Scientists at Texas A&M AgriLife Research and the U.S. Department of Energy's National Renewable Energy Laboratory used artificial intelligence to surpass a targeted level of productivity of algae as an alternative fuel source.
Relatively low yield has constrained algal biofuel's commercialization, with growth limitations induced 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 to sustain optimal cell growth and minimize mutual shading," the scientists say in their published research.
The team employed a patented AI advanced learning model to forecast algae light penetration, growth, and optimal density by accommodating continual harvest via hydroponics. They realized a biomass yield of 43.3 grams per square meter per day, versus the latest DOE target range of 25 grams per square meter per day.
From AgriLife Today
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