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AI Helps in Growth of Algae for Producing Clean Biofuel

Machine learning is being used by researchers to aid cell growth and prevent mutual shading. A sedimentation approach based on aggregation is also being developed to accomplish low-cost biomass collection and cost-effective semi-continuous algae production (SAC).

Shivam Dwivedi
Picture of Green Algae
Picture of Green Algae

Algae has so much potential as a biofuel source that scientists have been researching it for a long time. They also constructed 3D printed artificial algae leaves to supply oxygen for our Mars explorations. Now, Texas A&M AgriLife Research experts are utilizing artificial intelligence to set a new world record for generating algae as a reliable biofuel source, paving the way for a greener and more cost-effective fuel source for jet planes and other modes of transportation.  

The study was published in Nature Communications. Joshua Yuan, PhD, is leading the study, which is financed by the US Department of Energy's Fossil Energy Office.

Removing the Obstacles

Due of mutual shading and the high cost of collection, one of the key issues with algae's prominence was their growth restrictions. But this, too, is going to be overcome. Machine learning is being used by researchers to aid cell growth and prevent mutual shading. A sedimentation approach based on aggregation is also being developed to accomplish low-cost biomass collection and cost-effective semi-continuous algae production (SAC).

The study team set a new record for biomass production by using an outdoor pond system to produce 43.3 grammes per square metre per day.  The Department of Energy's most recent target range was 25 grammes per square metre each day. The minimum biomass selling price is reduced to roughly $281 per tonne with this technique.

Corn costs $260 per tonne, making it the most common low-cost biomass feedstock for ethanol. It must, however, be pounded and the mush heated before fermentation. Yuan's method, on the other hand, does not necessitate any expensive pre-treatments prior to fermentation.

Despite the numerous barriers to algae commercialization, this technology appears to be cost-effective and contributes to the advancement of algae as a viable alternative energy source. Furthermore, Yuan believes that by addressing these challenges, sustainable algal biofuels will be able to reduce carbon emissions, mitigate climate change, reduce petroleum dependency, and alter the bio-economy.

More About Algal Biofuel

Algal biofuel is recognized as one of the ultimate renewable energy alternatives, but its commercialization is hampered by mutual shading-induced growth restrictions and high harvest costs. These obstacles were resolved by incorporating machine learning into the design of semi-continuous algal cultivation (SAC) to ensure optimal cell development while reducing mutual shading.

Then, to achieve low-cost biomass collecting and cost-effective SAC, an aggregation-based sedimentation (ABS) technique is devised. The ABS is made possible by genetically modifying a fast-growing strain, Synechococcus elongatus UTEX 2973, to produce limonene, which improves the hydrophobicity of cyanobacterial cell surfaces and allows for effective cell aggregation and sedimentation.  

In photobioreactors, SAC unleashes cyanobacterial growth potential with 0.1 g/L/hour biomass productivity and 0.2 mg/L/hour limonene productivity over time. When the SAC is scaled up with an outdoor pond system, a biomass production of 43.3 g/m2/day is achieved, lowering the minimum biomass selling price to around $281 per tonne.

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