Chemostat Design ProgressFor

Chlorella vulgaris

ENVE 797Independent Research

Woody ArnoldSpring 2014

Prof. Temesgen Garoma

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Table of Contents

I. Abstract pg. 3

II. Introduction pg. 4

III. Materials and Methods pg. 5 – 8

IV. Results pg. 9 – 13

V. Discussion and Progress pg. 14 - 15

VI. References pg. 16

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I. Abstract

In this project Chlorella vulgaris was analyzed for the optimization of growth for the design of a chemostat. Parameters such as pH, CO2 intake, mixing, and consistency of the experimental procedure were assessed. For the first harvest, tryptose wasn’t used and 1L of the algae was used for the 10L algae/broth mix was used instead of the 500 mL amount of the algae used for the second and third harvests. In the second and third harvests, tryptose was added, but problems arose due to the consistency of the experimental procedures for ensuring a sterile environment. Although it was suspected that Escherichia coli contaminated the second reactor during the second harvest due to brownish floc being stirred with the algae, it didn’t hamper the algae growth. For the linearization of the growth curves the Logistic Model was used to obtain proportionality constants or the K values to get R2 closest to 1 for the optimization of the operation of the batch reactor system through trial-and-error. For the second harvest, the pH was never balanced to 7.5, but still continued to grow and was discovered that the pH range needed for optimum growth is between 6.4 and 6.8, but to be on the safe side, a pH of 7.2 after autoclaving was recommended according to procedure. The recommendations were to monitor the growth of Chlorella vulgaris for a couple more harvests at different air and CO2 flow rates to come up with a more accurate determination of the rate for the exponential growth rate for use in the chemostat design, ensure a sterile environment, use of mirrors or cardboard wrapped with aluminum foil to reflect light to ensure the algae get adequate sunlight, and the installation of a separate sampling tube at each batch reactor to prevent interfering with the bubbling.

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II. Introduction

Over the past decades, with the large concern of our environment and the need for renewing resources, algae shows the biggest promise for the creation of biofuels and as a means to fixate the CO2 in the environment. With current environmental issues that we face today, it isn’t surprising that businesses are arising that focus on the creation of these biofuels out of algae. Changing market dynamics, such as increasing oil prices, growing recognition of the environmental impacts of global warming, technological innovations in energy and environmental fields, and altered consumer attitudes toward green products and services, encourage firms to change their existing business models too. According to Johnson and Suskewicz (2009), governments and businesses need to balance four components to help green industries evolve: enabling technological enabling technological systems, an innovative and customized business model, a market adoption strategy that assures a foothold, and favorable government policies (S. Nair, H. Paulose, 2013). Petroleum shortages and the climate implications of combusting proven reserves have driven research and business ventures into algae-based fuels. This attention stems from several of algae’s seemingly desirable characteristics that set it apart from other biomass sources. The first of these is thatalgae tend to produce more biomass than terrestrial plants per unit area, and unlike terrestrial plants, they can be cultivated on otherwise marginal land using freshwater or saltwater. A fast-growing aquatic alternative to conventional crops is appealing since most developed nationsconsume more energy than they could offset using slow growing terrestrial crops. A second characteristic is that algae do not compete directly with food crops. The United States ethanol boom of 2008 was one of many factors that contributed to a spike in corn prices worldwide, raising complex ethical issues that could be avoided by production of separate crops for food and fuel. The third characteristic is that algae, by virtue of their fast growth rates and aquatic habitat, could be cultivated in systems designed for simultaneous biomass production, uptake of anthropogenic CO2, and removal of certain water pollutants. Although both algae and terrestrial photosynthetic organisms tend to grow faster in the presence of slightly elevated CO2 and nutrient levels, these nutrients are more easily delivered to algae than to terrestrial plants (A.F. Clarens, E.P. Resurreccion, M.A. White, L.M. Colosi, 2009). In this study, Chlorella vulgaris, a microalgae species is observed in batch reactors mixed in broth to determine the microalgae’s growth curve and how pH, the flow ratios of air to CO2, light intensity, limiting nutrients, and other factors such as the contamination of bacteria effect the growth of Chlorella vulgaris for the design of a chemostat.

