ASHRAE Journal - July 2023 - 112

REFRIGERATION APPLICATIONS
COLUMN
Andy Pearson
AI In Refrigeration
BY ANDY PEARSON, PH.D., C.ENG., FELLOW ASHRAE
The rapid advancements in artificial intelligence (AI) over the past decade have left
a profound impact on many industries, and the refrigeration sector is no exception.
The integration of AI into refrigeration systems presents numerous opportunities
to improve efficiency, reduce costs, and minimize the environmental footprint.
This column delves into some of the most promising applications of AI in the
refrigeration industry.
One of the significant challenges in the refrigeration industry
is energy consumption. AI-driven energy optimization
systems can help businesses save energy and reduce operational
costs by continuously monitoring equipment performance
and adjusting settings in real-time. These systems
can learn and predict optimal settings for various conditions,
ensuring that the refrigeration system operates at
maximum efficiency. This is especially crucial for
industries with strict temperature requirements,
such as food processing or pharmaceuticals,
where even minor fluctuations can result in spoilage
or product recalls.
Defrosting cycles in refrigeration systems can
consume a considerable amount of energy and
are often executed based on fixed schedules. AI
can optimize defrosting cycles by using real-time
data on factors such as humidity, temperature,
and frost accumulation. By implementing smart
defrosting solutions, refrigeration systems can reduce energy
consumption, cut down on defrosting time, and lower the risk
of damaging sensitive products.
Compressors play a critical role in maintaining the desired
temperature within a refrigeration system. AI can optimize
compressor performance by analyzing data on variables such
as temperature, pressure, and power consumption. With
AI-based control, compressors can adapt to changing conditions
and operate at peak efficiency, resulting in reduced
energy consumption and increased system reliability.
AI-driven predictive maintenance can optimize refrigeration
system operations and extend the life of equipment by
identifying potential issues before they escalate. By analyzing
data from sensors, AI algorithms can detect patterns and
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anomalies in equipment performance, allowing technicians
to perform targeted maintenance before a breakdown occurs.
This results in reduced downtime, improved efficiency, and
lower maintenance costs.
Autonomous HVAC and refrigeration system design AI can
also revolutionize the design process for heating, ventilation,
air conditioning, and refrigeration (HVACR)
systems. Machine learning algorithms can
evaluate countless design options based on
factors such as energy consumption, system
performance, and initial cost. This approach
allows engineers to optimize HVACR system
designs and deliver tailored solutions that cater
to each client's unique needs.
Finally, AI can contribute to the education and
All this creative writing was
giving Robby a headache.
training of refrigeration technicians. Virtual
and augmented reality platforms can simulate
various scenarios, allowing trainees to develop
problem-solving skills in a safe, immersive environment.
Furthermore, AI-driven support tools can assist technicians
in diagnosing and resolving issues more efficiently.
In conclusion, the integration of AI into the refrigeration
industry holds enormous potential for enhancing efficiency,
reducing costs, and minimizing environmental impacts. By
embracing these innovative technologies, businesses in the
refrigeration sector can stay ahead of the competition and
drive the industry towards a more sustainable future.
Here's a challenge for you: can you tell what percentage of
this column (by word count) was written by an artificial intelligence
algorithm?
Andy Pearson, Ph.D., C.Eng., is group managing director at Star Refrigeration in
Glasgow, UK.
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