CSIRO Project Report

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

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CSIRO Project Report

  1. 1. Raceway Bio-dieselEffect of inserts on raceway pond efficiency Supervisor: Dr. Richard ManassehRoshane Nanayakkara 7022395Cecil Tiew Siew Hsi 4208492Pan Sze Chung 4207416
  2. 2. AcknowledgementFirst and foremost, the authors would like to take this opportunity to express our appreciationto our supervisor of this project, Dr Richard Manasseh for his guidance. Without guidancefrom him, the completion of this report would not be possible. Special thanks are given to theCSIRO for providing the laboratory facilities for us to complete the research. We deeplyappreciate the assistance from Dr. Kurt Liffman and Dr. Peter Liovic for providing the detailsand tasks for our research.Other than that, the authors would like to express their gratitude to Dr. David Paterson whoalready come back to assist and provide significant information for us in the research afterretiring. The knowledge and experience from Mr. Paterson helped a lot in the collection ofour 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 problemswith our project. His humorous and good personality has greatly reduced our stress when wefaced difficulties. i
  3. 3. DeclarationWe hereby declare that the project work entitled ―Effect of inserts on raceway pondefficiency‖ submitted to the Swinburne University Technology, is a record of an original workdone by us under the guidance of Dr. Richard Manasseh, Senior Lecturer, Faculty ofEngineering and Industrial Sciences from Swinburne University of Technology, and thisproject work has not performed the basis for the award of any Degree or diploma/associateship/fellowship and similar project if any, except where due reference is made in the text ofthe report.Hence, to the best of the candidate’s knowledge contains no material previously published orwritten by another person except where due reference is made in the text of the report. ii
  4. 4. ContentsAcknowledgement ................................................................................................................................. iDeclaration .............................................................................................................................................. ii1.0 Abstract ........................................................................................................................................... 12.0 Introduction ..................................................................................................................................... 13.0 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 .............................................................................................. 114.0 Methodology ................................................................................................................................. 12 4.1 Experiments.............................................................................................................................. 12 4.2 Calculation of Time Required For Algae to Be In Darkness ............................................. 13 4.3 Reynolds number calculation................................................................................................. 155.0 Risk Assessment ......................................................................................................................... 16 5.1 Justifications ............................................................................................................................. 16 6.0 Apparatus and Experimental Procedure .............................................................................. 17 6.1 Apparatus:............................................................................................................................. 17 6.2 Procedure ................................................................................................................................. 187.0 Results and Discussion .............................................................................................................. 19 7.1 Practical Results ...................................................................................................................... 19 7.1.1 Comparison of Raceway with Insert and Raceway without Insert. ........................... 22 iii
