The Impact of Raceway Mixing and Light Penetration on Algal Growth A

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The Impact of Raceway Mixing and Light Penetration on Algal Growth

A thesis presented to

the faculty of

the Russ College of Engineering and Technology of Ohio University

In partial fulfillment

of the requirements for the degree

Master of Science

Thatchai Thanapisudwong

December 2016

This thesis titled

The Impact of Raceway Mixing and Light Penetration on Algal Growth

THATCHAI THANAPISUDWONG

has been approved for

the Department of Civil Engineering

and the Russ College of Engineering and Technology by

R. Guy Riefler

Associate Professor of Civil Engineering

Dennis Irwin

Dean, Russ College of Engineering and Technology 3

ABSTRACT

THANAPISUDWONG, THATCHAI, M.S., December 2016, Civil Engineering

The Impact of Raceway Mixing and Light Penetration on Algal Growth

Director of Thesis: R. Guy Riefler

Raceway ponds for microalgae are broadly used in cultivating microalgae for high production because of their effectiveness and cost savings. One of the challenging factors of microalgae growth is how to optimize the impact of mixing during cultivation. The effects of light characteristics, LED, and fluorescence, are also significant for growing microalgae. Mixing paddlewheel speeds were investigated using speeds at 11 rpm, 13 rpm, and 15 rpm. Maximum algae concentrations and growth rates were higher with higher rpm. Shear calculations showed that these mixing rates were not high enough to injure cells. Maximum velocities in raceways varied from 16.5 to 41.8 cm/s, adequate for mixing. LED lights were more effective than fluorescent likely due to higher intensity and better radiation spectra. Growth nearly stopped after six days due to high turbidities that greatly diminished light penetration. A CFD model matched measured velocities well and showed eddy problems were more severe at lower mixing rates. 4

DEDICATION

I would provide all knowledge from my thesis to the Thai King Rama V and IX who established and supported Chulachomklao Royal Military Academy. Also, I would thank

all outcomes from my thesis to my family, Dr. Ben J. Stuart, and Dr. R. Guy Riefler. 5

ACKNOWLEDGMENTS

I would sincerely thank my advisor, Dr. R. Guy Riefler, who always supports many things and provides all knowledge from the beginning process until the end for helping my thesis. I really appreciate and thank you again to Dr. Sarah Davis, Dr. Shad

Sargand, and Dr. Ben Sperry for serving on my thesis committees. Furthermore, I would like to thank Prof. Jesus M. Pagan, who encourages and fulfills my thesis works. 6

TABLE OF CONTENTS

Page

Abstract ...... 3 Dedication ...... 4 Acknowledgments...... 5 List of Tables ...... 8 List of Figures ...... 10 Chapter 1: Introduction ...... 12 1.1 Background ...... 12 1.2 Objective ...... 16 Chapter 2: Literature Review ...... 17 2.1 Microalgae ...... 17 2.1.1 Scenedesmus Dimorphus...... 18 2.2 Cultivation System of Microalgae ...... 18 2.2.1 Open Channel Raceway Pond ...... 19 2.2.2 Light on Microalgae Growth ...... 20 2.2.3 Culture Medium ...... 21 2.3 Effects of Turbulence on Microalgae Growth ...... 22 2.4 Mixing Microalgae in Raceway Pond ...... 23 2.4.1 Shear and Eddy Mixing ...... 24 2.5 Mathematical Equations ...... 24 2.5.1 Mathematics of Algae Cultivation System ...... 25 2.5.2 Modeling of the Raceway Pond ...... 25 2.5.3 CFD Modeling System ...... 25 2.5.4 Mathematics of Microalgae Growth Rate ...... 30 Chapter 3: Methodology ...... 31 3.1 Microalgae Culturing ...... 31 3.2 Raceway Pond Operation ...... 33 3.3 Experimental Measurements ...... 36 3.3.1 Light Sensor ...... 36 7

3.3.2 Velocity Measurements...... 37 3.3.3 Algae Concentrations ...... 38 3.3.4 Growth Calculations ...... 39 3.3.5 Shear Calculations ...... 39 3.3.6 Statistical Analysis of Results ...... 41 3.4 CFD Development ...... 41 Chapter 4: Result and Discussion ...... 46 4.1 Microalgal Growth under Different Light and Mixing Conditions ...... 46 4.2 Light Penetration in Raceway Ponds ...... 52 4.3 Statistical Analysis of Variables Affecting Growth ...... 53 4.4 CFD Simulation of Raceway Ponds ...... 58 4.5 Shear Rate ...... 69 Chapter 5: Conclusions ...... 74 Chapter 6: Recommendations ...... 78 References ...... 79 Appendix A: Cartesian Coordinates ...... 87 Appendix B: Input Data of the CFD Model...... 88 Appendix C: Statistics Data of the Light Intensity ...... 98 Appendix D: Descriptive Measurements ...... 108 8

LIST OF TABLES

Page

Table 1. The Comparable Resource of Biodiesel (Chisti, 2007)...... 18 Table 2. Status of Microalgae Cultivation in Open Channel Raceway Ponds (Sheets, 2013)...... 20 Table 3. Chemical Components of BG Medium 11 (Stanier et al., 1971)...... 33 Table 4. Light Quality Conditions Used in the Study with S. dimorphus...... 35 Table 5. The Parameters and Boundary Conditions for the CFD Model ...... 42 Table 6. The Data for CFD Simulation of a Vane Model ...... 43 Table 7. Descriptive Statistics of Dependent Variables: Maximum Optical Density and Maximum Growth Rate ...... 55 Table 8. Between-Subjects Factors ...... 56 Table 9. Tests of between Subjects Effects on Dependent Variables between Maximum Density and Maximum Growth Rate...... 57 Table 10. Percent Differences between the Results of the CFD Model and the Flow Meter ...... 68 Table 11. The Reynolds Number Calculation for Channel Flow at 25.0ºC at Six Locations...... 69 Table 12. The Percent Dead Zone from a Flow Meter at Six Locations...... 70 Table 13. The Rotational Speed (RPM) with Shear Rate (s-1) and Shear Stress (Michels et al. 2010) ...... 71 Table 14. Measured Standard Light Intensity for LED and Fluorescent Lights ...... 98 Table 15. Scan of Light Wavelength Detected from Fluorescent Bulbs at Position 1 ..... 98 Table 16. Scan of Light Wavelength Detected from Fluorescent Bulbs at Position 1 ..... 99 Table 17. Scan of Light Wavelength Detected from Fluorescent Bulbs at Position 1 .. 100 Table 18. Scan of Light Wavelength Detected from Fluorescent Bulbs at Position 1 ... 101 Table 19. Scan of Light Wavelength Detected from LED Bulbs at Position 1 ...... 101 Table 20. Scan of Light Wavelength Detected from LED Bulbs at Position 1 ...... 102 Table 21. Scan of Light Wavelength Detected from LED Bulbs at Position 1 ...... 103 Table 22. Scan of Light Wavelength Detected from LED Bulbs at Position 1 ...... 104 Table 23. Optical Density (λ = 680 nm) of Algae Grown in the Raceway Pond ...... 104 Table 24. The Specific Growth Rate for Algae Grown in Raceway Pond ...... 105 Table 25. Light Penetration at a Depth of 4 in in a Raceway Pond with an LED Light Source ...... 105 Table 26. Light Penetration at a Depth of 4 in with an LED Light Source ...... 105 Table 27. Light Penetration at a Depth of 4 in with an LED Light Source ...... 106 Table 28. Light Penetration at a Depth of 4 in in a Raceway Pond with a Fluorescent Light Source ...... 106 Table 29. Light Penetration at a Depth of 4 in in a Raceway Pond with a Fluorescent Light Source ...... 106 Table 30. Light Penetration at a Depth of 4 in in a Raceway Pond ...... 107 9

Table 31. The Interaction Effects between the Light Source and Paddle Wheel Speeds on Microalgae Maximum Concentrations and Growth Rates...... 108 Table 32. The Results of the CFD Model and the Flow Meter ...... 108 Table 33. The Calculations of the Reynolds Number for each Location ...... 109 Table 34. The Calculations of the Reynolds Number for each Location ...... 109 Table 35. The Calculations of the Reynolds Number for each Location ...... 109 Table 36. The Calculations of the Reynolds Number for each Location ...... 110 Table 37. The Calculations of the Reynolds Number for each Location ...... 110 Table 38. The Calculations of the Reynolds Number for each Location ...... 110 Table 39. The Calculations of the Dead Zone for each Location ...... 111 Table 40. The Calculation of Shear Rate and Shear Stress ...... 111 10

LIST OF FIGURES Page

Figure 1. Magnified details of S. dimorphus (CCALA, 1962)...... 33 Figure 2. 100 L raceway pond, pH control, and CO2 supply for microalgae growth...... 35 Figure 3. The main components of Apogee light sensor...... 36 Figure 4. Locations of the light sensor readings in the raceway pond...... 37 Figure 5. The general details of an electromagnetic open channel flow meter...... 38 Figure 6. The main components of the vane channel raceway pond...... 42 Figure 7. The main vane raceway pond on the CFD model...... 43 Figure 8. The mesh used for the CFD model of the raceway pond. The blue color is free fluid cells, and the green color is partial cells...... 44 Figure 9. Measured light intensity for LED and fluorescent lights for six locations at a depth of 4 in growth media with no algae...... 47 Figure 10. Scan of light wavelength detected from fluorescent bulbs at position 1 with no algae at a depth of 4 in fresh media. The optimum wavelength for algae ...... 48 Figure 11. Scan of light wavelength detected from LED bulbs at position 1 with no algae at a depth of 4 in fresh media...... 48 Figure 12. Optical density (λ = 680 nm) of algae grown in raceway ponds under different light (fluorescent and LED) and mixing conditions (error bars show one standard deviation from samples in triplicate)...... 50 Figure 13. The specific growth rate for algae grown in raceway ponds under different light (fluorescent and LED) and mixing conditions (error bars show one standard deviation from samples in triplicate)...... 51 Figure 14. Light penetration at a depth of 4 in in a raceway pond with an LED light source...... 52 Figure 15. Light penetration at a depth of 4 in in a raceway pond with a fluorescent light source...... 53 Figure 16. Descriptive dependent variables between the light source and paddle wheel speeds on microalgae maximum concentrations...... 54 Figure 17. Descriptive dependent variables between the light source and paddle wheel speeds on microalgae maximum growth rates...... 54 Figure 18. The velocity field of a vane model at 11 rpm on the water surface...... 59 Figure 19. The vertical velocity field of a vane model at 11 rpm of the location 1 and 6. 59 Figure 20. The vertical velocity field of a vane model at 11 rpm of the location 2 and 5. 59 Figure 21. The vertical velocity field of a vane model at 11 rpm of the location 3 and 4. 60 Figure 22. The particle studies of a vane model at 11 rpm released from a paddle wheel for 50s...... 60 Figure 23. The vertical tracking of the particle studies at 11 rpm released from a paddle wheel for 50s ...... 60 Figure 24. The velocity field of a vane model at 13 rpm on the water surface...... 61 Figure 25. The vertical velocity field of a vane model at 13 rpm of the location 1 and 6. 62 Figure 26. The vertical velocity field of a vane model at 13 rpm of the location 2 and 5. 62 Figure 27. The vertical velocity field of a vane model at 13 rpm of the location 3 and 4. 62 11

Figure 28. The particle studies of a vane model at 13 rpm released from a paddle wheel for 50s...... 63 Figure 29. The particle studies of a vane model at 13 rpm released from a paddle wheel for 50s...... 63 Figure 30. The velocity field of a vane model at 15 rpm on the water surface...... 64 Figure 31. The vertical velocity field of a vane model at 15 rpm of the location 1 and 6. 64 Figure 32. The vertical velocity field of a vane model at 15 rpm of the location 2 and 5. 65 Figure 33. The vertical velocity field of a vane model at 15 rpm of the location 3 and 4. 65 Figure 34. The particle studies of a vane model at 15 rpm released from a paddle wheel for 50s...... 65 Figure 35. The particle studies of a vane model at 15 rpm released from a paddle wheel for 50s...... 66 Figure 36. Average measured velocities compared with CFD model predicted results at six locations in the center of the raceway pond at different mixing rates (error bars indicated one standard deviation from three replicated measurements) ...... 67 Figure 37. A linear regression compared with the CFD model and the flow meter predicted results at six locations in the center of the raceway pond at different mixing rates...... 67 Figure 38. The main 3 files: a body part, a paddle wheel part, and an assembly part...... 88 Figure 39. The body part is modeled in three dimensions...... 89 Figure 40. The paddle wheel part is modeled in a length 25.40 cm and a width 15.24 cm with 8 blades...... 89 Figure 41. The assembly part is combined with the body and the paddle wheel...... 90 Figure 42. The main input wizard data for creating project name and configuration...... 90 Figure 43. The main input wizard data for designing unit system and parameter...... 91 Figure 44. The main input wizard data for designing analysis type and physical feature. 91 Figure 45. The main input wizard data for creating fluid type and flow characteristic. ... 92 Figure 46. The main input wizard data for designing wall parameter...... 92 Figure 47. The main input wizard data for designing thermodynamic, velocity, and turbulent values...... 93 Figure 48. The main input wizard data for designing refinement meshes and optimized resolutions...... 93 Figure 49. The main input boundary conditions for a mass flow...... 94 Figure 50. The main input boundary conditions for an environmental pressure...... 94 Figure 51. The main input boundary conditions for an ideal wall...... 95 Figure 52. The analyzing step for solving and meshing the raceway model...... 95 Figure 53. After designing all steps, the close system with the ideal wall is shown in this model...... 96 Figure 54. The close system with the environmental pressure is shown in this model. ... 96 Figure 55. The fluid cells are analyzed after solving from the model’s calculation ...... 97 Figure 56. The partial cells are analyzed after solving from the model’s calculation...... 97

