This post we will look at an article in the "Biofuel Research Journal" where researchers explore various factors that affect the growth of a specific strain of algae as well as the lipid content.  The article citation and a link to the article are at the bottom of this post.

A little backgroundMicroalgae has great potential as a biofuel feedstock.  There are many reasons why this is the case but four stand out among the rest: Microalgae has the ability to grow in places that are unsuitable for traditional agriculture, either

due to climate or soil conditions. Growing microalgae can be accomplished in a much smaller area than any other biomass. The small footprint along with other factors make it highly suited for sequestering carbon dioxide. Microalgae produce lipids instead of sugars, which makes the process of transforming them into a

useable fuel much simpler. Instead of creating ethanol from sugars that must be mixed with petroleum products to be used in traditional vehicles, the lipids can be easily, and inexpensively, processed into a biodiesel. 

Why this study is importantIf algae are to be successfully grown commercially for use as a biofuel feedstock, optimal growth conditions must be identified.  Compared with crude oil, producing green crude (as it is known) from algae is currently more expensive.  Minimizing expenses by increasing production efficiency will be key in making this a viable fuel source.

The ExperimentWhat they didBased on previous research, the researchers determined that the Chlorella species of algae was the most suitable candidate for growth for biofuel conversion. This was based on the growth rate in atmospheric conditions and the ability to be cultivated in open ponds.  

Four factors known to alter the growth rate and lipid content were identified, and used for independent variables.  These factors are light intensity, CO2 concentration, nitrogen content (used NaNO3 as a nitrogen source), as well as aeration rate.  Two values for each variable were used, with eight total variations.

Using the Taguchi method, the experimenters compared these independent variables without having to use separate experimental apparatuses to explore each variable.  This allowed the researchers to use eight experimental setups instead of 48, saving time and money. A loss function, which calculates a signal/noise ratio, was used to qualify the validity of results.

An enclosed photobioreactor was used for each apparatus, with halogen bulbs as the light source.  A schematic can be viewed in the study (link at the bottom)

What they found

Effect on biomass productivityBiomass productivity was almost four times more active in the most active system when compared with the least active.  Results showed the most important condition (within the parameters of experimental values) was nitrogen content, followed by CO2 concentration, with light intensity being less significant.  It was found that aeration had no statistically significant effect on biomass production.

Effects on lipid production

The conditions used varied lipid content of the algae from 8.6% to 19.7%.  The most important factor influencing lipid concentration was nitrogen content, with aeration playing a smaller but significant role; light intensity as well as CO2 concentration were not found to be statistically significant. The lipid content dropped appreciably with increased nitrogen within the bounds of the experiment.

My Thoughts One part of the analysis that struck me was the complete separation of optimizing biomass and lipid content; both situations were analyzed independently of each other.  Given that the end goal is to process the algae using a transesterification reaction, the total quantity of lipids is an important metric to be evaluated.  Below this paragraph I have included a chart and graph (Chart I, Figure I) which does so.  I multiplied the average biomass production by the average percentage of lipids for each condition. Although experiments 2&3 account for the highest values for biomass production and lipid concentration respectively, this shows that experiment 6 would be the most productive for the end goal of creating the most biofuel.

Table 1 Experiment Average biomass [mg/(L*day)] Average lipids % of biomass Average lipid accumulation [mg/(L*day)]

1 61 13.7 8.3572 210.9 8.2 17.29383 85.9 19.7 16.92234 105.4 8.6 9.06445 54.3 16.2 8.79666 196.9 11.2 22.05287 67.2 12.3 8.26568 72.9 9.1 6.6339

Figure 1

Below (Figure 2) illustrates the importance of combining total biomass production as well as lipid concentration. Without this metric it would seem that experiments 2 and 3 are the most productive, even though experiment 6 was over 27% more effective at producing lipids than the second highest situation.

Figure 2

While CO2 concentration and light intensity alter the rate of production, they are statistically insignificant when looking at lipid concentration.  Conversely, while aeration affects lipid content, it is statistically insignificant when dealing with biomass production.  It follows that three of the four variables are significant in either the biomass production or lipid concentration, but not both.  With that information, it is clear which values are ideal for production. Alternatively, increased NaNO3 concentration was effective in generating more biomass, but reduced lipid content; there were competing effects, which makes looking at a single metric (total lipid mass) even more important. This way the end goal can be directly compared to the inputs.

Experiment 6 had the most productive levels of all the variables except aeration.  It seems logical that experiment 6 would be even more productive if it used an aeration rate of 3.33 vvm with all the other variables kept the same.

The values used for light intensity were a bit interesting to me as well. The highest light intensity used was 14.5 klux, which corresponds closely with a shaded area on a bright sunny day. It seems that it would be more realistic to use a value that would be incurred with direct radiation on a sunny day. My guess is that these values are based upon data previously available, that the light intensity of direct afternoon light is stifling to production. This is backed up by the fact that the lower intensity situations actually corresponded to about a 17% increase in production. Given the small number of data points, more investigation is needed to know if there is maximum production between the two values.

The fact that lower intensity light makes the microalgae thrive seems to be beneficial, at least for closed systems. Instead of building flat ponds, it seems plausible that an array of angled photobioreactors could be used so that area is maximized while keeping light intensity to a reasonable level.

It would be interesting to see how the wavelength of the light used affected production as well. This experiment was done with a halogen bulb,which is reasonably close in the spectrum of the sun, but nonetheless has a different spectrum. It is possible that different wavelength spectrum contained in sunlight would affect the optimal intensity.

Another area that may warrant additional investigation is the idea of using these variables in a time varying approach. The authors cited a couple different studies concluding that certain conditions, such as low medium homogenization and nutrient deprivation, can cause the algae to produce a higher lipid content as a response to stress.  This effect comes at the cost of biomass production.  It would be interesting to see how this could be used with a time varying approach.  It seems plausible that conditions favorable to biomass productivity could initially be used in the algal lifecycle, by growing the culture to size, with the later part of the cycle becoming focused on maximizing lipid content. The efficacy of such an approach would highly depend upon the rate at which nutrient deprivation affects lipid concentration.

Andrea Vallati, Stefano Grignaffini, and Marco Romagna. “A New Method to Energy Savings in a Micro Grid” Sustainability 7 (2015) 13904-13919. doi:10.3390/su71013904

http://www.mdpi.com/2071-1050/7/10/13904/pdf (.pdf)