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III. Materials and Methods

A. Materials Used

1. Description of Chlorella vulgaris

Chlorella vulgaris is a green, single-celled algae that is typically spherical in shape and where the cell sizes are approximately 2-10 µm in diameter. This algae contains both chlorophyll-A and –B and the photosynthetic process is highly efficient with up to 8% efficient. Chlorella vulgaris is one of the most studied and researched out of all algae as a resource for replacing fossil fuels.

A. Materials Used

3 10L Fisherbrand Bottles 3 Tri-Port caps Aalborg Mass Flow Controller GFC Omega Mass Flow Controller FMA 5400/5500 4 4L VWR Flasks 3 Mass Flowmeters for each reactor 3 Fisher Scientific Isotemp Magnetic Stirrers 3 Magnets Tuttnauer Model 5050/5075 Autoclave Machine Mettler AE200 Weighing Scale White Cardboard Philips Fluorescent Light DI Water Thermo Scientific BioMate-3S UV-Visible Spectrometer PVC Tubing 1M Solution of HCL 1M Solution of NaOH 2 400mL Pyrex Beakers Flexaframe 60 ml Syringe Fisherbrand Wash Bottle Thermo Scientific 1300 Series A2 Clean Bench Hach HQ 440d Laboratory Meter 1L Nalgene Cylinder Dessicating Sand 1 mL Cuvettes 2 250mL Pyrex Bottles Air and COs Tanks Yeast Extract (Remel, USA) Beef Extract (Remel, USA)

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Tryptose (Oxoid, UK) FeSO4∙7H2O (Fisher Scientific, USA) Dextrose (Fisher Scientific, USA) Pump

B. Preparation of the Broth

For the procedure for preparing the broth, 6L of the broth was prepared for each 10L Fisherbrand bottle and 2L of broth were prepared for each 4L VWR flasks. So in total 8L of broth had to be prepared with 500 mL of the Chlorella vulgaris culture. For 8L of the broth needed for the bioreactors, 2.67g of beef extract, 2.67g. of yeast extract, 5.33g. of tryptose, 0.011g. of FeSO4

·7H20, 26.67g. of dextrose, and 8L of DI water were needed in total.

C. Overall Set Up of The Batch Reactor System

For the overall setup of the batch reactor system, the air and CO2 tanks were connected with PVC tubing to both the Aalborg Mass Flow Controller GFC for the air inlet flow and to the Omega Mass Flow Controller FMA 5400/5500 for the CO2 inlet flow. Then the PVC tubings from both mass flow meters were connected to a plastic fitting to combine both of the flows into one PVC tube which is then connected to a 4-way fitting that separates the combined flow 3 ways into each batch reactor. For each batch reactor, 3-prong caps were put in place to ensure that air flow was bubbling in the reactors and that the air outlet flow was going to a 250 mL Pyrex bottle filled with 200 mL of dessicating sand while maintaining a closed system within the batch reactors. After the setup with the dessicator bottles to prevent water from going into the individual flow meters for each reactor, PVC tubing were set up to connect to the flow meters to be able to adjust the air flow for each batch reactor and ensure they each get equal flow. For the stirring of the algae/broth solution mixture in the batch reactors, a magnet was dropped in each of the batch reactors and each reactor was being stirred continuously with a Fisher Scientific Isotemp magnetic stirrer. Also a Philips Fluorescent light was attached to the top of a Flexaframe to ensure the algae in the batch reactors receive adequate light for photosynthesis and the whole batch reactor system was covered with white cardboard to ensure the algae are absorbing the light.

D. Experimental Procedure

First, the broth was prepared placing 6L of DI water in each 10L Fisherbrand bottle. Then the yeast extract, beef extract, tryptose, FeSO4∙7H2O, and dextrose were weighed out using aMettler AE200 weighing scale with weights of 2g, 2g, 4g, 0.008g, and 20g respectively. To ensure that all the measurements of each ingredient were consistent and accurate, 5L of DI water was poured into the 10L bottle first while placing the remaining 1L of DI water to be left in a 1L Nalgene cylinder to be used, poured into a Fisherbrand wash bottle, and sprayed on the weighing tray to ensure all of each broth ingredient goes into the 10L bottle. This was done for each 10L bottle. Then 4 4L VWR flasks were prepared with 2L of broth each. Using the same