CHAPTER 1: INTRODUCTION

1.1 Background

Microalgae-based fuels related to biofuel commercialization have been investigated since 1950. When the oil crisis in 1973 occurred, an extensive investigation known as the marine program began. However, the productive technology of microalgae fuels could not be competitive with the petroleum oil even after roughly 18 years of this project (Benemann et al., 1998). Thallophytes, which were plants without stems, roots, and leaves, were roughly known as algae. Their organisms encompassed both multicellular and unicellular forms and also have chlorophyll as the primary photosynthetic pigment (Lee, 2008). Globally, the number of photosynthetic algae species have been estimated at 72500 (Guiry, 2012). 2.70 billion years ago, the oldest algae produced the first oxygen on Earth. Through photosynthesis, light energy was captured by algae and converted into chemical energy, which was stored as carbohydrate molecules from carbon dioxide (CO2) and water (H2O). To create essential membrane lipids, algae synthesized fatty acids within the algal chloroplasts from this chemical energy. In this case the lipids of algae can accumulate to approximately 5.00 to 20.0 % of the total algal mass under optimal growth (Darzins et al., 2008). However, during nutrient limitations and other stresses lipids can accumulate to higher levels. Algae responded to stress by adjusting their lipid synthetic process into accumulating neutral lipids in the form of triacylglycerides that are used to store carbon and energy (Guschina and

Harwood, 2006). An average of 45.7% lipid content was observed after nutrient starvation or photo-oxidative stress (Darzins et al., 2008). In another study, at least 50.0% 13 of dry mass of lipid contents was produced from microalgae. However, the current production scales of biofuels are insufficient to supply high demands and displace the use of conventional fuels (Chisti, 2007). Therefore, this research will examine the relationship between light exposure and mixing speeds in raceway ponds to improve microalgae growth rates.

Recently, several studies in this area have been supported by the Bioenergy

Program for Advanced Biofuels and the Biomass Research & Development Initiative in

2008 and 2012, respectively (Simon and Ziolkowska, 2014). Most researchers concentrated on the increase in biomass productivity through the identification of species with high lipid content and cost minimization during harvesting and extraction of lipid processes (Brennan and Owende, 2009). Additionally, other parameters such as temperature, light intensity, and nutrient could impact microalgae cultivation. Growing either photobioreactors or open raceway ponds could lead to increased algae production during cultivation as well (Al-Barwani et al., 2012). In terms of the light intensity, a light/dark cycle was necessary for the photosynthetic process due to energy capture in the light period and biochemical synthesis in the dark period (Al-Barwani et al., 2012)

Prabakaran and Ravindran (2012) had formerly compared Scenedesmus sp. to other species for retrieving biodiesel production. The results showed that Scenedesmus sp. were the most useful for converting to biodiesel because of its high oleic acid and lipid content. However, the challenges for cultivating Scenedesmus sp. at a large scale include temperature control, nutrient supply, CO2 supply, light intensity, and water availability (Christenson and Sims, 2011). 14

Posten (2009) demonstrated that light intensity, which was one of the reasons for designing photobioreactors, greatly impact growth rates of microalgae. Light intensity effectiveness could be increased through sufficient agitation and aeration. LED light could be used to distribute light into a reactor more uniformly. Another necessary parameter, CO2 can be delivered through surface contact with air, or submerged aerators

(Terry and Raymond, 1985). To compare with closed photobioreactors, open channel raceways were considered to be the most feasible design due to saving capital costs, requiring lower energy inputs, and providing higher algae production (Bassi et al., 2009).

More operational conditions in open raceways for phototrophic algae cultivation have continued since the 1950s (Borowitzka, 1999). In 2006, the total area globally used in the operation of microalgae raceway ponds was facilitated at 440,000 m² (Duran, Isambert,

Joannis, and Spolaore, 2006). Furthermore, the U.S. Department of Energy has encouraged the study of open channel raceways to improve the biomass production from microalgae for producing biofuels (Sheehan et al., 1998).

To sustain microalgae growth rates, water, light, nutrients, and CO2 are required during cultivation. Thus, mixing was an important key to maintaining an equal distribution of algae cells in the water column, so they were equally exposed to nutrients and CO2 that had equal time near water surfaces where the light intensity was highest. In the raceway pond, mixing by paddle wheel was the most common tool to minimize the energy that was needed to run the system (Ketheesan and Nirmalakhandan, 2011). In

1998, Benemann and other researchers stated that one of the major aims for mixing microalgae was how to circulate microalgae in and out of shaded areas. Based on 15 microalgae cultivation in raceway ponds, selecting the optimal water depth is also challenging. In one case, Sheehan and other researchers (1998) described that they attempted to increase microalgae cell production by increasing the depth. However, they found that resulted in the lower cell densities than expected. Increased depth may not increase production because of limited light penetration through the turbid water. Thus, these factors need to be evaluated for effectiveness in operating raceway ponds.

Additional problems cultivating algae include contamination, evaporation, and temperature. For contamination, systems for achieving maximum productivity could be disrupted by the inherent threat from invasive algae species (Borowitzka, 1999). In term of evaporation, lost water needs to be replaced resulting in a potential increase in ion concentration in the medium (Bux, Mutanda, Rawat, and Ranjith, 2013). For the stable temperature, it was especially difficult to control the range of temperature and CO2 solubility for large scales (Mcbride et al., 2014).

The launch of the CFD model allows the impact of the percentage of dead zone and the mixing behavior on algae growth to be tested (Yu et al., 2009). Algae culture systems that are optimized using mathematical models are likely to be more economically feasible. Other properties which are viscous dissipation rates, shear rates, and deformation rates are also known to be important to algae growth (Thomas and Gibson,

1990). Optimizing culture systems using CFD before being built could help save time and costs in the future. 16

1.2 Objective

The objective of this thesis is to determine the impact of raceway mixing and light under LED and fluorescent cultivation on algae growth. Two main objective details are shown in the following specific goals.

The first objective is to assess algal growth in a raceway pond with three different mixing speeds and two different light sources. The response variables measured were concentrations and growth rate. Differences in responses across treatments were assessed statistically.

The second objective is to calculate turbulence and potential impacts of shear by simulating fluid flow behavior in the raceway with CFD, and using light sensors to measure light penetration in variable conditions.

CHAPTER 2: LITERATURE REVIEW

The literature review on this chapter is divided into four parts that relate to how microalgae are grown under several growth conditions. The first section of this research starts with general terms of microalgae species. The second section describes growing systems for microalgae, while the third part explains how turbulence impacts microalgae growth. Lastly, mathematical equations consisting of a microalgae reactor system, mixing model, CFD model, turbulent flow and microalgae growth rate, provide explanations for how those equations are usefully applied and related to this thesis.

2.1 Microalgae

Microalgae can be found in various environments, terrestrial, and marine.

(Tomaselli, 2004). Physically, microalgae species are minimal photosynthetic plants due to no roots, leaves, and stems. Some researchers have estimated more 40,000 microalgae species in the environments (Hu et al., 2008). Microalgae species are autotrophic and can produce protein, polysaccharides, and lipid which are useful for food and biodiesel

(Lavens and Sorgeloos, 1996).

The demand for energy is dramatically increasing. Biofuels from microalgae is being considered as a renewable energy. Previous researchers described that microalgae can be used to produce polysaccharides, oils, and compounds (Borowitzka, 1992 and

Munro et al., 1999). In this case, microalgae consume light to yield oil (Ma and Hanna,

1999). Based on the renewable energy, some kinds of crops: animal fats, soybeans and corn can also be converted to biofuels. In 2007, Christi found that microalgae’s efficiency and the capability of producing oil are considerably better than other plants. The 18 comparable sources of biodiesel in Table 1 show that microalgae can provide the highest oil yields for producing biofuels per hectare of land used.

Table 1.

The Comparable Resource of Biodiesel (Chisti, 2007).

2.1.1 Scenedesmus Dimorphus.

An aquatic unicellular algae, S. dimorphus, is refered to chlorophyceae class. A strain of algae can rapidly grow and synthesize huge quantities of lipid and protein. The specific growth rates mixing with medium have been suggested from 0.232 d−1 to 0.770 d−1 (Welter et al., 2013). The amounts of 35.0% protein, 60.0% carbohydrate, and 37% lipid content were included in the biomass of S. dimorphus. They can be flexibly cultivated over a range of pH from 6.50 to 8.00 (Jiang et al., 2013).

2.2 Cultivation System of Microalgae

Raceway ponds are effective for microalgae cultivation, because the systems are constantly operated and whole mixing circulates CO2 and nutrients during cultivation. 19

These systems only require adequate light, CO2, and nutrients to maintain microalgae growth. Economically, the large cultivation scales of microalgae can reduce high cost and provide high amounts of biofuels (Borowitzka, 1999).

2.2.1 Open Channel Raceway Pond

Open channel raceway ponds have been utilized for large scale production of microalgae biomass since the 1950s (Oswald, 1995). The device is called “raceway” due to its geometry, coupled with a paddlewheel that combines and circulates microalgae growth medium (Demirbas, 2010). These systems are constantly operated, in which the mixing circulates the CO2 and nutrients while cultivating. A controlled semi-continuous system could allow daily harvesting and nutrient addition to maintain steady biomass concentrations. In order to minimize the self-shading effect caused by biomass accumulation, open raceway ponds are most effective when operated at shallow depths and high surface areas, allowing maximum light penetration through the pond surface

(Terry and Raymond, 1985).

Despite difficulties facing open raceway pond systems, researchers continue to study biofuel producing strains in open raceway ponds due to the low capital and operating costs for large scale cultivation. Because capital costs for photobioreactors are so high, these systems will likely not be used for large scale production of microalgae for biofuels. Nevertheless, different light sources and mixing controls impact microalgae productivity in raceway ponds. Table 2 displays a summary of the key different factors between an open raceway pond and a photobioreactor.

Table 2.

Status of Microalgae Cultivation in Open Channel Raceway Ponds (Sheets, 2013).

2.2.2 Light on Microalgae Growth

Light intensity is a limiting parameter in large scale cultivation (Ogbonna et al.,

1995). Light intensity is not homogeneous throughout a growth system, especially when high biomass density causes self-shading. Open raceway ponds rely on available sunlight, meaning seasonal variation likely has significant impact on microalgae biomass and lipid productivity. Since light quality can affect the improvement and biochemistry of algae, artificial light may be especially utilized to manipulate the biomass for high end markets for special uses. To design an artificial light system for micro algal development, the photosynthetic efficiency of LEDs and fluorescents will be evaluated.

The number of photons that may be captured by a particle of chlorophyll in the algae depends upon chloroplast organization, pigment composition, and the cellular architecture. Interestingly, the evolution of algae has been presented for growth rates under either red (λ at 660 - 675 nm) or blue (λ at 450 - 475 nm) light. They provided high oleic acids and lipid contents greater than other plants using light sources for cultivation

(Keeling, 2013). 21

Spectrum quality and light intensity can impact the metabolisms and growth rates of photosynthesis on algae. Microalgae cells may decrease if the light intensity are not close enough to the water surface due to Lambert Beer's law. The light intensity will be declined as a function of the distance from the light surface (Sastre, Csogor, Perner-

Nochta, Fleck-Schneider, and Posten, 2007).

Algae growth rates increase rapidly with increasing light intensity. The saturation constant of light is the light intensity where the distinct biomass growth rate shows 50.0% of the maximum specific growth rate. The maximum sunlight intensity is typically greater than the constant light saturation for the microalgae at noon. Higher light intensity beyond the maximum results in a lower microalgae growth rate due to photoinhibition

(Chisti, 2007).

2.2.3 Culture Medium

Inorganic media for microalgae cultivation must include trace elements, nitrogen and phosphorous sources, chelating agents, and salts (Richmond, 2004). Energy assessments showed that artificial media like BG11 as a medium supplied the most power generation for biodiesel production. However, using domestic secondary effluent with N and P supplementation produced the greatest net energy due to decreased media costs

(Fan, Lui, Yang, and Zhang, 2014).

In their studies, different microalgae media mixes were tested for the best cultivation of S. dimorphus. Five different freshwater microalgae media such as Bold's

Basal medium, M4N medium, BG11 medium, N8 medium and M8 medium were utilized for culturing S. Dimorphus in specific cultures. S. dimorphus was the fastest growth in 22

Bold's Basal Medium or BBM when it was compared to the other media. The next highest growth was observed in M4N and BG11.

2.3 Effects of Turbulence on Microalgae Growth

Hondzo and Wong (2002) investigated the impact of fluid movement on the growth of the freshwater Stigeocloniom, Spirogyra and Periphyton species. The only factor limiting growth was mixing; all the other environmental conditions (temperature, light intensity, and nutrient concentrations) were not limiting growth. Similarly, photosynthetic rates and the Periphyton growth increase were the lowest in a fluid that was stagnant.

For small scale cultivation of microalgae, the effects of water turbulence are significant. Turbulence has two main positive influences on microalgae: moving algae to different light regimes and decreasing the boundary layers for nutrient delivery. Within a certain range of turbulence, microalgae growth rates could be improved with increased mixing (Grobbelaar, 1991). Ogbonna and Yada (1995) investigated the effective movements of microalgae cells by mixing different depths of photobioreactors and observing the impact on algae growth. For a shallow reactor, productivity increased while the productivity for a deep reactor decreased. The researchers summarized that microalgae cells growth and productivity could depend on the species of algae, mixes of each pond culture, requirements of each light intensity, and depths of each raceway pond.