  5. 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.................................................................................................. 278.0 Conclusion .................................................................................................................................... 28 8.1 Recommendations for further work ....................................................................................... 289.0 References ................................................................................................................................... 29Appendix A – Experimental Results ...................................................................................................... 33Appendix B – Readings taken for statistical Analysis ............................................................................ 49Appendix C - Temperatures Data .......................................................................................................... 51Appendix D – Raceway Coordinate system .......................................................................................... 52Appendix 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. 6. 1.0 AbstractThe 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 suchas Chlorella for a number of years. For maximum production of lipids through photosynthesisthe algae require exposure to CO2 and sunlight. Algae productivity has been found toincrease when exposed to flickering light where the light/dark cycle is around 8.5/4.4seconds. To produce this flickering effect, eddy’s are formed through turbulence created bya paddle wheel that also keeps flow at around 20 cm/s. Depth is maintained at around 30cm. A raceway pond with modifications to reduce energy consumption and variation invelocity has been provided by the Commonwealth Scientific and Industrial ResearchOrganisation (CSIRO). Computational Fluid Dynamics (CFD) simulations on these modifiedraceways have already shown a potential decrease in energy consumption, which couldlower the capital cost and operating expenses of the raceway pond. The task of the researchteam is to practically confirm these results while also introducing their own improvements. Tothis extent, the research team has generated a model to predict the effect of turbulenceintensity of the water channel in relation to flickering light which is further discussed insection 4.0 (Methodology).2.0 IntroductionIn a world where people are facing the effects of global warming and climatic change, theneed for greener, cleaner fuels to power our cars, ships and factories has never beengreater. This has provided a strong incentive for developing alternatives to fossil fuels andhas led researchers along different paths to produce bio fuels that leave a smaller carbonfootprint on the environment. While some researchers are still working on producingcompletely new energy sources, some others are working on producing these alternativefuels 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 calleda raceway pond which is a relatively old technique dating about 30 years. We were provideda raceway pond by the CSIRO and we have conducted experiments on their model whichhas certain modifications done to it. The pond through CFD shows a reduction in energyconsumption. The modifications reduce friction when the water takes sharp bends and alsoaims to reduce large fluctuations in velocity. Our task was to practically test and confirmthese computer derived results while also looking at other ways we can improve the racewaypond. A statistical inference was performed from a range of practical figures that were 1
  7. 7. compiled after running tests on the CSIRO’s pond. This would provide the CSIRO with anestimate of where their CFD model results should fall into.The authors have done an exhaustive search on the physical conditions required to get themaximum productivity from micro algae, particularly the chlorella species.There are certain physical attributes that are accepted as industry standard and varyingthem produce little or no effect on bio mass production. In this context the depth of racewayponds are usually kept in the range of 300mm and flow rates in the range of 15-20 cm/s. Wealso found that micro algae produces more bio mass when exposed to flickering light asopposed to constant illumination and that depending on the algae species, the requiredlight/darkness ratio varies.In a raceway pond, turbulence has to be provided to achieve this flicker effect of light andalso mix the algae. Reynolds numbers above 20000 are used in practice when designingraceway ponds. The device that provides this turbulence and also velocity to the water is apaddlewheel.3.0 Literature ReviewA clean, renewable source of energy that is easily affordable is of major importance for thesurvival of society in this day and age where the effects of global warming are beingexperienced on a daily basis. Although solar energy and wind energy have been utilized forproducing electricity and are zero emission energy sources, we still need a clean energysource that can power our engines and turbines. In this context bio-diesel is a very greensource of fuel where the net output of in its life cycle is quite low. Since the 1980’s, biodiesel plants have opened in many countries and some cities have run buses on bio diesels(Demirbas 2010).3.1 Bio-dieselBio-diesel can be made from any oil/lipid source; the major components of these sources aretricylglycerol molecules (Wen et al. 2009). Pure vegetable oil (virgin oils), animal fats (yellowgrease), and waste cooking oils are the main sources of oil for bio diesel presently (Wen etal. 2009). The most familiar of virgin oils are soybean oil, rapeseed oil, mustard seed oil andalgae oil. These virgin oils have been greatly utilized during the past few years in big nationslike USA and Europe to help preserve natural resources. More and more investments arebeing made into algae oil compared to other food crops because the yields of oil and fuelsfrom algae are much higher (10-100times) compared to competing energy crops. The annualproductivity and oil content of algae are far greater than seed crops (Campbell M 2008). 2