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procedure as was done for the 3 10L bottles, the measurements for the yeast extract, the beef extract, tryptose, FeSO4∙7H2O, and dextrose were weighed out with the measurements of 0.67g, 0.67g, 1.33g, 0.0027g, and 6.67g respectively. All of the 10 L bottles and 4L flasks were shaked and stirred to ensure a homogenous solution in each and then the pH of each bottle and flask were checked using a Hach HQ 440d Laboratory Meter. If the pH < 7.5, a couple drops from a 1M solution of NaOH was used to increase the pH, but if pH > 7.5, a couple drops from a 1M solution of HCL was used to decrease the pH. The reason that the broth solution needs to be at around a pH of 7.5 prior to autoclaving because the autoclave process will decrease the pH to 7.2. Hence ensuring the broth solution won’t become too acidic for the algae. Then the broth solutions along with necessary equipment such as tubings, tri-port caps, magnets, and measuring cylinders were placed into the Tuttnauer Model 5050/5075 autoclave machine to go through the autoclave process for 30 min. at 131° C and cooled off for 24 hours in the Thermo Scientific 1300 Series A2 clean bench under UV light to ensure no contamination occurs.

For the first harvest, Chlorella vulgaris was harvested from a 10L bottle containing primary sewage effluent. Then the algae were sucked out using a pump in order to transfer 500 mL of it into the 10L bottle and added 500 mL of the broth into the mix to have 10L total of the algae/broth solution. Afterwards, the first initial OD reading of each batch reactor was taken using a Thermo Scientific BioMate-3S UV-Visible Spectrometer for 600nm. The sampling on each batch reactor was done by disconnecting the inlet airflow tube from the reactor and then hooking up a 60ml syringe to the reactor to suck out around 20 ml worth of the algae and then filling up 3 1mL cuvettes. Then the remaining algae in the syringe was placed into a 400mL Pyrex beaker for waste with the other 400 mL beaker, which was filled with DI water to be used to rinse the syringe before taking samples on the next reactor. This was done on each reactor and the syringe was rinsed with DI water three times and the tip sprayed with propanol to ensure it is clean before taking more samples. The ideal air flow rate was set to 192.5 mL/min and the CO2 flow rate was set to 7.5 mL/min. Then the 1 mL cuvettes for each reactor was inverted and OD readings were taken. In this first harvest, tryptose wasn’t added and OD readings were taken daily until the growth plateaued and the algae needed to be harvested again. The experimental procedure for the second and third harvest is the same as the first one, but for the second harvest, the air flow rate was set to 145.5 mL/min with a CO2 flow rate at 4.5 mL/min. and the third harvest set for the air and CO2 rates at the same rates. For the actual procedure for the harvesting of the second and third harvests, instead of transferring the algae from one 10L bottle to the next, all of the transferring were done under the Thermo Scientific 1300 Series A2 clean bench where the algae was poured carefully into a 500 ml cylinder and then transferred to a fresh 10L bottle with newly prepared broth. 2L of broth from the 4L flasks were poured in each reactor and then 500 mL of the broth from the fourth 4L flasks was poured in each reactor in order to have 10L total of the algae/broth mix in total each. Based on the sampling the algae and taking OD readings at 600nm, growth curves were created using Microsoft Office Excel to visually show each growth phases of Chlorella vulgaris of each batch reactor. Then the growth curves were linearized using the following equation based on the Logistic Model:

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ln (( KN )−1)=ln [( K−No

No )−rt ]Where

K = proportionality constant N = OD reading at a certain time (abs)No = Initial OD reading (abs)r = growth rate (1/days)t = time (days)

Using this formula, the K values for each reactor were calculated using Goal Seek on Microsoft Office Excel to determine a K value closest to R2 = 1. Having an R2 equaling to 1 defines an ideal system, but in reality it never happens and the closer the value to 1, the more optimized the system is.

IV. Results

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Based on the OD readings and the sampling of the algae, figures 1-3 show the data collected for Chlorella vulgaris at each reactor:

Figure 1: OD Readings of Chlorella vulgaris for the first harvest.

Figure 2: OD Readings of Chlorella vulgaris for the second harvest.

