Artificial intelligence helps grow algae for producing clean biofuel
Algae has such immense potential as a biofuel source that scientists have long been studying it for sustainable energy. They even created 3D printed artificial leaves out of algae to produce oxygen for our investigations of Mars.
Now, scientists from Texas A&M AgriLife Research are using artificial intelligence to break a new world record for producing algae as a reliable biofuel source, so that a greener and more economical fuel source for jet aircraft and other kinds of transportation could be achieved. The research project is conducted by Joshua Yuan, PhD., and funded by the U.S. Department of Energy Fossil Energy Office.
Removing the obstacles
One of the major problems with algaes' prominence was their growth limitations due to mutual shading and the high cost of harvest. But this is about to be overcome as well.
The team is employing machine learning to support cell growth and hinder mutual shading. Also, an aggregation-based sedimentation method is designed to achieve low-cost biomass harvesting and economical semi-continuous algal cultivation (SAC).
By means of an outdoor pond system, the research team has broken the record with the production of 43.3 grams per square meter per day of biomass. The latest target range by the Department of Energy was 25 grams per square meter per day. This system lowers the minimum biomass selling price to around $281 per ton.
Corn, which appears as the general low-cost biomass feedstock for ethanol, costs $260 per ton. However, it needs to be ground and the mush must be cooked before fermentation. Yuan's technique, on the other hand, does not require any costly pre-treatments before fermentation.
In spite of all the obstacles in front of the commercialization of algae, this technique appears cost-effective and helps the advancement of algae as a viable alternative energy source. Plus, Yuan believes that sustainable algal biofuels will be able to decrease carbon emissions, alleviate climate change, ease petroleum dependency and transform the bio-economy with the overcoming of these obstacles.
The study was published in Nature Communications.
Algal biofuel is regarded as one of the ultimate solutions for renewable energy, but its commercialization is hindered by growth limitations caused by mutual shading and high harvest costs. We overcome these challenges by advancing machine learning to inform the design of a semi-continuous algal cultivation (SAC) to sustain optimal cell growth and minimize mutual shading. An aggregation-based sedimentation (ABS) strategy is then designed to achieve low-cost biomass harvesting and economical SAC. The ABS is achieved by engineering a fast-growing strain, Synechococcus elongatus UTEX 2973, to produce limonene, which increases cyanobacterial cell surface hydrophobicity and enables efficient cell aggregation and sedimentation. SAC unleashes cyanobacterial growth potential with 0.1 g/L/hour biomass productivity and 0.2 mg/L/hour limonene productivity over a sustained period in photobioreactors. Scaling-up the SAC with an outdoor pond system achieves a biomass yield of 43.3 g/m2/day, bringing the minimum biomass selling price down to approximately $281 per ton.
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