  8. 8. Moreover, algae can be grown practically anywhere, whereas food crops depend more onland and labour costs in mass production. Algae are the only feedstock that has the potentialto completely replace the world’s consumption of transportation fuels compare to otheralternatives. Table 1: Comparison of various sources of Bio Diesel (Scott S 2010)3.2 Micro algaeMicro 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 cangrow 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 basedon whether they grow optimally in freshwater or saltwater. The level of salinity influences theoverall productivity, as well as individual production rates of lipids and carbohydrates in eachstrain of algae (Kunjapur et al. 2010).Using waste water for this application provides two significant benefits: the algae receive aninexpensive medium rich in required nutrients and the waste water is further treated in theprocess (Kunjapur et al. 2010). Not all strains of micro algae can grow in open ponds dueexposure to atmospheric air that contains contaminants. Dunaliella, Spirulina, and Chlorellastrains grow in environments exceptionally high in salinity, alkalinity, and nutrientsrespectively (Lee Y K 2001). For example, the cyanobacterium, Spirulina Platensis, growsbest in highly alkaline media with a pH of up to 10; Dunaliella Salina is the most salt-toleranteukaryotic alga known, and produces its maximum intracellular concentrations ofcommercially 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 massiveamounts of . Hence, it is reported that Chlorella sp. can be grown under 40% CO2 3
  9. 9. conditions and has commercial value (Huntley et al. 2006). The percentage of oil contentbased on dry weight of Chlorella sp. is around 28-32% (Campbell 2008).3.3 Effect of LightSunlight is the ultimate energy source of micro algae. Although the wavelength range ofsolar radiation is very broad, only radiation of the range between 400 and 700 nm can beused by micro algae (Janssen 2002). It has been found that when Algae are subjected tocertain light/dark cycles where the light period is characterized by a light gradient, that theselight/dark cycles will give higher productivity and biomass yield compared to algae exposedto constant light. (Barbosa 2003) Light/dark cycles are associated with two basicparameters: first, the light fraction, i.e., the ratio between the light period and the cycle timeand second, the frequency of the light/dark cycle. (Barbosa M 2003)The overall biomass yield (specific growth rate over specific light absorption rate) can varyover different light regimes. In experiments that have been carried out (Janssen 2002), itwas found that the overall biomass yield of Chlorella reinhardtii under an 8.5/4.4s light/darkcycle was considerably lower than the yield under continuous illumination. As a result, thedark period will have to be shorter than 4.4s for optimal light utilization efficiency (Janssen2002). 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 algalproductivity (Janssen 2002).The graph (Fig 1) obtained from the research done by Marcel Janssen shows how thePhoton Flux Density (PFD) varies with increasing depth. For raceway ponds, sunlightimpinges on the surface and is absorbed inside the culture; the photon flux density willdecrease with increasing depth (Janssen 2002). 4
  10. 10. Figure 1 – Variation of PFD with depthFurther the formulation of the correction due to non-optimal illumination is derived fromSteele’s equationwhere 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 isreached, and beyond that optimal value, production varies in accordance with the type oflight source. That is, algal growth curves under condition of continuous light and intermittentlight (typically 14 hours of light, 10 hours of dark) are unique and species dependent. Subjectto intermittent light, growth rates approach a constant value, which is a function of theintermittency of the light, as the light intensity increases. (James et al. 2010) Below is agraph that shows the variation of growth with respect to the light intensity. 5