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Reading 1 Reading 2 Reading 3 Reading 1 Reading 2 Reading 3 Reading 1 Reading 2 Reading 3

2/26/14 03:00 PM 0.00 7.50 200.00 0.273 0.272 0.272 0.308 0.310 0.312 0.286 0.291 0.294 0.272 0.310 0.290 0.001 0.002 0.004

2/27/14 07:10 PM 1.17 7.90 200.00 0.64 0.634 0.634 0.826 0.821 0.806 0.744 0.745 0.735 0.636 0.818 0.741 0.003 0.010 0.006

2/28/14 10:52 AM 1.83 7.60 200.00 0.751 0.750 0.738 0.972 0.976 0.959 0.853 0.831 0.875 0.746 0.969 0.853 0.007 0.009 0.022

3/1/14 12:00 AM 2.38 7.40 195.00 0.984 0.963 0.961 1.243 1.242 1.237 1.083 1.070 1.081 0.969 1.241 1.078 0.013 0.003 0.007

3/2/14 02:24 PM 3.97 7.10 193.00 1.243 1.241 1.233 1.397 1.392 1.372 1.292 1.252 1.284 1.239 1.387 1.276 0.005 0.013 0.021

3/3/14 05:07 PM 5.09 7.60 200.00 1.366 1.370 1.371 1.151 1.151 1.161 1.506 1.508 1.507 1.369 1.154 1.507 0.003 0.006 0.001

3/4/14 04:42 PM 6.07 7.70 199.00 1.406 1.407 1.407 1.697 1.699 1.681 1.581 1.588 1.584 1.407 1.692 1.584 0.001 0.010 0.004

3/5/14 03:10 PM 7.01 7.00 189.00 1.468 1.481 1.459 1.749 1.750 1.744 1.670 1.667 1.670 1.469 1.748 1.669 0.011 0.003 0.002

3/6/14 07:30 PM 8.19 7.60 197.00 1.537 1.524 1.531 1.856 1.851 1.852 1.694 1.681 1.677 1.531 1.853 1.684 0.007 0.003 0.009

3/7/14 07:41 PM 9.20 7.50 192.00 1.662 1.664 1.674 1.946 1.946 1.952 1.831 1.830 1.830 1.667 1.948 1.830 0.006 0.003 0.001

3/8/14 11:00 AM 9.83 7.20 199.00 1.738 1.733 1.735 1.989 1.986 1.997 1.883 1.892 1.892 1.735 1.991 1.889 0.003 0.006 0.005

3/10/14 10:12 AM 11.80 7.40 204.00 1.830 1.834 1.826 2.050 2.057 2.053 1.980 2.000 2.002 1.830 2.053 1.994 0.004 0.004 0.012

St Dev

Reactor #3

Reactor # 3 (600 nm)

Avg

Reactor #1

(600 nm)

Avg

Reactor #2

(600 nm)

Avg

Reactor #3

(600 nm)

St Dev

Reactor #1

St Dev

Reactor #2Date and Time

Time

Elapsed

(Days)

CO2

(mL/min)

Air

(mL/min)

Reactor # 1 (600 nm) Reactor # 2 (600 nm)

Reading 1 Reading 2 Reading 3 Reading 1 Reading 2 Reading 3 Reading 1 Reading 2 Reading 3

3/10/14 07:30 PM 0.00 4.10 146.00 0.166 0.169 0.170 0.168 0.164 0.162 0.174 0.173 0.178 0.168 0.165 0.175 0.002 0.003 0.003

3/11/14 08:31 PM 1.04 4.40 144.00 0.304 0.301 0.302 0.430 0.436 0.437 0.439 0.437 0.442 0.302 0.434 0.439 0.002 0.004 0.003

3/12/14 01:43 PM 1.76 4.20 139.00 0.315 0.322 0.315 0.552 0.561 0.560 0.512 0.505 0.508 0.317 0.558 0.508 0.004 0.005 0.004

3/13/14 04:14 PM 2.86 4.10 140.00 0.353 0.352 0.359 0.645 0.632 0.643 0.651 0.653 0.665 0.355 0.640 0.656 0.004 0.007 0.008

3/14/14 12:53 PM 3.72 4.00 146.00 0.391 0.389 0.396 0.694 0.707 0.697 0.748 0.774 0.776 0.392 0.699 0.766 0.004 0.007 0.016

3/15/14 02:31 PM 4.79 4.00 139.00 0.410 0.408 0.409 0.787 0.788 0.790 0.865 0.870 0.710 0.409 0.788 0.815 0.001 0.002 0.091

3/16/14 06:00 PM 5.94 3.90 143.00 0.430 0.436 0.437 0.831 0.822 0.818 1.013 1.017 1.012 0.434 0.824 1.014 0.004 0.007 0.003

