0.4 0.8 0.8 0.6 0.6
Max 0.8 1.1 1.3 1.3 1.3
Avg 0.6 0.9 1.1 1.1 1.1
P6 Min 0 0 0 0 0
Max 0.2 0.4 0.4 0.4 0
Avg 0 0 0 0 0
P7 Min 0.8 0.8 0.9 0.9 0.8
Max 1.1 1.5 1.5 1.3 1.3
Avg 0.9 1.1 1.1 1 1.1
P8 Min 0.2 0.6 0.4 0.2 0.2
Max 0.8 0.9 0.9 0.9 0.8
Avg 0.5 0.7 0.6 0.6 0.6
P9 Min 0 0.2 0 0 0
Max 0.6 0.6 0.8 0.8 0.6
Avg 0.2 0.4 0.4 0.3 0.2
P10 Min 0 0 0.2 0 0.4
Max 0 0.8 0.9 0.8 0.8
Avg 0 0.3 0.6 0.5 0.6
P11 Min 0.4 0.6 0.6 0.6 0.6
Max 0.6 0.9 1.1 0.9 0.9
Avg 0.5 0.8 0.8 0.8 0.8
P12 Min 0.4 0.4 0.4 0.6 0.6
Max 0.6 0.9 1.1 0.9 0.9
Avg 0.5 0.7 0.8 0.8 0.8
P13 Min - - - - -
Max - - - - -
Avg - - - - -
P14 Min - - - - -
Max - - - - -
Avg - - - - -
P15 Min - - - - -
Max - - - - -
Avg - - - - -
Duration Times(s) 48 29.5 20.5 16 -
for 10 revs RPM 12.5 20.339 29.268 37.5 -
Table A2 – 70mm Depth on 26/8/2011
4/8/2011
Depth:80mm
Raceway Velocity Paddle Speed (Hz)
Location Category (ft/s) 1.5 2 2.5 3 3.5
P1 Min 0.8 0.9 1.1 0.9 1.3
Max 1.1 1.3 1.5 1.7 1.7
Avg 0.9 1.2 1.2 1.4 1.6
P2 Min 0.4 0.8 1.3 1.5 1.7
Max 0.9 1.1 1.9 2.1 2.3
Avg 0.7 1 1.7 1.9 1.9
P3 Min 0 0 0 0 0
Max 0 0.2 0 0 0
34

40. Avg 0 0 0 0 0
P4 Min 0.9 1.5 1.7 1.7 1.3
Max 1.5 1.9 2.1 2.5 2.3
Avg 1.3 1.7 1.9 2 1.9
P5 Min 0.6 0.9 1.1 1.1 1.1
Max 1.1 1.3 1.7 1.9 1.9
Avg 0.8 1.1 1.4 1.6 1.5
P6 Min 0 0.4 0.2 0 0
Max 0.4 0.8 0.8 0.9 1.1
Avg 0.1 0.6 0.5 0.4 0.2
P7 Min 0.8 1.3 1.3 1.1 1.1
Max 1.5 1.7 1.9 1.7 1.7
Avg 1.1 1.5 1.6 1.5 1.4
P8 Min 0.2 0.8 0.6 0.6 0.4
Max 0.8 1.1 1.3 1.3 1.3
Avg 0.5 0.9 0.9 1 0.9
P9 Min 0 0.2 0.2 0 0
Max 0.4 0.8 0.8 0.9 1.1
Avg 0.2 0.5 0.5 0.4 0.6
P10 Min 0.2 0.8 0.4 0.2 0.8
Max 0.8 1.1 1.1 1.3 1.1
Avg 0.5 1 0.9 0.9 0.9
P11 Min 0 0.8 0.8 0.9 0.9
Max 0.9 1.1 1.3 1.3 1.3
Avg 0.5 0.9 1 1.1 1.1
P12 Min 0 0.6 0.8 0.8 0.8
Max 0.8 0.9 1.1 1.3 1.3
Avg 0.5 0.8 0.9 1 1
P13 Min 0 0 0 0 0
Max 0.2 0.6 0.8 0.6 0.6
Avg 0 0.3 0.3 0.2 0.2
P14 Min 0.4 0.8 0.8 0.8 0.9
Max 0.9 0.9 0.9 1.3 1.1
Avg 0.6 0.9 0.9 1 1
P15 Min 0.4 0.8 0.9 1.1 1.1
Max 1.1 1.1 1.5 1.5 1.5
Avg 0.7 0.9 1.2 1.3 1.2
Duration Times(s) 44 28.5 20.7 18.1 -
for 10 revs RPM 13.636 21.052 28.986 33.149 -
Table A3 – 80mm Depth on 4/8/2011
10/8/2011
Depth:90mm
Raceway Velocity Paddle Speed (Hz)
Location Category (ft/s) 1.5 2 2.5 3 3.5
P1 Min 0.9 1.3 1.3 1.1 1.3
