Identifying the best algae for biofuels

Tuesday, 17 January, 2017

Identifying the best algae for biofuels

Discovering which algae species is best suited to make biofuel is no small task. Researchers have tried to evaluate algae in test tubes, but often find lab results don’t always mirror what happens when the substance is grown in outdoor ponds.

The Algae DISCOVR (Development of Integrated Screening, Cultivar Optimization and Validation Research) Project is now trying out a new approach that could reduce the cost and the time needed to move promising algal strains from the laboratory and into production. At the end of the three-year pilot project, scientists hope to identify four promising strains from at least 30 initial candidates.

“Algae biofuel is a promising clean energy technology, but the current production methods are costly and limit its use,” said lead researcher Michael Huesemann of the US Department of Energy’s (DOE) Pacific Northwest National Laboratory (PNNL). “The price of biofuel is largely tied to growth rates. Our method could help developers find the most productive algae strains more quickly and efficiently.”

Led by PNNL and including three other DOE labs, as well as the Arizona Center for Algae Technology and Innovation, the project’s work relies on PNNL’s Laboratory Environmental Algae Pond Simulator (LEAPS) mini-photobioreactors. This system mimics the frequently shifting water temperatures and lighting conditions that occur in outdoor ponds at any given place on Earth.

The LEAPS glass-column photobioreactors act like small ponds and are placed in rows to allow scientists to simultaneously grow multiple different types of algae strains. Each row is exposed to unique temperature and lighting regimens thanks to heaters, chillers and heat exchangers, as well as coloured lights simulating the sunlight spectrum — all of which can be changed every second.

LEAPS mimics the frequently shifting water temperatures and lighting conditions that occur in outdoor ponds at any given place on Earth.

The first phase of the team’s screening process uses the photobioreactors to cultivate all 30 algal strains under consideration and evaluate their growth rates. Strains with suitable growth will be studied further to measure their oil, protein and carbohydrate content, all of which could be used to make biofuels. The algae will also be tested for valuable co-products such as the food dye phycocyanin, which could make algae biofuel production more cost-effective. This phase will also involve evaluating how resistant strains are to harmful bacteria and predators that can kill algae.

Next, the team will look for strains that produce 20% more biomass than two well-studied algae strains. The top-performing strains will then be sorted to find individual cells best suited for biofuel production, such as those that contain more oil. Those strains will also be exposed to various stresses to encourage rapid evolution so they can, for example, survive in the higher temperatures outdoor ponds experience in the summer.

After passing those tests, the remaining strains will be grown in large outdoor ponds in Arizona. Researchers will examine how algae growth in the outdoor ponds compares with the algal biomass output predicted in earlier steps. Biomass will also be harvested from outdoor-grown algae for future studies.

Finally, the team will further study the final algae strains that fare best outdoors to understand how fast they grow in different lighting and temperature conditions. That data will then be entered into PNNL’s Biomass Assessment Tool, which uses detailed data from weather stations and other sources to identify the best possible locations to grow algae. Data and strains will be made public in the hopes that algae companies and other researchers will consider growing the most productive strains identified by the project.

Top image: PNNL biochemical engineer Mattias Greer examines algae samples for their suitability to make biofuel.

Images courtesy of Pacific Northwest National Laboratory - PNNL under CC BY-NC-SA 2.0

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