  11. 11. Figure 2 – Variation of algae growth with respect to light intensity (James et al. 2010)3.4 Types of Algae Growth SystemsSuspend-based open pond raceways and enclosed photo bioreactors are the two mainmethods used for algal-bio-fuel production presently (Wen et al. 2009). Although photobioreactors (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 areconsidered 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 verydifficult to maintain a monoculture of one desired strain of algae in an outdoor openenvironment. (Kunjapur et al. 2010) Although it is not so much of a problem once a higheralgal density is obtained. Loss of water is considered another drawback of open pondsystems 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 concentrationof algae.A comparison of the pond systems shows that open ponds cost $76,000 per hectare whilethe raceway pond that is a special type of open pond that costs $161,000 and the closedbioreactors cost $348,000. (Johnson et al. 1988) Table 2: Comparison of Biomass production systems (Brennan et al 2009)3.5 Photo BioreactorsThe three main categories most generally suitable for large-scale cultivation aretubular/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. 12. pumping, or energy used to compress gas for sparing), and have much embodied energy inthe materials of construction, although this might be offset by the higher productivity ofclosed systems (Scott 2010).A photo bioreactor is a more modern design for growing algae in which the growth area isentirely enclosed. Nutrients are added, cooling is done actively, more elaborate pumpingmechanisms are used, and active removal of waste by products is accomplished. By using aclosed system, PBRs are able to use a mono-culture of algae which allows for higher-lipidcontent strains to be selectively grown. Photo bioreactors are also typically able to have ahigher concentration of algae, at approximately 28 times the biomass concentration in thebroth. This increased concentration allows for more efficient extraction from solution (Neltneret al. 2008).3.6 Raceway PondsRaceway systems are plastic lined, shallow (30 cm deep) ponds, which allow good controlover conditions (such as supply). They use paddle wheels to mix the algae. Individualgrowth ponds are up to about 0.5 ha in size, although larger sizes are feasible (Beneman etal. 1993).Our research project is focused on the open system of algae cultivation, mainly the openraceway pond system. This will be further discussed in the section 3.16 (ExperimentalRaceway Pond).3.7 Effect of TemperatureMicroalgae 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 beenattributed to increased photo inhibition at sub-optimal low morning temperatures. (Richmondet al 1980) A 10 to 15 increase in the morning culture medium temperature results in asignificant increase in the yield of Chlorella grown in outdoor raceway ponds. Results alsoindicate that a 4 increase in the culture temperature in the morning can significantlyincrease the daily biomass production, as the optimum growth temperature of this strain isbetween 23 and 28 . These results suggest that this increase in productivity is mainly dueto 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 AlgaeTo produce lipids the micro algae must be exposed to gas. It has been experimentallyshown that Chlorella cells exposed to high partial pressures (p ) experienced 7
  13. 13. declined growth rates. can be supplied via diffusion through a gas permeablemembrane in order to provide sufficient to the entire culture. This also preventsinhibition at high gaseous p concentrations from 1% to 5% (by volume) often leading tomaximum growth (Lee et al. 1991).3.9 DimensionsOblong 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 betweenraceways is assumed to be 3 meters, and space for the burn in the centre of the raceway isassumed to be 2 meters (Richardson et al. 2010).3.10 TurbulenceIn practice, for the algae to experience light/dark cycles then turbulence must be provided sothat the algae are not allowed to stagnate. Local motion has important consequences tomicro algae cells. If it is too large, viscous stresses may mechanically damage the cells orotherwise interfere with growth processes. If it is too small, vital mass transfer of nutrientsand wastes may be impeded (Thomas et al. 1990). Large-scale turbulence is important inalgal culturing in as it can intermittently mix cells in dense cultures into lighted zones formaximum photosynthesis and growth (Thomas et al. 1990). The effects of turbulence onorganisms such as micro algae smaller than the Kolmogorov inertial-viscous length scale depend on the stress, , where is the dynamic viscosity, p is thedensity, 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 andkinematic viscosity of fluid v. Due to this (depending on the algae growing) there needs to besome form of turbulent flow around the raceway ponds to obtain exposure to the light sourceintermittently for the algae, and in order to achieve this effect paddle wheels are used. (Lew2010)In practical cases, utilization of high-intensity light would be enhanced only by inducingturbulent streaming in culture suspension (Ketheesan et al. 2011).3.11 Velocity/FlowA range of velocities have been recommended by various researchers over the years incultivating algae. Tredici (2004) states that, in practice, mixing velocities of 30–35cm sec-1 8