Max 1.1 1.5 1.7 1.7 1.7
Avg 1 1.4 1.6 1.4 1.6
P2 Min 0.8 0.9 1.1 1.9 1.9
Max 1.1 1.3 1.7 2.3 2.3
Avg 0.9 1.1 2.5 2 2
P3 Min 0 0.2 0 0 0
Max 0.4 0.6 0.4 0.4 0.4
Avg 0.2 0.4 0.1 0.2 0.2
P4 Min 1.1 1.7 1.9 1.9 1.9
Max 1.5 2.1 2.3 2.5 2.7
Avg 1.3 1.9 2.1 2.3 2.2
P5 Min 0.8 1.1 1.3 1.3 0.9
Max 1.1 1.5 1.7 1.9 1.9
Avg 1 1.3 1.6 1.7 1.7
P6 Min 0.4 0.6 0.6 0.4 0
Max 0.8 0.9 1.1 1.1 1.3
Avg 0.5 0.8 0.9 0.7 0.6
P7 Min 1.1 1.5 1.7 1.9 1.1
Max 1.5 1.9 2.1 2.3 1.9
Avg 1.3 1.8 1.9 2 1.6
P8 Min 0.8 1.1 1.1 0.8 0.8
35

41. Max 0.9 1.5 1.5 1.5 1.7
Avg 0.9 1.2 1.3 1.2 1.3
P9 Min 0.2 0.6 0.6 0.6 0.4
Max 0.8 0.9 1.1 1.1 1.1
Avg 0.5 0.8 0.9 0.9 0.8
P10 Min 0.6 0.9 1.3 1.1 1.1
Max 0.9 1.5 1.7 1.3 1.5
Avg 0.7 1.3 1.5 1.2 1.3
P11 Min 0.6 1.1 1.1 0.9 0.9
Max 0.9 1.3 1.5 1.5 1.5
Avg 0.8 1.2 1.3 1.3 1.3
P12 Min 0.6 0.8 0.9 0.9 0.9
Max 0.9 1.1 1.3 1.5 1.5
Avg 0.8 0.9 1.1 1.3 1.3
P13 Min 0.4 0.6 0.6 0 0.6
Max 0.8 0.9 0.9 1.1 0.9
Avg 0.5 0.8 0.8 0.8 0.8
P14 Min 0.8 0.9 1.1 1.1 1.1
Max 1.1 1.3 1.5 1.3 1.5
Avg 0.9 1.1 1.3 1.2 1.3
P15 Min 0.6 1.1 1.3 1.1 1.1
Max 1.3 1.3 1.5 1.5 1.5
Avg 0.9 1.2 1.4 1.4 1.5
Duration Times(s) 51.8 34.6 23.2 17.8 -
for 10 revs RPM 11.583 17.341 25.862 33.708 -
Table A4 – 90mm Depth on 10/8/2011
5/8/2011
Depth:100mm
Raceway Velocity Paddle Speed (Hz)
Location Category (ft/s) 1.5 2 2.5 3 3.5
P1 Min 0.8 1.3 1.3 1.3 1.3
Max 1.1 1.5 1.7 1.7 1.9
Avg 0.9 1.5 1.6 1.6 1.6
P2 Min 0.8 0.9 1.3 1.3 1.7
Max 1.1 1.3 1.7 2.1 2.1
Avg 0.9 1.1 1.5 1.9 1.9
P3 Min 0.4 0.4 0 0 0
Max 0.8 0.8 0.6 0.4 0.6
Avg 0.5 0.5 0.4 0.1 0.1
P4 Min 1.1 1.9 2.3 2.5 2.3
Max 1.7 2.3 2.7 2.7 2.8
Avg 1.4 2 2.4 2.6 2.6
P5 Min 0.8 1.1 1.3 1.5 1.3
Max 0.9 1.5 1.5 1.9 1.9
Avg 0.9 1.3 1.4 1.7 1.7
P6 Min 0.4 0.8 0.9 0.8 0.6
Max 0.8 0.9 1.3 1.5 1.3
Avg 0.6 0.9 1.1 1.1 1
P7 Min 1.3 1.3 1.9 2.1 1.9
Max 1.5 2.1 2.3 2.5 2.5
Avg 1.4 1.8 2.1 2.2 2.1
P8 Min 0.6 0.8 0.8 1.3 0.9
Max 1.1 1.5 1.7 1.7 1.9
Avg 0.9 1.2 1.3 1.5 1.5
P9 Min 0.4 0.6 0.8 0.6 0.6
Max 0.8 0.8 1.9 1.1 1.1
Avg 0.5 0.7 1.9 0.9 0.8
P10 Min 0.6 1.1 1.5 1.5 0.9
Max 1.1 1.7 1.9 1.9 1.7
Avg 0.9 1.4 1.6 1.7 1.4
P11 Min 0.8 1.1 1.1 1.3 1.3
Max 1.3 1.3 1.7 1.9 1.9
Avg 1 1.2 1.4 1.5 1.5
P12 Min 0.8 0.9 1.3 1.1 1.1
Max 1.1 1.3 1.5 1.7 1.9
Avg 1 1.1 1.4 1.4 1.5
36