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Data processing and machine learning

A new machine learning technique is boosting algae as a renewable, carbon-neural source of fuel for airplanes and other vehicles typically powered by fossil fuels.

What’s new: Researchers at Texas A&M and the National Renewable Energy Laboratory developed a system that helps algae farmers keep an algal colony growing at top speed.

How it works: Individual algae cells shade out their neighbors if they grow too densely, keeping the colony from taking full advantage of available light. The authors built an algal growth simulator that lets farmers know when to harvest algae to optimize the colony’s density for growth. The training data consisted of grayscale images of algal colonies under six lighting conditions and at 23 intervals over time. Each example included its average algal concentration, and each pixel was labeled with the light intensity.

  • The authors trained a separate support-vector regression (SVR) model for each pixel to estimate the light intensity.
  • They further labeled each pixel with the SVR’s estimated light intensity and used the relabeled images to train a random forest to predict the average growth rate.
  • At inference, these techniques combined to predict algal growth. Given a picture of a colony and its initial algal concentration, the SVRs estimated light intensities per pixel, and the random forest used the estimates to determine how the algae would grow.

Results: The authors found that growth rates across all lighting conditions were at their highest when pixels darkened by algal growth accounted for between 43 percent and 65 percent of an image. They used their system to determine when to harvest indoor and outdoor algae farms. The outdoor farm produced 43.3 grams of biomass per day, the indoor pond 48.1 grams per day. A commercial operation using the authors’ method would produce a biofuel sale price of $281 per ton. That’s comparable to the $260-per-ton price of ethanol derived from corn, which requires expensive processing that algae doesn’t.

Behind the news: Depending on the species and processing method, algae can be turned into a variety of fuel products including diesel, alcohol, jet fuel, gasoline, hydrogen, and methane. It was first proposed as a source of fuel in the 1950s and has been a growing area of sustainable-energy research since the 1970s. However, algal fuels have made little commercial headway due largely to low yields and the cost of processing harvested biomass.

Why it matters: Converting algae into fuel is attractive because the biomass is renewable, absorbs as much atmospheric carbon as it emits, and works with internal-combustion engines. To date, it hasn’t scaled well. If machine learning can make it more productive, it could revitalize this approach to alternative energy.

We’re thinking: Between this work, Fraunhofer Institute’s similar algal growth system, and Hypergiant’s AI-powered algae bioreactor, machine learning applications for algae are blooming!


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