  14. 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 inraceway ponds for algae cultivation (Benemann 1994). Mixing also prevents settling of cellsand avoids thermal and oxygen stratification in the pond. A velocity of 5 cm/s is sufficient toavoid 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 channelvelocity can be used to calculate the velocity of the flow and also calculate power required tomaintain flow (Ketheesan et al. 2011).3.12 PaddlewheelsPaddle wheels have emerged as the preferred method for mixing high-rate ponds for thefollowing 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, whichimproves harvest ability; (3) They are mechanically simple, requiring a minimum ofmaintenance; (4) Their drive train can easily be designed to accommodate a wide range ofspeeds (high turn-down ratio) without drastic changes in efficiency; (5) They do not requireintake 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 thealgae and creating flow in the raceway pond. Presently paddle wheels are considered themost efficient and cost effective method of mixing the algae. A paddle wheel is a liquid flowcreator in which a number of scoops are set around the periphery of the wheel. The caneasily create flow velocities of 20-30 cm/s.The dimensions and specifications for paddle wheels in open raceway ponds may vary, butfor large scale operations, diameters of 1500mm have been recommended. The use of asump of around 100 mm depth for higher efficiency is good. The shaft and spokes should becreated from mild steel pipe and the blades from 2mm mild steel plate and also crimped toprovide greater resistance to bending (Andersen R et al. 2005). Alternatives for the paddlewheel 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, especiallyat lower pond depths. However they create dead zones of flow and a vortex that is notsuitable for the algae (Lew 2010). Airlift driven raceway reactors have also been thoughtabout to replace paddle wheels. Although not practically used in large-scale ponds yettheoretically they seem to be over 80% more efficient than paddle wheels for similar flowvelocities (Ketheesan et al. 2011). 9
  15. 15. 3.13 Depth/PFDIn practice the depths usually used are in the range of 300mm (0.3m). The reason higherdepths are not used is because light intensity/photon flux density decreases with increasingdepth. According to experiments recorded by Janssen (2002) (Fig 1) for a 300mm pond witha photon flux density (PFD) of 1000 micro mole per m2/s, the PFD gets to nearly 0 at a300mm depth. (Janssen 2002) Borowitzca (in Andersen 2005) mentions an equation derivedby 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 lightpenetration to about ⅔ its depth (Borowitzca in Andersen R et al. 2005). Using higherdepths means using more energy to create flow as well. Therefore using a depth of 300mmis 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 PowerThe hydraulic power required for maintaining flow can be used as a measuring tool to checkthe efficiency of the pond when we add improvements to the design. The electrical powerused by the motor powering the paddlewheel will also give us an indication of the efficiencyof the system.3.15 Use of InsertsThe use of inserts in raceway ponds is something that has been implemented very recentlyand is still in an initial research stage. The main objective of using inserts in raceway pondsis to deflect the flow to the outer edge of the bend before the start of the bend, in order tomaximize the turning circle and so minimize centrifugal effects (Paterson et al. 2010). This 10
  16. 16. would also decrease the power consumption of the paddlewheel as it can operate at a lowerspeed 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 examplesare islands and asymmetric inserts. The figures below are some examples of how thisinserts look like when implemented in an algae raceway pond. Figure 5 (Ben-Amotz C) Figure 4 (Ben-Amotz B)3.16 Experimental Raceway PondThe modified raceway pond under consideration is one that has been modified by theCSIRO’s own staff. The raceway pond built by the CSIRO is designed in the shape of anautomotive raceway circuit, where the water moves around in a rectangular and shallowpond. This artificial pond used in the cultivation of algae is lined with plastic. There arepresently two identical ponds built side by side at the CSIRO site. This enables us to useone as a control during experiments. Each pond contains a paddle wheel to provide motiveforce and keeps the algae circulating around the pond. It has changes made to reduce lossof kinetic energy around hairpin bends and also to keep the channel cross-sectional areaperpendicular 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 thedepth near the hairpin bends. Presently the tests performed on the design have been donethrough CFD (Computational Fluid Dynamics) which have given favourable results. The taskof our team is to practically see if the results agree with the theoretical ones. What should benoted here is that in the computer simulation the length and width of the pond has beengiven as 96m x 5m, which is much larger than the dimensions of the test pond. The testpond is approximately 1.5m long and 1m wide. 11