Turbulence can have a negative impact on algae growth. High rates of mixing can cause shear that injures the algae cells. Thomas and Gibson (1990) ranked the sensitivity of different microalgae groups to mixing turbulence. In this case, the relative scale of 23 sensitive inhibition to turbulence were green microalgae < blue-green microalgae < diatoms < dinoflagellates. Furthermore, Warnaars and Hondzo (2006) stated that the flow parameters of turbulence that are the most significant to microalgae survival are shear rate and shear stress.

2.4 Mixing Microalgae in Raceway Pond

Mixing microalgae is not only necessary for the circulation of an algaculture, but also for delivering crucial nutrients to the microalgae.

Effects of the geometry of the system are said to impact development, merchandise quality, and hydrodynamic stress (Gudin and Chaumont, 1991; Perner et al.,

2003; Pruvost et al., 2006). Developed systems may be found by many layouts of open raceway ponds. Among the deciding factors for the layout selections, the fluid is included. For running a bioreactor system, smaller volumes of fluid are included. Either high biomass yields or high value products will need to be reached; so, bioreactor is useful to provide a higher level of a control.

A paddle wheel is used to mix microalgae in standard open pond systems. These systems generate laminar regimes downstream and high turbulence at the wheel (Thomas and Gibson, 1990). Large disturbance at the water wheel can be found (Alias et al.,

2004). The high turbulence can damage microalgae. In one study, doubling fluid velocity report in a raceway pond from 15.0 to 30.0 cm/s. It was believed that the effect of flashing light caused cells to move toward the lit surface and shaded bottom of the raceway ponds (Richmond and Vonshak, 1978). There was no significant increase in 24 algae productivity when the flow rates in another 100 m² were increased from 1.00 to

30.0 cm/s (Weissman et al., 1988).

2.4.1 Shear and Eddy Mixing

There is a limitation to the amount of mixing that can improve growth rates. A systematic balance must be reached between having the ability to spread nutrients and light throughout the cells and damping cells from shear. A lot of valuable algae forms are sensitive to shear. On the other hand, the precise shear speeds that correlate with declines in growth rates is not known and needs to be eliminated to assist with better designs.

Bronnenmeier and Markl (1982) were the first to identify that the overcritical hydrodynamic load would harm micro-organisms due to shear stress that was applied, microorganisms more immune to harm are those of spherical shape and small size. On the other hand, longer rod-shaped microalgae forms like Spirulina sp. were damaged at reduced speeds of shear stress and more susceptible to mechanical forces.

A fluid flow which includes eddy currents defines a powerful indication of the mixing turbulence. These currents advantageously help to move algae cells from poorly lit areas to well lit areas. One study indicates that microalgae growth decreases with the increasing eddy size (Alias et al., 2004).

2.5 Mathematical Equations

A paddle wheel in a real situation is installed in a raceway pond with almost half the number of paddle blades submerged in the water for driving the water flow. To integrate with CFD codes, they are computed by 3D modeling. Water volumes moving through the raceway with a rotating paddle wheel can only be described mathematically 25 by including multi-phases and multi-physics. Thus, computational development in CFD codes can help to solve by dividing through multi-dimensional problems.

2.5.1 Mathematics of Algae Cultivation System

Algae reactor systems have numerous designs when it comes to blending and geometry. It is necessary to comprehend the development routines and model the system mathematically before scaling up. Hopefully, CFD systems can be designed and optimized before being assembled in the future. This may reduce costs on scientific testing of reactor systems which are not adequate.

2.5.2 Modeling of the Raceway Pond

The velocity rate of 5.00 cm. s−1 in raceway pond is adequate to remove thermal stratification and keep most species of algae in suspension. Nevertheless, such a low rate is hard to maintain in a huge pond due to frictional losses in the corners and the channels in the raceway pond. In practice, flow velocities of at least 20.0 to 30.0 cm. s−1 are needed and, if experience demonstrates that more turbulence substantially enhances development, subsequently an additional passive turbulence creating apparatus can be installed in the pond (Oswald 1988a). Nevertheless, higher flow rates need more energy, so increasing operational costs.

2.5.3 CFD Modeling System

Raceway pond design could be optimized using CFD simulations. When the CFD models the bending of the raceway pond, the results demonstrate the prevalence of stagnation areas and the energy reduction (Bandopadhayay et al., 2013). Sompech and other researchers (2012) clearly removed dead zones and found that the raceway 26 arrangement with 3 semi-circular deflector baffles is the most energy efficient.

Additionally, a novel raceway was designed that could create a better mix for photosynthetic development on microalgae (Xu and Waller., 2012). Within the launch of

CFD, some dead zones were generally found in the velocity values less than 9.14 cm/s

Thus, the fraction of the dead zones could be calculated by (Hadiyanto, 2013).

U DZ u0.3 x100% (1) U Average

Variation in dead zone percent and turbulent kinetic energy (TKE) could further impact the effect of hydrodynamics on algae growing (Yu et al., 2009). Several CFD simulations are used to design for photobioreactors. Bitog and other researchers (2011) have presented an evaluation of 35 cases, so the CFD technique was used to study and describe the 35 cases by theoretical CFD models.

The application of fluid flow simulations or CFD is widely acceptable and attractive as CFD can not only aid in the design, but can also provide more reliable and predictable recommendations for all processes. Gavriliouk (1993) presented CFD from

SOLIDWORKS simulation as a usefully developed code for engineering. The modified turbulence model with two equations accurately simulates a broad range of all turbulent scenarios. For boundary Cartesian meshing, it immerses all boundaries to validate all flow resolution. Laminar flows also solved with CFD codes, although they have undoubtedly unique solutions.

Turbulent flows need finely computing meshes to solve them. The fundamental

Navier Stokes equations are used for the turbulent effects that relate to the time average fluid flow characteristics in the volume by semi-empirical models (Wilcox, 1994). In 27

SolidWorks Flow Simulation technique, the equational model is applied to various additional empirical improvements that cover turbulent industrial scenarios. Besides, to solve turbulent conditions, affected boundary layers are significant for simulating near bodies and walls as they can pose difficulties due to the high velocities. Hence, Launder and Spalding (1974) propose that wall functions can be calculated to reduce mesh sizes for the Navier Stokes equations. The Two scale wall functions (2SWF) which are comprised of two approaches will be assembled to calculate thick and thin boundary layers.

The thick boundary layer was shown in:

  Ay. (2) The thin boundary layer was shown in:   Ay. (3) where the boundary of thickness is , the length between computational meshes near a wall and cell fluid meshes near a center to a main wall is y, and if A ≥ 1, flow conditions will depend on a coefficient. To generate the traditionally restricted CFD codes, both of two approaches provide proper fine meshes near the walls and also immerse Cartesian meshes in the calculation domain for whole geometries (Kalitzin and Iaccarino, 2002).

Both the classical k  turbulent models and the Launder-Spalding wall functions specify for wall boundary conditions and Navier-Stokes equations which are called

Enhanced turbulence modeling (ETM).

Turbulent flows in a raceway pond were typically generated by a paddle wheel.

Downstream could become laminar flow in the raceway pond. In general, the flow simulation of SOLIDWORKS was used to generate the Cartesian meshes for the multi- 28 physics conjugate calculations by calculating solid cells, fluid cells and partial cells. For the fluid cells, they were solved by the Navier-Stokes equations, which were formulated through momentum, mass, and energy conservation laws.

Both turbulent and laminar flows could be investigated by the SOLIDWORKS flow simulation. Reynolds number could be used to predict flow behaviors. If the

Reynolds number was less than 2000, it presented laminar flows. The transitional flows generally were observed when the Reynolds number was between 2000 and 4000. More than 4000 would generally correspond to turbulent flows. In this study, the Navier-Stokes equations were used to calculate the turbulent flows where time dependent effects on flow parameters were included. To integrate this system with equations, the turbulent kinetic energy (k) and the dissipation rate (ɛ) transported equations for calculating flows.

Lam and Bremhorst (1981) stated that laminar, turbulent, and transitional flows were combined with the governing equations (see Appendix) and damping functions for the k-ε turbulence model:

kkku   u i  t R i      ij tP B , (4) t  xi  x i  kx i  x j

2  uui t   R i      C f C  P  f C , (5)      1 1 ij  B t B 2 2 t xi x i tx i k x j k  2 22uuiku j ij s ij,  ij   t s ij   k ij, s ij    ij , (6) 33xj  x i  xk

gi 1  PB  , (7) Bix

Where CCC 0.0900,12  1.44,  1.92,k  1.00,  1.30,B  0.900,CB  1.00 29

If PB  0,

CB  0 if PB  0 , the viscosity is defined by:

2 Ck    f () (8) t  

Damping function of Lam and Bremhorst (1981), f , is defined by:

2  0.025RY 20.5 fe 1   1  , (9) Rt

 ky R  , (10) y 

k 2 R  , (11) t 

Where a position from a point to a wall is presented by y and both f1 and f2 from Lam and Bremhorst damping function are formulated by:

3 0.05  f1 1  (12) f

R2 t fe2 1 (13)

In term of Lam and Bremhorst (1981) damping functions , , , both the turbulent viscosity and the turbulent energy are decreased, while the turbulent dissipation rate is increased when the Reynolds number that is moderated by distances from the wall and the average velocity of fluctuations provides small numbers. Also, if the number of ,

, is equal to 1, then the result will present the original k-ε model. 30

2.5.4 Mathematics of Microalgae Growth Rate

The increasing speeds in microalgae cells are related to the amounts of algae cells.

They show the cultivating culture during an exponential growth phase. In this case, µ is used for the exponential growth rate of the microalgae population (Guillard, 1973).

dC  C (14) dt

The transformed solution is

t Ct  C0 e (15)

C ln(t ) CCCln ln  00t (16) tt where the size of tested population at the beginning time period is 퐶0 , the size of tested population at the end of the time period is 퐶푡, and µ is the specific growth rate.

CHAPTER 3: METHODOLOGY

Large scale microalgae production in outdoor raceway ponds for the purpose of creating biofuels is still largely untested. Many operational conditions including mixing rate, temperature, light intensity, light quality, carbon dioxide (CO2) supply, nutrient supply and culture medium impact microalgae cultivation. Currently, few studies investigate the optimal effects for mixing flows and different light sources for microalgae cultivation. Testing these effects was the main experimental objective for this work.

In addition, CFD simulation techniques were explored to mathematically model the mixing and flow in raceway ponds. The flow regime can directly affect microalgae production, and operating costs at the small scales and large scales. Use of CFD techniques could potentially be used to optimize raceway pond and mixing design. In this work, a CFD model was developed and its performance compared to real velocity measurements.

This chapter consists of four parts including algae cultivating, open channel raceway pond operation, experimental measurements, and CFD development.

3.1 Microalgae Culturing

Scenedesmus dimorphus, green freshwater microalgae with high oleic acid and lipid content, were used for experiments (Prabakaran and Ravindran, 2012, see Figure 1).

S. dimorphus was previously ordered to experiment with in other studies and was maintained in a 100 L conical plastic tank. For this study, S. dimorphus was initially diluted with a sock filter and restored in another conical plastic tank at room temperature with a 12/12 hr light dark cycle. Sterile Bristol Medium BG11 (recipe in Table 3) was 32 used for all culturing including the raceway pond (Stanier, Kunisawa, Mandel, and

Cohenbaz, 1971). In this study, we need to weight out 30.0 g of 푁푎푁푂3, and dissolved it in 1.00 L of ultrapure to make a stock solution. Then diluted 1.00 L of the stock solution into a total volume of 5.00 L to make the BG11. Then diluted 5.00 L of BG 11 into a total volume of 100 L raceway pond. We repeated these steps with other components. Hence, this media has an initial total Nitrogen of 250 mg/L and total Phosphorous of 4.93 mg/L by the total Nitrogen and Phosphorous method. After two weeks of culturing, 100 L of dark green algae were concentrated using a 10.0 µm sock filter to a final volume of 95.0

L. After that, the algae concentrate was then blended with fresh BG11 at 5.00 L to total

100 L in the raceway pond.

Filter bags were used because the algae population got contaminated with filamentous cyanobacteria. The sizes of cyanobacteria were bigger than the S. dimorphus, so they were captured in the filter bags while the S. dimorphus pass through. The filtration step allows experimentation with a relatively pure culture of S. dimorphus.

Using 25.0 µm filter bags definitely worked much better than 10.0 µm, but the sizes of microalgae consistent enough to test with the light intensity after dilution. After the dilution, the results of a microscopy purely reported 100% of S. dimorphus species for the study.

Figure 1. Magnified details of S. dimorphus (CCALA, 1962).

Table 3.

