Scientists have been studying algae as a biofuel source for a long time since it has so much potential. They also created fake algae leaves that were 3D printed to provide oxygen for our Mars missions. Experts from Texas A&M AgriLife Research are now using artificial intelligence to set a new world record for producing algae as a dependable biofuel source, paving the way for a cleaner and more cost-effective fuel source for jet jets and other means of transportation.
The findings were reported in the journal Nature Communications. The project is led by Joshua Yuan, PhD, and is funded by the US Department of Energy’s Fossil Energy Office.
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.
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.
An aggregation-based sedimentation (ABS) technique is then created to enable low-cost biomass collection and cost-effective SAC. The ABS was made possible by genetically altering Synechococcus elongatus UTEX 2973 to synthesise limonene, which enhances the hydrophobicity of cyanobacterial cell surfaces and enables for successful cell aggregation and sedimentation.
SAC unleashes cyanobacterial growth potential in photobioreactors, producing 0.1 g/L/hour biomass and 0.2 mg/L/hour limonene over time. The SAC can produce 43.3 g/m2/day of biomass when scaled up with an outdoor pond system, cutting the minimum biomass selling price to roughly $281 per tonne.