  17. 17. 4.0 Methodology4.1 ExperimentsThe 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 forthe sponges to travel one lap around the pond was measured using a stopwatch. This iscalled particle tracking velocimetry. Using this technique we were able to determine theflowpath of the sponge which corresponded to the highest velocity. The path of the spongewhere the flow was highest was observed as depicted by the red line in (Figure 11) Figure 6 - Top View of Raceway 12
  18. 18. Figure 7 – Side View of Raceway Pond Figure 8 – Water Level Height/Probe Depth VariationThe intention of the team was to practically prove that the CFD results can be obtained inreal life and to determine if the changes made to the standard raceway pond can helpreduce energy consumption. This would help reinforce the idea that producing bio diesel viamicro algae may become cost effective and viable method.4.2 Calculation of Time Required For Algae to Be In DarknessThe distribution of turbulence intensity in a channel is an indicator of a flow’s abilityto 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 thealgae 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 crudeassumptions, but it is imperative that some numbers are formulated for this area wherefurther research has been conducted. Data from numerous studies have been used tocalibrate a theoretical model for the downstream turbulence intensity. (Wren 2000) 13
  19. 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 turbulenceintensity 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 whileV’ and W’ do not make much of a contribution (Nagakawa 1975). These assumptions wereused 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 bedUsually slopes are in the range of 1/100 (Mulbry 2008) Therefore using g=9.81 m/s2, h=0.3m, S=0.01 the mean friction velocity was calculated asUsing 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. 20. The mean instantaneous velocity was used to calculate the mean instantaneous angularvelocity 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 formedduring calculations.Using the equation , the period for the algae to make one whole cycle within thedepth was found to be 2.14s.Field observations have indicated that light is permitted to penetrate two thirds (2/3) of theactual culture depth. (Borowitzka)Using simple trigonometric calculations the angle covered by the algae during the dark 1/3was 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 canbe seen that the present flow rate and depth are acceptable to be used with the chlorellaspecies.4.3 Reynolds number calculationThe turbulence of the water can be calculated by finding the Reynolds NumberRe = VD/μ (1) where Re- Reynolds Number, V- velocity of Water, D- Hydrodynamic Diameter, μ– Kinematic ViscosityReynolds 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 theliquidHydraulic radius = Rh = A/P, where A is the cross sectional area of flow and P is the wettedperimeter, i.e. the perimeter of the channel that is in direct contact with the water.Density of sea water, ρ = 1029kg/m3 15
  21. 21. Mean velocity, u = 0.20m/sViscosity of sea water, µ = 1.08x10-3 Pa.sCross sectional area of flow, A = 0.3m deep x 0.429m wide = 0.1287m2Wetted perimeter, P = 0.3m deep + 0.429m wide + 0.3m deep = 1.029mHydraulic radius, Rh = 0.1287m2/1.029m = 0.125mReCHANNEL = 1029x0.125x0.20/ (1.008x10-3) = 23819As ReCHANNEL is greater than 1000 therefore flow in the paddlewheel pond is turbulent. (LewS 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 solutionBreak down of the Detailed operating instruction for safe operation ofraceway ponds 5 ponds before experiment.Paddlewheel gearsand blades catching on Place a protective cage over any moving parts of thehands of operator 4 paddlewheel.Motor of thepaddlewheel 3 Set a speed limit for the motor avoid overheat.Electric controls near Waterproof containment box for all electricalwater 3 equipment to avoid short circuit.Power box next to Waterproof containment box for all electricalwater tanks 3 equipment to avoid short circuit.Weight of the water Ensure the joints and links are able to supports theand structure for ponds 3 heavy weight of the ponds. Table 3 – Risk Assessment Analysis5.1 Justifications  The priority number stands for the importance of the case. 16
  22. 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 Procedure6.1 Apparatus:1. FP111-FP211 Global Water Flow Probe details Figure 10 – Global Water Flow Probea. Range: 0.3-19.9 FT/S (0.6-6.1M/S)b. Accuracy: 0.1 FT/Sc. Averaging: True digital running average updated once per secondd. Display: LCD, Glare and UV Protectede. Sensor Type: Turbo-Prop propeller with magnetic pickupf. Instrument weight: FP111: 2 Lbs. FP21: Lbs 17