3/17/14 11:17 AM 6.66 4.50 143.00 0.438 0.433 0.433 0.859 0.859 0.879 1.097 1.134 1.142 0.435 0.866 1.124 0.003 0.012 0.024

3/20/14 03:30 PM 9.83 3.80 142.00 1.385 1.391 1.385 1.387 0.003

3/21/14 02:37 PM 10.80 3.90 145.00 1.460 1.470 1.470 1.467 0.006

3/22/14 06:45 PM 11.97 4.20 147.00 1.557 1.558 1.552 1.556 0.003

3/23/14 01:44 PM 12.76 3.90 147.00 1.597 1.596 1.600 1.598 0.002

3/24/14 11:11 AM 13.65 3.80 147.00 1.626 1.629 1.626 1.627 0.002

3/25/14 12:29 PM 14.71 4.40 147.00 1.650 1.661 1.664 1.658 0.007

St Dev

Reactor #3

Reactor # 3 (600 nm)

Avg

Reactor #1

(600 nm)

Avg

Reactor #2

(600 nm)

Avg

Reactor #3

(600 nm)

St Dev

Reactor #1

St Dev

Reactor #2Date and Time

Time

Elapsed

(Days)

CO2

(mL/min)

Air

(mL/min)

Reactor # 1 (600 nm) Reactor # 2 (600 nm)

Figure 3: OD Readings of Chlorella vulgaris for the second harvest.

In Figure 2, for reactors 1 and 2, samplings of the algae and OD readings weren’t conducted due to contamination. The contamination maybe due to the way the experimental procedures were conducted and lack of ensuring sterile conditions throughout. Contamination was suspected because of brown flocculation floating around inside both reactors 1 and 2. Based on the Logistic Model used for the linearization of the growth curves the K values; the proportionality constants were determined. The K values for the first harvest were 1.505, 1.784, and 1.734 for the first, second and third reactors respectively. For the second harvest, the values were 0.5, 1.0, and 1.731 and for the third harvest, the values were 1.313, 1.26, and 1.212. The following figures shows the growth curves and the linearization of the curves for each harvest and the R2 values are shown in each of the linearization curves that correspond to the proportionality constants given.

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Reading 1 Reading 2 Reading 3 Reading 1 Reading 2 Reading 3 Reading 1 Reading 2 Reading 3

3/27/14 05:45 PM 0.00 4.40 145.00 0.122 0.117 0.119 0.124 0.121 0.124 0.119 0.120 0.118 0.119 0.123 0.119 0.003 0.002 0.001

3/30/14 05:34 PM 2.99 4.50 144.00 0.873 0.869 0.872 0.809 0.810 0.815 0.771 0.774 0.779 0.871 0.811 0.775 0.002 0.003 0.004

4/1/14 11:16 AM 4.73 4.20 148.00 0.994 0.994 0.994 0.908 0.915 0.919 0.799 0.806 0.813 0.994 0.914 0.806 0.000 0.006 0.007

4/7/14 03:09 PM 10.89 4.10 145.00 1.187 1.195 1.193 1.030 1.053 1.057 0.999 1.013 1.005 1.192 1.047 1.006 0.004 0.015 0.007

4/8/14 12:33 PM 11.78 4.10 151.00 1.227 1.225 1.222 1.076 1.061 1.079 1.020 1.028 1.031 1.225 1.072 1.026 0.003 0.010 0.006

4/10/14 06:58 PM 14.05 3.40 146.00 1.256 1.251 1.250 1.109 1.099 1.098 1.079 1.077 1.092 1.252 1.102 1.083 0.003 0.006 0.008

4/13/14 07:52 PM 17.09 4.00 148.00 1.290 1.295 1.296 1.184 1.180 1.186 1.135 1.133 1.130 1.294 1.183 1.133 0.003 0.003 0.003

4/15/14 06:52 PM 19.05 8.10 173.00 1.302 1.325 1.300 1.213 1.214 1.208 1.178 1.179 1.171 1.309 1.212 1.176 0.014 0.003 0.004

4/17/14 03:31 PM 20.91 8.10 174.00 1.310 1.311 1.316 1.240 1.249 1.251 1.210 1.196 1.203 1.312 1.247 1.203 0.003 0.006 0.007

St Dev

Reactor #3

Reactor # 3 (600 nm)

Avg

Reactor #1

(600 nm)

Avg

Reactor #2

(600 nm)

Avg

Reactor #3

(600 nm)

St Dev

Reactor #1

St Dev

Reactor #2Date and Time

Time

Elapsed

(Days)

CO2

(mL/min)

Air

(mL/min)

Reactor # 1 (600 nm) Reactor # 2 (600 nm)

0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.000.000

0.500

1.000

1.500

2.000

2.500

1st Harvest

Reactor 1Reactor 2Reactor 3

Time(days)

Abs

Figure 4: Growth Curve of Chlorella vulgaris from the 1st harvest.