Chemical Components of BG Medium 11 (Stanier et al., 1971). Stock Solution Final Stock Number Component Amount Concentration Concentration

1 푁푎푁푂3 10.0 mL/L 30.0 g/200mL d퐻2푂 17.6 mM 2 퐾2퐻푃푂4 10.0 mL/L 0.800 g/200mL d퐻2푂 0.230 mM 3 푀푔푆푂47퐻2푂 10.0 mL/L 1.50 g/200mL d퐻2푂 0.300 mM 4 퐶푎퐶푙22퐻2푂 10.0 mL/L 0.720 g/200mL d퐻2푂 0.240 mM 5 Critic Acid. 퐻2푂 10.0 mL/L 0.120 g/200mL d퐻2푂 0.0310 mM 6 Ferric Ammonium Citrate 10.0 mL/L 0.120 g/200mL d퐻2푂 0.0210 mM 7 푁푎2퐸퐷푇퐴. 2퐻2푂 10.0 mL/L 0.0200 g/200mL d퐻2푂 0.00270 mM 8 푁푎2퐶푂3 10.0 mL/L 0.400 g/200mL d퐻2푂 0.190 mM 9 Tracer Metal 1.00 mL/L

3.2 Raceway Pond Operation

The raceway pond held a working volume of 100 L at a depth of 20.3 cm, a width of 19.9 cm, and surface area of 405 cm². Mixing was provided using a 19.9 cm diameter paddle wheel and operated at 3 different speeds: 11 rpm, 13 rpm, and 15 rpm for 14 days.

These rotation rates were selected by observations of flow. The proper ranges for running 34 the paddle wheel was 11 rpm to 15 rpm. Speeds lower than 11 rpm were too slow and not good for cultivating, whereas at higher than 15 rpm mixing of algae was too fast for cultivating and water splashed out of the raceway pond. Hence, the velocities at 11 rpm,

13 rpm, and 15 rpm would be suitable and minimal with little shear on the algae cultivation. The paddle wheel propelled the fluid around the track and mixed the algae vertically in the water column.

Both pH control and CO2 supply were linked and worked by a pH controller that was set at pH 7.00 (see Figure 2). When pH in the water increased, CO2 delivery would be increased to the pond. The acidic dissolved CO2 adjusted pH back to the set value of

7.00. Light for photosynthesis was provided by either 12 fluorescent bulbs or 288 LEDs which were measured by the light intensity in µmol. m−2. 푠−1(see Table 4). Both of them were operated continuously for the run. It was expected that algae receive resting dark periods as they circulate into deeper parts of the water column. Evaporated water was replaced with reverse osmosis water to maintain a working volume of 100 L. Once inoculated with algae each experiment was run at a certain paddle wheel and light condition (either fluorescent or LED) for 14 days. Over that period measurements of light penetration, algae concentration by optical density, and fluid velocities were made.

Table 4.

Light Quality Conditions Used in the Study with S. dimorphus. Light Source Condition LED Fluorescence Input Voltage 110 v 110 v Input Current 3.45 A 6.90 A Watt 760 W 760 W Frequency 50.0 HZ 50.0 Hz Height from Water Surface 12.5 in 12.5 in 430-470 nm 420-480 nm Wavelength 600-680 nm, 520-620 nm, Red, Blue Color White Orange, White

pH control

CO2 supply

12.5 in

Figure 2. 100 L raceway pond, pH control, and CO2 supply for microalgae growth.

3.3 Experimental Measurements

Six types of measurements were taken in this study: light intensity, water velocity, algae concentrations, growth calculations, shear calculations, and statistics.

3.3.1 Light Sensor

Photosynthetically Active Radiation (PAR) was measured. In this study, a light sensor, Apogee Instruments Quantum Sensors in Figure 3, was used to measure a light intensity at depths of 10.2 cm or half of the water surface in the raceway pond at the six locations (see Figure 4). The units of PAR were μmol m-2.s-1. Both wide dynamic range for high-resolution measurements and low light conditions could be measured by the light sensor. Mechanically, it might be better to test the light intensity with different water levels. However, the results were not significantly different when light intensity was measured closer to the surface. LED or fluorescent light intensity were measured during

14 days at a depth of 4 in to determine the impact of algae growth on light available to the organisms.

Figure 3. The main components of Apogee light sensor.

Figure 4. Locations of the light sensor readings in the raceway pond.

3.3.2 Velocity Measurements

An electromagnetic flow meter (Valeport) was used to determine local velocities at specific locations in the raceway pond (see Figure 5). This small sensor was designed for measuring flow in open channels. As ions in the water passed through the electromagnetic field installed by the instrument, a current was generated.

Electromagnetic flow meters correlate the measured current with the fluid flow velocity.

Precision was reported by the manufacturer for velocity reading of ± 0.500%.

The electromagnetic flow meter was used to investigate water velocity at the three different mixing rates in the raceway pond. The probe was a flat sensor with 2.00 cm in height and 1.00 cm in width and was submerged 1.27 cm under the water. Measurements were taken at six locations. As shown in Figure 4, 1 was after the water was propelled by the water wheel in the straight section, 2 was in the first bend, 3 was the first half of the next straight section, 4 was the second half, 5 was in the second bend, and 6 was in the straight section before the water wheel. These were the same locations where light was 38 measured. At each of the six locations velocity measurement was determined at the center of the channel width.

Figure 5. The general details of an electromagnetic open channel flow meter.

3.3.3 Algae Concentrations

Algae concentration was approximated using optical density. A 5.00 mL sample was collected from location 3 and placed in a zeroed spectrophotometer (HACH

DR3900) set at a wavelength of 680 nm. Absorbance units were used to describe algae concentrations. Another researcher in the group had previously confirmed a linear 39 relationship between absorbance and mass concentration at this wavelength with this organism over this range of absorbance values (Abu Hajar, 2017).

3.3.4 Growth Calculations

Growth rates were estimated assuming exponential growth:

dc t  e (17) dt where, C is the algae concentration, µ is the specific growth rate, and t is the time. Using

OD (optical density) in place of C, and approximating the integral using forward differences, results in:

2.303 log OD  log OD    21 (18) tt21 – 

Using the optical density measurements collected over time, the specific growth rate was estimated at each time step.

3.3.5 Shear Calculations

The turbulence of flow in this study was calculated by finding the Reynolds number for a channel flow with

Ru R  h (19) e µ where ρ was the density of the liquid, 푅ℎ was the hydraulic radius for channel, u was the velocity of each six location, and µ was the dynamic viscosity of the liquid. For 푅ℎ, it could be transformed into 푅ℎ was A/P ;where the cross sectional area was A (width x depth), while the wetted perimeter ((2 x depth) + width) was P. For this study, we 40 obtained these parameters for the water at 25.0ºC and used a raceway width at 19.9 cm and depth at 20.3 cm resulting in 푅ℎ at point 1 to 6 was 6.69 cm.

The result of shear stress on the utility of S. dimorphus was examined. The shear paddle wheel had the following measurements: an inner blade 24.1 cm and an outer cylinder with a radius of 26.7 cm. The shear rate was provided as:

2(RR )   OI 22 (20) ()RROI where the shear rate (푠−1) was γ, the angular velocity was ω, the outer cylinder (cm) was

Ro, and the inner cylinder (cm) was Ri.

2   (21) 60 where the rotational speed (rpm) of the paddle wheel was Ɵ.

The shear stress was provided by:

  ˙  (22) where the shear stress (Pa) was τ, the shear rate (푠−1 ) was γ˙, and the normal viscosity of the water (Pa.s) was η. Shear stress levels might be altered by raising the normal viscosity or by increasing the shear speed. Michels et al. (2010) determined that the shear rate applied in the shear cylinders was suitable to rotate speeds with various levels of shear stress by a Couette device. The results reported that the range of shear stress from 1.20 to

5.40 Pa may affect a reduction in viability of microalgae. Thus, this calculation was compared with the other research papers that if shear rate and shear stress increase, they might affect the microalgae species. 41

3.3.6 Statistical Analysis of Results

Statistical analyses were used to determine if raceway operation impacted cell densities and if the CFD model adequately mimicked actual velocities in the raceway pond. A two-way ANOVA was used to evaluate if different mixing speeds and different light sources affected the cell density and growth of S. dimorphus. A correlation analysis was used to test the relationship between local velocity measurements in the raceway pond and CFD predictions. SPSS software was used with an α level of 95.0%, and p– values of 0.01 and 0.05 to test for significance.

3.4 CFD Development

The CFD model, a vane model as shown in Figure 8, was created for analyzing and comparing the fluid flow field. CFD development produced by 3 steps: a pre- processing step, a solving step, and a post-processing step.

The pre-processing step involves defining the model domain with all parameters, defining the boundary conditions, and discretizing the domain. SOLIDWORKS Flow

Simulation was used to execute the CFD model. This finite element package solves k-ε turbulent model. A rotating circular paddle wheel was created and linked to a raceway pond model (see Figure 6). Parameters and boundary conditions for the CFD model were shown in Table 5. All domain boundaries were considered as a closed system. The finite element meshes included fluid cells and partial cells for this domain. The partial cells span the boundary of the fluid cells as they got split at the boundary (see Table 6 and

Figure 8). Complete in put screens to the model were provided in Appendix.

Table 5.

The Parameters and Boundary Conditions for the CFD Model Parameter Model Water Density Temperature Model Dimension (cm³) Material (kg/m³)(103) (°C) Raceway (88.3).(39.9).(20.3) Plastic 1.00 25.0 Bend π.(19.9²).(20.3) Plastic 1.00 25.0 Wheel π.(24.1²).(15.2) Plastic 1.00 25.0 Environmental Turbulent Turbulent Model Pressure (KPa) Energy Dissipation (ɛ) Wall Condition (κ) (10-4) Raceway 101 0.0165 7.00 Ideal reflection Bend 101 0.0165 7.00 Ideal reflection Wheel 101 0.0165 7.00 Ideal reflection

Figure 6. The main components of the vane channel raceway pond.

Figure 7. The main vane raceway pond on the CFD model.

Table 6.

The Data for CFD Simulation of a Vane Model Paddle Wheel Speed (rpm) Characteristics 11 13 15 Channel depth (cm) 20.3 20.3 20.3 Channel width (cm) 19.9 19.9 19.9 Turn radius (cm) 19.9 19.9 19.9 Iterations 243 241 240 Partial cells 1.16 x10³ 1.15 x10³ 1.42 x10³ Fluid cells 1.09 x10³ 1.09 x10³ 1.19 x10³

Figure 8. The mesh used for the CFD model of the raceway pond. The blue color is free fluid cells, and the green color is partial cells.

To run all processes, both inlet and outlet boundary conditions were required. For a time-dependent solver, full results were used to simulate between the raceway pond and the paddlewheel model, using periodic wall clocks for 50.0 s with a time step of 0.250 s to generate pulsatile fluid flows which are the flows with periodic variations. To contour the pulsated velocities, the inlet and outlet of the CFD system were linked to the main domains which provided the turbulent effects in the raceway pond. For setting the flow inlet and outlet conditions in the CFD model, the environmental pressure was used as the flow metric. SOLIDWORKS interpreted this value as a total pressure for incoming flows and as a static pressure for outgoing flows. To calculate the model systems, the k-ε turbulent model with the governing equations (see Appendices) were solved in a conservation of mass, linear momentum, and energy (White, 1991). 45

In conclusion, S. dimorphus was cultivated to analyze the optical density and the growth rates using LED and fluorescent light sources. Three different speeds (11 rpm, 13 rpm, and 15 rpm) were also tested during cultivations and measurements such as the wavelengths of light, velocity measurements and algae concentrations recorded. Growth rates and shear stresses were calculated and the results analyzed statistically investigate of the impact of mixings and different light sources in the study. Finally, a CFD model was developed to depict flow in the raceway pond under the different mixing rates and compared with the velocity measurements.

CHAPTER 4: RESULT AND DISCUSSION

The results here address the impact of paddlewheel mixing speeds and light penetration on microalgae growth. CFD simulations are compared with fluid flow at mixing speeds in actual raceway ponds.

This chapter is broken down into five sections describing this work. The first details cell growth with different sources and mixing speeds. The second section presents the light penetration in raceway ponds. The third part uses statistical analysis to interpret the impact mixing and light has on microalgae growth. The fourth section demonstrates

CFD simulation of raceway ponds and the correlation of the CFD model predictes velocities with actual velocity measurements, while the remaining section estimates shearing stress exerted on microalgae in the raceway pond.

4.1 Microalgal Growth under Different Light and Mixing Conditions

Because light served as the energy source for algae, LED and fluorescent light sources were used to illuminate the raceway pond during different tests. We measured initial light intensity because they would be compared how strength intensity would be changed during cultivation. The bank of LEDs had a higher light intensity than the fluorescent bulbs. Max light intensity was at position 1 for LEDs at 234 μmol m-2.s-1 and position 3 for fluorescents at 201 μmol m-2.s-1, whereas the lowest light intensity was at position 5 for LEDs and fluorescents at 25.0 μmol m-2.s-1 and 8.00 μmol m-2.s-1. Bulbs were placed over one half of the raceway pond. Thus, positions 1, 2, and 3 showed the highest results, while positions 4, 5, and 6 showed the lowest results. Average intensities 47 at the six positions were 150 μmol m-2.s-1 for LEDs and 88.0 μmol m-2.s-1 for the fluorescents.

300

250

200

150

100

0 1 2 3 4 5 6 Light sensor position Day 0 of LED Day 0 of FLU Figure 9. Measured light intensity for LED and fluorescent lights for six locations at a depth of 4 in growth media with no algae.

The range of wavelengths emitted by the sources were different because of the light characteristics of light waves (see Figure 10-11) for both fluorescents and LEDs.

Hence, optimum wavelengths from 500 to 600 nm were proper for algae photosynthesis.

1.4

1.2

0.8

0.6

0.4 Relative Relative irradiation intensity 0.2

0 400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700

Wavelength (nm) Figure 10. Scan of light wavelength detected from fluorescent bulbs at position 1 with no algae at a depth of 4 in fresh media. The optimum wavelength for algae

2 1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 Relative Relative irradiation intensity 0 400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700

Wavelength (nm) Figure 11. Scan of light wavelength detected from LED bulbs at position 1 with no algae at a depth of 4 in fresh media.