  23. 23. g. Approx. Length: FP222 3’ to 6’ FP211 5’ to 15’h. Materials: Probe: PVC and anodized aluminum with stainless steel water bearingi. 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 ProcedureRaceway 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. 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 pondRaceway Pond without InsertFollow procedure for raceway pond with insert for points P1, P2, P3, P13, P14 and P15 only.7.0 Results and Discussion7.1 Practical ResultsParticle tracking velocimetry was performed using approximately 1cm3 sponge pieces totrack where the flow was highest. The red line shows the path of the highest velocity. Figure 11 - Observation from particle tracking velocimetryThe speed of the paddlewheel could be controlled using the motor speed controller. The barcharts below (Figure 12 & 13) give the velocity taken at points 1-15 for 5 speed controllervalues at water heights 80mm and 120mm. The velocity values are in m/s. The Figure 14shows how paddle wheel RPM changes on different days when all other variables are keptconstant. 19
  25. 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 PointsFigure 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 PointsFigure 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 HzFigure 14 - Variation of Paddle wheel RPM vs Controller Hz for 80mm height for readings ondifferent days 20
  26. 26. Since 2 readings per depth isn’t adequate to do a statistical analysis on the data, 10repetitions were done at the depth of 80mm in the positions P2, P5, P8, P11, P14. Anotherset of 10 readings were taken at a water height of 120mm at depths 25mm, 60mm, 95mmmeasured from the top of the water surface. To minimize the error due to varyingpaddlewheel speeds for the same depth, the velocities were divided by the paddle speed tonormalize it. Below is a plot of the average value of the 10 readings with upper control limitsand lower control limits calculated at 95% confidence interval. The values of the initial twosets 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. 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 theraceway without inserts. Two sets of readings were taken from the raceway without insert fordepths 55mm to 95 mm. 55mm in the raceway without inserts correspond to 80 mm in theraceway with inserts as it has a slope of maximum height 25mm the raceway without insertsdoesn’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. 28. 7.2 Computational Fluid Dynamics Results for 80mm water heightWhen the CFD tests were performed a velocity of 0.3 m/s was provided to the fluid. Theassumption 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. 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 1-157.3 Comparison on CFD and Practical resultsCSIRO used two different CFD models to compare with our practical values. Below are theresults they obtained by using the Rigid Free Surface: Steady state Solution model and theDeformable Free Surface: Time- averaged solution models at depths 80mm and 120mm forthe raceway with the insert. 24
  30. 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 case7.4 Observations7.4.1 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. 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 DiscussionWhen 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 pondOf these Objectives the team was able to get experimental values that could be analyzed toprove 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 dataover a larger range than initially anticipated, the goal of investigating the effect of lightflickering on algae could not be accomplished. 26
  32. 32. The main objective of the team was to provide as much experimental data as possible to theCSIRO so that they could use them to compare with their CFD models. We have managedto provide the CSIRO with two sets of readings for water heights of 70 – 120 mm. Fromfurther readings we took at 80mm and 120mm water level height, we also have provided astatistical 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 similarresults in most instances. This confirms that the CSIRO’s CFD calculations are consistentwith practical estimates.There is much less variation in velocity across the breadth of the channel in the raceway withthe insert when compared to the one without. This shows that the flow of the fluid issmoother around the insert and that the insert reduces the reverse flow around hairpinbends.Because there is considerably less reverse flow around hairpin bends it can be seen that byusing an insert it saves energy that would otherwise be lost to make the water flow in theright 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. 