0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00

-4

-3

-2

-1

0

1

2

f(x) = − 0.609848432219841 x + 1.22405344986432R² = 0.982952130962613

f(x) = NaN x + NaNR² = 0f(x) = NaN x + NaNR² = 0 1st Harvest (Linearized)

Reac-tor 1

Linear (Reac-tor 1)

Time(days)

ln(k

/n -

1)

Figure 5: Linearized Growth Curve of Chlorella vulgaris from the 1st harvest.

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0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.000.000

0.200

0.400

0.600

0.800

1.000

1.200

1.400

1.600

1.800

2nd Harvest

Reactor 1Reactor 2Reactor 3

Time(days)

Abs

Figure 6: Growth curve of Chlorella vulgaris from the 2nd harvest.

0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00

-4

-3

-2

-1

0

1

2

3

f(x) = − 0.318825454961148 x + 1.6013230151056R² = 0.984484723272918

f(x) = NaN x + NaNR² = 0f(x) = NaN x + NaNR² = 0 2nd Harvest (Linearized)

Se-ries2

Linear (Series2)

Time(days)

ln(k

/n -

1)

Figure 7: Linearized growth curve of Chlorella vulgaris from the 2nd harvest.

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0.00 5.00 10.00 15.00 20.00 25.000.000

0.200

0.400

0.600

0.800

1.000

1.200

1.400

3rd Harvest

Reactor 1Reactor 2Reactor 3

Time(days)

Abs

Figure 8: Growth curve of Chlorella vulgaris from the 3rd harvest.

0.00 5.00 10.00 15.00 20.00 25.00

-7

-6

-5

-4

-3

-2

-1

0

1

2

3

f(x) = − 0.257391292068975 x + 1.18041689511828R² = 0.893337111375333f(x) = − 0.240077906819707 x + 1.02841426820909R² = 0.871856946042425f(x) = − 0.931443365879719 x + 1.0167608483324R² = 0.900046329365304

3rd Harvest (Linearized)

Reac-tor 1

Linear (Reac-tor 1)

Time(days)

ln(k

/n -

1)

Figure 9: Linearized growth curve of Chlorella vulgaris from the 3rd harvest.

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V. Discussion and Progress

Based on the results shown, the first harvest grew very rapidly in approximately 12 days before it need to be harvested again without tryptose. In this harvest pH was balanced and the procedure was done very carefully to prevent contamination. In addition, in the second harvest, the growth of algae was almost as quick as the first harvest despite not ensuring that the pH of the broth is at around 7.5 before the autoclaving process and with the addition of the tryptose. In one study, the optimum pH for maximum growth is between 6.4 and 6.8 with an optimum temperature at 32.4° C. C. vulgaris survived and even grew under very acidic conditions because the photosynthetic system for this algae, which is in the chloroplasts, is surrounded by cytoplasm of neutral pH, but at a temperature of 40° C, the algae becomes less resistant to the acidity. (A.W. Mayo, 1997). As mentioned with the bacterial contamination during the monitoring of the second harvest, although uncertain, it is suspected that it was Escherichia coli that contaminated the second batch reactor because the contamination didn’t effect the growth rate. Although it was theorized that E. coli would slow down the productivity of the algae due to competition for the carbon substrate, it was concluded that a co-culture of the green algae C. minutissima withE. coli under mixotrophic conditions can enhance algal productivity. Although the co-culture represents a simplification of a “real” algae cultivation system, the results provide insight into how algae can respond to a heterotrophic organism (B.T. Higgins, J.S. VanderGheynst, 2014). If this algae, C. minutissima, is able to benefit from growing with E. coli, perhaps this may be beneficial for C.vulgaris as well and should be considered for further research. In another study showing the possibility of how bacteria such as E. coli can benefit C. vulgaris growth, in the case of the gasification of algal biomass, this study analyzed only the use of wild type E. coli for boosting oil yields via a regrowth pathway. In the corresponding model, over half of the organic carbon present in the aqueous phase is unused, which provides a fertile venue for further research on improving biomass growth with the aqueous phase. Microbial species other than E. coli could provide the same utility. Examples that have been examined in other research include P. putida and Saccharomyces cerevisiae. Additionally, E. coli and other species can be engineered for enhanced growth performance with the aqueous phase. Desirable traits include high conversion efficiency of organic carbon into biomass, high oil yield of these organisms when processed via HTL, and high toxicity tolerance for regrowth on aqueous phase products. If the fraction of organic carbon that could be converted into microbial biomass were to be increased from the baseline (45%) to 75% or 100%, for example, the cost of algal oil could be further reduced from$1.61 to $1.55 or $1.50 L-oil−1, respectively investment (N.D. Orfield, A.J. Fang, P.J. Valdez, M.C. Nelson, P.E. Savage, X.N. Lin, G.A. Keoleian, 2014).