In this study, maximum wavelength for fluorescent bulbs was at 456 nm. Strong emission was also detected from 520 nm to 620 nm. Most plants use chlorophyll a and b.

For type a, light is absorbed up to around 470 nm, and also between 560 nm and 700 nm. 49

Type b absorbs light more weakly from 420 nm to 490 nm, and from 600 nm to 680 nm

(Barreira et al, 2014).

Max wavelength observed for LED was at 681 nm with strong emission from

430-470 nm and 600-680 nm. Max wavelength for fluorescent bulbs was at 456 nm with strong emission from 520-620 nm. Both the fluorescent and LED spectrum appear to match the chlorophyll a range well, although fluorescent bulbs emit significant radiation from 520–560 nm that would likely not be harvested. LED spectra match chlorophyll b detection range better than fluorescent bulbs because all of the radiation from 520-600 nm would likely be lost. It is clear that from the light results, that the algae grown under

LED lights in this study had a significant advantage over algae grown under fluorescent lights because of both higher LED intensity and a better LED light spectrum. Therefore, the results of this study would be useful to choose what kinds of light sources matched well for growing microalgae.

Optical density in raceway pond media over the 14 d runs are shown in Figure 12.

Under both light conditions, algae concentrations increased rapidly over the first 4 d and then stabilized at a constant or slightly decreasing concentration for the remaining 14 d.

The comparative results showed similar concentration trends between the LED and the fluorescent results during day 1 to day 14. All cases experienced exponential growth phase in the similar period from the beginning to the 4th day. LED lights at 15 rpm produced the highest result at 1.47, whereas the lowest result was fluorescent lights at 11 rpm at 0.700. For all experiments with fluorescent lights with mixing speeds at 11 rpm,

13 rpm and 15 rpm, the optical density results peaked at 0.700, 0.890, and 1.22, 50 respectively. For all LED experiments, optical density results with mixing speeds at 11 rpm, 13 rpm and 15 rpm peaked at 0.820, 1.13 and 1.47, respectively.

1.8 1.6 1.4 1.2 1 0.8

Absorbance 0.6 0.4 0.2 0 0 2 4 6 8 10 12 14 Time (day) FLU 55.4411 rpm RPM LED 55.4411 rpm RPM FLU 57.1213 rpm RPM LED 57.1213 rpm RPM FLU 58.8015 rpm RPM LED 58.8015 rpm RPM Figure 12. Optical density (λ = 680 nm) of algae grown in raceway ponds under different light (fluorescent and LED) and mixing conditions (error bars show one standard deviation from samples in triplicate).

As expected, maximum algae concentrations were higher for all LED experiments, consistent with the observation that the LEDs provided a higher light intensity with a better spectrum than the fluorescent bulbs. Interestingly, maximum algae concentrations were higher with more rapid mixing for both lighting choices. A detailed statistical analysis is provided in the following section.

Specific growth rates were estimated using a differencing approximation as described in section 3. For all experiments using LED lights with mixing speeds at 11 rpm, 13 rpm, and 15 rpm, the specific growth rates were maximum initially at 0.520 d−1,

0.670 d−1 and 0.750 d−1, respectively (see Figure 13). For all experiments with 51 fluorescent lights with mixing speeds at 11 rpm, 13 rpm, and 15 rpm, the specific growth rates also peaked initially at 0.470 d−1, 0.400 d−1, and 0.600 d−1, respectively. Growths rate decreased over time approaching zero or becoming negative after day 6. After 14 days, the total specific growth rates showed negative results for both cases.

0.9000

0.8000

0.7000

0.6000

0.5000

0.4000

0.3000

0.2000

0.1000 Specific Growth Specific Growth Rate, µ (1/d) 0.0000 0 2 4 6 8 10 12 14 16 -0.1000

-0.2000 Time (day)

FLU 55.4411 rpm RPM LED 55.4411 rpm RPM FLU 57.1213 rpm RPM LED 57.1213 rpm RPM FLU 58.8015 rpm RPM LED 58.8015 rpm RPM Figure 13. The specific growth rate for algae grown in raceway ponds under different light (fluorescent and LED) and mixing conditions (error bars show one standard deviation from samples in triplicate).

This rapid decrease in growth rate was typical of batch growth because of the culture running out of nutrients or energy. Nitrogen and phosphorous concentrations were not measured in the work, so it is not clear if nutrient deficiency limited algae growth.

However, as shown in the next section by day 4 no light was detected at a depth of 4 in due to turbidity from high algae concentrations, so it is reasonable to assume light loss 52 reduced growth rates. High mixing speeds under LED and fluorescent light significantly impact algae growth rates. A maximum speed at 15 rpm with LEDs yielded the highest specific growth rate at 0.750 d−1, while lowest speed at 13 rpm with fluorescent light yielded the lowest initial growth rate of 0.400 d−1. The highest LED results in this study were close to the previous study (Welter et al., 2013), but the results of fluorescent light were not. Researchers indicated that S. dimorphus should be grown in the range of 0.230 d−1 to 0.770 d−1. Thus, we could see that the high speed under LED light could be suitable for growing S. dimorphus.

4.2 Light Penetration in Raceway Ponds

Light intensity was measured at a depth of 4 in with six locations described earlier. LED light absorbed more than doubled after two days of growth (see Figure 14).

By day four, light detected at depth in the raceway was minimal because high concentrations of microalgae were produced during this exponential growth phase. Light detected did not vary significantly with different mixing rate.

300 250 1) -

s 200 2. - 150 100 (μmol m Light intensity 50 0 1 2 3 4 5 6 Light sensor position LED at Day 0 LED 11 at Day 2 LED 13 at Day 2 LED 15 at Day 2 LED 11 at Day 4 LED 13 at Day 4

Figure 14. Light penetration at a depth of 4 in in a raceway pond with an LED light source. 53

Similar results were observed in the raceway ponds with fluorescent light (see

Figure 15). By day two, light detected was reduced to half the original intensity and by day four light detected was even lower though not as low as with LED lights. Light absorption did not vary significantly with the different mixing rates. As expected from the algae concentration curves, reduction in light intensity was more pronounced in the

LED cultures compared with the fluorescent cultures because of the more rapid growth in the LED cultures.

300

250 )

1 200 - s 2. - 150

100 μmol m ( Light intensity 50

0 1 2 3 4 5 6 Light sensor position

FLU at Day 0 FLU 11 at Day 2 FLU 13 at Day 2 FLU 15 at Day 2 FLU 11 at Day 4 FLU 13 at Day 4 FLU 15 at Day 4 Figure 15. Light penetration at a depth of 4 in in a raceway pond with a fluorescent light source.

4.3 Statistical Analysis of Variables Affecting Growth

The raceway pond results indicate that maximum concentration and growth rates increase with mixing speed and are higher for LED illumination than fluorescent illumination (see Figure 16 and 17). Increasing speeds can affect the mixing turbulent flows, and these can advantageously help to circulate microalgae cells from poorly 54 lighted areas to well lighted areas. Then, microalgae can accelerate growth rates and increase in concentration. In addition, LED lights produced higher growth rates than fluorescents. LED light has a relatively narrow emission spectrum that could provide significant energy efficiency for plants. Moreover, LED light produced more energy compared with fluorescent light.

1.800 1.600 1.400 1.200 1.000 0.800 0.600 0.400 Maximum Maximum optical density 0.200 0.000 0 11 rpm1 13 rpm2 15 rpm3 4 LED Fluorescent Figure 16. Descriptive dependent variables between the light source and paddle wheel speeds on microalgae maximum concentrations.

0.900 0.800 0.700 0.600 0.500 0.400 0.300

Maximum Maximum growth rate 0.200 0.100 0.000 0 11 rpm1 13 2rpm 15 rpm3 4 LED Fluorescent Figure 17. Descriptive dependent variables between the light source and paddle wheel speeds on microalgae maximum growth rates. 55

To clearly interpret all results, two-way ANOVA using SPSS Statistics were used to determine if mixing or light source significantly controlled with maximum concentrations or maximum growth rates.

From the Table 7, the descriptive statistics reported the maximum optical density and the maximum growth rate under different light sources and paddle speeds. Table 8 showed between-subjects factors of all cases that were included in mixing paddlewheel speeds and different light sources. For those descriptive results, we could see that the highest results were shown in both LED and fluorescent light at 15 rpm, while the mixing paddlewheel speeds under the light sources at 11 rpm presented the lowest results with one exception. The maximum growth rate for fluorescent lights at 13 rpm was lower than for 11 rpm.

Table 7.

Descriptive Statistics of Dependent Variables: Maximum Optical Density and Maximum Growth Rate Maximum Maximum Optical Density Growth Rate Light Paddle Wheel (푑−1) (푑−1) N LED 11 rpm 0.820 0.520 3 13 rpm 1.13 0.670 3 15 rpm 1.47 0.750 3 Fluorescent 11 rpm 0.660 0.470 3 13 rpm 0.890 0.400 3 15 rpm 1.22 0.600 3

Table 8.

Between-Subjects Factors Value Label N Light 1 LED 9 2 Fluorescent 9 Paddle Wheel 1 11 rpm 6 2 13 rpm 6 3 15 rpm 6

An analysis of variance for the two-way ANOVA (2 x 3) was evaluated to conduct the effects of the paddle speed mixing under LED and fluorescent light. The two independent variables in this study were different light sources and speed mixings: 11 rpm, 13 rpm, and 15 rpm. The dependent variables were the maximum optical density and the maximum growth rate.

Both standardized skewness and the Shapiro-Wilks test were examined by the normality test which showed the statistical normal curve. The homogeneity of variance was not significant. It met the assumption of the two-way ANOVA was met. The result of two-way ANOVA showed that there was a significant main effect between the paddle wheel mixing and the maximum density, F(2, 2) = 151, p(0.007) < 0.05, but a main effect with the maximum growth was observed, F(2, 2) = 2.98, p(0.251) > 0.05 (see Table 9).

Table 9.

Tests of between Subjects Effects on Dependent Variables between Maximum Density and Maximum Growth Rate. Type III Dependent Sum of Source Variable Squares df Mean Square F Sig. Corrected Max Density .438a 3 .146 120 .00800 Model Max Growth .0730b 3 .0240 4.18 .199 Intercept Max Density 6.39 1 6.39 5249 .000 Max Growth 1.94 1 1.94 333 .00300 Paddle Wheel Max Density .367 2 .184 151 .00700 Max Growth .0350 2 .0170 2.98 .251 Light Source Max Density .070 1 .0700 57.9 .0170 Max Growth .038 1 .0380 6.57 .124 Error Max Density .00200 2 .00100 Max Growth .0120 2 .00600 Total Max Density 6.83 6 Max Growth 2.02 6 Corrected Total Max Density .440 5 Max Growth .0850 5 a. R Squared = .994 (Adjusted R Squared = .986) b. R Squared = .862 (Adjusted R Squared = .656)

The results showed statistically significant interaction between the different light sources and the maximum optical density, F(1, 2) = 57.9, p(0.170) < 0.05, but no significant main effect with the maximum growth, F(1, 2) = 6.57, p(0.124) > 0.05 indicating that any differences between the different light sources were independent upon which the maximum growth of the subject was. Based on the statistical results, we could see that changing paddle speeds from 11 rpm to 15 rpm with different light sources were statistically different with the maximum optical density. 58

4.4 CFD Simulation of Raceway Ponds

A raceway model using CFD was developed to depict the experimental raceway pond with three different mixing speeds: 11 rpm, 13 rpm, and 15 rpm. For each mixing rate, the flow field was estimated using the CFD model. Results were depicted using a color map to show water velocity with arrows to designate velocity direction. These representations are the velocity field on the water surface. Note that yellow represents a zero velocity, green to blue represents negatives velocities or the right to left flow direction, and orange and red represent positive velocities or the left to right flow direction. A second depiction of the flow field was provided using particle tracking.

For these, 300 particle examples were released into the flow field at the paddle wheel and their positions after 50.0 s per round were shown. Particles were also colored based on their final velocity.

For all mixing rates surface speeds increased tremendously after leaving the paddle wheel. Velocities in 11 and 13 rpm runs at the 1st point reached 28.5 cm/s and

34.1 cm/s, respectively. After turning around the first bend, the average velocity at point

2 was around 7.28 and 7.73 cm/s for both cases (see Figure 18 and 24). The particle tracking demonstrates the formation of a large eddy after the first bend for the slowest mixing speed. A significant number of particles collect in this eddy which demonstrates very inefficient mixing and may limit the growth potential in that system. For the 13 rpm model the eddy is greatly reduced. For the results of point 3 to 5, velocities sharply decreased to below 1.00 cm/s because of changing steady streamlines and fluctuated directions in the opposite ways. An eddy does not form after the second bend. 59

Figure 18. The velocity field of a vane model at 11 rpm on the water surface.

Figure 19. The vertical velocity field of a vane model at 11 rpm of the location 1 and 6.

Figure 20. The vertical velocity field of a vane model at 11 rpm of the location 2 and 5. 60

Figure 21. The vertical velocity field of a vane model at 11 rpm of the location 3 and 4.

Figure 22. The particle studies of a vane model at 11 rpm released from a paddle wheel for 50s.

Figure 23. The vertical tracking of the particle studies at 11 rpm released from a paddle wheel for 50s 61

The vertical tracking of 11 rpm (see Figure 23) showed that the large eddy currents after first bend also had a vertical mixing component. This phenomenon was not good for algae growth that were captured in the eddy and not circulated to other parts of the raceway due to the insufficient mixing. This vertical component to the eddy was not present at a mixed rate of 13 rpm (see Figure 29).