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 ConclusionThroughout 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. Itreduces reverse flow that is common around hairpins. Apart from that, there is also lessvariation across the breath of the channel where the insert is present. This results in lessloss 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 thepoints P2, P5, P8, P11 and P14 at a water level height of 80mm, the CFD should be in therange plotted in the Figure * with an allowance for an extra +/- 30% outside the range due toexperimental errors.The team is able to state with 95% confidence that the range of normalized velocities versusdepth for the points P5, P8 and P11 at a water level height of 120mm, the CFD should be inthe range plotted in the Figure * with an allowance for an extra +/- 30% outside the rangedue to experimental errors.When establishing this statistical claim, we have made the assumption that the 5 pointstested are applied to all 15 points in the raceway system.8.1 Recommendations for further workSome of the recommendations we can offer are that for future research into the algaeraceway 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, araceway that is ideally scaled and practically proportionate should be used in furtherexperiments. Ideally, any future raceway designs should incorporate two inserts on eitherend.On top of that, the inserts should also be varied to have different radii to investigate theeffect it has on the flow around hairpins. Another point worth nothing is the apparatus usedto measure the flow in the raceway pond. Smaller diameter probes should be used to allowfor the analysis of velocities at shallower depths in the raceway pond. 28
  34. 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. 35. Donna A. Johnson, Joseph Weissman, Raymond. Goebel, 1988, An Outdoor TestFacility for the Large-Scale Production of Microalgae, Presented at the Institute of GasTechnology Energy from Biomass and Wastes XIIE. 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 microalgaeHiroji Nagakawa, Iehisa Nezu & Hiroshi Ueda, September 1975, Turbulence of openchannel flow over smooth and rough bedsJ. C. Welssman, R. P. Goebel, 1987, Design and Analysis of Microalgal Open PondSystems for the Purpose of Producing Fuels, A Subcontract ReportJames W. Richardson, Joe L. Outlaw, and Marc Allison, 2010, The Economics ofMicroalgae OilJohn R. Benemann J, 2008, Photobioreactors – Comparative Economics, Presented at5th Annual World Congress on Industrial Biotechnology, ChicagoLiam Brennan, Philip Owende, 2009, Biofuels from microalgae—A review oftechnologies for production, processing, and extractions of biofuels and co-productsMarcel Janssen, 2002, Cultivation of microalgae: effect of light/dark cycles on biomassyieldMaria J. Barbosa, Marcel Janssen, Nienke Ham, Johannes Tramper, René H. Wijffels,2003, Microalgae Cultivation in Air-Lift Reactors: Modeling Biomass Yield and GrowthRate as a Function of Mixing FrequencyMario R. Tredici, 2004, Handbook of Micro algal Culture 30
  36. 36. Mark E. Huntley and Donald G. Redalje, 2006, CO2 Mitigation and renewable oil fromphotosynthetic microbes: a new appraisalMark E. Huntley and Donald G. Redalje, 2006, CO2 Mitigation and renewable oil fromphotosynthetic microbes: a new appraisalMatthew N Campbell, 2008, Biodiesel: Algae as a Renewable Source for Liquid FuelMicheal A Borowitzka, Culturing Micro Algae in Outdoor Ponds, Algal CulturingTechniques, Elsevier Academic Press.Navid Reza Moheimani, Michael A. Borowitzka, 2006, Limits to Productivity of the AlgaPleurochrysis Carterae(Haptophyta) Grown in Outdoor Race way PondsScott C. James Aand Varun Boriah, 2010, Modeling Algae Growth in an Open-ChannelRaceway, Journal Of Computational Biology Volume 17, Number 7, Pp. 895–906Simon Lew, 2010, Experimental Investigation into Raceway Ponds and the use ofImpellers as a potential paddlewheel replacement for water propulsionStuart 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 andprospectsWalter Mulbry ,Mulbry, Shannon Kondrad, Carolina Pizarro, Elizabeth Kebede-Westhead, 19/5/2008, Treatment, Treatment of dairy manure effluent using freshwateralgae: Algal productivity and recovery of manure nutrients using pilot-scale algal turfscrubbers 31
  37. 37. William H. Thomas and Carl H. Gibson, 1990, Effects of small-scale turbulence onmicroalgaeYuan-Kun Lee & Hong-Soon Tay, 1991, High CO2partial pressure depresses productivityand bioenergetic growth yield of chlorellaYuan-Kun Lee, 2011, Microalgae mass culture systems and methods: Their limitationand potentialYusuf Chisti, 2007, Biodiesel from microalgaeZhiyou Wen, Michael B. Johnson, 2009, Microalgae as a Feedstock for BiofuelProduction, PUBLICTION 442-886 32
  38. 38. Appendix A – Experimental ResultsFirst set of result with the insert26/8/2011 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. 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 26/8/20114/8/2011 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. 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 4/8/201110/8/2011 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. 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 10/8/20115/8/2011 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

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