Artificial intelligence predicts algae potential as alternative energy
source
Jet fuel, animal feed among potential products from algae

Date:
March 4, 2022
Source:
Texas A&M AgriLife Communications
Summary:
Texas A&M AgriLife Research scientists are using artificial
intelligence to set a new world record for producing algae as
a reliable, economic source for biofuel that can be used as an
alternative fuel source for jet aircraft and other transportation
needs.



FULL STORY ========================================================================== Texas A&M AgriLife Research scientists are using artificial intelligence
to set a new world record for producing algae as a reliable, economic
source for biofuel that can be used as an alternative fuel source for
jet aircraft and other transportation needs.


========================================================================== Joshua Yuan, Ph.D., AgriLife Research scientist, professor and chair
of Synthetic Biology and Renewable Products in the Texas A&M College
of Agriculture and Life Sciences Department of Plant Pathology
and Microbiology, is leading the research project. The project
team includes Bin Long, a graduate student from the Department of
Plant Pathology and Microbiology; Bart Fischer, Ph.D., co-director
of the Texas A&M Agricultural and Food Policy Center and Texas A&M
Department of Agricultural Economics; Henry Bryant, Ph.D., Department
of Agricultural Economics; and Yining Zeng, Ph.D., staff scientist with
the U.S. Department of Energy National Renewable Energy Laboratory.

Solving the algae limitations as a biofuel "The commercialization of
algal biofuel has been hindered by the relatively low yield and high
harvesting cost," Yuan said. "The limited light penetration and poor cultivation dynamics both contributed to the low yield." Overcoming these challenges could enable viable algal biofuels to reduce carbon emissions, mitigate climate change, alleviate petroleum dependency and transform
the bioeconomy, Yuan said.

Yuan has previously been successful at finding methods to convert corn
stubble, grasses and mesquite into biodegradable, lightweight materials
and bioplastics.

His latest project utilizes a patented artificial intelligence advanced learning model to predict algae light penetration, growth and optimal
density.

The prediction model allows for continual harvest of synthetic algae
using hydroponics to maintain the rapid growth at the optimal density
to allow best light availability.



========================================================================== Texas A&M AgriLife Research scientists are using artificial intelligence
for producing algae as a reliable, economic source for biofuel. This illustration depicts integration of machine learning-informed
semi-continuous algal cultivation (SAC) and aggregation-based
sedimentation (ABS) for biofuel production. (Illustration: Texas A&M
AgriLife Research) The method Yuan and team have successfully achieved in
an outdoor experiment is 43.3 grams per square meter per day of biomass productivity, which would be a world record. The latest DOE target range
is 25 grams per square meter per day.

"Algae can be used as an alternative energy source for many industries, including biofuel and as jet fuel," Yuan said. "Algae is a good
alternative fuel source for this industry. It's an alternate feedstock
for bioethanol refinery without the need for pretreatment. It's lower
cost than coal or natural gas. It also provides for a more efficient way
of carbon capture and utilization." Yuan said algae can also be used as
a source for animal feed. AgriLife Research has previously investigated
algae as a source of livestock protein.

Algae as a renewable energy Algae 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," he said.

Yuan said he is using an aggregation-based sedimentation strategy designed
to achieve low-cost biomass harvesting and economical SAC.

"The aggregation-based sedimentation is achieved by engineering a
fast-growing blue-green algae strain, Synechococcus elongatus UTEX2973,
to produce limonene, which increases cyanobacterial cell surface
hydrophobicity and enables efficient cell aggregation and sedimentation,"
he said.

Making algae economical energy Scaling-up the SAC with an outdoor pond
system achieves a biomass yield of 43.3 grams per square meter per day, bringing the minimum biomass selling price down to approximately $281
per ton, according to the journal article. In comparison, the standard
low-cost feedstock for biomass in ethanol is corn, which is currently approximately $6 per bushel or $260 per ton. However, Yuan's process
does not call for costly pre-treatment before fermentation. Corn must
be ground and the mash must be cooked before fermentation.

"Algae as a renewable fuel source was a hot topic a decade ago,"
Fischer said.

"As a result, there's a lot of skepticism. I was even skeptical. However,
the work that Joshua is doing is incredibly innovative. We were excited
to partner on this project. At the productivity levels they obtain --
and given the low- cost harvest that the strain allows -- it shows a
lot of promise." Yuan said despite significant potential and extensive efforts, the commercialization of algal biofuel has been hindered by
limited sunlight penetration, poor cultivation dynamics, relatively low
yield, and the absence of cost-effective industrial harvest methods.

"This technology is proven to be affordable and help propel algae as a
true alternative form of energy," he said.

The team's findings were published in January in Nature
Communications. Ongoing research is funded by the U.S. Department of
Energy Fossil Energy Office. The work is also being funded by a gift
from Dr. John '90 and Sally '92 Hood, who recently met with Yuan to
discuss his biofuels research program. The gift is managed by the Texas
A&M Foundation.

========================================================================== Story Source: Materials provided
by Texas_A&M_AgriLife_Communications. Original written by Blair
Fannin. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Bin Long, Bart Fischer, Yining Zeng, Zoe Amerigian, Qiang Li, Henry
Bryant, Man Li, Susie Y. Dai, Joshua S. Yuan. Machine
learning-informed and synthetic biology-enabled semi-continuous
algal cultivation to unleash renewable fuel productivity. Nature
Communications, 2022; 13 (1) DOI: 10.1038/s41467-021-27665-y ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/03/220304124008.htm

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