However, the CFD model had slightly error between the paddle wheel and the particle tracking due to analyzing within a close system. In this case, the model would analyze all fluid flow areas within the close boundary condition and provide all particle movements after a solving step. To compare with the real paddle wheel movement, the water from the raceway pond would similarly splash out when circulating by the paddle wheel. Therefore, we could see a lot of particle results above the paddle wheel after running the model.

Figure 24. The velocity field of a vane model at 13 rpm on the water surface.

Figure 25. The vertical velocity field of a vane model at 13 rpm of the location 1 and 6.

Figure 26. The vertical velocity field of a vane model at 13 rpm of the location 2 and 5.

Figure 27. The vertical velocity field of a vane model at 13 rpm of the location 3 and 4. 63

Figure 28. The particle studies of a vane model at 13 rpm released from a paddle wheel for 50s.

Figure 29. The particle studies of a vane model at 13 rpm released from a paddle wheel for 50s.

The 3rd speed at 15 rpm showed surface velocities that significantly improved after circulating by a paddle wheel. They provided the highest speed at the 1st point at

38.7 cm/s and similarly decreased around the raceway pond. The average velocity at the

2nd point was 8.47 cm/s, but the velocity was greatly improved compared to lower mixing rates. The particle tracking showed a significant decrease in the eddy and very little accumulation of particles. This flow field behavior may explain the improved 64 growth rate at higher mixing velocities. For the results of point 3 to 5, velocities sharply decreased to under 3.05 cm/s as with the other mixing velocities (see Figure 30).

The vertical tracking of 15 rpm (see Figure 35) showed that there were no large vertical eddy currents and that particles were well distributed. Vertically, we could see in the water column that a higher speed affected particle movements and would improve algae cultivation.

Figure 30. The velocity field of a vane model at 15 rpm on the water surface.

Figure 31. The vertical velocity field of a vane model at 15 rpm of the location 1 and 6. 65

Figure 32. The vertical velocity field of a vane model at 15 rpm of the location 2 and 5.

Figure 33. The vertical velocity field of a vane model at 15 rpm of the location 3 and 4.

Figure 34. The particle studies of a vane model at 15 rpm released from a paddle wheel for 50s. 66

Figure 35. The particle studies of a vane model at 15 rpm released from a paddle wheel for 50s.

Speeds were measured with an electromagnetic flow meter at specified locations in the raceway pond. For comparison to the CFD model, speeds at point 1 after the paddle wheel were highest from 30.0 cm/s to 40.5 cm/s (see Figure 36). Second highest velocities were at point 6 as the water accelerated toward the paddle wheel from 17.4 cm/s to 25.3 cm/s. Velocities were significantly lower at all others under 9.14 cm/s because the bends could impact the fluid flow directions. As shown in the particle studies, the eddy currents were presented after passing the first bend. However, the results of this study showed that velocities gradually improved by raising the paddle speeds.

Based on the previous study, Weissman and other researchers (1988) revealed that there was no significant increase in microalgae growth when the flow rates were changed from 11 rpm to 15 rpm. We could assume that changing speeds in raceway ponds could help to simultaneously mix microalgae from the bottom to the surface. In this case, it appears that higher mixer rates improve light interception. 67

50.0 45.0 40.0 35.0 30.0 25.0 20.0 15.0

Velocity Velocity cm/s in 10.0 5.0 0.0 -5.0 1 2 3 4 5 6 -10.0 Position in raceway pond CFD at 11 rpm Flow meter at 11 rpm CFD at 13 rpm Flow meter at 13 rpm CFD at 15 rpm Flow meter at 15 rpm Figure 36. Average measured velocities compared with CFD model predicted results at six locations in the center of the raceway pond at different mixing rates (error bars indicated one standard deviation from three replicated measurements)

45.0

40.0

35.0

30.0 y = 1.0495x + 0.0473 R² = 0.9999 25.0

20.0

15.0

10.0 Flow Flow meter velocitiy in cm/s 5.0

0.0 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 45.0 CFD velocity in cm/s 11 rpm 13 rpm 15 rpm

Figure 37. A linear regression compared with the CFD model and the flow meter predicted results at six locations in the center of the raceway pond at different mixing rates. 68

To compare the velocities of CFD model and the flow meter, percent differences between the model predictions and the measurements were calculated (see Table 10). For the six locations, the highest percent error was 6.38% and the lowest percent error 4.06%.

In general, there was very close correlation between the CFD model and the measured velocities. The high correlative results of the CFD model and the flow meter provided an excellent linear regression of the water flows in the raceways which was y = 1.05x + 0.0473 and R² = 0.999. In term of scaling up, scaling up algae cultivation was one of the most challenging because it was difficult to control light source, nutrient, contamination, and also water evaporation during cultivation. However, an experiment on a lab scale with a CFD technique could adapt the best mixing and cultivation system ratios from the lab scale into a large scale system because it could be useful to exhibit fluid flow behaviors, microalgae growth rates, various light distributions. Therefore, this is the best way to forecast a low investment and algae cultivation system before scaling up in the future.

Table 10.

Percent Differences between the Results of the CFD Model and the Flow Meter % Change for six locations Position 11 rpm 13 rpm 15 rpm 1 4.91% 4.20% 4.44% 2 5.68% 5.28% 5.78% 3 5.21% 4.27% 4.66% 4 6.38% 4.06% 5.03% 5 6.21% 4.64% 4.86% 6 5.61% 4.70% 5.89% 69

4.5 Shear Rate

Based on the discussion on the effects of turbulence on microalgae growth, the impact of fluid flow movement can affect the growth of microalgae species. Thomas and

Gibson (1990) found that different microalgae groups exhibited sensitivity to mixing turbulence. To examine this potential, the turbulence of fluid flow in the study was estimated by finding the Reynolds number from Table 11 and equation 19. The water density and viscosity were referenced by an international standard at 25.0°C of 997 kg/m³ and 8.90 x 10−4 Pa/s, while the velocities were measured from 6 locations by the cross sectional area was 19.9 cm. The wetted perimeter was solved (20.3 cm x 20.3 cm) + 19.9 cm, so we obtained hydraulic radius by cross sectional area of a wetted perimeter and should for 푅푒 equation (see Appendices). The results of Table 11 showed that Reynolds number from 6 locations were greater than 4000. The highest result was shown in the 1st location at 1.19 x 106, while the lowest result was found in the 4th location at 3.70 x 103.

Therefore, we determined that the fluid flows in the raceway pond were turbulent which is advantageous for circulating microalgae in the raceway pond.

Table 11.

The Reynolds Number Calculation for Channel Flow at 25.0ºC at Six Locations. Raceway pond Location 11 rpm 13 rpm 15 rpm 1 8.85 x105 1.05 x106 1.19 x106 2 2.28 x105 2.41 x105 2.50 x105 3 3.00 x104 6.80 x104 1.03 x105 4 3.70 x103 6.80 x103 5.02 x103 5 8.31 x103 9.64 x103 9.87 x103 6 4.86 x105 6.86 x105 7.65 x105 70

The percent dead zone on Table 12 indicated that the highest dead zone was occurred more than 60.0% at location 2, while location 3 was reported between 10.0% and 30.0% as well. This dead zone is due to the eddy and not circulated to other parts of the raceway due to the insufficient mixing. Mortality in the other locations were not significantly different as high as the 2nd and 3rd locations. In 2013, Hadiyanto investigated that some dead zones could be often found in the velocity values less than 9.14 cm/s.

Therefore, the fractions of the dead zones in this study were useful for predicting the dead zone in the raceway pond.

Table 12.

The Percent Dead Zone from a Flow Meter at Six Locations. Percent Dead Zone (DZ) Location 11 rpm 13 rpm 15 rpm 1 0.00 0.00 0.00 2 83.2 70.0 64.4 3 11.0 19.8 26.7 4 1.35 1.98 1.29 5 3.04 2.80 2.55 6 0.00 0.00 0.00

Based on several discussions on the shear stress from chapter 2, the fluid flow parameters of turbulence most crucial to microalgae survival are shear stress and rate of strain (Warnaars and Hondzo, 2006). We calculated the shear stress using equation 22

(Michels, et al., 2010).

The results of shear stress in this study showed that the shear stresses of the paddle wheel mixing were lower than 1 (see Table 13). To compare the results of previous researches, Michels and other researchers (2010) investigated the effect of shear 71 stress on the viability of Chaetoceros muelleri by using an inner and outer radius of a paddle wheel with a radius of 20.0 and 21.0 mm.

Table 13.

The Rotational Speed (RPM) with Shear Rate (푠−1) and Shear Stress (Michels et al. 2010) Rotational speed Shear rate Shear Stress (rpm) (푆−1) (Pa) 11 11.5 0.0100 13 13.6 0.0120 15 15.7 0.0140

Shear stresses of this study stayed within the ranges of Michels's results between

20.0 and 100 rpm. According to their results, they stated that shear stress values close to

1.00 Pa had no effect on the viability. However, at shear stress levels higher than 1.30 Pa, a sharp drop in viability could be seen with resulting viabilities. Thus, the comparisons between previous research and this study indicate that there was no effect on the microalgae viability due to shear stress from mixing. This was verified by our results that growth rate increased with increasing mixing rates within the bounds tested. we can apply these calculations to predict how much Reynolds number and shear stress affected flow movements before scaling up. Thus, this study could be applied to address more innovative raceway ponds such as adding bends, designing blades, and divining channels to increase the effectiveness of cost, mixing, and energy consumption, which was a main factor to obtain and optimize high microalgae productivity. 72

In summary, S. dimorphus was cultivated to analyze the optical density and the growth rates using LED and fluorescent light sources. Three different speeds: 11 rpm, 13 rpm, and 15 rpm were used during cultivation. The results of microalgae growth showed that under both light conditions, algae concentrations increased rapidly over the first 4 d and then stabilized at a constant or slightly decreasing concentration for the remaining 14 d. The comparative results showed similar concentration trends between the LED and the fluorescent results during day 1 to day 14. The light penetration results indicate that light absorption did not vary significantly with the different mixing rates. As expected from the algae concentration curves, reduction in light intensity was more pronounced in the

LED cultures compared with the fluorescent cultures because of the more rapid growth in the LED cultures.

A statistically significant interaction was observed between the different light sources and the maximum optical density; we could see that changing paddle speeds from

11 rpm to 15 rpm with different light sources resulted in statistically different the maximum optical density. For the CFD simulation of raceway ponds, there were improved flow behaviors such as eddy currents and velocities from 11 rpm to 15 rpm.

Percent differences between the model predictions and the measurements were also measured for the six locations. the highest percent error was 6.38 % and the lowest percent error 4.06 %. Overall, there was a high correlation between the two.

Finally, Reynolds numbers from 6 locations were used to calculate for the effects of turbulent flows. In this study, the highest result was shown in the 1st location at

1.19x106, while the lowest result was found in the 4th location at 3.70x106. Therefore, 73 we could obtain that the fluid flows in the raceway pond were turbulent which is advantageous for circulating microalgae in the raceway pond. Based on the fluid flow parameters of turbulence, the most crucial impacts on microalgae are shear stress and shear rate. There was no effect on the microalgae due to shear stress from paddle wheel mixing which was lower than 1.00 Pa. Scaling up algae cultivation was one of the most challenging tasks which depended on control light source, nutrient, contamination, and so on during cultivation. Nevertheless, the lab experiment with the CFD technique could help to predict the best mixing and cultivation system ratios before application in a large scale system. In addition, it was useful to exhibit fluid flow behaviors, microalgae growth rates, various light distributions for scaling up. Hence, this study is able to be adapted with more innovative raceway ponds such as adding bends, designing blades, and divining channels to obtain the effective cost, mixing, and energy consumption, which are crucial factors to optimize high microalgae productivity in the future. 74

CHAPTER 5: CONCLUSIONS

Raceway ponds were utilized for growing microalgae. However, one of the challenging terms of microalgae growth was how to optimize the impact of mixing during cultivation. This study focused on the impact of mixing and light on microalgae growth. Scenedesmus dimorphus was initially cultured with different speeds under LED and fluorescent lights. Based on these experiments, microalgae were cultivated with

BG11 medium in a raceway pond at 100 L. In this study, the use of continuous LED and fluorescent illumination rapidly increased the optical density of microalgae for the first

4 d. After that turbidity reduced light penetration significantly and growth dramatically slowed. Increasing the paddlewheel speeds (11 rpm, 13 rpm, and 15 rpm) resulted in increased algae optical densities and growth rates. Growing microalgae under LED light was more efficient than under fluorescent light, because the specific wavelengths of LED provided better spectra for growing plants and the light intensity was higher. The results of cells grown during 14 d demonstrated that growing algae under LED light was better than fluorescent light. Microalgae could reach the maximum optical density at 1.47 with

15 rpm under LEDs, while the fluorescent light with the same conditions was only 1.22.

Thus, we could indicate that under both light conditions, algae concentrations increased rapidly over the first 4 d and then stabilized at a constant or slightly decreasing concentration for the remaining 14 d. The comparative results showed similar concentration trends between the LED and the fluorescent results during day 1 to day 14 which the light absorption did not significant affect with the different mixing rates.

