Can Machine Learning Lead to Cleaner Water and More Energy-Efficient Ionic Separations? U.S. Department of Energy Invests $1.5M in LSU and Penn State Researchers Who Think So
An LSU-Penn State research team is leveraging data science and machine learning to lower the world’s energy footprint for securing freshwater supplies and recover critical minerals.

As much as 15% of U.S. energy is spent on separating and purifying chemicals. Electrically-driven separations augmented with functional materials, such as electrodeionization and membrane-capacitive deionization, which are studied by the LSU-Penn State team, could save a lot of energy—especially in Louisiana, where chemical manufacturing is a major industry. They could also help stabilize freshwater supplies for energy production.
The above illustration shows ionic separation of sodium and chloride (green and red dots) from water using advanced Janus bipolar ion-exchange resin wafers.
Illustration courtesy of Lauren Valentino and Ellen Weiss, Argonne National Laboratory
BATON ROUGE, September 29, 2021—An interdisciplinary team of LSU-Penn State researchers has been awarded $1.5M from
the U.S. Department of Energy (DOE) to develop a smarter approach to ionic separations,
which are important for water treatment, resource recovery, and energy production.
Using machine learning, the researchers are building a platform for accelerated materials
and processes discovery. It combines continuously refined and cross-linked data on
both the molecular level, the materials level, and the device level. The platform
bridges transport, thermodynamic, and kinetic phenomena from the super-small, or nano-level,
to plant-scale equipment.
The LSU research team consists of three investigators: Chris Arges, adjunct professor
in the LSU Department of Chemical Engineering and associate professor in the Penn
State Department of Chemical Engineering; Revati Kumar, associate professor in the
LSU Department of Chemistry with a joint appointment in the LSU Center for Computation
& Technology; and Cain Endowed Chair José Romagnoli, professor in the LSU Department
of Chemical Engineering.
“This research will take advantage of the rapid growth of artificial intelligence and machine learning to accelerate the discoveries needed for more efficient energy generation, storage, and use, while eliminating or reducing the emission of greenhouse gases and the use of critical resources.”—Steve Binkley, acting director of the U.S. Department of Energy‘s Office of Science
Through this highly competitive DOE award, LSU joins a selective group of 10 universities and national labs, including Caltech, Cornell, Harvard, Stanford, and Los Alamos, all working to develop
cutting-edge research tools that will lead to the discovery of new clean-energy technologies.
Their shared focus is on data-driven models of complex chemical or materials systems.
“Data science, and especially artificial intelligence and machine learning, provides
unique opportunities to leapfrog to novel capabilities for understanding fundamental
properties and processes in physical and chemical systems,” said Steve Binkley, acting
director of DOE’s Office of Science. “This research will take advantage of the rapid
growth of artificial intelligence and machine learning to accelerate the discoveries
needed for more efficient energy generation, storage, and use, while eliminating or
reducing the emission of greenhouse gases and the use of critical resources.”
The goal of the LSU-Penn State project is to help optimize common and often costly
processes in the chemical manufacturing and energy industries, the extraction and
recycling of valuable minerals and elements, such as lithium and copper, and water
purification. All while producing less waste and lowering energy costs.
“A challenge in the development of new materials for separation science is the scarce
availability of data that limits the implementation and effectiveness of machine learning
methods,” Romagnoli said. “In this project, data generated across all levels will
be used to train machine learning regression algorithms to be incorporated into compositional
physics-informed models. Synthetic data generated from these models will then be used
to train machine learning surrogate models, suitable for devise optimization, alleviating
the cost and time required to generate large datasets from physical experiments.”
“And then we follow up with physical experiments to make sure the data we get is both
accurate and useful,” Arges added.
He and Romagnoli have previously used machine learning to improve fuel cells. That
work helped them establish what they call a “playbook” for how to get answers to their
research questions more quickly. This new project adds yet another dimension via collaboration
with the LSU College of Science and Kumar, who is an expert on computational chemistry,
using high-performance computing to simulate and predict outcomes of ionic transport
and reaction kinetics at the molecular level.
“Machine learning is beautiful because you don’t have to have a priori knowledge of the relationships between things. That’s what the machine learning does—it takes large data sets and helps you see patterns.”—Revati Kumar, associate professor in the LSU Department of Chemistry with a joint appointment in the LSU Center for Computation & Technology
“Machine learning is beautiful because you don’t have to have a priori knowledge of
the relationships between things,” Kumar said. “That’s what the machine learning does—it
takes large data sets and helps you see patterns. Normally, if you look at all of
that data, you have no chance of making sense of it, so this method is a big improvement
and there are many validation steps.”
Their collaborative project addresses several separation science needs across multiple
areas within the U.S. Department of Energy. It includes securing the supply chain
for critical minerals and materials, extracting valuable organic acids from biofuel
streams, and the water-energy nexus. The latter refers to the interdependence of water
for energy production and energy to extract, purify, deliver, heat, cool, treat, and
dispose of water. In Louisiana, large amounts of both energy and freshwater are needed
by the chemical industry, the second-largest in the U.S. Meanwhile, rising seas and
saltwater intrusion are threatening many established freshwater sources along the
coast. This is not only a problem in Louisiana, but around the world. Through more
efficient ionic separations, however, both seawater and contaminated water could more
easily be turned into freshwater, helping to slow or halt what many already describe
as a global water crisis. Both droughts and floods can limit freshwater access.
Beyond freshwater production, the machine learning system the LSU-Penn State researchers
are working to develop can also help recover key minerals and elements from wastewater,
including raw sewage, as well as fermentation broths used in the production of biofuels.
“There are many value-added chemicals in that mixture that would be good building
blocks for commodity chemicals and new products, such as biodegradable plastics,”
Arges said. “Also, think about how commonplace lithium-ion batteries are today. Soon,
they’ll be even more common, and meanwhile lithium is a limited resource. The price
of lithium depends on how much can be made available.”
Other impacts of more efficient ionic separations could lead to lower energy and water
bills.
“There are safety and national security implications of this, too,” Arges added. “In
electric power plants, you heat water to create steam to turn a turbine to produce
electrical power. If you don’t have a reliable freshwater resource to do this, you
don’t have a reliable electrical grid.”
Even power plants and chemical plants with secure access to freshwater rely on ionic
separations to reuse water, or soon will.
“Since water evaporates over time, the salt concentration of freshwater goes up, and
it’s important to get the salt out, or you end up with corrosion and scaling,” Arges
said. “There are many uses of this technology, and we look forward to sharing our
playbook with the wider scientific community so they can use the tools we’re developing
to advance their own research objectives in the area of ionic separations.”
Elsa Hahne
LSU Office of Research & Economic Development
ehahne@lsu.edu