Optimizing phase change material usage could reduce power plant water consumption

Date:
July 29, 2021
Source:
Texas A&M University
Summary:
The food-water-energy nexus dictates that there is a direct link
between these three necessities, and stressing one directly impacts
the supply of the other two. As the population grows, human demand
for energy and food has caused our freshwater reserves to slowly
deplete. Power plants are one of the main culprits contributing
to this issue, as they use trillions of gallons of fresh water
annually to prevent overheating.



FULL STORY ==========================================================================
The food-water-energy nexus dictates that there is a direct link between
these three necessities, and stressing one directly impacts the supply
of the other two. As the population grows, human demand for energy and
food has caused our freshwater reserves to slowly deplete. Power plants
are one of the main culprits contributing to this issue, as they use
trillions of gallons of fresh water annually to prevent overheating.


==========================================================================
A research group led by Dr. Debjyoti Banerjee, professor in the J. Mike
Walker '66 Department of Mechanical Engineering at Texas A&M University,
has shown that specific phase change materials (PCMs) can cool steam
turbines used in power plants, averting fresh water usage. Simultaneously,
they used machine- learning techniques to enhance the reliability and
energy storage capacity of various PCM-based cooling platforms to develop powerful "cold batteries" that dispatch on demand.

Their publication, "Leveraging Machine Learning (Artificial Neural
Networks) for Enhancing Performance and Reliability of Thermal Energy
Storage Platforms Utilizing Phase Change Materials," was published in
the American Society of Mechanical Engineers Journal of Energy Resources Technology.

Power plants and process industries use fresh water in cooling towers
to reduce costs and improve reliability. Water runs through the cooling
tower, absorbing the heat and turning into vapor, which is then used to condense the steam from the turbine exhaust.

With high demands on fresh water, alternate methods like using PCMs that
can morph from a solid to a liquid state by absorbing heat energy are
gaining more attention for cooling power plants and process industries.

The first material the team examined was bioderived waxy materials
(similar to lard): natural products with low carbon footprints that are relatively cheap.

Although effective, the researchers showed that waxes (paraffins) could
not store as much energy nor deliver the cooling power they originally hypothesized, therefore, not providing enough cooling for extreme climates
or providing safety amid extreme weather events.

This led to testing another PCM called salt hydrates that are also
inexpensive and safe for the environment. Salt hydrates pack more punch
than waxes and lards, approximately harboring two to three times the
amount of energy while melting at faster rates. However, these materials
have a known flaw -- they take too long to solidify (as they need to be "subcooled"). Without a reliable melting and freezing method, the salt
hydrates are ineffective.

"Think of the process as an electric car battery -- you want it to take
very little time to recharge, but it needs to run for a long time,"
said Banerjee.

"The same concept can be applied to PCMs. We need a PCM to recharge
(freeze) quickly, yet melt over long durations." To ramp up the
reliability and speed up freezing of these PCMs, the researchers turned
to machine learning. Using the readings from just three miniature
temperature sensors that act like thermometers, they recorded the
melting-time history. They then implemented machine learning to predict
when and how much of the PCM will melt and when the freezing will start, maximizing both cooling power and capacity.

"Using this method, we found that if you melt only 90% of the salt
hydrate and leave 10% solidified, then the moment you start the cooling
cycle, it immediately starts freezing," said Banerjee. "The beauty of
this method is that with a bare-bones apparatus of three sensors and a
simple computer program, we have created a system that is cost-effective, reliable and sustainable." Currently, other machine-learning algorithms require years of data to achieve this type of accuracy for power plants
whereas Banerjee's new method requires only a few days. The algorithm
can tell the operator within one hour (and as much as three hours)
before the system will reach the peak percentage for melting with a 5-to-10-minute prediction accuracy. The technique can be retrofitted on
any existing cooling unit in any process industry or power plant.

The co-authors of this publication are Aditya Chuttar and Ashok
Thyagarajan, students in the mechanical engineering department.

========================================================================== Story Source: Materials provided by Texas_A&M_University. Original written
by Michelle Revels. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Aditya Chuttar, Ashok Thyagarajan, Debjyoti Banerjee. Leveraging
Machine
Learning (Artificial Neural Networks) for Enhancing Performance
and Reliability of Thermal Energy Storage Platforms Utilizing
Phase Change Materials. Journal of Energy Resources Technology,
2022; 144 (2) DOI: 10.1115/1.4051048 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/07/210729111908.htm

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