Moreover, the highest LED results in this study were similar to the previous research 75 which indicated the suitable range of S. dimorphus from 0.230 d−1 to 0.770 d−1 (Welter et al., 2013), but the results of fluorescent light were not. Therefore, we suggested changing paddle speeds from 11 rpm to 15 rpm with LED and fluorescent light sources statistically affected with the maximum optical density, p(0.170) < 0.05, but there was no significant main effect on the maximum growth, p(0.124) > 0.05. We could see that changing paddle speeds from 11 rpm to 15 rpm with different light sources were significantly affected the maximum optical density.

The fluid flow behaviors were studied using a CFD simulation. A three- dimensional vane model was created for a geometry. The generating meshes were iteratively moved over times so that the combinations between the static and rotation structures could simultaneously calculate the fluid flow fields. Thus, varying fluid flow rates could be characterized by the steady state of rotor speeds. The steady state of the

CFD model properly provided initial conditions for running the whole patterns to automatically adjust the rotating speeds at 11 rpm, 13 rpm, and 15 rpm. The CFD model generated more than 10,000 meshing elements. Meshing calculations generated a structural raceway from the domain data. After post-processing installation, the average highest speed at 11 rpm was reported at 28.5 cm/s, whereas the highest velocities of 13 rpm and 15 rpm were shown at 34.1 cm/s and 38.7 cm/s, respectively. As the fluid flows hit the walls and the curves inside the raceway pond, the CFD predicted formation of a large eddy. Particle studies from the CFD model showed an accumulation of particles in the eddy particularly at the lowest mixing velocities. The eddy was significantly diminished at higher mixing rates. The speeds of 13 rpm and 15 rpm decreased particle 76 stagnation which improved fluid flows. To validate the CFD model, the surface velocities of the CFD model and velocities measured with an electromagnetic flow meter were compared.

Percent differences between the model predictions and the measurements were also measured for the six locations. The highest percent error was 6.38 % and the lowest percent error 4.06 %. This showed the high correlations with each other. Based on the previous study, Weissman and other researchers (1988) revealed that there was no significant increase in microalgae growth when the flow rates were changed from 1.00 cm/s to 30.0 cm/s. However, the results in this study argued that microalgae growth could be significantly improved when increasing paddle wheel speeds. Therefore, we could assume that changing speeds in raceway ponds could help to simultaneously mix microalgae from the bottom to the surface. In this case, microalgae could obtain light sources for their photosynthetic processes.

Based on the discussion on the effects of turbulence on microalgae growth, the impact of fluid flow movement can affect the growth of microalgae species. Reynolds numbers from 6 locations were used to calculate for the effects of turbulent flows. In this study, the highest result was shown in the 1st location at 1.19x106, while the lowest result was found in the 4th location at 3.70 x103. Fluid flows in the raceway pond were turbulent which is advantageous for circulating microalgae in the raceway pond. The fluid flow parameters of turbulence most crucial to microalgae survival are shear stress and shear rate. The results in this study were no effect on the microalgae due to shear 77 stress from paddle wheel mixing which was lower than 1 related to Michels’s study in

2010.

Overall, the effects of light characteristics, LED, and fluorescence, is also significant for growing microalgae. Mixing paddlewheel speeds were investigated using speeds at 11 rpm, 13 rpm, and 15 rpm. Maximum algae concentrations and growth rates were higher with higher RPM. Shear calculations showed that these mixing rates were not high enough to injure cells with max velocities in raceways varied from 30.0 to 40.5 cm/s, adequate for mixing. LED lights were more effective than fluorescent likely due to higher intensity and better radiation spectra. Growth nearly stopped after six days due to high turbidities that greatly diminished light penetration. A CFD model matched measured velocities well and showed eddy problem were more severe at lower mixing rates. The concept of the study was to investigate the impact of mixing and the light on microalgae growth. Based on the results above, we see that increasing paddle wheel speeds under LED light improved microalgae growth. Furthermore, the high correlative results of the CFD model and the flow meter can also use and apply the CFD technique to optimize economical approach for improving the future raceway models as well.

CHAPTER 6: RECOMMENDATIONS

This thesis is performed to present the impact mixing and light on microalgae growth. A CFD model was used to study the fluid flow compared with an electromagnetic flow meter measurements.

Based on the results on previous chapter, five future recommendation are suggested. First, this research should be scaled up with another more innovative raceway pond such as a double vane pond to determine the impact of mixing both an indoor and an outdoor cultivation. Second, the experimental light penetrations have not been performed with sunlight as a light source. Therefore, this study should be investigated under sunlight cultivation to compare the results with the other light sources. Third, this study should be tested with the other microalgae species to compare optimal growth rates which were advantageous for scaling up in the future. Finally, the CFD model has not been tested with the other raceway pond designs; hence, we recommend to model and compare with a different CFD model for a different raceway pond to optimize the best results.

Based on the several recommendations above, we can see that there are more details to analyze those issues for the best optimization, including different mixing speeds, CFD simulations, light cultivations for improving a large amounts of microalgae growth rates that will be useful in the future.

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APPENDIX A: CARTESIAN COORDINATES

Appendix A presents the governing equation for Cartesian Coordinates which are energy equations and Navier Stokes equations are valid for Newtonian fluids. So, this equation will be used to calculate all parameters and boundary conditions from CFD model. Also, κ, ρ, and µ are constant (Currie, 1974).

APPENDIX B: INPUT DATA OF THE CFD MODEL

Appendix B demonstrates the main input data of the CFD model from the beginning step to the end step. The aim of the appendix B will show how to combine a body part, a paddle wheel part, and an assembly part together. Next, it will present how to input all parameters and boundary conditions in the CFD model. Finally, post processing step will show how to report the results.

Figure 38. The main 3 files: a body part, a paddle wheel part, and an assembly part. 89

Figure 39. The body part is modeled in three dimensions.

Figure 40. The paddle wheel part is modeled in a length 25.40 cm and a width 15.24 cm with 8 blades. 90

Figure 41. The assembly part is combined with the body and the paddle wheel.

Figure 42. The main input wizard data for creating project name and configuration. 91

Figure 43. The main input wizard data for designing unit system and parameter.

Figure 44. The main input wizard data for designing analysis type and physical feature. 92

Figure 45. The main input wizard data for creating fluid type and flow characteristic.

Figure 46. The main input wizard data for designing wall parameter. 93

Figure 47. The main input wizard data for designing thermodynamic, velocity, and turbulent values.

Figure 48. The main input wizard data for designing refinement meshes and optimized resolutions. 94

Figure 49. The main input boundary conditions for a mass flow.

Figure 50. The main input boundary conditions for an environmental pressure. 95

Figure 51. The main input boundary conditions for an ideal wall.

Figure 52. The analyzing step for solving and meshing the raceway model. 96

Figure 53. After designing all steps, the close system with the ideal wall is shown in this model.

Figure 54. The close system with the environmental pressure is shown in this model. . 97

Figure 55. The fluid cells are analyzed after solving from the model’s calculation

Figure 56. The partial cells are analyzed after solving from the model’s calculation. 98

APPENDIX C: STATISTICS DATA OF THE LIGHT INTENSITY

This part will show statistical data of LED and fluorescent wavelength and light intensity. Both microalgae concentrations and specific growth rates will be shown in the tables as well.

Table 14.

Measured Standard Light Intensity for LED and Fluorescent Lights Position LED at Day 0 Day 0 of FLU 1 234 160 2 180 64.3 3 219 201 4 123 58.3 5 25.9 8.44 6 117 36.7

Table 15.

Scan of Light Wavelength Detected from Fluorescent Bulbs at Position 1 Wavelength Data Wavelength Data Wavelength Data 400 0.0119 501 0.0816 603 0.714 401 0.0143 502 0.0769 604 0.768 402 0.0165 503 0.0729 605 0.826 403 0.0203 504 0.0696 606 0.892 404 0.0257 505 0.0671 607 0.965 405 0.0309 506 0.0644 608 1.04 406 0.0350 507 0.0626 609 1.12 407 0.0388 508 0.0616 610 1.20 408 0.0456 509 0.0609 611 1.27 409 0.0530 510 0.0599 612 1.30 410 0.0575 511 0.0590 613 1.31 411 0.073 512 0.0584 614 1.31 412 0.084 513 0.0589 615 1.32 413 0.100 514 0.0598 616 1.33 414 0.118 515 0.0603 617 1.34 415 0.141 516 0.0605 618 1.35 99

Table 16.

Scan of Light Wavelength Detected from Fluorescent Bulbs at Position 1 Wavelength Data Wavelength Data Wavelength Data 416 0.166 517 0.0608 619 1.36 417 0.194 518 0.0618 620 1.37 418 0.227 519 0.0629 621 1.37 419 0.266 520 0.0637 622 1.38 420 0.314 521 0.0649 623 1.39 421 0.367 522 0.0666 624 1.40 422 0.423 523 0.0687 625 1.41 423 0.488 524 0.0703 626 1.41 424 0.561 525 0.0716 627 1.42 425 0.640 526 0.0716 628 1.43 426 0.722 527 0.0731 629 1.44 427 0.818 528 0.0755 630 1.45 428 0.925 529 0.0779 631 1.45 429 1.04 530 0.0807 632 1.46 430 1.16 531 0.0833 633 1.47 431 1.30 532 0.0833 634 1.48 432 1.42 533 0.0893 635 1.49 433 1.53 534 0.0923 636 1.49 434 1.61 535 0.0966 637 1.50 435 1.64 536 0.101 638 1.50 436 1.64 537 0.106 639 1.51 437 1.63 538 0.112 640 1.52 438 1.63 539 0.120 641 1.52 439 1.63 540 0.130 642 1.52 440 1.62 541 0.144 643 1.53 441 1.62 542 0.166 644 1.53 442 1.61 543 0.192 645 1.55 443 1.61 544 0.208 646 1.55 444 1.61 545 0.212 647 1.56 445 1.61 546 0.212 648 1.57 446 1.62 547 0.202 649 1.58 447 1.62 548 0.184 650 1.58 448 1.62 549 0.173 651 1.59 449 1.63 550 0.172 652 1.60 450 1.63 551 0.171 653 1.60 100

Table 17.

Scan of Light Wavelength Detected from Fluorescent Bulbs at Position 1 Wavelength Data Wavelength Data Wavelength Data 451 1.63 552 0.170 654 1.61 452 1.64 553 0.168 655 1.61 453 1.64 554 0.167 656 1.62 454 1.65 555 0.164 657 1.63 455 1.65 556 0.165 658 1.64 456 1.66 557 0.168 659 1.64 457 1.66 558 0.171 660 1.65 458 1.66 559 0.173 661 1.67 459 1.65 560 0.174 662 1.68 460 1.65 561 0.177 663 1.69 461 1.65 562 0.179 664 1.70 462 1.65 563 0.183 665 1.71 463 1.64 564 0.187 666 1.71 464 1.63 565 0.189 667 1.72 465 1.61 566 0.193 668 1.73 466 1.57 567 0.196 669 1.74 467 1.50 568 0.199 670 1.75 468 1.42 569 0.203 671 1.75 469 1.33 570 0.207 672 1.76 470 1.22 571 0.210 673 1.77 471 1.11 572 0.212 674 1.78 472 1.02 573 0.217 675 1.79 473 0.930 574 0.223 676 1.80 474 0.844 575 0.228 677 1.81 475 0.763 576 0.235 678 1.82 476 0.690 577 0.240 679 1.82 477 0.624 578 0.246 680 1.84 478 0.565 579 0.254 681 1.84 479 0.510 580 0.261 682 1.80 480 0.461 581 0.267 683 1.68 481 0.418 582 0.277 684 1.53 482 0.378 583 0.286 685 1.35 483 0.346 584 0.292 686 1.19 484 0.319 585 0.298 687 1.04 485 0.293 586 0.308 688 0.915 101

Table 18.

Scan of Light Wavelength Detected from Fluorescent Bulbs at Position 1 Wavelength Data Wavelength Data Wavelength Data 486 0.270 587 0.319 689 0.800 487 0.248 588 0.326 690 0.702 488 0.228 589 0.332 691 0.617 489 0.210 590 0.342 692 0.545 490 0.192 591 0.355 693 0.482 491 0.185 592 0.365 694 0.419 492 0.161 593 0.389 695 0.378 493 0.147 594 0.406 696 0.336 494 0.135 595 0.425 697 0.297 495 0.123 596 0.447 698 0.265 496 0.114 597 0.474 699 0.235 497 0.106 598 0.506 498 0.0996 599 0.538 499 0.0933 600 0.573 500 0.0873 601 0.613

Table 19.

Scan of Light Wavelength Detected from LED Bulbs at Position 1 Wavelength Data Wavelength Data Wavelength Data 400 0.0119 501 0.0816 602 0.660 401 0.0143 502 0.0769 603 0.714 402 0.0165 503 0.0729 604 0.768 403 0.0203 504 0.0696 605 0.825 404 0.0257 505 0.0671 606 0.892 405 0.0309 506 0.0644 607 0.965 406 0.0350 507 0.0626 608 1.04 407 0.0388 508 0.0616 609 1.12 408 0.0456 509 0.0609 610 1.20 409 0.0530 510 0.0599 611 1.27 410 0.0575 511 0.0590 612 1.30 411 0.0727 512 0.0584 613 1.31 412 0.0847 513 0.0589 614 1.31 413 0.100 514 0.0598 615 1.32 414 0.118 515 0.0603 616 1.33 102

Table 20.