In addition to the possibility of further research on the co-growth of E. coli with C. vulgaris related to productivity, the first harvest grew rapidly because of the CO2 intake. The inflow rates were monitored daily and kept at around 200.00 ml/min. and 7.5 mL/min. for the air and CO2 in flows respectively and 1L of the algae culture was used instead of 500 mL for the algae/broth mixture of 10L total. Because this harvest grew so fast and hit the lag phase fairly quickly was the reason a transfer of 500 mL of the algae for the next harvest was used. From a study in

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South Africa, it is recommended to use a CO2 composition for around 4% of the total airflow and a light intensity in the range of 540 to 720 Dmol/m2s. 540 to 720 Dmol/m2s. It is recommended that shorter batch experimental runs be performed as it is within the first three days that the reactor has a chance of achieving a NER greater than , that these reactors should run continuously, and adjustments made in order to constantly harvest a reactor stream with a greater energy content than that of the input stream. This method is more practical for the commercialization of the reactor design (T. Arthur and F. De Flamingh, 2011). NER stands for the net energy ratio.

As for the progress of this project, it is recommended to have ensure that each batch reactor has tube installed on the tri-port caps for sampling without interfering with the air flow, ensure that all procedures are conducted under a sterile environment, and to cover the white cardboards covering the batch reactor system with aluminum foil to reflect more light onto the algae or invest in the use of mirrors. As for the designing of the chemostat, due to setbacks with figuring out why the algae batch reactors were becoming contaminated during the determination of the growth and linear curves, it is recommended to run a couple more harvests, each with varying air and CO2 flows different from what was used previously, to determine a more accurate calculation of the average growth rates for C. vulgaris to keep them in exponential growth for the chemostat design.

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VI. References

Arthur T., De Flamingh F. “Optimising the Delivery of Energy (For Mixing and Mass Transfer) and Light to Algal Photobioreactors” (2011)

Higgins, Brendan T., and Jean S. VanderGheynst. "Effects of Escherichia coli on Mixotrophic Growth of Chlorella minutissima and Production of Biofuel Precursors." PloS one 9.5 (2014): e96807.

Nair, Sujith, and Hanna Paulose. "Emergence of green business models: The case of algae biofuel for aviation." Energy Policy 65 (2014): 175-184.

Mayo, Aloice W. "Effects of temperature and pH on the kinetic growth of unialga Chlorella vulgaris cultures containing bacteria." Water Environment Research(1997): 64-72.

Higgins, Brendan T., and Jean S. VanderGheynst. "Effects of Escherichia coli on Mixotrophic Growth of Chlorella minutissima and Production of Biofuel Precursors." PloS one 9.5 (2014): e96807.

Clarens, Andres F., et al. "Environmental life cycle comparison of algae to other bioenergy feedstocks." Environmental science & technology 44.5 (2010): 1813-1819.

Orfield, Nolan D., et al. "Life cycle design of an algal biorefinery featuring hydrothermal liquefaction: effect of reaction conditions and an alternative pathway including microbial regrowth." ACS Sustainable Chemistry & Engineering 2.4 (2014): 867-874.

Johnson M.W., Suskewicz J. “How to Jump-Start the Clean Tech Economy” Harvard Bus rev. 87 (11) 52-60 (2009)

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