Scan of Light Wavelength Detected from LED Bulbs at Position 1 Wavelength Data Wavelength Data Wavelength Data 415 0.141 516 0.0605 617 1.34 416 0.166 517 0.0608 618 1.35 417 0.194 518 0.0618 619 1.36 418 0.227 519 0.0629 620 1.37 419 0.266 520 0.0637 621 1.37 420 0.314 521 0.0649 622 1.38 421 0.367 522 0.0666 623 1.39 422 0.390 523 0.0696 623 1.40 423 0.457 524 0.0701 624 1.40 423 0.488 524 0.0703 625 1.41 424 0.561 525 0.0716 626 1.41 425 0.640 526 0.0716 627 1.42 426 0.722 527 0.0731 628 1.43 427 0.818 528 0.0755 629 1.44 428 0.925 529 0.0779 630 1.45 429 1.05 530 0.0807 631 1.45 430 1.17 531 0.0833 632 1.46 431 1.30 532 0.0833 633 1.47 432 1.43 533 0.0893 634 1.48 433 1.53 534 0.0923 635 1.49 434 1.61 535 0.0966 636 1.49 435 1.64 536 0.101 637 1.50 436 1.64 537 0.106 638 1.50 437 1.63 538 0.112 639 1.51 438 1.63 539 0.120 640 1.52 439 1.63 540 0.130 641 1.52 440 1.62 541 0.144 642 1.52 441 1.62 542 0.166 643 1.53 442 1.61 543 0.192 644 1.53 443 1.61 544 0.208 645 1.55 444 1.61 545 0.212 646 1.55 445 1.61 546 0.212 647 1.56 446 1.62 547 0.202 648 1.57 447 1.62 548 0.184 649 1.58 448 1.62 549 0.173 650 1.58 103

Table 21.

Scan of Light Wavelength Detected from LED Bulbs at Position 1 Wavelength Data Wavelength Data Wavelength Data 449 1.63 550 0.172 651 1.59 450 1.63 551 0.171 652 1.60 451 1.63 552 0.170 653 1.60 452 1.64 553 0.168 654 1.61 453 1.64 554 0.166 655 1.61 454 1.65 555 0.164 656 1.62 455 1.65 556 0.165 657 1.63 456 1.66 557 0.168 658 1.64 457 1.66 558 0.171 659 1.64 458 1.66 559 0.173 660 1.65 459 1.65 560 0.174 661 1.67 460 1.64 561 0.176 662 1.68 461 1.65 562 0.179 663 1.69 462 1.65 563 0.183 664 1.70 463 1.64 564 0.187 665 1.71 464 1.63 565 0.189 666 1.71 465 1.61 566 0.193 667 1.72 466 1.57 567 0.196 668 1.73 467 1.50 568 0.199 669 1.74 468 1.42 569 0.203 670 1.75 469 1.33 570 0.207 671 1.75 470 1.22 571 0.210 672 1.76 471 1.11 572 0.214 673 1.77 472 1.02 573 0.217 674 1.78 473 0.930 574 0.223 675 1.79 474 0.844 575 0.228 676 1.80 475 0.763 576 0.235 677 1.81 476 0.690 577 0.240 678 1.82 477 0.624 578 0.246 679 1.82 478 0.565 579 0.254 680 1.84 479 0.510 580 0.261 681 1.84 480 0.461 581 0.267 682 1.80 481 0.418 582 0.277 683 1.68 482 0.378 583 0.286 684 1.53 483 0.346 584 0.292 685 1.35 104

Table 22.

Scan of Light Wavelength Detected from LED Bulbs at Position 1 Wavelength Data Wavelength Data Wavelength Data 484 0.319 585 0.298 686 1.19 485 0.293 586 0.308 687 1.04 486 0.270 587 0.319 688 0.915 487 0.248 588 0.326 689 0.800 488 0.228 589 0.332 690 0.702 489 0.210 590 0.342 691 0.617 490 0.192 591 0.355 692 0.545 491 0.175 592 0.371 693 0.482 492 0.161 593 0.389 694 0.426 493 0.147 594 0.406 695 0.378 494 0.135 595 0.425 696 0.336 495 0.123 596 0.447 697 0.297 496 0.114 597 0.474 698 0.265 497 0.106 598 0.506 699 0.235 499 0.0933 600 0.573 500 0.0873 601 0.613

Table 23.

Optical Density (λ = 680 nm) of Algae Grown in the Raceway Pond Time Optical Density of LED Optical Density of Fluorescent (day) 11 rpm 13 rom 15 rpm 11 rpm 13 rpm 15 rpm 0 0.130 0.130 0.130 0.130 0.130 0.130 2 0.370 0.500 0.580 0.330 0.290 0.430 4 0.660 0.860 1.22 0.630 0.600 0.890 6 0.700 0.980 1.47 0.660 0.820 1.15 8 0.780 1.06 1.41 0.640 0.880 1.20 10 0.820 1.13 1.37 0.580 0.890 1.22 12 0.760 1.09 1.22 0.520 0.870 1.18 14 0.720 1.05 1.21 0.500 0.790 1.17 Mean 0.710 1.02 1.22 0.550 0.805 1.16 105

Table 24.

The Specific Growth Rate for Algae Grown in Raceway Pond Time µ of LED Cultivation µ of Fluorescent Cultivation (day) 11 rpm 13 rpm 15 rpm 11 rpm 13 rpm 15 rpm 2 0.523 0.674 0.748 0.466 0.401 0.598 4 0.289 0.271 0.372 0.323 0.364 0.364 6 0.0290 0.0650 0.0930 0.0230 0.156 0.128 8 0.0540 0.0390 -0.021 -0.0150 0.0350 0.0210 10 0.0250 0.0320 -0.0140 -0.0490 0.00600 0.00800 12 -0.0380 -0.0180 -0.0580 -0.0550 -0.0110 -0.0170 14 -0.0270 -0.0190 -0.00400 -0.0200 -0.0480 -0.00400

Table 25.

Light Penetration at a Depth of 4 in in a Raceway Pond with an LED Light Source LED LED 11 rpm LED 13 rpm LED 15 rpm Position Day 0 Day 2 Day 2 Day 2 1 235 110 99.9 103 2 180 47.0 40.6 50.2 3 219 120 97.9 114 4 123 54.7 49.8 54.2 5 25.9 11.4 8.60 4.90 6 117 34.9 27.6 30.1

Table 26.

Light Penetration at a Depth of 4 in with an LED Light Source LED 11 rpm LED 13 rpm LED 15 rpm Position Day 4 Day 4 Day 4 1 9.34 6.17 3.74 2 2.26 1.40 0.416 3 12.3 8.36 5.14 4 2.83 0.528 0.848 5 0.0110 0.00 0.00 6 1.06 0.575 0.155 106

Table 27.

Light Penetration at a Depth of 4 in with an LED Light Source LED 11 rpm LED 13 rpm LED 15 rpm Position Day 6 Day 6 Day 6 1 0.838 0.762 0.00 2 0.142 0.00 0.00 3 1.02 0.821 0.00 4 0.0560 0.00 0.00 5 0.00 0.00 0.00 6 0.00 0.00 0.00

Table 28.

Light Penetration at a Depth of 4 in in a Raceway Pond with a Fluorescent Light Source FLU FLU 11 rpm FLU 13 rpm FLU 15 rpm Position Day 0 Day 2 Day 2 Day 2 1 160 79.0 79.4 82.6 2 64.3 26.7 27.5 29.9 3 201 81.9 93.5 88.1 4 58.3 30.4 35.8 35.1 5 8.44 2.26 19.6 4.07 6 36.7 21.1 4.78 20.2

Table 29.

Light Penetration at a Depth of 4 in in a Raceway Pond with a Fluorescent Light Source FLU 11 rpm FLU 13 rpm FLU 15 rpm Position Day 4 Day 4 Day 4 1 25.6 18.7 16.4 2 8.95 7.00 4.09 3 30.3 22.9 20.5 4 10.4 7.94 8.20 5 1.22 0.406 0.648 6 7.27 4.16 4.25 107

Table 30.

Light Penetration at a Depth of 4 in in a Raceway Pond with a Fluorescent Light Source FLU 11 rpm FLU 13 rpm FLU 15 rpm Position Day 6 Day 6 Day 6 1 5.87 4.88 0 2 1.61 1.41 0 3 6.69 6.31 0 4 1.61 1.94 0 5 0.0240 0.00400 0 6 1.07 0.329 0 108

APPENDIX D: DESCRIPTIVE MEASUREMENTS

Appendix D demonstrates descriptive measurements of maximum optical density and maximum growth rate under different cases. Velocity results between the CFD model and the flow meter are also shown in this section. Finally, the significant data of

Reynolds number, dead zone, and shear stress will be presented as well.

Table 31.

The Interaction Effects between the Light Source and Paddle Wheel Speeds on Microalgae Maximum Concentrations and Growth Rates. Maximum Maximum Optical Growth Condition Density Rate LED 11 rpm 0.820 0.523 LED 13 rpm 1.13 0.674 LED 15 rpm 1.47 0.748 FLU 11 rpm 0.660 0.466 FLU 13 rpm 0.890 0.401 FLU 15 rpm 1.22 0.598

Table 32.

The Results of the CFD Model and the Flow Meter CFD Model Raceway Pond Position 11 rpm 13 rpm 15 rpm 11 rpm 13 rpm 15 rpm 1 28.5 34.1 38.7 30.0 35.6 40.5 2 7.28 7.73 7.98 7.72 8.16 8.47 3 0.965 2.21 3.34 1.02 2.30 3.51 4 0.117 0.193 0.220 0.125 0.201 0.232 5 0.263 0.311 0.319 0.280 0.326 0.335 6 16.4 21.3 23.8 17.4 22.3 25.3 Note: RPM is presented in a round per minute 109

Table 33.

The Calculations of the Reynolds Number for each Location Raceway pond Parameter Unit 11 rpm 13 rpm 15 rpm Density kg/m³ 997 997 997 Velocity at 1st point cm/s 30.0 35.6 40.4 Viscosity pa/s 8.90 x10−4 8.90 x10−4 8.90 x10−4 Cross sectional area cm² 405 405 405 Wetted perimeter cm 60.6 60.6 60.6 Hydraulic Radius cm 6.69 6.69 6.69 Re(channel) 8.86 x105 1.05 x106 1.19 x106

Table 34.

The Calculations of the Reynolds Number for each Location Raceway pond Parameter Unit 11 rpm 13 rpm 15 rpm Density kg/m³ 997 997 997 Velocity at 2nd point cm/s 7.71 8.17 8.47 Viscosity pa/s 8.90 x10−4 8.90 x10−4 8.90 x10−4 Cross sectional area cm² 405 405 405 Wetted perimeter cm 60.6 60.6 60.6 Hydraulic Radius cm 6.69 6.69 6.69 Re(channel) 2.28 x105 2.41 x105 2.50 x105

Table 35.

The Calculations of the Reynolds Number for each Location Raceway pond Parameter Unit 11 rpm 13 rpm 15 rpm Density kg/m³ 997 997 997 Velocity at 3rd point cm/s 1.01 2.32 3.51 Viscosity pa/s 8.90 x10−4 8.90 x10−4 8.90 x10−4 Cross sectional area cm² 405 405 405 Wettled perimeter cm 60.6 60.6 60.6 Hydraulic Radius cm 6.69 6.69 6.69 Re(channel) 3.00 x104 6.80 x104 1.03 x105 110

Table 36.

The Calculations of the Reynolds Number for each Location Raceway pond Parameter Unit 11 rpm 13 rpm 15 rpm Density kg/m³ 997 997 997 Velocity at 4th point cm/s 0.122 0.243 0.183 Viscosity pa/s 8.90 x10−4 8.90 x10−4 8.90 x10−4 Cross sectional area cm² 405 405 405 Wettled perimeter cm 60.6 60.6 60.6 Hydraulic Radius cm 6.69 6.69 6.69 Re(channel) 3.70 x103 6.80 x103 5.02 x103

Table 37.

The Calculations of the Reynolds Number for each Location Raceway pond Parameter Unit 11 rpm 13 rpm 15 rpm Density kg/m³ 997 997 997 Velocity at 5th point cm/s 0.274 0.335 0.365 Viscosity pa/s 8.90 x10−4 8.90 x10−4 8.90 x10−4 Cross sectional area cm² 405 405 405 Wettled perimeter cm 60.6 60.6 60.6 Hydraulic Radius cm 6.69 6.69 6.69 Re(channel) 8.31 x103 9.64 x103 9.87 x103

Table 38.

The Calculations of the Reynolds Number for each Location Raceway pond Parameter Unit 11 rpm 13 rpm 15 rpm Density kg/m³ 997 997 997 Velocity at 6th point cm/s 16.5 23.3 25.9 Viscosity pa/s 8.90 x10−4 8.90 x10−4 8.90 x10−4 Cross sectional area cm² 405 405 405 Wettled perimeter cm 60.6 60.6 60.6 Hydraulic Radius cm 6.69 6.69 6.69 Re(channel) 4.86 x105 6.86 x105 7.65 x105 111

Table 39.

The Calculations of the Dead Zone for each Location Percent Dead Zone (DZ) Location 11 rpm 13 rpm 15 rpm 1 0.00 0.00 0.00 2 83.2 70.0 64.4 3 11.0 19.8 26.7 4 1.35 1.98 1.29 5 3.04 2.80 2.55 6 0.00 0.00 0.00

Table 40.

The Calculation of Shear Rate and Shear Stress Rotational speed Outer Inner Viscousity of Shear rate Shear Stress (rpm) (cm) (cm) Water (Pa.s) (푠−1) (Pa) 11 26.7 24.1 8.94 x10−4 11.5 0.0100 13 26.7 24.1 8.94 x10−4 13.6 0.0120 15 26.7 24.1 8.94 x10−4 15.7 0.0140 ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !

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